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Commit e9cc7d51 authored by Simeon's avatar Simeon
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added Trich data

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analysis/Trich/Trich_DADA2_results_060921/.DS_Store 0 → 100644
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analysis/Trich/Trich_DADA2_results_060921/ASV_custom_BLAST_taxonomy.fasta 0 → 100644
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>ASV1;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Heteroderidae;Globodera;Globodera tabacum;100
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTA
CGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAA
CCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGA
TTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAA
CCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATT
CTATGCATTTTGAGAGCTGGAATTACCG
>ASV2;Eukaryota;Metazoa;Nematoda;Enoplea;Triplonchida;Trichodoridae;Paratrichodorus;Paratrichodorus pachydermus;100
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAAC
CAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAA
GCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATAT
ACGCTATTGGAGCTGGAATTACCG
>ASV3;NA;NA;NA;NA;NA;NA;NA;NA;NA
CCGTAAACCACTTCGATCAGTACTGGCATCGCATCCAACTGCGTAATGAAGCAATGTCGATTCCAGCGCCATGTCTCCGC
TGGCAACGAATGAATGAACTGAATCCTGTGGAGCGTTTCATATGGGACAACGTGCAGAGTTCGCGCCGAATACTCGACTT
CGGTTCAGGAGATCAGTCGTTAAGGAAGAAATTCCTCGCTGCCGGTTATCGCGGTAAGTACGAGACATTCGATATTT
>ASV4;Eukaryota;Metazoa;Nematoda;Enoplea;Dorylaimida;Diphterophoridae;Diphterophora;Diphterophora communis;98.345
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAAC
CAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAA
GTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATA
CGCTATTGGAGCTGGAATTACCG
>ASV5;Eukaryota;Metazoa;Chordata;Mammalia;Primates;Cercopithecidae;Macaca;Macaca mulatta;83.673
CACCCGGAACGAGCAGTGATTTTGCCATTTTATCGACTTTTTCTTGCAATACTGCCTGATTAATCGGCTTTAGTTCCGCT
GCCTGGCAAACCATGTTTGCTAATAAGAACAGAATGGTTATGATTCTCAAAAATTATCTCCTTTTCATCTTCAATGGCGC
TTATGTCCTTTAGGCTTACATTAGTATAAGCCAGGCTTTAAAATTCCGCTACCTTCTGAAG
>ASV6;Bacteria;NA;Proteobacteria;Betaproteobacteria;Nitrosomonadales;Nitrosomonadaceae;Nitrosospira;Nitrosospira multiformis;80.165
GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCA
AATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGC
CAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTC
CGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC
>ASV7;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Meloidogynidae;Meloidogyne;Meloidogyne hapla;100
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCA
CGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAA
CCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGA
TTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACA
CCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTA
TTAGCAGAGCTGGAATTACCG
>ASV8;Eukaryota;Metazoa;Mollusca;Gastropoda;NA;Lottiidae;Lottia;Lottia gigantea;81.818
GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAA
CAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGTACTTTCAAAGTTGCAGAGTGAAG
GAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCG
ATAAAGGCTCTCCTTC
>ASV9;Eukaryota;Metazoa;Nematoda;Enoplea;Dorylaimida;Diphterophoridae;Diphterophora;Diphterophora obesa;99.528
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTACAGCGATAAC
CAATAAAAGGCTGCAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCTAGAGAAAACCGAATGAGTAGTCCAGCCGAC
ATAGTACCACCCAGTAGGGGGACCACATCAGCCAGACGAAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATAT
ACGCTATTGGAGCTGGAATTACCG
>ASV10;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Rhabditidae;Rhabditis;Rhabditis cf. terricola JH-2004;100
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCA
CGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAAC
CAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTT
GATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGA
GACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTT
ACGCTAGTGGAGCTGGAATTACCG
>ASV11;Eukaryota;Metazoa;Nematoda;Enoplea;Dorylaimida;Diphterophoridae;Diphterophora;Diphterophora communis;99.291
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAAC
CAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAA
GTAGTGCCACCCAGTGGGGGACCACCTCAGCCGAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATA
CGCTATTGGAGCTGGAATTACCG
>ASV12;Bacteria;NA;Bacteroidetes;Sphingobacteriia;Sphingobacteriales;Sphingobacteriaceae;Pedobacter;Pedobacter sp. eg;67.488
GCCGCTGAAAATTCTGTAACCTGCTGATACCGGATCTTACATTCCTGTCAACCTTTGCTTTTTTAACTTCATCCAAATCG
GTCAGTTGGTTTAATACCAGCTGCGGAAAAACGGCTGAAGTAGTCTGCTCCACACATCCATGTGGGTATTCTATCAGGTA
GTTCAAGCGTTTTTGCAAATTCATAGATGGTACACTGGAAATTTCCAACACAGCTGTACTTGAAGTTATCACACCAATAG
GGTTGGCCATTGTTTTCCATTGCTGCCCTGCCGATAAAGTCATTTCAGTAACCTGTGTTACCGGCGGATTTGGGTTGCGT
ATTTCAAGTTCCACTTCATAATCT
>ASV13;Bacteria;NA;Bacteroidetes;Chitinophagia;Chitinophagales;Chitinophagaceae;Chitinophaga;Chitinophaga caeni;75.117
CCATCACTGCTTTATAAGAATAATTGACCAAACCAATGGCCAGTACCCGCTGATAAGGCCAGCCCTTACCGATGCTGAAG
GCTATGAACAATACAATGTAGCTGTCTATCAGCTGTGAGATGATGGTAGAACCTGTAGCCCGGAGCCAGACCTTTTTCTC
ACCGGTCACCTTCTTGATCTTATGGAACACCCATACATCCACCACCTGGCTCACCAGGAAAGC
>ASV14;Eukaryota;Fungi;Ascomycota;Sordariomycetes;Hypocreales;Hypocreaceae;Trichoderma;Trichoderma reesei;96.296
CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAG
CGATCCAGCGCCATTACCCTTCAAAAGACCAGTGAGAGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGCGTGCTCG
AGACGTCCGTCGAAAGATCGATGAGCGATGTTGAGACAA
>ASV15;Bacteria;NA;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingomonas;Sphingomonas alpina;86.207
CCTCGGTATTGGCGACCTCGAAATTCCAGGTCGATTGCTCGATCTCATTCTCGAGGAACACGTCGCCATATGTCACGCCG
CGATCATTATAAGCGAGATCGTAGATGCTGTCCTTGTTCTGCAGATACAGCGCCAGCCGCTCAAGCCCATAGGTCAGCTC
GCCAGCGACCGGCTTGCAGTCGAAACCGCCCATCTGCTGGAAATAGGTGAACTGGGTGACCTCCATACCATCG
>ASV16;Bacteria;NA;Proteobacteria;Betaproteobacteria;Nitrosomonadales;Nitrosomonadaceae;Nitrosospira;Nitrosospira multiformis;80.992
GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCA
AATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGC
CAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTC
CGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGTTCGGTCGCGTGGAGAC
>ASV17;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Heteroderidae;Globodera;Globodera rostochiensis;100
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTA
CGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAA
CCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGA
TTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAGCCAAC
CGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTC
TATGCATTTTGAGAGCTGGAATTACCG
>ASV18;Eukaryota;Metazoa;Chordata;Mammalia;Perissodactyla;Rhinocerotidae;Ceratotherium;Ceratotherium simum;85.366
GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAA
CAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGCACTTTCAAAGTTGCAGAGTGAAG
GAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCG
ATAAAGGCTCTCCTTC
>ASV19;Eukaryota;Fungi;Ascomycota;Sordariomycetes;Hypocreales;Hypocreaceae;Trichoderma;Trichoderma reesei;96.296
CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAG
CGATCCAGCGCCATTACCTTTCAAAAGACCGGTGAGCGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGAGTGCTCG
AGACATCGGTCGAAAGATCGATGAGAGACGTTGAGATGA
>ASV20;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Neodiplogasteridae;Pristionchus;Pristionchus lucani;100
TCGCCTTCGATCCTCTGACTTTCGTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCAATAGGTCGTCTCGCTG
CGGTCCAAGAATTTCACCTCTCACGCAGCGATACGGATGCCCCCGAGTTGTTCCTTTTAATCATTACTTCAATCCCAAGA
CCAACAGAATGGACCGAAGTCATATTCTATTATTCCATGATAAAGCATTCAAGCAAACGCCTGTTTTAATCACATTGATT
TAATCAAAGTTACAACACCAGTCACCGCAGCCGAAGCTACGGAAAACCGGCGAAACAGTCCAGACTAGCAGTATCTCACA
TGGAGAACTACAGTCATGAACTTAGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGATATACACGTTGGGAGCTGG
AATTACCG
>ASV21;Bacteria;NA;Acidobacteria;Acidobacteriia;Bryobacterales;Bryobacteraceae;Paludibaculum;Paludibaculum fermentans;67.47
CCAAATTCGCGCAGCACCTCCAGGTGCCCCACTTGGTTGCGCGGAAAAGCAAAGGAGCGCAGCTCAATATCCATCGCCTG
TGCCAATCGCACGCACTCCGCGATTTCACTGCGCGCCGCCGTCTCGGAACAACCCGCATCGCCGAAAATGACGTGCGAAA
AAGAATGGCACCCAATCTCCTGAGGCATGGGCGATGCCTTGATTTTTTC
>ASV22;Eukaryota;Viridiplantae;Streptophyta;Magnoliopsida;Malvales;Malvaceae;Gossypium;Gossypium turneri;69.672
CCAAAAATGATTTTAATTAGTCAAAAAATATTAATTTACATAACTTTATAAAATTGCAAACATTAGCATCAACACCAATT
TCGTAATTTTATAAATTAAATTTGAAACTATTTTTTTCAACTTTCCGGCTAACGCGATTGGCAAGGGCGTAGCAAAACAA
TGCGCAGATGGCGCAAAACTTGTCATACAATGCT
>ASV23;Eukaryota;Viridiplantae;Streptophyta;Magnoliopsida;Poales;Poaceae;Setaria;Setaria viridis;86.047
CCAAACGATTCACAGAAAATGCTGTCAGCGGGCTGGAATTCATGAGCCAGGGCCACTACCATACTTTGCCCACCTAACTC
ATCTTCTCTTCGTAGCCGCCGCAATAGCGAAAGCGAAAGGGAGAGCCATGCCCAAAGCCGCAAGCGGGCTCTAGGACAAT
TCCTTGCCGAGGCTTCAGCCTTGGCCTTTCCGATGAACACCTTCTCGTCAGCCTCGATCTGATTCTCGCCTGCTTTGGCA
TGGCGCTGCGTTTGCGCTGCTCTGGTCCATGCGTTACAACA
>ASV24;Bacteria;NA;Proteobacteria;Betaproteobacteria;Burkholderiales;NA;Inhella;Inhella inkyongensis;72.619
CCCTTCCCCGATGAGCACGAGCAGCCCCTTCATCTGCGGTGCCTCGTGCGCCGAGTGAATATCGACCGCCATCGAAATCG
AATGCGCGAGCAGCGCGCCGAGGAGGACAGCGACAACCACGCCGCCGACTTGAAGTACCCACCGCGGCAGCCAGGGCGCC
AATCGAACCGGCCGTTGTTCGAACAGCCCGAAGGCCAGCACCGC
>ASV25;Bacteria;NA;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Alteromonas;Alteromonas mediterranea;89.474
CCCGAAATGCTGGGATTGACCCTGGCGGAAGGCAAAGCCATCTTGCGGGAACTCCAACGCGTCGTAGTAGAACATCAAAT
CGCGGAATTCGTGGCGGCGCACCGTCACTGTTCCGAGTGCGGACAGCTCCGACGGAGCCGCGGGTGCCACGATATTCCGA
TGCGCGCCGTGTTCGGCAAGATTAAGATTCCAAGCCCGCGCTTTGTGCATTGTGATTGCCAGCCGCACGATACCGAGAGC
TTCAGCCCGTTGGCGCAAGTGCTGCCGGAGCG
>ASV26;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Rhabditidae;Pellioditis;Pellioditis marina;86.461
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTATAGGGCGTCCCACTG
CGGTCCACGAATTTCACCTCTCGCGCAATGATACGAATGCCCCCGAATTGTCCCTCTTAACCATTAATTCAGTTCCAGAA
CCAATAAAAAGAACCGAAGTCCTCTTTTATTATTCCATGATCGAATATGCAGGCAAACGCCTGTTTTGAGCACTCTAATT
TAATCAAAGTAAACTCGCCAATCATCACACCGACGTTTCCGCCAATGCAAAAAATTGGCACAAGTAGACAAATCTTAGTT
AACGCCAGAGGCGTACCAAGATCTGCTACGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGGCTTACATTAATGGA
GCTGGAATTACCG
>ASV27;Bacteria;NA;Proteobacteria;Alphaproteobacteria;Rhizobiales;Rhizobiaceae;Agrobacterium;Agrobacterium tumefaciens;73.737
CCGATCGAGAACAGCTTCAAGCCGGGAGTTCCTCCCGAGGTCTACATGAAGGACCCGGCGAACAGCGACGAGCGCTTCTT
CATTCCCTTGAGCGAGACGGTTCTCTCGCGGCCGATGTGGATTTCGCCTCAGCGCAACATGTGGGCCGACATCCTATGGG
CCAAGACGGCCGGCCTGGTGAACCGGCACTACCACCCG
>ASV28;Bacteria;NA;Bacteroidetes;Sphingobacteriia;Sphingobacteriales;Sphingobacteriaceae;Mucilaginibacter;Mucilaginibacter ginsenosidivorax;79.798
ACTCAACCAGAAAGTTGAAAAAGATGAGGTTGAAATCGAAAAATCAAACATCATCATGGTGGGCGAAACTGGTACCGGCA
AAACCCTGCTGGCAAAAACTCTCGCCAAAATATTAAATGTGCCCTTCTGTATATGCGATGCAACCGTTCTAACGGAAGCT
GGTTATGTTGGGGAGGATGTCGAAAGTATTCTTACACGTCTG
>ASV29;Bacteria;NA;Planctomycetes;NA;NA;NA;NA;Planctomycetes bacterium Enr10;77.381
CAAAGCGGCTAAGCATCGCCAGGCCGCTGTGAAATTACCTCGGGAAAGCCAACAGCTTGCTGTCATGGAGCATACAAGAT
GAAGCTTTGAGCAGCTGACTTTCATGCTTTCCTAGACGCTACGGCATTACGGGAGATTTGTTTGGCCCATTAAGTAACGG
TCGCACTCCCGCGCTGCGCCCCGGCCCTCGTTGATCGCCCATAC
>ASV30;Eukaryota;Metazoa;Chordata;Actinopteri;Cypriniformes;Cyprinidae;Cyprinus;Cyprinus carpio;79.31
GGTCGGCAAATTCATATCAAAATTTCAAGAGAATATCATGCCAAAGTGCGCTATTTGTTCTGAATATAAATTCCGTCTCC
CGTTCCTATCCAAATTTTATTATCTTTTGCTTGTTCAACCGCAAATACACCTTCAAACTTTGTGGTTCCGGCTGCTGTCC
ATTTATTCCCATCATAAATACTACAGGTAGTGCCATCGCTG
>ASV31;Bacteria;NA;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Burkholderia;Burkholderia multivorans;86.364
CACTACTAGAGCTTGCCATCGTTCCTATGGCGTCAGGAGTCTTCTGTCAGTATCATTGCATCGTTCGCCTTATTGCTGAG
AACGATCTACTTTTTGCCGAAAGGCGCGCTGCACCGCGCACATGCAGAAAATGAGCACCGAGGGCAGCGCCGCGCTCATC
GTGTACTAATGCGGCAGGCGCTCGCTTTTAGCCGCCCGTACGATCAGCGCGGTTCCGGAGCGGCTTCA
>ASV32;Bacteria;NA;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Gramella;Gramella forsetii;78.947
GCATATTAGTTCAGAACACAATTGATATAAGTCCGGAACTAAAGGCATATCAGTTCAGAGGTGATATGCATTAGGGTTGG
AAGTGGGGTGCCTTATACCCGGAGGTAACAGCCGAAGTGACGTCATTGATAGTTATACTTAAAAAACGTCATTTCGAACA
GAACGTAGTGGAGTGAGAAATCTCATGAATTGTAGGAAAATGCTTTTATGATCCGTCAACACTT
>ASV33;Bacteria;NA;Proteobacteria;Alphaproteobacteria;Rhizobiales;Methylobacteriaceae;Methylobacterium;Methylobacterium terrae;67.692
CCCCGGGATGCTCGTCGACTCGTATGGACCGGTGCGCACCGTGCCCTTGTCGTTGAGCTCGCAACCAAGCGATTTTGCCA
GATCGGAGCACTGCGTCTGGCCCGTCGTGAAGAAAAGCGCGCGCCGCGCCAGCGCCTCGCCGCTGGCAAAAACGATGCGC
TCGAGCCGGCCGTCGCGGCCGTCGAGCCGCGCAATGCGATCCTCGCG
>ASV34;Bacteria;NA;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Salmonella;Salmonella enterica;91.892
CACGCCCTTGGTATCCTGGTATATCGAGCGAACCTGATCTCCAGCCAGCCCATCGCGTTCGTCCAATGAGGACCAAGCGA
TGCCATCGAAGTGTGAAACCCCGCCATCGGTTCCGAACCAAAAGTGACCCTCGGAATCGCGGAAGATTGCGTGGATGAAA
TTGCCCGCGAGCCCGCTTTTGCCAGCCGTATAAGTGGTGAACTTACGGCCATCAAAGTG
>ASV35;Eukaryota;Metazoa;Arthropoda;Insecta;Thysanoptera;Thripidae;Frankliniella;Frankliniella occidentalis;83.333
GCATACTGTTTGTTACACTATTTTCAATTGCCACAGAAGCAGATAAAGTAATTCTATCTTTCAAAACCGTCACACCAACT
TTAGATGAAGGAGATTGGAGTTTGGGAGAGACGATGGAGCTATCAGAGGATGCACTGCCGCCCACAGAGCGCGACACTTG
ACACGTCTGCACAGGGATGAACACTTGAGGTTTATTCGGTGGAATAGGAGGCGGGAATCCGCGCCCCATCGGGGACAGCT
TCCTCTGTTGAC
>ASV36;Archaea;NA;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halobacterium;Halobacterium hubeiense;96.296
GCAAAGAAGCTTGGTCTCACTCCCCGGGAATATATTCAACAACTTATCGCCAACGACCTCGCTCTGGACCGGAAGGCACA
ATCGTCGTCGCTCGATGAGCTCGCGGCCCCCTTCCGAAAAGCTCTCAAAGATGTCTCCGACAAGGAACTTGACCGGATGG
TCGATGCCGCTCGTCATCGAAGAGTATCGCCGTCCTCCAGGAAACTGCCTCAAGC
>ASV37;Bacteria;NA;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Variovorax;Variovorax sp. PBL-E5;68.617
CAGTCGCCGCTTTCGCAGACGGGGCCGACAATGTCGGTTTTTTTGGTCGAACCCTTCGTTTTATTAATGGGGACGATCTC
ATGGTAGCTGCCGTAGAGGGACGGCCGAAGCAGATCATTCATCGCTGCGTCCACGACGAGAAAATCCTTCTCCTTGCGAA
CTTTGCGATAGAGGACCTCAGTGAGAAGCA
>ASV38;Eukaryota;Metazoa;Chordata;Actinopteri;Beloniformes;Adrianichthyidae;Oryzias;Oryzias latipes;84.444
GGAAAGCGGTAGTCGTCCTGGTTATCGGTTTGGCCGTCAGCAGGCATCACAAATGTGGCGTTGTAAAGCAGCCACAGATC
GTGCATGTGGGTGCTCTGGTTGGACGGCGAGTTCATGCCGACTTGGTCGTAGTACATGGCCTGCAACGCGAGCTCTGATC
ACACTCTTTGAAATAGCTCGTGTCTCACCTGGCTCTGATTCACGCGATAGCTGCCCGCTCCAG
>ASV39;Bacteria;NA;Proteobacteria;Alphaproteobacteria;NA;NA;Polymorphum;Polymorphum gilvum;83.721
GGCTCCCTGCCGATCAACTCCTTGACACACACCAACTGTCACACGTTCGCCTGCCGTGTGTTGAACAGTGCGACCACGCA
AGAAACACATCCGATCCCATTGGTCGGCCAGTCGCGGATCGTCGGCACTCAACAGACTGAGATGCGATTCCAGTTGTCGC
AGCACGTTCACCAGGACGTCTTCGAGGGTCCCTCGTCGCTGCGAGAGATCGCATAAAGCCGTGGCGCGATCGGCGATTTC
CGCAGGGGCCGACTGGACGGAATTGTTGACATTGATCCCAATCCCGATCACCAGACTTGCCCC
>ASV40;Bacteria;NA;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Erythrobacteraceae;Altererythrobacter;Altererythrobacter namhicola;76.552
GCCATGAGGATGTTCCGCCATACCAGGGTCACCCGGTACCTGATTCGAAAGGGTTCATAGGAGAGAGCGCTATGGGAATT
CGTGAAGACATGCCGCTGCTGGCGCGCCACGAGGGTGTGTGGGACGGTGTCTACACTTATTACAACGCCGCGGGTGAGAA
GATCGACGAGCACGGATCGCGGCTTTTCTGTCGCTTCCCGGAAGGCAGCAAGTATCCGTAT
>ASV41;Eukaryota;Metazoa;Chordata;Mammalia;Primates;Hominidae;Homo;Homo sapiens;80.645
GGAGCGGAATGTGACAGTGGGAATAAACATCGTAAAGTACCTTACAGCTCGCTCGACTACGAGCGATCGCTTGATGAAAC
ATTGTACTCTGGAAAATTACGGGCGGAAAAGAAAAACTTCGGTAAGTGGTGTCCGGCTTGACCTAGAAAAGGGAGCACCG
GTTTCCCGATACTCCCGCTTGCTAGAATATTTCGAGTCTGCCTTCAATTCGAACCGCGGTGCCCGCTTCCTCCATTGAAG
TAGATTTGTGCTTTGCGCCATGCCTGTTCGCC
>ASV42;Bacteria;NA;Actinobacteria;Actinobacteria;Propionibacteriales;Nocardioidaceae;Friedmanniella;Friedmanniella luteola;74.074
CCCAAACTTCAGGTTTTTGGAGCGCACGTATTCTGCGACGGGCGCAAGTCCGTTGGGCAGCTTTGCACCGTCCACCCGAT
ACCAGTTACCAAGCCCTGCCGAGAAGTCGTAGCCCTTTCCGGTGCCGCTGTACCAGCCGGCGTCCAGCAGGAAGTACTCT
TCTCCAAGCGCTGCGGCTGCATCAGCTAGTTTGCGCAGCAATTCCTCGTCGAAGTCGACCCCAACGTTCCAGAAGTGATC
GTAGGTCATCACAGGCGTGAACGGGCGGCCACCAAGCCGCGGCGTGTACACCTCGCG
>ASV43;Eukaryota;Metazoa;Nematoda;Chromadorea;Rhabditida;Pratylenchidae;Pratylenchus;Pratylenchus crenatus;89.865
ACGGTATCTGATCGCCTCGAACCTCCGTCGTTCTTGATTACTGTAAATTAGACATTCTTGGCAAATGCTTTCGCTGTAGT
CTGTCTGGCAACGGTCCCAAATTTCACCTCTCACGTTGCCATACAAATGCCCCCGTTTGTCTCCTTGCAGCACCGGACAT
CCACGCACCATTGAAGGGCATGCCGAACACTGCAGGAAAGCCAGTGCTGACAAGTGAGGTGAGACTAGTACACGTCCGAA
GACGGA
analysis/Trich/Trich_DADA2_results_060921/ASVs_raw.fasta 0 → 100644
+ 215
− 0
View file @ e9cc7d51
>ASV1
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTA
CGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAA
CCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGA
TTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAA
CCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATT
CTATGCATTTTGAGAGCTGGAATTACCG
>ASV2
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAAC
CAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAA
GCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATAT
ACGCTATTGGAGCTGGAATTACCG
>ASV3
CCGTAAACCACTTCGATCAGTACTGGCATCGCATCCAACTGCGTAATGAAGCAATGTCGATTCCAGCGCCATGTCTCCGC
TGGCAACGAATGAATGAACTGAATCCTGTGGAGCGTTTCATATGGGACAACGTGCAGAGTTCGCGCCGAATACTCGACTT
CGGTTCAGGAGATCAGTCGTTAAGGAAGAAATTCCTCGCTGCCGGTTATCGCGGTAAGTACGAGACATTCGATATTT
>ASV4
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAAC
CAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAA
GTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATA
CGCTATTGGAGCTGGAATTACCG
>ASV5
CACCCGGAACGAGCAGTGATTTTGCCATTTTATCGACTTTTTCTTGCAATACTGCCTGATTAATCGGCTTTAGTTCCGCT
GCCTGGCAAACCATGTTTGCTAATAAGAACAGAATGGTTATGATTCTCAAAAATTATCTCCTTTTCATCTTCAATGGCGC
TTATGTCCTTTAGGCTTACATTAGTATAAGCCAGGCTTTAAAATTCCGCTACCTTCTGAAG
>ASV6
GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCA
AATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGC
CAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTC
CGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC
>ASV7
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCA
CGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAA
CCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGA
TTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACA
CCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTA
TTAGCAGAGCTGGAATTACCG
>ASV8
GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAA
CAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGTACTTTCAAAGTTGCAGAGTGAAG
GAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCG
ATAAAGGCTCTCCTTC
>ASV9
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTACAGCGATAAC
CAATAAAAGGCTGCAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCTAGAGAAAACCGAATGAGTAGTCCAGCCGAC
ATAGTACCACCCAGTAGGGGGACCACATCAGCCAGACGAAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATAT
ACGCTATTGGAGCTGGAATTACCG
>ASV10
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCA
CGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAAC
CAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTT
GATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGA
GACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTT
ACGCTAGTGGAGCTGGAATTACCG
>ASV11
TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGA
CGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAAC
CAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATT
TTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAA
GTAGTGCCACCCAGTGGGGGACCACCTCAGCCGAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATA
CGCTATTGGAGCTGGAATTACCG
>ASV12
GCCGCTGAAAATTCTGTAACCTGCTGATACCGGATCTTACATTCCTGTCAACCTTTGCTTTTTTAACTTCATCCAAATCG
GTCAGTTGGTTTAATACCAGCTGCGGAAAAACGGCTGAAGTAGTCTGCTCCACACATCCATGTGGGTATTCTATCAGGTA
GTTCAAGCGTTTTTGCAAATTCATAGATGGTACACTGGAAATTTCCAACACAGCTGTACTTGAAGTTATCACACCAATAG
GGTTGGCCATTGTTTTCCATTGCTGCCCTGCCGATAAAGTCATTTCAGTAACCTGTGTTACCGGCGGATTTGGGTTGCGT
ATTTCAAGTTCCACTTCATAATCT
>ASV13
CCATCACTGCTTTATAAGAATAATTGACCAAACCAATGGCCAGTACCCGCTGATAAGGCCAGCCCTTACCGATGCTGAAG
GCTATGAACAATACAATGTAGCTGTCTATCAGCTGTGAGATGATGGTAGAACCTGTAGCCCGGAGCCAGACCTTTTTCTC
ACCGGTCACCTTCTTGATCTTATGGAACACCCATACATCCACCACCTGGCTCACCAGGAAAGC
>ASV14
CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAG
CGATCCAGCGCCATTACCCTTCAAAAGACCAGTGAGAGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGCGTGCTCG
AGACGTCCGTCGAAAGATCGATGAGCGATGTTGAGACAA
>ASV15
CCTCGGTATTGGCGACCTCGAAATTCCAGGTCGATTGCTCGATCTCATTCTCGAGGAACACGTCGCCATATGTCACGCCG
CGATCATTATAAGCGAGATCGTAGATGCTGTCCTTGTTCTGCAGATACAGCGCCAGCCGCTCAAGCCCATAGGTCAGCTC
GCCAGCGACCGGCTTGCAGTCGAAACCGCCCATCTGCTGGAAATAGGTGAACTGGGTGACCTCCATACCATCG
>ASV16
GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCA
AATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGC
CAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTC
CGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGTTCGGTCGCGTGGAGAC
>ASV17
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTA
CGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAA
CCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGA
TTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAGCCAAC
CGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTC
TATGCATTTTGAGAGCTGGAATTACCG
>ASV18
GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAA
CAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGCACTTTCAAAGTTGCAGAGTGAAG
GAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCG
ATAAAGGCTCTCCTTC
>ASV19
CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAG
CGATCCAGCGCCATTACCTTTCAAAAGACCGGTGAGCGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGAGTGCTCG
AGACATCGGTCGAAAGATCGATGAGAGACGTTGAGATGA
>ASV20
TCGCCTTCGATCCTCTGACTTTCGTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCAATAGGTCGTCTCGCTG
CGGTCCAAGAATTTCACCTCTCACGCAGCGATACGGATGCCCCCGAGTTGTTCCTTTTAATCATTACTTCAATCCCAAGA
CCAACAGAATGGACCGAAGTCATATTCTATTATTCCATGATAAAGCATTCAAGCAAACGCCTGTTTTAATCACATTGATT
TAATCAAAGTTACAACACCAGTCACCGCAGCCGAAGCTACGGAAAACCGGCGAAACAGTCCAGACTAGCAGTATCTCACA
TGGAGAACTACAGTCATGAACTTAGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGATATACACGTTGGGAGCTGG
AATTACCG
>ASV21
CCAAATTCGCGCAGCACCTCCAGGTGCCCCACTTGGTTGCGCGGAAAAGCAAAGGAGCGCAGCTCAATATCCATCGCCTG
TGCCAATCGCACGCACTCCGCGATTTCACTGCGCGCCGCCGTCTCGGAACAACCCGCATCGCCGAAAATGACGTGCGAAA
AAGAATGGCACCCAATCTCCTGAGGCATGGGCGATGCCTTGATTTTTTC
>ASV22
CCAAAAATGATTTTAATTAGTCAAAAAATATTAATTTACATAACTTTATAAAATTGCAAACATTAGCATCAACACCAATT
TCGTAATTTTATAAATTAAATTTGAAACTATTTTTTTCAACTTTCCGGCTAACGCGATTGGCAAGGGCGTAGCAAAACAA
TGCGCAGATGGCGCAAAACTTGTCATACAATGCT
>ASV23
CCAAACGATTCACAGAAAATGCTGTCAGCGGGCTGGAATTCATGAGCCAGGGCCACTACCATACTTTGCCCACCTAACTC
ATCTTCTCTTCGTAGCCGCCGCAATAGCGAAAGCGAAAGGGAGAGCCATGCCCAAAGCCGCAAGCGGGCTCTAGGACAAT
TCCTTGCCGAGGCTTCAGCCTTGGCCTTTCCGATGAACACCTTCTCGTCAGCCTCGATCTGATTCTCGCCTGCTTTGGCA
TGGCGCTGCGTTTGCGCTGCTCTGGTCCATGCGTTACAACA
>ASV24
CCCTTCCCCGATGAGCACGAGCAGCCCCTTCATCTGCGGTGCCTCGTGCGCCGAGTGAATATCGACCGCCATCGAAATCG
AATGCGCGAGCAGCGCGCCGAGGAGGACAGCGACAACCACGCCGCCGACTTGAAGTACCCACCGCGGCAGCCAGGGCGCC
AATCGAACCGGCCGTTGTTCGAACAGCCCGAAGGCCAGCACCGC
>ASV25
CCCGAAATGCTGGGATTGACCCTGGCGGAAGGCAAAGCCATCTTGCGGGAACTCCAACGCGTCGTAGTAGAACATCAAAT
CGCGGAATTCGTGGCGGCGCACCGTCACTGTTCCGAGTGCGGACAGCTCCGACGGAGCCGCGGGTGCCACGATATTCCGA
TGCGCGCCGTGTTCGGCAAGATTAAGATTCCAAGCCCGCGCTTTGTGCATTGTGATTGCCAGCCGCACGATACCGAGAGC
TTCAGCCCGTTGGCGCAAGTGCTGCCGGAGCG
>ASV26
TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTATAGGGCGTCCCACTG
CGGTCCACGAATTTCACCTCTCGCGCAATGATACGAATGCCCCCGAATTGTCCCTCTTAACCATTAATTCAGTTCCAGAA
CCAATAAAAAGAACCGAAGTCCTCTTTTATTATTCCATGATCGAATATGCAGGCAAACGCCTGTTTTGAGCACTCTAATT
TAATCAAAGTAAACTCGCCAATCATCACACCGACGTTTCCGCCAATGCAAAAAATTGGCACAAGTAGACAAATCTTAGTT
AACGCCAGAGGCGTACCAAGATCTGCTACGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGGCTTACATTAATGGA
GCTGGAATTACCG
>ASV27
CCGATCGAGAACAGCTTCAAGCCGGGAGTTCCTCCCGAGGTCTACATGAAGGACCCGGCGAACAGCGACGAGCGCTTCTT
CATTCCCTTGAGCGAGACGGTTCTCTCGCGGCCGATGTGGATTTCGCCTCAGCGCAACATGTGGGCCGACATCCTATGGG
CCAAGACGGCCGGCCTGGTGAACCGGCACTACCACCCG
>ASV28
ACTCAACCAGAAAGTTGAAAAAGATGAGGTTGAAATCGAAAAATCAAACATCATCATGGTGGGCGAAACTGGTACCGGCA
AAACCCTGCTGGCAAAAACTCTCGCCAAAATATTAAATGTGCCCTTCTGTATATGCGATGCAACCGTTCTAACGGAAGCT
GGTTATGTTGGGGAGGATGTCGAAAGTATTCTTACACGTCTG
>ASV29
CAAAGCGGCTAAGCATCGCCAGGCCGCTGTGAAATTACCTCGGGAAAGCCAACAGCTTGCTGTCATGGAGCATACAAGAT
GAAGCTTTGAGCAGCTGACTTTCATGCTTTCCTAGACGCTACGGCATTACGGGAGATTTGTTTGGCCCATTAAGTAACGG
TCGCACTCCCGCGCTGCGCCCCGGCCCTCGTTGATCGCCCATAC
>ASV30
GGTCGGCAAATTCATATCAAAATTTCAAGAGAATATCATGCCAAAGTGCGCTATTTGTTCTGAATATAAATTCCGTCTCC
CGTTCCTATCCAAATTTTATTATCTTTTGCTTGTTCAACCGCAAATACACCTTCAAACTTTGTGGTTCCGGCTGCTGTCC
ATTTATTCCCATCATAAATACTACAGGTAGTGCCATCGCTG
>ASV31
CACTACTAGAGCTTGCCATCGTTCCTATGGCGTCAGGAGTCTTCTGTCAGTATCATTGCATCGTTCGCCTTATTGCTGAG
AACGATCTACTTTTTGCCGAAAGGCGCGCTGCACCGCGCACATGCAGAAAATGAGCACCGAGGGCAGCGCCGCGCTCATC
GTGTACTAATGCGGCAGGCGCTCGCTTTTAGCCGCCCGTACGATCAGCGCGGTTCCGGAGCGGCTTCA
>ASV32
GCATATTAGTTCAGAACACAATTGATATAAGTCCGGAACTAAAGGCATATCAGTTCAGAGGTGATATGCATTAGGGTTGG
AAGTGGGGTGCCTTATACCCGGAGGTAACAGCCGAAGTGACGTCATTGATAGTTATACTTAAAAAACGTCATTTCGAACA
GAACGTAGTGGAGTGAGAAATCTCATGAATTGTAGGAAAATGCTTTTATGATCCGTCAACACTT
>ASV33
CCCCGGGATGCTCGTCGACTCGTATGGACCGGTGCGCACCGTGCCCTTGTCGTTGAGCTCGCAACCAAGCGATTTTGCCA
GATCGGAGCACTGCGTCTGGCCCGTCGTGAAGAAAAGCGCGCGCCGCGCCAGCGCCTCGCCGCTGGCAAAAACGATGCGC
TCGAGCCGGCCGTCGCGGCCGTCGAGCCGCGCAATGCGATCCTCGCG
>ASV34
CACGCCCTTGGTATCCTGGTATATCGAGCGAACCTGATCTCCAGCCAGCCCATCGCGTTCGTCCAATGAGGACCAAGCGA
TGCCATCGAAGTGTGAAACCCCGCCATCGGTTCCGAACCAAAAGTGACCCTCGGAATCGCGGAAGATTGCGTGGATGAAA
TTGCCCGCGAGCCCGCTTTTGCCAGCCGTATAAGTGGTGAACTTACGGCCATCAAAGTG
>ASV35
GCATACTGTTTGTTACACTATTTTCAATTGCCACAGAAGCAGATAAAGTAATTCTATCTTTCAAAACCGTCACACCAACT
TTAGATGAAGGAGATTGGAGTTTGGGAGAGACGATGGAGCTATCAGAGGATGCACTGCCGCCCACAGAGCGCGACACTTG
ACACGTCTGCACAGGGATGAACACTTGAGGTTTATTCGGTGGAATAGGAGGCGGGAATCCGCGCCCCATCGGGGACAGCT
TCCTCTGTTGAC
>ASV36
GCAAAGAAGCTTGGTCTCACTCCCCGGGAATATATTCAACAACTTATCGCCAACGACCTCGCTCTGGACCGGAAGGCACA
ATCGTCGTCGCTCGATGAGCTCGCGGCCCCCTTCCGAAAAGCTCTCAAAGATGTCTCCGACAAGGAACTTGACCGGATGG
TCGATGCCGCTCGTCATCGAAGAGTATCGCCGTCCTCCAGGAAACTGCCTCAAGC
>ASV37
CAGTCGCCGCTTTCGCAGACGGGGCCGACAATGTCGGTTTTTTTGGTCGAACCCTTCGTTTTATTAATGGGGACGATCTC
ATGGTAGCTGCCGTAGAGGGACGGCCGAAGCAGATCATTCATCGCTGCGTCCACGACGAGAAAATCCTTCTCCTTGCGAA
CTTTGCGATAGAGGACCTCAGTGAGAAGCA
>ASV38
GGAAAGCGGTAGTCGTCCTGGTTATCGGTTTGGCCGTCAGCAGGCATCACAAATGTGGCGTTGTAAAGCAGCCACAGATC
GTGCATGTGGGTGCTCTGGTTGGACGGCGAGTTCATGCCGACTTGGTCGTAGTACATGGCCTGCAACGCGAGCTCTGATC
ACACTCTTTGAAATAGCTCGTGTCTCACCTGGCTCTGATTCACGCGATAGCTGCCCGCTCCAG
>ASV39
GGCTCCCTGCCGATCAACTCCTTGACACACACCAACTGTCACACGTTCGCCTGCCGTGTGTTGAACAGTGCGACCACGCA
AGAAACACATCCGATCCCATTGGTCGGCCAGTCGCGGATCGTCGGCACTCAACAGACTGAGATGCGATTCCAGTTGTCGC
AGCACGTTCACCAGGACGTCTTCGAGGGTCCCTCGTCGCTGCGAGAGATCGCATAAAGCCGTGGCGCGATCGGCGATTTC
CGCAGGGGCCGACTGGACGGAATTGTTGACATTGATCCCAATCCCGATCACCAGACTTGCCCC
>ASV40
GCCATGAGGATGTTCCGCCATACCAGGGTCACCCGGTACCTGATTCGAAAGGGTTCATAGGAGAGAGCGCTATGGGAATT
CGTGAAGACATGCCGCTGCTGGCGCGCCACGAGGGTGTGTGGGACGGTGTCTACACTTATTACAACGCCGCGGGTGAGAA
GATCGACGAGCACGGATCGCGGCTTTTCTGTCGCTTCCCGGAAGGCAGCAAGTATCCGTAT
>ASV41
GGAGCGGAATGTGACAGTGGGAATAAACATCGTAAAGTACCTTACAGCTCGCTCGACTACGAGCGATCGCTTGATGAAAC
ATTGTACTCTGGAAAATTACGGGCGGAAAAGAAAAACTTCGGTAAGTGGTGTCCGGCTTGACCTAGAAAAGGGAGCACCG
GTTTCCCGATACTCCCGCTTGCTAGAATATTTCGAGTCTGCCTTCAATTCGAACCGCGGTGCCCGCTTCCTCCATTGAAG
TAGATTTGTGCTTTGCGCCATGCCTGTTCGCC
>ASV42
CCCAAACTTCAGGTTTTTGGAGCGCACGTATTCTGCGACGGGCGCAAGTCCGTTGGGCAGCTTTGCACCGTCCACCCGAT
ACCAGTTACCAAGCCCTGCCGAGAAGTCGTAGCCCTTTCCGGTGCCGCTGTACCAGCCGGCGTCCAGCAGGAAGTACTCT
TCTCCAAGCGCTGCGGCTGCATCAGCTAGTTTGCGCAGCAATTCCTCGTCGAAGTCGACCCCAACGTTCCAGAAGTGATC
GTAGGTCATCACAGGCGTGAACGGGCGGCCACCAAGCCGCGGCGTGTACACCTCGCG
>ASV43
ACGGTATCTGATCGCCTCGAACCTCCGTCGTTCTTGATTACTGTAAATTAGACATTCTTGGCAAATGCTTTCGCTGTAGT
CTGTCTGGCAACGGTCCCAAATTTCACCTCTCACGTTGCCATACAAATGCCCCCGTTTGTCTCCTTGCAGCACCGGACAT
CCACGCACCATTGAAGGGCATGCCGAACACTGCAGGAAAGCCAGTGCTGACAAGTGAGGTGAGACTAGTACACGTCCGAA
GACGGA
analysis/Trich/Trich_DADA2_results_060921/blastnt-dbAp21_ASVs_raw.txt 0 → 100644
+ 11239
− 0
View file @ e9cc7d51
Source diff could not be displayed: it is too large. Options to address this: view the blob.
analysis/Trich/Trich_DADA2_results_060921/custom_BLAST_taxonomy_nt.txt 0 → 100644
+ 44
− 0
View file @ e9cc7d51
"Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "Species"
"ASV1" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Heteroderidae" "Globodera" "Globodera tabacum"
"ASV2" "Metazoa" "Nematoda" "Enoplea" "Triplonchida" "Trichodoridae" "Paratrichodorus" "Paratrichodorus pachydermus"
"ASV3" NA NA NA NA NA NA NA
"ASV4" "Metazoa" "Nematoda" "Enoplea" "Dorylaimida" "Diphterophoridae" "Diphterophora" "Diphterophora communis"
"ASV5" "Metazoa" "Chordata" "Mammalia" "Primates" "Cercopithecidae" "Macaca" "Macaca mulatta"
"ASV6" NA "Proteobacteria" "Betaproteobacteria" "Nitrosomonadales" "Nitrosomonadaceae" "Nitrosospira" "Nitrosospira multiformis"
"ASV7" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Meloidogynidae" "Meloidogyne" "Meloidogyne hapla"
"ASV8" "Metazoa" "Mollusca" "Gastropoda" NA "Lottiidae" "Lottia" "Lottia gigantea"
"ASV9" "Metazoa" "Nematoda" "Enoplea" "Dorylaimida" "Diphterophoridae" "Diphterophora" "Diphterophora obesa"
"ASV10" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Rhabditidae" "Rhabditis" "Rhabditis cf. terricola JH-2004"
"ASV11" "Metazoa" "Nematoda" "Enoplea" "Dorylaimida" "Diphterophoridae" "Diphterophora" "Diphterophora communis"
"ASV12" NA "Bacteroidetes" "Sphingobacteriia" "Sphingobacteriales" "Sphingobacteriaceae" "Pedobacter" "Pedobacter sp. eg"
"ASV13" NA "Bacteroidetes" "Chitinophagia" "Chitinophagales" "Chitinophagaceae" "Chitinophaga" "Chitinophaga caeni"
"ASV14" "Fungi" "Ascomycota" "Sordariomycetes" "Hypocreales" "Hypocreaceae" "Trichoderma" "Trichoderma reesei"
"ASV15" NA "Proteobacteria" "Alphaproteobacteria" "Sphingomonadales" "Sphingomonadaceae" "Sphingomonas" "Sphingomonas alpina"
"ASV16" NA "Proteobacteria" "Betaproteobacteria" "Nitrosomonadales" "Nitrosomonadaceae" "Nitrosospira" "Nitrosospira multiformis"
"ASV17" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Heteroderidae" "Globodera" "Globodera rostochiensis"
"ASV18" "Metazoa" "Chordata" "Mammalia" "Perissodactyla" "Rhinocerotidae" "Ceratotherium" "Ceratotherium simum"
"ASV19" "Fungi" "Ascomycota" "Sordariomycetes" "Hypocreales" "Hypocreaceae" "Trichoderma" "Trichoderma reesei"
"ASV20" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Neodiplogasteridae" "Pristionchus" "Pristionchus lucani"
"ASV21" NA "Acidobacteria" "Acidobacteriia" "Bryobacterales" "Bryobacteraceae" "Paludibaculum" "Paludibaculum fermentans"
"ASV22" "Viridiplantae" "Streptophyta" "Magnoliopsida" "Malvales" "Malvaceae" "Gossypium" "Gossypium turneri"
"ASV23" "Viridiplantae" "Streptophyta" "Magnoliopsida" "Poales" "Poaceae" "Setaria" "Setaria viridis"
"ASV24" NA "Proteobacteria" "Betaproteobacteria" "Burkholderiales" NA "Inhella" "Inhella inkyongensis"
"ASV25" NA "Proteobacteria" "Gammaproteobacteria" "Alteromonadales" "Alteromonadaceae" "Alteromonas" "Alteromonas mediterranea"
"ASV26" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Rhabditidae" "Pellioditis" "Pellioditis marina"
"ASV27" NA "Proteobacteria" "Alphaproteobacteria" "Rhizobiales" "Rhizobiaceae" "Agrobacterium" "Agrobacterium tumefaciens"
"ASV28" NA "Bacteroidetes" "Sphingobacteriia" "Sphingobacteriales" "Sphingobacteriaceae" "Mucilaginibacter" "Mucilaginibacter ginsenosidivorax"
"ASV29" NA "Planctomycetes" NA NA NA NA "Planctomycetes bacterium Enr10"
"ASV30" "Metazoa" "Chordata" "Actinopteri" "Cypriniformes" "Cyprinidae" "Cyprinus" "Cyprinus carpio"
"ASV31" NA "Proteobacteria" "Betaproteobacteria" "Burkholderiales" "Burkholderiaceae" "Burkholderia" "Burkholderia multivorans"
"ASV32" NA "Bacteroidetes" "Flavobacteriia" "Flavobacteriales" "Flavobacteriaceae" "Gramella" "Gramella forsetii"
"ASV33" NA "Proteobacteria" "Alphaproteobacteria" "Rhizobiales" "Methylobacteriaceae" "Methylobacterium" "Methylobacterium terrae"
"ASV34" NA "Proteobacteria" "Gammaproteobacteria" "Enterobacterales" "Enterobacteriaceae" "Salmonella" "Salmonella enterica"
"ASV35" "Metazoa" "Arthropoda" "Insecta" "Thysanoptera" "Thripidae" "Frankliniella" "Frankliniella occidentalis"
"ASV36" NA "Euryarchaeota" "Halobacteria" "Halobacteriales" "Halobacteriaceae" "Halobacterium" "Halobacterium hubeiense"
"ASV37" NA "Proteobacteria" "Betaproteobacteria" "Burkholderiales" "Comamonadaceae" "Variovorax" "Variovorax sp. PBL-E5"
"ASV38" "Metazoa" "Chordata" "Actinopteri" "Beloniformes" "Adrianichthyidae" "Oryzias" "Oryzias latipes"
"ASV39" NA "Proteobacteria" "Alphaproteobacteria" NA NA "Polymorphum" "Polymorphum gilvum"
"ASV40" NA "Proteobacteria" "Alphaproteobacteria" "Sphingomonadales" "Erythrobacteraceae" "Altererythrobacter" "Altererythrobacter namhicola"
"ASV41" "Metazoa" "Chordata" "Mammalia" "Primates" "Hominidae" "Homo" "Homo sapiens"
"ASV42" NA "Actinobacteria" "Actinobacteria" "Propionibacteriales" "Nocardioidaceae" "Friedmanniella" "Friedmanniella luteola"
"ASV43" "Metazoa" "Nematoda" "Chromadorea" "Rhabditida" "Pratylenchidae" "Pratylenchus" "Pratylenchus crenatus"
analysis/Trich/Trich_DADA2_results_060921/descriptors.txt 0 → 100644
+ 22
− 0
View file @ e9cc7d51
SampleIDs Sample Alias
1-trich 1 S-01
10-trich 10 S-10
11-trich 11 S-11
12-trich 12 S-12
13-trich 13 S-13
14-trich 14 S-14
15-trich 15 S-15
16-trich 16 S-16
17-trich 17 S-17
18-trich 18 S-18
2-trich 2 S-02
3-trich 3 S-03
5-trich 5 S-05
6-trich 6 S-06
7-trich 7 S-07
8-trich 8 S-08
9-trich 9 S-09
PositivK-1-trich Control Control
PositivK-2-trich Control Control
PositivK-3-trich Control Control
PositivK-4-trich Control Control
\ No newline at end of file
analysis/Trich/Trich_DADA2_results_060921/descriptors_blank.txt 0 → 100644
+ 22
− 0
View file @ e9cc7d51
"SampleIDs" "ExampleProperty1" "ExampleProperty2" "ExampleProperty3"
"1-trich" "" "" ""
"10-trich" "" "" ""
"11-trich" "" "" ""
"12-trich" "" "" ""
"13-trich" "" "" ""
"14-trich" "" "" ""
"15-trich" "" "" ""
"16-trich" "" "" ""
"17-trich" "" "" ""
"18-trich" "" "" ""
"2-trich" "" "" ""
"3-trich" "" "" ""
"5-trich" "" "" ""
"6-trich" "" "" ""
"7-trich" "" "" ""
"8-trich" "" "" ""
"9-trich" "" "" ""
"PositivK-1-trich" "" "" ""
"PositivK-2-trich" "" "" ""
"PositivK-3-trich" "" "" ""
"PositivK-4-trich" "" "" ""
analysis/Trich/Trich_DADA2_results_060921/mergers.txt 0 → 100644
+ 90
− 0
View file @ e9cc7d51
"sample" "sequence" "abundance" "forward" "reverse" "nmatch" "nmismatch" "nindel" "prefer" "accept"
"1-trich" "CACCCGGAACGAGCAGTGATTTTGCCATTTTATCGACTTTTTCTTGCAATACTGCCTGATTAATCGGCTTTAGTTCCGCTGCCTGGCAAACCATGTTTGCTAATAAGAACAGAATGGTTATGATTCTCAAAAATTATCTCCTTTTCATCTTCAATGGCGCTTATGTCCTTTAGGCTTACATTAGTATAAGCCAGGCTTTAAAATTCCGCTACCTTCTGAAG" 100 1 1 201 0 0 2 TRUE
"1-trich" "GCCGCTGAAAATTCTGTAACCTGCTGATACCGGATCTTACATTCCTGTCAACCTTTGCTTTTTTAACTTCATCCAAATCGGTCAGTTGGTTTAATACCAGCTGCGGAAAAACGGCTGAAGTAGTCTGCTCCACACATCCATGTGGGTATTCTATCAGGTAGTTCAAGCGTTTTTGCAAATTCATAGATGGTACACTGGAAATTTCCAACACAGCTGTACTTGAAGTTATCACACCAATAGGGTTGGCCATTGTTTTCCATTGCTGCCCTGCCGATAAAGTCATTTCAGTAACCTGTGTTACCGGCGGATTTGGGTTGCGTATTTCAAGTTCCACTTCATAATCT" 33 2 2 238 0 0 2 TRUE
"1-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 13 3 5 155 0 0 1 TRUE
"1-trich" "CAAAGCGGCTAAGCATCGCCAGGCCGCTGTGAAATTACCTCGGGAAAGCCAACAGCTTGCTGTCATGGAGCATACAAGATGAAGCTTTGAGCAGCTGACTTTCATGCTTTCCTAGACGCTACGGCATTACGGGAGATTTGTTTGGCCCATTAAGTAACGGTCGCACTCCCGCGCTGCGCCCCGGCCCTCGTTGATCGCCCATAC" 13 4 3 184 0 0 2 TRUE
"1-trich" "CACGCCCTTGGTATCCTGGTATATCGAGCGAACCTGATCTCCAGCCAGCCCATCGCGTTCGTCCAATGAGGACCAAGCGATGCCATCGAAGTGTGAAACCCCGCCATCGGTTCCGAACCAAAAGTGACCCTCGGAATCGCGGAAGATTGCGTGGATGAAATTGCCCGCGAGCCCGCTTTTGCCAGCCGTATAAGTGGTGAACTTACGGCCATCAAAGTG" 8 5 4 199 0 0 2 TRUE
"10-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 65 1 1 154 0 0 2 TRUE
"10-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 23 2 2 159 0 0 2 TRUE
"10-trich" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC" 18 3 3 274 0 0 1 TRUE
"10-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 5 6 6 158 0 0 1 TRUE
"10-trich" "ACGGTATCTGATCGCCTCGAACCTCCGTCGTTCTTGATTACTGTAAATTAGACATTCTTGGCAAATGCTTTCGCTGTAGTCTGTCTGGCAACGGTCCCAAATTTCACCTCTCACGTTGCCATACAAATGCCCCCGTTTGTCTCCTTGCAGCACCGGACATCCACGCACCATTGAAGGGCATGCCGAACACTGCAGGAAAGCCAGTGCTGACAAGTGAGGTGAGACTAGTACACGTCCGAAGACGGA" 2 7 4 226 0 0 1 TRUE
"11-trich" "CCGTAAACCACTTCGATCAGTACTGGCATCGCATCCAACTGCGTAATGAAGCAATGTCGATTCCAGCGCCATGTCTCCGCTGGCAACGAATGAATGAACTGAATCCTGTGGAGCGTTTCATATGGGACAACGTGCAGAGTTCGCGCCGAATACTCGACTTCGGTTCAGGAGATCAGTCGTTAAGGAAGAAATTCCTCGCTGCCGGTTATCGCGGTAAGTACGAGACATTCGATATTT" 99 1 1 217 0 0 2 TRUE
"11-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 30 2 2 159 0 0 1 TRUE
"11-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 29 4 3 154 0 0 2 TRUE
"11-trich" "CCCGAAATGCTGGGATTGACCCTGGCGGAAGGCAAAGCCATCTTGCGGGAACTCCAACGCGTCGTAGTAGAACATCAAATCGCGGAATTCGTGGCGGCGCACCGTCACTGTTCCGAGTGCGGACAGCTCCGACGGAGCCGCGGGTGCCACGATATTCCGATGCGCGCCGTGTTCGGCAAGATTAAGATTCCAAGCCCGCGCTTTGTGCATTGTGATTGCCAGCCGCACGATACCGAGAGCTTCAGCCCGTTGGCGCAAGTGCTGCCGGAGCG" 17 3 4 252 0 0 2 TRUE
"11-trich" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC" 14 5 5 274 0 0 2 TRUE
"12-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 29 1 2 154 0 0 1 TRUE
"12-trich" "CCAAAAATGATTTTAATTAGTCAAAAAATATTAATTTACATAACTTTATAAAATTGCAAACATTAGCATCAACACCAATTTCGTAATTTTATAAATTAAATTTGAAACTATTTTTTTCAACTTTCCGGCTAACGCGATTGGCAAGGGCGTAGCAAAACAATGCGCAGATGGCGCAAAACTTGTCATACAATGCT" 20 2 1 174 0 0 2 TRUE
"12-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 7 4 4 159 0 0 1 TRUE
"12-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTATAGGGCGTCCCACTGCGGTCCACGAATTTCACCTCTCGCGCAATGATACGAATGCCCCCGAATTGTCCCTCTTAACCATTAATTCAGTTCCAGAACCAATAAAAAGAACCGAAGTCCTCTTTTATTATTCCATGATCGAATATGCAGGCAAACGCCTGTTTTGAGCACTCTAATTTAATCAAAGTAAACTCGCCAATCATCACACCGACGTTTCCGCCAATGCAAAAAATTGGCACAAGTAGACAAATCTTAGTTAACGCCAGAGGCGTACCAAGATCTGCTACGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGGCTTACATTAATGGAGCTGGAATTACCG" 6 3 3 169 0 0 1 TRUE
"12-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 3 5 6 158 0 0 1 TRUE
"13-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 51 1 1 154 0 0 1 TRUE
"13-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 8 2 2 158 0 0 2 TRUE
"14-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 49 1 1 154 0 0 1 TRUE
"14-trich" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC" 10 2 5 274 0 0 1 TRUE
"14-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 2 3 6 158 0 0 1 TRUE
"15-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTACAGCGATAACCAATAAAAGGCTGCAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCTAGAGAAAACCGAATGAGTAGTCCAGCCGACATAGTACCACCCAGTAGGGGGACCACATCAGCCAGACGAAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 57 1 1 158 0 0 1 TRUE
"15-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 33 2 2 154 0 0 1 TRUE
"15-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 7 3 3 158 0 0 2 TRUE
"16-trich" "CCGTAAACCACTTCGATCAGTACTGGCATCGCATCCAACTGCGTAATGAAGCAATGTCGATTCCAGCGCCATGTCTCCGCTGGCAACGAATGAATGAACTGAATCCTGTGGAGCGTTTCATATGGGACAACGTGCAGAGTTCGCGCCGAATACTCGACTTCGGTTCAGGAGATCAGTCGTTAAGGAAGAAATTCCTCGCTGCCGGTTATCGCGGTAAGTACGAGACATTCGATATTT" 63 1 1 217 0 0 2 TRUE
"16-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 62 2 2 159 0 0 1 TRUE
"16-trich" "CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAGCGATCCAGCGCCATTACCCTTCAAAAGACCAGTGAGAGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGCGTGCTCGAGACGTCCGTCGAAAGATCGATGAGCGATGTTGAGACAA" 30 3 3 179 0 0 2 TRUE
"16-trich" "GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAACAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGTACTTTCAAAGTTGCAGAGTGAAGGAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCGATAAAGGCTCTCCTTC" 21 4 4 235 0 0 2 TRUE
"16-trich" "TCGCCTTCGATCCTCTGACTTTCGTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCAATAGGTCGTCTCGCTGCGGTCCAAGAATTTCACCTCTCACGCAGCGATACGGATGCCCCCGAGTTGTTCCTTTTAATCATTACTTCAATCCCAAGACCAACAGAATGGACCGAAGTCATATTCTATTATTCCATGATAAAGCATTCAAGCAAACGCCTGTTTTAATCACATTGATTTAATCAAAGTTACAACACCAGTCACCGCAGCCGAAGCTACGGAAAACCGGCGAAACAGTCCAGACTAGCAGTATCTCACATGGAGAACTACAGTCATGAACTTAGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGATATACACGTTGGGAGCTGGAATTACCG" 16 6 5 174 0 0 2 TRUE
"16-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 10 5 9 155 0 0 1 TRUE
"16-trich" "CCCCGGGATGCTCGTCGACTCGTATGGACCGGTGCGCACCGTGCCCTTGTCGTTGAGCTCGCAACCAAGCGATTTTGCCAGATCGGAGCACTGCGTCTGGCCCGTCGTGAAGAAAAGCGCGCGCCGCGCCAGCGCCTCGCCGCTGGCAAAAACGATGCGCTCGAGCCGGCCGTCGCGGCCGTCGAGCCGCGCAATGCGATCCTCGCG" 9 7 8 187 0 0 1 TRUE
"16-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCACGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAACCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACACCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTATTAGCAGAGCTGGAATTACCG" 9 11 6 161 0 0 2 TRUE
"16-trich" "GGAAAGCGGTAGTCGTCCTGGTTATCGGTTTGGCCGTCAGCAGGCATCACAAATGTGGCGTTGTAAAGCAGCCACAGATCGTGCATGTGGGTGCTCTGGTTGGACGGCGAGTTCATGCCGACTTGGTCGTAGTACATGGCCTGCAACGCGAGCTCTGATCACACTCTTTGAAATAGCTCGTGTCTCACCTGGCTCTGATTCACGCGATAGCTGCCCGCTCCAG" 5 12 7 197 0 0 2 TRUE
"17-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 57 1 1 154 0 0 2 TRUE
"17-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCACGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAACCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACACCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTATTAGCAGAGCTGGAATTACCG" 39 2 2 161 0 0 2 TRUE
"17-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 14 4 4 158 0 0 2 TRUE
"17-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 10 3 3 159 0 0 1 TRUE
"17-trich" "TCGCCTTCGATCCTCTGACTTTCGTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCAATAGGTCGTCTCGCTGCGGTCCAAGAATTTCACCTCTCACGCAGCGATACGGATGCCCCCGAGTTGTTCCTTTTAATCATTACTTCAATCCCAAGACCAACAGAATGGACCGAAGTCATATTCTATTATTCCATGATAAAGCATTCAAGCAAACGCCTGTTTTAATCACATTGATTTAATCAAAGTTACAACACCAGTCACCGCAGCCGAAGCTACGGAAAACCGGCGAAACAGTCCAGACTAGCAGTATCTCACATGGAGAACTACAGTCATGAACTTAGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGATATACACGTTGGGAGCTGGAATTACCG" 6 5 5 174 0 0 2 TRUE
"18-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 66 1 1 154 0 0 2 TRUE
"18-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 17 4 3 159 0 0 2 TRUE
"18-trich" "GGTCGGCAAATTCATATCAAAATTTCAAGAGAATATCATGCCAAAGTGCGCTATTTGTTCTGAATATAAATTCCGTCTCCCGTTCCTATCCAAATTTTATTATCTTTTGCTTGTTCAACCGCAAATACACCTTCAAACTTTGTGGTTCCGGCTGCTGTCCATTTATTCCCATCATAAATACTACAGGTAGTGCCATCGCTG" 13 3 2 179 0 0 2 TRUE
"18-trich" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC" 8 5 7 274 0 0 2 TRUE
"18-trich" "GCAAAGAAGCTTGGTCTCACTCCCCGGGAATATATTCAACAACTTATCGCCAACGACCTCGCTCTGGACCGGAAGGCACAATCGTCGTCGCTCGATGAGCTCGCGGCCCCCTTCCGAAAAGCTCTCAAAGATGTCTCCGACAAGGAACTTGACCGGATGGTCGATGCCGCTCGTCATCGAAGAGTATCGCCGTCCTCCAGGAAACTGCCTCAAGC" 6 7 5 194 0 0 2 TRUE
"18-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 4 6 6 158 0 0 1 TRUE
"2-trich" "CCATCACTGCTTTATAAGAATAATTGACCAAACCAATGGCCAGTACCCGCTGATAAGGCCAGCCCTTACCGATGCTGAAGGCTATGAACAATACAATGTAGCTGTCTATCAGCTGTGAGATGATGGTAGAACCTGTAGCCCGGAGCCAGACCTTTTTCTCACCGGTCACCTTCTTGATCTTATGGAACACCCATACATCCACCACCTGGCTCACCAGGAAAGC" 33 1 1 203 0 0 2 TRUE
"2-trich" "GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAACAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGCACTTTCAAAGTTGCAGAGTGAAGGAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCGATAAAGGCTCTCCTTC" 23 2 2 235 0 0 2 TRUE
"2-trich" "CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAGCGATCCAGCGCCATTACCTTTCAAAAGACCGGTGAGCGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGAGTGCTCGAGACATCGGTCGAAAGATCGATGAGAGACGTTGAGATGA" 23 3 3 179 0 0 2 TRUE
"2-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 7 4 4 154 0 0 1 TRUE
"3-trich" "GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAACAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGTACTTTCAAAGTTGCAGAGTGAAGGAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCGATAAAGGCTCTCCTTC" 20 1 1 235 0 0 2 TRUE
"3-trich" "CCGATCGAGAACAGCTTCAAGCCGGGAGTTCCTCCCGAGGTCTACATGAAGGACCCGGCGAACAGCGACGAGCGCTTCTTCATTCCCTTGAGCGAGACGGTTCTCTCGCGGCCGATGTGGATTTCGCCTCAGCGCAACATGTGGGCCGACATCCTATGGGCCAAGACGGCCGGCCTGGTGAACCGGCACTACCACCCG" 15 2 2 178 0 0 2 TRUE
"3-trich" "CAGTCGCCGCTTTCGCAGACGGGGCCGACAATGTCGGTTTTTTTGGTCGAACCCTTCGTTTTATTAATGGGGACGATCTCATGGTAGCTGCCGTAGAGGGACGGCCGAAGCAGATCATTCATCGCTGCGTCCACGACGAGAAAATCCTTCTCCTTGCGAACTTTGCGATAGAGGACCTCAGTGAGAAGCA" 6 7 3 170 0 0 2 TRUE
"3-trich" "GGCTCCCTGCCGATCAACTCCTTGACACACACCAACTGTCACACGTTCGCCTGCCGTGTGTTGAACAGTGCGACCACGCAAGAAACACATCCGATCCCATTGGTCGGCCAGTCGCGGATCGTCGGCACTCAACAGACTGAGATGCGATTCCAGTTGTCGCAGCACGTTCACCAGGACGTCTTCGAGGGTCCCTCGTCGCTGCGAGAGATCGCATAAAGCCGTGGCGCGATCGGCGATTTCCGCAGGGGCCGACTGGACGGAATTGTTGACATTGATCCCAATCCCGATCACCAGACTTGCCCC" 5 3 5 266 0 0 1 TRUE
"3-trich" "CCCAAACTTCAGGTTTTTGGAGCGCACGTATTCTGCGACGGGCGCAAGTCCGTTGGGCAGCTTTGCACCGTCCACCCGATACCAGTTACCAAGCCCTGCCGAGAAGTCGTAGCCCTTTCCGGTGCCGCTGTACCAGCCGGCGTCCAGCAGGAAGTACTCTTCTCCAAGCGCTGCGGCTGCATCAGCTAGTTTGCGCAGCAATTCCTCGTCGAAGTCGACCCCAACGTTCCAGAAGTGATCGTAGGTCATCACAGGCGTGAACGGGCGGCCACCAAGCCGCGGCGTGTACACCTCGCG" 3 4 4 277 0 0 1 TRUE
"5-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 41 1 1 154 0 0 1 TRUE
"5-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCACGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAACCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACACCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTATTAGCAGAGCTGGAATTACCG" 8 2 2 161 0 0 2 TRUE
"5-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 4 3 4 158 0 0 1 TRUE
"6-trich" "CCAAATTCGCGCAGCACCTCCAGGTGCCCCACTTGGTTGCGCGGAAAAGCAAAGGAGCGCAGCTCAATATCCATCGCCTGTGCCAATCGCACGCACTCCGCGATTTCACTGCGCGCCGCCGTCTCGGAACAACCCGCATCGCCGAAAATGACGTGCGAAAAAGAATGGCACCCAATCTCCTGAGGCATGGGCGATGCCTTGATTTTTTC" 22 1 1 189 0 0 2 TRUE
"6-trich" "GCATATTAGTTCAGAACACAATTGATATAAGTCCGGAACTAAAGGCATATCAGTTCAGAGGTGATATGCATTAGGGTTGGAAGTGGGGTGCCTTATACCCGGAGGTAACAGCCGAAGTGACGTCATTGATAGTTATACTTAAAAAACGTCATTTCGAACAGAACGTAGTGGAGTGAGAAATCTCATGAATTGTAGGAAAATGCTTTTATGATCCGTCAACACTT" 10 4 2 203 0 0 2 TRUE
"6-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 6 5 3 154 0 0 2 TRUE
"6-trich" "GGAGCGGAATGTGACAGTGGGAATAAACATCGTAAAGTACCTTACAGCTCGCTCGACTACGAGCGATCGCTTGATGAAACATTGTACTCTGGAAAATTACGGGCGGAAAAGAAAAACTTCGGTAAGTGGTGTCCGGCTTGACCTAGAAAAGGGAGCACCGGTTTCCCGATACTCCCGCTTGCTAGAATATTTCGAGTCTGCCTTCAATTCGAACCGCGGTGCCCGCTTCCTCCATTGAAGTAGATTTGTGCTTTGCGCCATGCCTGTTCGCC" 4 2 5 250 0 0 1 TRUE
"7-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 35 1 2 154 0 0 1 TRUE
"7-trich" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGTTCGGTCGCGTGGAGAC" 26 2 4 274 0 0 1 TRUE
"7-trich" "CCCTTCCCCGATGAGCACGAGCAGCCCCTTCATCTGCGGTGCCTCGTGCGCCGAGTGAATATCGACCGCCATCGAAATCGAATGCGCGAGCAGCGCGCCGAGGAGGACAGCGACAACCACGCCGCCGACTTGAAGTACCCACCGCGGCAGCCAGGGCGCCAATCGAACCGGCCGTTGTTCGAACAGCCCGAAGGCCAGCACCGC" 19 3 1 184 0 0 2 TRUE
"7-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 9 5 3 159 0 0 2 TRUE
"8-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGCCACCCAGTGGGGGACCACCTCAGCCGAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 37 3 2 159 0 0 2 TRUE
"8-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 33 2 5 154 0 0 2 TRUE
"8-trich" "CCTCGGTATTGGCGACCTCGAAATTCCAGGTCGATTGCTCGATCTCATTCTCGAGGAACACGTCGCCATATGTCACGCCGCGATCATTATAAGCGAGATCGTAGATGCTGTCCTTGTTCTGCAGATACAGCGCCAGCCGCTCAAGCCCATAGGTCAGCTCGCCAGCGACCGGCTTGCAGTCGAAACCGCCCATCTGCTGGAAATAGGTGAACTGGGTGACCTCCATACCATCG" 28 1 1 213 0 0 2 TRUE
"8-trich" "CCAAACGATTCACAGAAAATGCTGTCAGCGGGCTGGAATTCATGAGCCAGGGCCACTACCATACTTTGCCCACCTAACTCATCTTCTCTTCGTAGCCGCCGCAATAGCGAAAGCGAAAGGGAGAGCCATGCCCAAAGCCGCAAGCGGGCTCTAGGACAATTCCTTGCCGAGGCTTCAGCCTTGGCCTTTCCGATGAACACCTTCTCGTCAGCCTCGATCTGATTCTCGCCTGCTTTGGCATGGCGCTGCGTTTGCGCTGCTCTGGTCCATGCGTTACAACA" 20 5 3 261 0 0 2 TRUE
"8-trich" "ACTCAACCAGAAAGTTGAAAAAGATGAGGTTGAAATCGAAAAATCAAACATCATCATGGTGGGCGAAACTGGTACCGGCAAAACCCTGCTGGCAAAAACTCTCGCCAAAATATTAAATGTGCCCTTCTGTATATGCGATGCAACCGTTCTAACGGAAGCTGGTTATGTTGGGGAGGATGTCGAAAGTATTCTTACACGTCTG" 14 4 4 182 0 0 2 TRUE
"8-trich" "CACTACTAGAGCTTGCCATCGTTCCTATGGCGTCAGGAGTCTTCTGTCAGTATCATTGCATCGTTCGCCTTATTGCTGAGAACGATCTACTTTTTGCCGAAAGGCGCGCTGCACCGCGCACATGCAGAAAATGAGCACCGAGGGCAGCGCCGCGCTCATCGTGTACTAATGCGGCAGGCGCTCGCTTTTAGCCGCCCGTACGATCAGCGCGGTTCCGGAGCGGCTTCA" 11 7 6 208 0 0 2 TRUE
"8-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCACGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAACCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACACCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTATTAGCAGAGCTGGAATTACCG" 9 11 7 161 0 0 2 TRUE
"8-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTATAGGGCGTCCCACTGCGGTCCACGAATTTCACCTCTCGCGCAATGATACGAATGCCCCCGAATTGTCCCTCTTAACCATTAATTCAGTTCCAGAACCAATAAAAAGAACCGAAGTCCTCTTTTATTATTCCATGATCGAATATGCAGGCAAACGCCTGTTTTGAGCACTCTAATTTAATCAAAGTAAACTCGCCAATCATCACACCGACGTTTCCGCCAATGCAAAAAATTGGCACAAGTAGACAAATCTTAGTTAACGCCAGAGGCGTACCAAGATCTGCTACGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGGCTTACATTAATGGAGCTGGAATTACCG" 5 10 8 169 0 0 2 TRUE
"9-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" 45 1 1 154 0 0 2 TRUE
"9-trich" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC" 31 3 4 274 0 0 1 TRUE
"9-trich" "GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAACAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGTACTTTCAAAGTTGCAGAGTGAAGGAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCGATAAAGGCTCTCCTTC" 21 4 2 235 0 0 2 TRUE
"9-trich" "GCATACTGTTTGTTACACTATTTTCAATTGCCACAGAAGCAGATAAAGTAATTCTATCTTTCAAAACCGTCACACCAACTTTAGATGAAGGAGATTGGAGTTTGGGAGAGACGATGGAGCTATCAGAGGATGCACTGCCGCCCACAGAGCGCGACACTTGACACGTCTGCACAGGGATGAACACTTGAGGTTTATTCGGTGGAATAGGAGGCGGGAATCCGCGCCCCATCGGGGACAGCTTCCTCTGTTGAC" 8 5 8 231 0 0 1 TRUE
"9-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 7 11 6 158 0 0 2 TRUE
"9-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTATAGGGCGTCCCACTGCGGTCCACGAATTTCACCTCTCGCGCAATGATACGAATGCCCCCGAATTGTCCCTCTTAACCATTAATTCAGTTCCAGAACCAATAAAAAGAACCGAAGTCCTCTTTTATTATTCCATGATCGAATATGCAGGCAAACGCCTGTTTTGAGCACTCTAATTTAATCAAAGTAAACTCGCCAATCATCACACCGACGTTTCCGCCAATGCAAAAAATTGGCACAAGTAGACAAATCTTAGTTAACGCCAGAGGCGTACCAAGATCTGCTACGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGGCTTACATTAATGGAGCTGGAATTACCG" 5 12 12 169 0 0 1 TRUE
"9-trich" "GCCATGAGGATGTTCCGCCATACCAGGGTCACCCGGTACCTGATTCGAAAGGGTTCATAGGAGAGAGCGCTATGGGAATTCGTGAAGACATGCCGCTGCTGGCGCGCCACGAGGGTGTGTGGGACGGTGTCTACACTTATTACAACGCCGCGGGTGAGAAGATCGACGAGCACGGATCGCGGCTTTTCTGTCGCTTCCCGGAAGGCAGCAAGTATCCGTAT" 5 8 5 200 0 0 2 TRUE
"9-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" 4 9 7 158 0 0 2 TRUE
"9-trich" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCACGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAACCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACACCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTATTAGCAGAGCTGGAATTACCG" 4 10 10 161 0 0 1 TRUE
"PositivK-1-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 103 1 1 158 0 0 2 TRUE
"PositivK-2-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 63 1 1 158 0 0 1 TRUE
"PositivK-3-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 87 1 1 158 0 0 1 TRUE
"PositivK-4-trich" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" 87 1 1 158 0 0 2 TRUE
analysis/Trich/Trich_DADA2_results_060921/out.txt 0 → 100644
+ 24
− 0
View file @ e9cc7d51
"reads.in" "reads.out"
"1-trich_S138_L001_R1_001.fastq.gz.trim.fastq.gz" 3580 202
"10-trich_S142_L001_R1_001.fastq.gz.trim.fastq.gz" 2010 141
"11-trich_S145_L001_R1_001.fastq.gz.trim.fastq.gz" 2813 199
"12-trich_S148_L001_R1_001.fastq.gz.trim.fastq.gz" 1330 75
"13-trich_S151_L001_R1_001.fastq.gz.trim.fastq.gz" 786 67
"14-trich_S154_L001_R1_001.fastq.gz.trim.fastq.gz" 2973 91
"15-trich_S157_L001_R1_001.fastq.gz.trim.fastq.gz" 1327 120
"16-trich_S160_L001_R1_001.fastq.gz.trim.fastq.gz" 7324 245
"17-trich_S140_L001_R1_001.fastq.gz.trim.fastq.gz" 1771 134
"18-trich_S143_L001_R1_001.fastq.gz.trim.fastq.gz" 2562 149
"2-trich_S141_L001_R1_001.fastq.gz.trim.fastq.gz" 2235 102
"3-trich_S144_L001_R1_001.fastq.gz.trim.fastq.gz" 4719 74
"5-trich_S150_L001_R1_001.fastq.gz.trim.fastq.gz" 970 62
"6-trich_S153_L001_R1_001.fastq.gz.trim.fastq.gz" 1431 71
"7-trich_S156_L001_R1_001.fastq.gz.trim.fastq.gz" 3175 133
"8-trich_S159_L001_R1_001.fastq.gz.trim.fastq.gz" 9926 205
"9-trich_S139_L001_R1_001.fastq.gz.trim.fastq.gz" 2979 206
"PositivK-1-trich_S152_L001_R1_001.fastq.gz.trim.fastq.gz" 1675 106
"PositivK-2-trich_S155_L001_R1_001.fastq.gz.trim.fastq.gz" 1238 65
"PositivK-3-trich_S158_L001_R1_001.fastq.gz.trim.fastq.gz" 1267 88
"PositivK-4-trich_S161_L001_R1_001.fastq.gz.trim.fastq.gz" 1714 107
"Vann1-trich_S146_L001_R1_001.fastq.gz.trim.fastq.gz" 11 0
"Vann2-trich_S149_L001_R1_001.fastq.gz.trim.fastq.gz" 63 0
analysis/Trich/Trich_DADA2_results_060921/seqtab.nochim.txt 0 → 100644
+ 22
− 0
View file @ e9cc7d51
"TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTTTGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACAAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTCATAGCGATAACCAATAAAAGGCTACAAGGACCTCTTTCATTATTCCATGCACGAATATTCGGGCGATGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAAAGTTCCGGTCACCTAGGACACTCAGTGAAGAGCATCAAAGGAAAACCGAATGATTAGTCCGGGAGAAGCAGTAACAACCCATCGGGCGACCGCTTTCACCAGACGCAGATCCAACTACGAGCTTTTTAACCGCAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" "CCGTAAACCACTTCGATCAGTACTGGCATCGCATCCAACTGCGTAATGAAGCAATGTCGATTCCAGCGCCATGTCTCCGCTGGCAACGAATGAATGAACTGAATCCTGTGGAGCGTTTCATATGGGACAACGTGCAGAGTTCGCGCCGAATACTCGACTTCGGTTCAGGAGATCAGTCGTTAAGGAAGAAATTCCTCGCTGCCGGTTATCGCGGTAAGTACGAGACATTCGATATTT" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGTCGCCCAGTGGGGAACCACCTCAGCCAAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" "CACCCGGAACGAGCAGTGATTTTGCCATTTTATCGACTTTTTCTTGCAATACTGCCTGATTAATCGGCTTTAGTTCCGCTGCCTGGCAAACCATGTTTGCTAATAAGAACAGAATGGTTATGATTCTCAAAAATTATCTCCTTTTCATCTTCAATGGCGCTTATGTCCTTTAGGCTTACATTAGTATAAGCCAGGCTTTAAAATTCCGCTACCTTCTGAAG" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGCTCGGTCGCGTGGAGAC" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTGTAGTTTGTCTGGCCACGGTCCAAGAATTTCACCTCTCACGGGGCCATACAAATGCCCCCGTTTGTCTCTGTTAACCATTATCTCAGTCCGTAAAACCAATAAAACTGAACCGAAATCGTATTCTATTATTCCATGCACGATCATTCAAGCTAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTTGTAACGCCTGTCAGGAATCCTGTTAAGGACTCAAGACCGAACATTACGATAAATCGATAACACCTAGAGACACCCGAAGGGTTCCAGGGTATCGACACGAATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGTATTAGCAGAGCTGGAATTACCG" "GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAACAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGTACTTTCAAAGTTGCAGAGTGAAGGAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCGATAAAGGCTCTCCTTC" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTACAGCGATAACCAATAAAAGGCTGCAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCTAGAGAAAACCGAATGAGTAGTCCAGCCGACATAGTACCACCCAGTAGGGGGACCACATCAGCCAGACGAAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGGAGACATTCTTGGCAAATGCTTTCGCTATTGGTCGTCTCCTCACGGTCTACGGATTTCACCCCTCGCGCGAGGATACGTTTGCCCCCGAATTGTCCCTCTTAACCATTAAACGATTCTGGAACCAACAAATAGAACCGAAGTCCTCTTCTGTTATTCCTCGAGAAATCATACAAGCTTTCGCCTGTTTTGAGCACTCTGATTTGATCAAAGTAACTCGCTAGCCACCGAAGATCCGAAGACCAACAGCAAACCAGCAAAAATCGGCCGTGAATTAACGCCAGAGACGAATAAACGCCAACGACAAACCCAACGGACACGACTAGATTCAACTACGAGCTTTTTAACCGCAGCAGTAATATTTTACGCTAGTGGAGCTGGAATTACCG" "TCGCTGTCGAACCTCAGACTTTCGTTCTTGATTGATGAAAACATTCTTGGCAAATGCTTTCGCAGTAGTCCGTCTTGCGACGATCCAAGAATTTCACCTCTAACGTCGCAATACGAATGCCCCCGTCTGTCCCCGTCGATCATTTCCTTGCAGCGATAACCAATAAAAGGCTACAAAGACCTTTTCCATTATTCCATGCACGAATGTTCAGGCGTAGCGCCTGCTTTGAGCACTCTAATTTTTTCAAAGTAAACGTTCCGGCCACTCAAGACACTCAGTTAAGAGCATCAAGAGAAAACCGAATGATTAGTTCAGCCGAAGTAGTGCCACCCAGTGGGGGACCACCTCAGCCGAACGCAGATCCAACTACGAGCTTTTTAACCACAACAACTTTAATATACGCTATTGGAGCTGGAATTACCG" "GCCGCTGAAAATTCTGTAACCTGCTGATACCGGATCTTACATTCCTGTCAACCTTTGCTTTTTTAACTTCATCCAAATCGGTCAGTTGGTTTAATACCAGCTGCGGAAAAACGGCTGAAGTAGTCTGCTCCACACATCCATGTGGGTATTCTATCAGGTAGTTCAAGCGTTTTTGCAAATTCATAGATGGTACACTGGAAATTTCCAACACAGCTGTACTTGAAGTTATCACACCAATAGGGTTGGCCATTGTTTTCCATTGCTGCCCTGCCGATAAAGTCATTTCAGTAACCTGTGTTACCGGCGGATTTGGGTTGCGTATTTCAAGTTCCACTTCATAATCT" "CCATCACTGCTTTATAAGAATAATTGACCAAACCAATGGCCAGTACCCGCTGATAAGGCCAGCCCTTACCGATGCTGAAGGCTATGAACAATACAATGTAGCTGTCTATCAGCTGTGAGATGATGGTAGAACCTGTAGCCCGGAGCCAGACCTTTTTCTCACCGGTCACCTTCTTGATCTTATGGAACACCCATACATCCACCACCTGGCTCACCAGGAAAGC" "CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAGCGATCCAGCGCCATTACCCTTCAAAAGACCAGTGAGAGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGCGTGCTCGAGACGTCCGTCGAAAGATCGATGAGCGATGTTGAGACAA" "CCTCGGTATTGGCGACCTCGAAATTCCAGGTCGATTGCTCGATCTCATTCTCGAGGAACACGTCGCCATATGTCACGCCGCGATCATTATAAGCGAGATCGTAGATGCTGTCCTTGTTCTGCAGATACAGCGCCAGCCGCTCAAGCCCATAGGTCAGCTCGCCAGCGACCGGCTTGCAGTCGAAACCGCCCATCTGCTGGAAATAGGTGAACTGGGTGACCTCCATACCATCG" "GGAGGCTGCCAACGAGCACATGGTCATGCTGGAAAAATCACCTGGGCGTGTCAAAAGCTACTTCCAGGACCCCAGGGTCAAATATGCAGCCGGTTGAGTCTTCTTAAGGCCGGATCAATAGGCAGGGCTCGAGCAGCGGACTGACCTGCTGATACTCGGCCAAGGCGGTTCCCGCTTCGAGAGTAAGGTATAGAGCCTCGACACCCTTTCTGTTGAATCGGCCACCTTGCACTGCGGCTCCGGCACCGCGTAGCGGTGCAAACGACCACCGTGGCGTATGCGCCGATACAGTTCGGTCGCGTGGAGAC" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGCCAAATGCTTTCGCTGTAGTCCGTCTTGCTACGGTCCAAGAATTTCACCTCTCACGTAGCAATACGAATGGCCCCGTTTGTCTCTGTTAACCATTATCTCGGTCAGCAAAACCAATAAAATCGGACCGAAATCCTCTTCTATTATTCCATGCACGAACATTCAAGCGAAACGCCTGTTTGAAGCACTCTGATTTGTTCAAAGTAAACTCGCCAGCCCACCGACGCCGACCGGTGAAGGCCAACGCCGGGGAAAGACCGGCAGAAAGCCAACCGAACCAGTGCACACCCAGTGGGTGGACCGGCCGGCTAGCACAGATCCAACTACGAGCTTTTTAACCGCAGCAATAATTCTATGCATTTTGAGAGCTGGAATTACCG" "GCACTCAAGGATGAGTTCAAGAATAAAAGAGCAGAACAGAAGGCAGAATTCTGCGCAAGTGCTCAAGATATGCTTTCAAACAGATTTAAGGGAGCAATCTCGGCACTCGAACAATTCCAAGCAAAAGCAAGTGATGCACTTTCAAAGTTGCAGAGTGAAGGAAAGGACACGACTCTAGCGACAGAATCCCTAAACCTTTCAAAACAGAATCTAGCAGATGCGAAAGCAAAGTTACTTGCGATAAAGGCTCTCCTTC" "CCGCCCGTGCCGTTAGCGTAGAGAATGGAAGAACCGACAGTGGCAGGCGCGTAGTCGATGCCAGATACTGCAGTCAGAAGCGATCCAGCGCCATTACCTTTCAAAAGACCGGTGAGCGATGTCGAACCCGTACCGCCATGAGAAACCGGAAGAGTGCTCGAGACATCGGTCGAAAGATCGATGAGAGACGTTGAGATGA" "TCGCCTTCGATCCTCTGACTTTCGTTCTTGATTAATGAAAACATTCTTGGCAAATGCTTTCGCAATAGGTCGTCTCGCTGCGGTCCAAGAATTTCACCTCTCACGCAGCGATACGGATGCCCCCGAGTTGTTCCTTTTAATCATTACTTCAATCCCAAGACCAACAGAATGGACCGAAGTCATATTCTATTATTCCATGATAAAGCATTCAAGCAAACGCCTGTTTTAATCACATTGATTTAATCAAAGTTACAACACCAGTCACCGCAGCCGAAGCTACGGAAAACCGGCGAAACAGTCCAGACTAGCAGTATCTCACATGGAGAACTACAGTCATGAACTTAGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGATATACACGTTGGGAGCTGGAATTACCG" "CCAAATTCGCGCAGCACCTCCAGGTGCCCCACTTGGTTGCGCGGAAAAGCAAAGGAGCGCAGCTCAATATCCATCGCCTGTGCCAATCGCACGCACTCCGCGATTTCACTGCGCGCCGCCGTCTCGGAACAACCCGCATCGCCGAAAATGACGTGCGAAAAAGAATGGCACCCAATCTCCTGAGGCATGGGCGATGCCTTGATTTTTTC" "CCAAAAATGATTTTAATTAGTCAAAAAATATTAATTTACATAACTTTATAAAATTGCAAACATTAGCATCAACACCAATTTCGTAATTTTATAAATTAAATTTGAAACTATTTTTTTCAACTTTCCGGCTAACGCGATTGGCAAGGGCGTAGCAAAACAATGCGCAGATGGCGCAAAACTTGTCATACAATGCT" "CCAAACGATTCACAGAAAATGCTGTCAGCGGGCTGGAATTCATGAGCCAGGGCCACTACCATACTTTGCCCACCTAACTCATCTTCTCTTCGTAGCCGCCGCAATAGCGAAAGCGAAAGGGAGAGCCATGCCCAAAGCCGCAAGCGGGCTCTAGGACAATTCCTTGCCGAGGCTTCAGCCTTGGCCTTTCCGATGAACACCTTCTCGTCAGCCTCGATCTGATTCTCGCCTGCTTTGGCATGGCGCTGCGTTTGCGCTGCTCTGGTCCATGCGTTACAACA" "CCCTTCCCCGATGAGCACGAGCAGCCCCTTCATCTGCGGTGCCTCGTGCGCCGAGTGAATATCGACCGCCATCGAAATCGAATGCGCGAGCAGCGCGCCGAGGAGGACAGCGACAACCACGCCGCCGACTTGAAGTACCCACCGCGGCAGCCAGGGCGCCAATCGAACCGGCCGTTGTTCGAACAGCCCGAAGGCCAGCACCGC" "CCCGAAATGCTGGGATTGACCCTGGCGGAAGGCAAAGCCATCTTGCGGGAACTCCAACGCGTCGTAGTAGAACATCAAATCGCGGAATTCGTGGCGGCGCACCGTCACTGTTCCGAGTGCGGACAGCTCCGACGGAGCCGCGGGTGCCACGATATTCCGATGCGCGCCGTGTTCGGCAAGATTAAGATTCCAAGCCCGCGCTTTGTGCATTGTGATTGCCAGCCGCACGATACCGAGAGCTTCAGCCCGTTGGCGCAAGTGCTGCCGGAGCG" "TCGCCTTCGAACCTCTGACTTTCGTTCTTGATTAATGAAGACATTCTTGGCAAATGCTTTCGCTATAGGGCGTCCCACTGCGGTCCACGAATTTCACCTCTCGCGCAATGATACGAATGCCCCCGAATTGTCCCTCTTAACCATTAATTCAGTTCCAGAACCAATAAAAAGAACCGAAGTCCTCTTTTATTATTCCATGATCGAATATGCAGGCAAACGCCTGTTTTGAGCACTCTAATTTAATCAAAGTAAACTCGCCAATCATCACACCGACGTTTCCGCCAATGCAAAAAATTGGCACAAGTAGACAAATCTTAGTTAACGCCAGAGGCGTACCAAGATCTGCTACGATCCAACTACGAGCTTTTTAACCGCAGCAATGACGGCTTACATTAATGGAGCTGGAATTACCG" "CCGATCGAGAACAGCTTCAAGCCGGGAGTTCCTCCCGAGGTCTACATGAAGGACCCGGCGAACAGCGACGAGCGCTTCTTCATTCCCTTGAGCGAGACGGTTCTCTCGCGGCCGATGTGGATTTCGCCTCAGCGCAACATGTGGGCCGACATCCTATGGGCCAAGACGGCCGGCCTGGTGAACCGGCACTACCACCCG" "ACTCAACCAGAAAGTTGAAAAAGATGAGGTTGAAATCGAAAAATCAAACATCATCATGGTGGGCGAAACTGGTACCGGCAAAACCCTGCTGGCAAAAACTCTCGCCAAAATATTAAATGTGCCCTTCTGTATATGCGATGCAACCGTTCTAACGGAAGCTGGTTATGTTGGGGAGGATGTCGAAAGTATTCTTACACGTCTG" "CAAAGCGGCTAAGCATCGCCAGGCCGCTGTGAAATTACCTCGGGAAAGCCAACAGCTTGCTGTCATGGAGCATACAAGATGAAGCTTTGAGCAGCTGACTTTCATGCTTTCCTAGACGCTACGGCATTACGGGAGATTTGTTTGGCCCATTAAGTAACGGTCGCACTCCCGCGCTGCGCCCCGGCCCTCGTTGATCGCCCATAC" "GGTCGGCAAATTCATATCAAAATTTCAAGAGAATATCATGCCAAAGTGCGCTATTTGTTCTGAATATAAATTCCGTCTCCCGTTCCTATCCAAATTTTATTATCTTTTGCTTGTTCAACCGCAAATACACCTTCAAACTTTGTGGTTCCGGCTGCTGTCCATTTATTCCCATCATAAATACTACAGGTAGTGCCATCGCTG" "CACTACTAGAGCTTGCCATCGTTCCTATGGCGTCAGGAGTCTTCTGTCAGTATCATTGCATCGTTCGCCTTATTGCTGAGAACGATCTACTTTTTGCCGAAAGGCGCGCTGCACCGCGCACATGCAGAAAATGAGCACCGAGGGCAGCGCCGCGCTCATCGTGTACTAATGCGGCAGGCGCTCGCTTTTAGCCGCCCGTACGATCAGCGCGGTTCCGGAGCGGCTTCA" "GCATATTAGTTCAGAACACAATTGATATAAGTCCGGAACTAAAGGCATATCAGTTCAGAGGTGATATGCATTAGGGTTGGAAGTGGGGTGCCTTATACCCGGAGGTAACAGCCGAAGTGACGTCATTGATAGTTATACTTAAAAAACGTCATTTCGAACAGAACGTAGTGGAGTGAGAAATCTCATGAATTGTAGGAAAATGCTTTTATGATCCGTCAACACTT" "CCCCGGGATGCTCGTCGACTCGTATGGACCGGTGCGCACCGTGCCCTTGTCGTTGAGCTCGCAACCAAGCGATTTTGCCAGATCGGAGCACTGCGTCTGGCCCGTCGTGAAGAAAAGCGCGCGCCGCGCCAGCGCCTCGCCGCTGGCAAAAACGATGCGCTCGAGCCGGCCGTCGCGGCCGTCGAGCCGCGCAATGCGATCCTCGCG" "CACGCCCTTGGTATCCTGGTATATCGAGCGAACCTGATCTCCAGCCAGCCCATCGCGTTCGTCCAATGAGGACCAAGCGATGCCATCGAAGTGTGAAACCCCGCCATCGGTTCCGAACCAAAAGTGACCCTCGGAATCGCGGAAGATTGCGTGGATGAAATTGCCCGCGAGCCCGCTTTTGCCAGCCGTATAAGTGGTGAACTTACGGCCATCAAAGTG" "GCATACTGTTTGTTACACTATTTTCAATTGCCACAGAAGCAGATAAAGTAATTCTATCTTTCAAAACCGTCACACCAACTTTAGATGAAGGAGATTGGAGTTTGGGAGAGACGATGGAGCTATCAGAGGATGCACTGCCGCCCACAGAGCGCGACACTTGACACGTCTGCACAGGGATGAACACTTGAGGTTTATTCGGTGGAATAGGAGGCGGGAATCCGCGCCCCATCGGGGACAGCTTCCTCTGTTGAC" "GCAAAGAAGCTTGGTCTCACTCCCCGGGAATATATTCAACAACTTATCGCCAACGACCTCGCTCTGGACCGGAAGGCACAATCGTCGTCGCTCGATGAGCTCGCGGCCCCCTTCCGAAAAGCTCTCAAAGATGTCTCCGACAAGGAACTTGACCGGATGGTCGATGCCGCTCGTCATCGAAGAGTATCGCCGTCCTCCAGGAAACTGCCTCAAGC" "CAGTCGCCGCTTTCGCAGACGGGGCCGACAATGTCGGTTTTTTTGGTCGAACCCTTCGTTTTATTAATGGGGACGATCTCATGGTAGCTGCCGTAGAGGGACGGCCGAAGCAGATCATTCATCGCTGCGTCCACGACGAGAAAATCCTTCTCCTTGCGAACTTTGCGATAGAGGACCTCAGTGAGAAGCA" "GGAAAGCGGTAGTCGTCCTGGTTATCGGTTTGGCCGTCAGCAGGCATCACAAATGTGGCGTTGTAAAGCAGCCACAGATCGTGCATGTGGGTGCTCTGGTTGGACGGCGAGTTCATGCCGACTTGGTCGTAGTACATGGCCTGCAACGCGAGCTCTGATCACACTCTTTGAAATAGCTCGTGTCTCACCTGGCTCTGATTCACGCGATAGCTGCCCGCTCCAG" "GGCTCCCTGCCGATCAACTCCTTGACACACACCAACTGTCACACGTTCGCCTGCCGTGTGTTGAACAGTGCGACCACGCAAGAAACACATCCGATCCCATTGGTCGGCCAGTCGCGGATCGTCGGCACTCAACAGACTGAGATGCGATTCCAGTTGTCGCAGCACGTTCACCAGGACGTCTTCGAGGGTCCCTCGTCGCTGCGAGAGATCGCATAAAGCCGTGGCGCGATCGGCGATTTCCGCAGGGGCCGACTGGACGGAATTGTTGACATTGATCCCAATCCCGATCACCAGACTTGCCCC" "GCCATGAGGATGTTCCGCCATACCAGGGTCACCCGGTACCTGATTCGAAAGGGTTCATAGGAGAGAGCGCTATGGGAATTCGTGAAGACATGCCGCTGCTGGCGCGCCACGAGGGTGTGTGGGACGGTGTCTACACTTATTACAACGCCGCGGGTGAGAAGATCGACGAGCACGGATCGCGGCTTTTCTGTCGCTTCCCGGAAGGCAGCAAGTATCCGTAT" "GGAGCGGAATGTGACAGTGGGAATAAACATCGTAAAGTACCTTACAGCTCGCTCGACTACGAGCGATCGCTTGATGAAACATTGTACTCTGGAAAATTACGGGCGGAAAAGAAAAACTTCGGTAAGTGGTGTCCGGCTTGACCTAGAAAAGGGAGCACCGGTTTCCCGATACTCCCGCTTGCTAGAATATTTCGAGTCTGCCTTCAATTCGAACCGCGGTGCCCGCTTCCTCCATTGAAGTAGATTTGTGCTTTGCGCCATGCCTGTTCGCC" "CCCAAACTTCAGGTTTTTGGAGCGCACGTATTCTGCGACGGGCGCAAGTCCGTTGGGCAGCTTTGCACCGTCCACCCGATACCAGTTACCAAGCCCTGCCGAGAAGTCGTAGCCCTTTCCGGTGCCGCTGTACCAGCCGGCGTCCAGCAGGAAGTACTCTTCTCCAAGCGCTGCGGCTGCATCAGCTAGTTTGCGCAGCAATTCCTCGTCGAAGTCGACCCCAACGTTCCAGAAGTGATCGTAGGTCATCACAGGCGTGAACGGGCGGCCACCAAGCCGCGGCGTGTACACCTCGCG" "ACGGTATCTGATCGCCTCGAACCTCCGTCGTTCTTGATTACTGTAAATTAGACATTCTTGGCAAATGCTTTCGCTGTAGTCTGTCTGGCAACGGTCCCAAATTTCACCTCTCACGTTGCCATACAAATGCCCCCGTTTGTCTCCTTGCAGCACCGGACATCCACGCACCATTGAAGGGCATGCCGAACACTGCAGGAAAGCCAGTGCTGACAAGTGAGGTGAGACTAGTACACGTCCGAAGACGGA"
"1-trich" 0 0 0 0 100 0 0 0 0 0 0 33 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 8 0 0 0 0 0 0 0 0 0
"10-trich" 65 0 0 23 0 18 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
"11-trich" 29 0 99 30 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"12-trich" 29 0 0 7 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"13-trich" 51 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"14-trich" 49 2 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"15-trich" 33 0 0 0 0 0 0 0 57 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"16-trich" 0 0 63 62 0 0 9 21 0 0 0 0 0 30 0 0 10 0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 5 0 0 0 0 0
"17-trich" 57 0 0 10 0 0 39 0 0 14 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"18-trich" 66 0 0 17 0 8 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 6 0 0 0 0 0 0 0
"2-trich" 7 0 0 0 0 0 0 0 0 0 0 0 33 0 0 0 0 23 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"3-trich" 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 6 0 5 0 0 3 0
"5-trich" 41 0 0 0 0 0 8 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"6-trich" 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 4 0 0
"7-trich" 35 0 0 9 0 0 0 0 0 0 0 0 0 0 0 26 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"8-trich" 33 0 0 0 0 0 9 0 0 0 37 0 0 0 28 0 0 0 0 0 0 0 20 0 0 5 0 14 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0
"9-trich" 45 7 0 0 0 31 4 21 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 8 0 0 0 0 5 0 0 0
"PositivK-1-trich" 0 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"PositivK-2-trich" 0 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"PositivK-3-trich" 0 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
"PositivK-4-trich" 0 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
analysis/Trich/Trich_DADA2_results_060921/seqtab.rds 0 → 100644
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analysis/Trich/Trich_DADA2_results_060921/seqtab_nochim.rds 0 → 100644
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analysis/Trich/Trich_DADA2_results_060921/track.txt 0 → 100644
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"input" "filtered" "denoisedF" "denoisedR" "merged" "nonchim" "percent_retained"
"1-trich" 3580 202 184 174 167 167 4.66480446927374
"10-trich" 2010 141 127 120 113 113 5.62189054726368
"11-trich" 2813 199 189 189 189 189 6.71880554568077
"12-trich" 1330 75 66 71 65 65 4.88721804511278
"13-trich" 786 67 61 59 59 59 7.50636132315522
"14-trich" 2973 91 66 76 61 61 2.05179952909519
"15-trich" 1327 120 98 114 97 97 7.30972117558402
"16-trich" 7324 245 232 225 225 225 3.07209175314036
"17-trich" 1771 134 127 127 126 126 7.11462450592885
"18-trich" 2562 149 134 142 114 114 4.44964871194379
"2-trich" 2235 102 94 86 86 86 3.84787472035794
"3-trich" 4719 74 54 49 49 49 1.03835558381013
"5-trich" 970 62 53 55 53 53 5.4639175257732
"6-trich" 1431 71 59 66 42 42 2.93501048218029
"7-trich" 3175 133 106 113 89 89 2.80314960629921
"8-trich" 9926 205 181 175 157 157 1.58170461414467
"9-trich" 2979 206 180 188 130 130 4.36388049681101
"PositivK-1-trich" 1675 106 103 103 103 103 6.14925373134328
"PositivK-2-trich" 1238 65 64 63 63 63 5.08885298869144
"PositivK-3-trich" 1267 88 87 87 87 87 6.86661404893449
"PositivK-4-trich" 1714 107 106 105 87 87 5.07584597432905
"Vann1-trich" 11 0 NA NA NA NA NA
"Vann2-trich" 63 0 NA NA NA NA NA
analysis/Trich/Trich_Dada2_pipeline_S-mod_v4-3.rmd 0 → 100644
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---
title: "dada2 microbiome pipeline v.4.2"
author: "Simeon Rossmann, Marie L. Davey"
date: "16.12.2019"
output:
pdf_document: default
html_document: default
last_modified: "Simeon Rossmann (SWR)"
date_modified: "20.11.2020"
version: 4.3
---
This is an R markdown document for the analysis of MiSeq amplicon sequencing data. In order to customise the pipeline, search 'CHANGE ME' to find different settings you should adjust according to your data. All parameters requiring changes are located within the first 4 chunks. After the fourth chunk has been run, you can simply click 'Run'\>'Run all chunks below' in order to process the data
Comment SWR: A new chunk was added to allow for quick reanalysis of taxonomy and pooled analysis of multiple samples after error inference. Small changes in chunk 4 (exporting seqtab.RDP in addition) were made to facilitate this. Therefore it's not recommended to 'Run all chunks below' from chunk 4 in this version.
## Changelog
### v4 update
Comment SWR: Two more chunks were added to integrate [LULU](https://www.nature.com/articles/s41467-017-01312-x) post-processing into this pipeline. The first of the two new chunks is a shell script chunk that generates a match list (most similar ASVs for each ASV) using vsearch. The match list can also be generated by other means (f. eks. BLAST). The second of the new chunks is the core LULU chunk that takes the seqtab_nochim file from the dada2 analysis and the match list and performs the LULU post-processing with standard parameters. After this, a new seqtab_nochim, ASV-fasta and RDP taxonomy (optional) are generated in a lulu-subdirectory.
#### v4.3
This update addresses inconsistencies in code usage between authors that were the likely cause of occasional warning messages and errors when creating files and directories in-function on some machines/setups. These problems occurred inconsistently and were not reproduced but seemed to be fixed by cleaner file path encoding.
- minor code format fixes:
- implementation of file path concatenation cleaned up by replacing all uses of `paste0()` for file paths with `file.path()` and removal of leading and closing slashes `/`
- replaced all instances of in-code object assignment using `=` with `<-` to clearly distinguish it from user assignable objects and argument assignment in functions. This means that objects assigned with `=` are intended for user input while objects assigned with `<-` are not to be directly manipulated by users.
- other format fixes:
- improved cosmetics when knitting by manually adjusted line breaks in code chunks
- added spaces surrounding `=` and following `,` to conform with best practices
- more consistent line spacing in code chunks
- Better text formatting in the Markdown text portions
#### v4.2
- improved cutadapt with quality control and fwd primer removal, can be run through R in separate chunk
- filterAndTrim option expanded with trimLeft to remove the first n 5' bases regardless of content
#### v4.1
- fixed an error when attempting to plot empty files
- fixed an error when attempting to sample more files than available for plotting
## Setup R Environment
Comment SWR: If the OS is Linux, this chunk now passes the path and marker variables to the shell script chunk, making it unnecessary to specify this separately below and avoiding issues with misspelled variables. It is possible that this also works with Mac OS, but this was not included since no Mac machine was available for testing.
```{r setup, eval=FALSE}
############set paths and marker name
# CHANGE ME to the directory (ABSOLUTE FILEPATH e.g. on Linux /home/usr/...)
# containing the fastq files after unzipping.
path = "/home/simeon/Documents/Marte_Metabarcoding/Run_August21"
#CHANGE ME to the marker you have sequenced
marker = c("Trich")
# CHANGE ME to the path for the taxonomy database you will be using for
# identification (if any)
tax_database = "no/path/here"
#set knitr chunk options
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(eval = FALSE)
knitr::opts_chunk$set(tidy = TRUE)
knitr::opts_chunk$set(root.dir = file.path(path))
#load libraries for R session
library(dada2)
library(phyloseq)
library(ggplot2)
library(Biostrings)
library(grid)
library(gridExtra)
library(ShortRead)
library(tidyverse)
# Get info on the operating system. If Linux, make 'path' and 'marker' available
# globally, if not, warn that the script needs additional attention.
os <- Sys.info()['sysname']
if (os == "Linux") {
Sys.setenv(projectpath = path)
Sys.setenv(marker = marker)
}else{
cat(os, "Please run this script on Linux,
it is no longer tested and optimized for Windows")
}
```
## Primer presence and orientation check
This chunk is based on the tutorial for [ITS workflows from the DADA2 GitHub page](https://benjjneb.github.io/dada2/ITS_workflow.html) and offers an assessment of primer presence before cutadapt processing. This should be done to check the orientation used in cutadapt primer removal. Raw fastq.gz files must be located in 'path' or 'path/marker' or 'path/marker/raw_data'. If fastq.gz files are in 'path/marker' or 'path/marker/raw_data', all files will be used in the analysis, if fastq.gz files are in 'path', you may choose whether to only use files that contain the marker in the file name or all files.
```{r primer-check, eval=FALSE}
#CHANGE ME to number of samples to check. Numerical values (e.g. 1, 5, 10) or
# "all" are accepted inputs.
nSamp = "all"
#CHANGE ME to TRUE if you only want to move files from 'path' to
# 'path/marker/raw_data' if they contain the marker in the file name.
# E.g. marker = 16S, files = 'Sxxx_16S_L001_R1.fastq.gz'. If FALSE, all fastq.gz
# files will be moved from 'path' to 'path/marker/raw_data'. All fastq.gz files
# will be moved from 'path/marker' to 'path/marker/raw_data' regardless of this.
marker_in_filename = FALSE
#CHANGE ME by uncommenting the appropriate primer pair
#for the Oomycete ITS1 marker:
#Fprimer = "CGGAAGGATCATTACCAC"
#Rprimer = "AGCCTAGACATCCACTGCTG"
#for the Oomycete ITS2 marker:
#Fprimer = "TTGAACGCAYATTGCACTT"
#Rprimer = "TCCTCCGCTTATTGATATGC"
#for the Fungal ITS1 marker:
#Fprimer = "AAGTCGTAACAAGGTTTCC"
#Rprimer = "TGCVAGARCCAAGAGATC"
#for the Fungal ITS2 marker:
#Fprimer = "GTGARTCATCGAATCTTTG"
#Rprimer = "TCCTCCGCTTATTGATATGC"
#for the Bacterial 16S marker:
#Fprimer = "GTGYCAGCMGCCGCGGTAA"
#Rprimer = "GGACTACNVGGGTWTCTAAT"
#for the plant Sper marker:
#rc_Fprimer = "GGGCAATCCTGAGCCAA"
#rc_Rprimer = "ATTGAGTCTCTGCACCTATC"
#for the plant Trac marker:
#Fprimer = "CRTTGCCGAGAGTC"
#Rprimer = "AAGTCGTAACAAGG"
#for the nematode Nem marker
#Fprimer = "GAGGGCAAGTCTGGTG"
#Rprimer = "TTTACGGTYAGAACTAGGG"
#for the nematode Trich marker
Rprimer = "GAGGGCAAGTCTGGTG"
Fprimer = "TTTACGGTCAGAACTAAAG"
#### NO CHANGES NEEDED BELOW ####
#Check if Marker folder exists, otherwise create it
dir.create(file.path(path, marker))
#Check if nSamp is numeric or "all", otherwise set it to "all"
nSamp <- ifelse(is.character(nSamp) | is.numeric(nSamp), nSamp, "all")
nSamp <- ifelse(nSamp == "all" | is.numeric(nSamp), nSamp, "all")
# Move raw reads to their own subdirectory path/marker/raw_data. If files are in
# path, check whether some contain 'marker' in name. If they exist only move
# those, otherwise move all.
raw_dir <- file.path(path, marker, "raw_data")
dir.create(raw_dir)
if (length(list.files(file.path(path, marker),
pattern = ".fastq.gz", full.names = TRUE)) != 0) {
fnraw <- sort(list.files(file.path(path, marker),
pattern = "fastq.gz", full.names = TRUE))
}
if (length(list.files(path, pattern = ".fastq.gz", full.names = TRUE)) != 0) {
if (marker_in_filename) {
fnrawp <- sort(list.files(
path, paste0(marker,"(.*).fastq.gz"), full.names = TRUE))
}else{
fnrawp <- sort(list.files(path, pattern = ".fastq.gz", full.names = TRUE))
}
}
if (exists("fnraw")) {
invisible(file.copy(fnraw, file.path(raw_dir, basename(fnraw))))
invisible(file.remove(fnraw))
rm(fnraw)}
if (exists("fnrawp")) {
invisible(file.copy(fnrawp, file.path(raw_dir, basename(fnrawp))))
invisible(file.remove(fnrawp))
rm(fnrawp)}
#Create Lists of Forward and Reverse Filenames and a List of Sample Names
fnFs <- sort(list.files(file.path(path, marker, "raw_data"),
pattern = "_R1_001.fastq.gz", full.names = TRUE))
fnRs <- sort(list.files(file.path(path, marker, "raw_data"),
pattern = "_R2_001.fastq.gz", full.names = TRUE))
allOrients <- function(primer) {
# Create all orientations of the input sequence
require(Biostrings)
# Biostrings works w/ DNAString objects rather than character vectors
dna <- DNAString(primer)
orients <- c(Forward = dna,
Complement = complement(dna),
Reverse = reverse(dna),
RevComp = reverseComplement(dna))
return(sapply(orients, toString)) # Convert back to character vector
}
FWD.orients <- allOrients(Fprimer)
REV.orients <- allOrients(Rprimer)
# Put N-filtered files in filtN/ subdirectory
fnFs.filtN <- file.path(path, marker, "filtN", basename(fnFs))
fnRs.filtN <- file.path(path, marker, "filtN", basename(fnRs))
filterAndTrim(fnFs, fnFs.filtN, fnRs, fnRs.filtN, maxN = 0, multithread = TRUE)
fnFs.filtN <- sort(list.files(file.path(path, marker, "filtN"),
pattern = "_R1_001.fastq.gz", full.names = TRUE))
fnRs.filtN <- sort(list.files(file.path(path, marker, "filtN"),
pattern = "_R2_001.fastq.gz", full.names = TRUE))
primerHits <- function(primer, fn) {
# Counts number of reads in which the primer is found
nhits <- vcountPattern(primer, sread(readFastq(fn)), fixed = FALSE)
return(sum(nhits > 0))
}
primerHits_perSample <- function(sampnum, fwd = fnFs, rev = fnRs){
hit_table <- rbind(FWD_primer_R1_Reads = sapply(
FWD.orients, primerHits, fn = fwd[[sampnum]]),
FWD_primer_R2_Reads = sapply(FWD.orients, primerHits, fn = rev[[sampnum]]),
REV_primer_R1_Reads = sapply(REV.orients, primerHits, fn = fwd[[sampnum]]),
REV_primer_R2_Reads = sapply(REV.orients, primerHits, fn = rev[[sampnum]]))
hit_table <- as.data.frame(hit_table)
hit_table <- tibble::rownames_to_column(hit_table, "Primer_Read")
return(hit_table)
}
nSamp <- ifelse(nSamp == "all", length(fnFs.filtN), nSamp)
nSamp <- ifelse(nSamp > length(fnFs.filtN), length(fnFs.filtN), nSamp)
Samps <- c(1:nSamp)
primers_before <- lapply(Samps, primerHits_perSample,
fwd = fnFs.filtN, rev = fnRs.filtN)
names(primers_before) <- paste(basename(fnFs.filtN[1:nSamp]),
basename(fnRs.filtN[1:nSamp]))
primers_before <- dplyr::bind_rows(primers_before, .id = "Sample") %>%
as_tibble()
write.table(primers_before,
file.path(path, marker, paste0("primers_before_cutadapt_",
nSamp, "Samples_", format(Sys.time(), "%d-%m-%y_%H%M"),
".txt")), row.names = FALSE)
primers_before %>%
select(-Sample) %>%
group_by(Primer_Read) %>%
summarise_all(mean)
```
## Improved cutadapt
More flexible implementation of the cutadapt processing, allows changing orientation of fwd and rev primers if the chunk above indicated that the orientation for one or both primers is not as expected. Expectation: FWD primer matches R1 in Forward orientation and/or R2 in RevComp orientation, REV primer matches R2 in Forward orientation and/or R1 in RevComp orientation. All other scenarios will require adjusting the primer orientation for cutadapt! This chunk offers a possibility to simply flip the FWD or REV primers separately if necessary but more complex issues will have to be resolved manually.
Both, the new and old implementation of cutadapt wil NOT run successfully on a Windows system, even though the rest of the pipeline *can*.
```{r cutadapt-improved, eval=FALSE}
# CHANGE ME to the path of your cutadapt installation. This can be found out by
# running 'which cutadapt' in the bash command line
cutadapt = "/home/simeon/miniconda3/envs/bioinfo/bin/cutadapt"
# CHANGE ME to flip the FWD primer. Should be FALSE if the orientation was as
# expected in the chunk above.
flip_FWD = FALSE
#CHANGE ME to flip the REV primer. Should be FALSE if the orientation was as
# expected in the chunk above.
flip_REV = FALSE
#CHANGE ME to define the minimum length of reads after trimming. Reads shorter
# than this value will be discarded and won't appear in the output files. The
# description of this parameter in the deprecated chunk was not correct!
MIN_LENGTH = 15
##SWR: define a variable for the number of mismatches to be used in cutadapt
#CHANGE ME to the rate of mismatches (0 =< value < 1) you want cutadapt to
# accept when finding the primer. When choosing a value different from 0, it is
# recommended to compare how many sequences get trimmed when using both, 0 and
# the alternative value.
MISMATCH = 0
#### NO CHANGES NEEDED BELOW ####
# This should print the version of cutadapt you're using if the path is given
# correctly and system2 can access cutadapt.
# THIS WILL NOT WORK IN A WINDOWS SYSTEM!
system2(cutadapt, args = "--version")
#Make output directory for trimmed reads in path/marker/trim
trim_dir <- file.path(path, marker, "trim")
dir.create(trim_dir)
fnFs_trim <- file.path(trim_dir, paste0(basename(fnFs.filtN), ".trim.fastq.gz"))
fnRs_trim <- file.path(trim_dir, paste0(basename(fnRs.filtN), ".trim.fastq.gz"))
#Define primers and Revcomps, Flip if specified
F_fwd <- ifelse(flip_FWD, FWD.orients["RevComp"], FWD.orients["Forward"])
F_rc <- ifelse(flip_FWD, FWD.orients["Forward"], FWD.orients["RevComp"])
R_fwd <- ifelse(flip_REV, REV.orients["RevComp"], REV.orients["Forward"])
R_rc <- ifelse(flip_REV, REV.orients["Forward"], REV.orients["RevComp"])
# Trim FWD and the reverse-complement of REV off of R1 (forward reads)
R1.flags <- paste("-g", F_fwd, "-a", R_rc)
# Trim REV and the reverse-complement of FWD off of R2 (reverse reads)
R2.flags <- paste("-G", R_fwd, "-A", F_rc)
# Run Cutadapt
for (i in seq_along(fnFs.filtN)) {
# -n 2 required to remove FWD and REV from reads
system2(cutadapt, args = c(R1.flags, R2.flags, "-n", 2,
"-m", MIN_LENGTH, "-e", MISMATCH,
# output files
"-o", fnFs_trim[i], "-p", fnRs_trim[i],
# input files
fnFs.filtN[i], fnRs.filtN[i]),
stdout = file.path(trim_dir, paste0(basename(fnFs.filtN[[i]]),
"_cutadapt_output.txt")))
}
nSamp <- ifelse(nSamp > length(fnFs_trim), length(fnFs_trim), nSamp)
Samps <- c(1:nSamp)
primers_after <- lapply(Samps, primerHits_perSample,
fwd = fnFs_trim, rev = fnRs_trim)
names(primers_after) <- paste(basename(fnFs_trim[1:nSamp]),
basename(fnRs_trim[1:nSamp]))
primers_after <- dplyr::bind_rows(primers_after, .id = "Sample")
write.table(primers_after, file.path(path, marker, paste0(
"primers_after_cutadapt_", nSamp, "Samples_",
format(Sys.time(), "%d-%m-%y_%H%M"),
".txt")), row.names = FALSE)
primers_after %>%
select(-Sample) %>%
group_by(Primer_Read) %>%
summarise_all(mean)
```
## assess run quality
looks at a random sample of R1/R2 trimmed reads to assess run quality.
```{r, QC, eval=FALSE}
# Create lists of forward and reverse file names and a list of sample names
fnFs <- sort(list.files(file.path(path, marker, "trim"),
pattern = "_R1_001.fastq(.*)trim.fastq.gz",
full.names = TRUE))
fnRs <- sort(list.files(file.path(path, marker, "trim"),
pattern = "_R2_001.fastq(.*)trim.fastq.gz",
full.names = TRUE))
# SWR: Make a list of fnF and fnR for quality plotting that excludes very small
# files (default is < 100 bytes) which may cause problems in quality plotting
# and exit the chunk with error
min_file_size <- function(files, minsize=100){
info <- file.info(files)
index_bigs <- which(info$size > 100)
keep <- files[index_bigs]
}
fnFL <- min_file_size(fnFs)
fnRL <- min_file_size(fnRs)
sample_number <- if (length(fnFL) < 10) length(fnFL) else 10
sample_number <- if (length(fnRL) < sample_number) length(fnRL) else sample_number
plts_trim <- lapply(c(sample(fnFL, sample_number), sample(fnRL, sample_number)),
plotQualityProfile)
m_plts <- marrangeGrob(plts_trim, nrow = 2, ncol = 1)
ggsave(file.path(path, marker, "trim", "qual_plots_after_cutadapt.pdf"),
m_plts, width = 20, height = 26, unit = "cm", dpi = 300)
print(plts_trim)
```
## set parameters for dada2
This chunk contains several options for dada2 that will be used in the processing of the trimmed reads. The settings are saved to a file with a time stamp.
```{r, set_params, eval=FALSE}
# CHANGE ME according to the quality of the sequencing run. For good quality
# runs, analysing both R1/R2 is desirable. For very poor quality runs, it may
# be worthwhile to analyse only R1. Select from the following: "R01" "both"
analysis = "both"
# CHANGE ME use "TRUE" to assign taxonomy, "FALSE" to proceed without taxonomic
# assignments
assign_taxonomy = "FALSE"
# CHANGE ME use "TRUE" to plot quality profiles for each sample, and "FALSE" to
# speed up analysis by skipping this step (plotting takes some time) and "SUB"
# to plot a subset of 10 samples
plotQC = "SUB"
# CHANGE ME according to the quality of the sequencing run. This determines the
# maximum expected errors for R1 and R2 during the filtering step. A reasonably
# conservative threshold is (2,2). If the data is of lower quality, it may be
# worthwhile to run with higher EEs ex/ (3,5)
my_maxEEf = 2
my_maxEEr = 2
# CHANGE ME according to the quality of the sequencing run. This is a PHRED score
# quality treshold - all sequences will be truncated at the first base with a
# quality score below this value
my_truncQ = 2
# CHANGE ME according to the quality of the sequencing run and according to the
# length of the target region. This is the length to cut the (forward,reverse)
# sequences at. Use 0,0 for no truncation.
my_truncLen = c(0, 0)
# CHANGE ME to trim the left (5') part of all reads (forward, reverse) if a dip
# is observable in the quality plots
left_trim = c(10, 10)
# CHANGE ME according to the marker used - this is the minimum length of the
# reads after trimming
my_minLen = 180
# CHANGE ME specify the minimum number of bases to overlap during merging, must
# be over 10!
my_minoverlap = 60
# CHANGE ME to specify the minimum confidence interval for RDP assignment of
# taxonomy.
my_minBootstrap = 80
# SWR: CHANGE ME to TRUE and give a value (number of bases) to collapse ASVs
# with an overlap of 'Collaps_minOverlap' (recommended to be the length of the
# shortest ASV that is identical to a longer one). This is only recommended to
# be used in cases where a number of ASVs occur that differ in length but are
# otherwise identical. If this frequently occurs, it may be an issue of primer
# trimming.
Collapse_overlapping = FALSE
Collapse_minOverlap = 200
parameters <- paste(
paste("Run on", format(Sys.time(), "%d/%m/%y %H:%M")),
paste("Path: ",path),"Samples:",
paste(c(sapply(strsplit(basename(sort(
list.files(file.path(path, marker, "trim"),
pattern = "_R1_001.fastq(.*)trim.fastq.gz",
full.names = TRUE))), "_"), `[`, 1)), collapse = ";"),
paste("Analysis type:",analysis),
paste("Database type:",tax_database),
"\ndada2 Parameters\n",
paste("maximum expected errors R1:",my_maxEEf),
paste("maximum expected errors R2:",my_maxEEr),
paste("minimum length: ",my_minLen),
paste("truncate at first instance of Qscore: ",my_truncQ),
paste("truncation at length (0 = no trunc)",
paste(my_truncLen), collapse = ","),
paste("minimum overlap during merging: ",my_minoverlap),
paste("taxonomy bootstrap threshold: ",my_minBootstrap),
paste("Collapse overlapping ASVs: ", Collapse_overlapping),
paste("Minimum overlap if collapsing overlapping ASVs:",
Collapse_minOverlap),
paste("Trimming 5' (R1, R2):",
paste(left_trim, collapse = ",")),
sep = "\n")
write.table(parameters, file.path(path, marker, paste0("parameters_",analysis,
format(Sys.time(), "%d-%m-%y_%H%M") ,".txt")),
row.names = FALSE)
```
## Processing chunk
#### DO NOT ALTER SCRIPT BEYOND THIS POINT!!!
Comment SWR: This chunk now passes the output directory to a global variable on Linux, so that the shell script chunk that generates the match list can access it.
```{r, dada2, eval=FALSE}
# Create lists of forward and reverse file names and a list of sample names
fnFs <- sort(list.files(file.path(path, marker,"trim"),
pattern = "_R1_001.fastq(.*)trim.fastq.gz",
full.names = TRUE))
fnRs <- sort(list.files(file.path(path, marker, "trim"),
pattern = "_R2_001.fastq(.*)trim.fastq.gz",
full.names = TRUE))
# SWR: Make a list of fnF and fnR for quality plotting that excludes very small
# files (default is < 100 bytes) which may cause problems in quality plotting
# and exit the chunk with error
min_file_size <- function(files, minsize = 100){
info <- file.info(files)
index_bigs <- which(info$size > 100)
keep <- files[index_bigs]
}
fnFL <- min_file_size(fnFs)
fnRL <- min_file_size(fnRs)
#Extract sample names
sample.names <- sapply(strsplit(basename(fnFs), "_"), `[`, 1)
# begin commands for analysing R01/R02 together
if (analysis == "both") {
#Create paths for the filtered files in a subdirectory called filtered/
filtFs <- file.path(path,marker, "R01_R02", "filtered",
paste0(sample.names, "_F_filt.fastq"))
filtRs <- file.path(path,marker, "R01_R02", "filtered",
paste0(sample.names, "_R_filt.fastq"))
outp <- file.path(path, marker, "R01_R02")
#Filter sequences
##SWR: fixed paired end pairing by adding matchIDs=TRUE
out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,
maxEE = c(my_maxEEf,my_maxEEr), truncQ = my_truncQ,
minLen = my_minLen, trimLeft = left_trim, truncLen = my_truncLen,
rm.phix = TRUE, compress = TRUE, multithread = TRUE,
matchIDs = TRUE)
head(out)
write.table(out, file.path(path, marker, "R01_R02", "out.txt"))
if (plotQC == "TRUE") {
# Plot Forward/Reverse Quality scores for each sample (not strictly
# necessary, time intensive)
pl <- lapply(c(fnFL),plotQualityProfile)
print(pl)
ml <- marrangeGrob(pl, nrow = 2, ncol = 1)
ggsave(file.path(path, marker, "R01_R02", "qualityplots_fwd.pdf"),
ml, width = 20, height = 26, unit = "cm", dpi = 300)
plr <- lapply(c(fnRL),plotQualityProfile)
print(plr)
mlr <- marrangeGrob(plr, nrow = 2, ncol = 1)
ggsave(file.path(path, marker, "R01_R02", "qualityplots_rev.pdf"),
mlr, width = 20, height = 26, unit = "cm", dpi = 300)
}
if (plotQC == "SUB") {
# Plot Forward/Reverse Quality scores for each sample (not strictly
# necessary, time intensive)
sample_number <- if (length(fnFL) < 10) length(fnFL) else 10
sample_number <- if (length(fnRL) < sample_number) length(fnRL) else sample_number
pl <- lapply(sample(c(fnFL), sample_number), plotQualityProfile)
print(pl)
ml <- marrangeGrob(pl,nrow = 2,ncol = 1)
ggsave(file.path(path, marker, "R01_R02", "qualityplots_fwd.pdf"),
ml, width = 20, height = 26, unit = "cm", dpi = 300)
plr <- lapply(sample(c(fnRL), sample_number), plotQualityProfile)
print(plr)
mlr <- marrangeGrob(plr, nrow = 2, ncol = 1)
ggsave(file.path(path, marker, "R01_R02", "qualityplots_rev.pdf"),
mlr, width = 20, height = 26, unit = "cm", dpi = 300)
}
# dereplicate
derepFs <- derepFastq(filtFs[out[,2] > 0], verbose = TRUE)
derepRs <- derepFastq(filtRs[out[,2] > 0], verbose = TRUE)
# Name the derep-class objects by the sample names
names(derepFs) <- sample.names[out[,2] > 0]
names(derepRs) <- sample.names[out[,2] > 0]
#Train dada2 to your dataset
errF <- learnErrors(filtFs[out[,2] > 0], multithread = TRUE, nbases = 1e+09)
errR <- learnErrors(filtRs[out[,2] > 0], multithread = TRUE, nbases = 1e+09)
## Plot the Estimated Error Rates for the Transition Types
#check that model and data match reasonably well
eFplot <- plotErrors(errF, nominalQ = TRUE)
ggsave(file.path(path, marker, "R01_R02", "R1_error_profile.pdf"),
eFplot, width = 20, height = 26, unit = "cm", dpi = 300)
eRplot <- plotErrors(errR, nominalQ = TRUE)
ggsave(file.path(path, marker, "R01_R02", "R2_error_profile.pdf"),
eRplot, width = 20, height = 26, unit = "cm", dpi = 300)
# sample inference
dadaFs <- dada(derepFs, err = errF, multithread = TRUE)
dadaRs <- dada(derepRs, err = errR, multithread = TRUE)
# merge forward and reverse reads
mergers <- mergePairs(dadaFs, derepFs, dadaRs, derepRs,
minOverlap = my_minoverlap, verbose = TRUE)
# Inspect the merger data.frame from the first sample
head(mergers[[1]])
merge.tab <- data.frame(matrix(ncol = 10, nrow = 0))
colnames(merge.tab) <- c("sample", "sequence", "abundance", "forward",
"reverse", "nmatch", "nmismatch", "nindel", "prefer",
"accept")
for (i in (1:length(mergers))) {
if (nrow(mergers[[i]]) > 0) {
sub <- cbind(names(mergers)[i], mergers[[i]])
merge.tab <- rbind(merge.tab,sub)
} else {}
}
colnames(merge.tab) <- c("sample", "sequence", "abundance", "forward",
"reverse", "nmatch", "nmismatch", "nindel",
"prefer", "accept")
write.table(merge.tab, file.path(path, marker, "R01_R02", "mergers.txt"),
row.names = FALSE)
# make sequence table and distribution table for sequence lengths
seqtab <- makeSequenceTable(mergers)
# use only sequences longer than 50 bp as the rdp classifier can't accept less
# than that
seqtab <- seqtab[,nchar(colnames(seqtab)) > 49]
## SWR: If Collapse_overlapping=TRUE, collapses identical sequences together
# based on an overlap specified in 'Collapse_minOverlap'.
if (Collapse_overlapping == TRUE) {
seqtab <- collapseNoMismatch(seqtab, minOverlap = Collapse_minOverlap,
orderBy = "abundance", identicalOnly = FALSE,
vec = TRUE, verbose = FALSE)
}
dim(seqtab)
table(nchar(getSequences(seqtab)))
## SWR: save RDS format seqtab to allow for merging of multiple runs and later
# analysis without rerunning the whole pipeline
saveRDS(seqtab, file.path(path, marker, "R01_R02", "seqtab.rds"))
# remove chimeric sequences
seqtab.nochim <- removeBimeraDenovo(seqtab, method = "consensus",
multithread = TRUE, verbose = TRUE)
dim(seqtab.nochim)
# calculate percent of sequences that are non-chimeric
sum(seqtab.nochim)/sum(seqtab)
write.table(seqtab.nochim, file.path(path, marker,
"R01_R02", "seqtab.nochim.txt"))
## SWR: additional .RDS output table for easy downstream use in e.g. phyloseq
saveRDS(seqtab.nochim, file.path(path, marker,
"R01_R02", "seqtab_nochim.rds"))
# output table for each sample
# define a function that counts sequences in a file/object
getN <- function(x) sum(getUniques(x))
if (min(out[,2]) > 0) {
track <- cbind(out, sapply(dadaFs, getN),
sapply(dadaRs, getN),
sapply(mergers, getN),
rowSums(seqtab.nochim),
rowSums(seqtab.nochim)/out[,1]*100)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR",
"merged", "nonchim","percent_retained")
rownames(track) <- sample.names
} else {
track <- cbind(out[out[,2] > 0,], sapply(dadaFs, getN), sapply(dadaRs, getN),
sapply(mergers, getN), ... = rowSums(seqtab.nochim),
rowSums(seqtab.nochim)/out[,1][out[,2] > 0]*100)
# If processing a single sample, remove the sapply calls:
# e.g. replace sapply(dadaFs, getN) with getN(dadaFs)
track <- rbind(track, cbind(out[,1][out[,2] == 0],
out[,2][out[,2] == 0],
NA,NA,NA,NA,NA))
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR",
"merged", "nonchim","percent_retained")
rownames(track) <- c(sample.names[out[,2] > 0], sample.names[out[,2] == 0])
}
head(track)
write.table(track, file.path(path, marker, "R01_R02", "track.txt"))
if (assign_taxonomy == "TRUE") {
taxa <- assignTaxonomy(seqtab.nochim, tax_database, multithread = TRUE,
minBoot = my_minBootstrap, outputBootstraps = TRUE)
sum(rownames(taxa[[1]]) == colnames(seqtab.nochim)) == nrow(taxa[[1]])
tmp <- cbind(rownames(taxa[[1]]),taxa[[1]],taxa[[2]])
rownames(tmp) <- paste0("ASV", 1:length(colnames(seqtab.nochim)))
tmp <- data.frame(tmp)
write.table(tmp, file.path(path, marker,
"R01_R02", "taxonomy_ASVs_NC_R1R2.txt"))
## SWR: save RDS file omitting bootstrap values for downstream use in
# e.g phyloseq
saveRDS(taxa$tax, file.path(path, marker, "R01_R02", "taxa.rds"))
tmp2 <- cbind(t(seqtab.nochim), tmp)
rownames(tmp2) <- paste0("ASV", 1:length(colnames(seqtab.nochim)))
write.table(tmp2, file.path(path, marker,
"R01_R02", "seqtab.nochim_withtax.txt"))
# write raw ASVs to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV", 1:length(colnames(seqtab.nochim)))
writeXStringSet(seqsnochim, file.path(path, marker,
"R01_R02", "ASVs_raw.fasta"),
format = "fasta")
# write ASVs with taxonomy in the header to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV",
1:length(colnames(seqtab.nochim)),
"|","|",
paste(tmp$Kingdom, tmp$Phylum, tmp$Class,
tmp$Order, tmp$Family, tmp$Genus,
tmp$Species, sep = ";"))
writeXStringSet(seqsnochim, file.path(path, marker,
"R01_R02", "ASVs_withtax.fasta"),
format = "fasta")
} else {
#write raw ASVs to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV",1:length(colnames(seqtab.nochim)))
writeXStringSet(seqsnochim, file.path(path, marker,
"R01_R02", "ASVs_raw.fasta"),
format = "fasta")
}
} else {
######################################################################################
# Forward Reads Only
###################################################################################
# Create paths for the filtered files in a subdirectory called filtered/
filtFsR1 <- file.path(path, marker, "R01_only", "filtered",
paste0(sample.names, "_F_filt.fastq"))
outp <- file.path(path, marker, "R01_only")
# Filter sequences
outR1 <- filterAndTrim(fnFs, filtFsR1,
maxEE = my_maxEEf, truncQ = my_truncQ, minLen = my_minLen,
trimLeft = left_trim[1], rm.phix = TRUE,
compress = TRUE, multithread = TRUE)
head(outR1)
write.table(outR1, file.path(path, marker, "R01_only", "out.txt"))
if (plotQC == "TRUE") {
#Plot Forward/Reverse Quality scores for each sample (not strictly
# necessary, time intensive)
pl <- lapply(c(fnFL), plotQualityProfile)
print(pl)
ml <- marrangeGrob(pl, nrow = 2,ncol = 1)
ggsave(file.path(path, marker, "R01_only", "qualityplots_fwd.pdf"),
ml, width = 20, height = 26, unit = "cm", dpi = 300)
}
if (plotQC == "SUB") {
# Plot Forward/Reverse Quality scores for each sample (not strictly
# necessary, time intensive)
sample_number <- if (length(fnFL) < 10) length(fnFL) else 10
pl <- lapply(sample(c(fnFL), sample_number), plotQualityProfile)
print(pl)
ml <- marrangeGrob(pl, nrow = 2, ncol = 1)
ggsave(file.path(path, marker, "R01_only", "qualityplots_fwd.pdf"),
ml, width = 20, height = 26, unit = "cm", dpi = 300)
}
# Train dada2 to your dataset
errFR1 <- learnErrors(filtFsR1[outR1[,2] > 0], multithread = TRUE)
eFplot <- plotErrors(errFR1, nominalQ = TRUE)
ggsave(file.path(path, marker, "R01_only", "R1_error_profile.pdf"),
eFplot, width = 20, height = 26, unit = "cm", dpi = 300)
# dereplicate
derepFsR1 <- derepFastq(filtFsR1[outR1[,2] > 0], verbose = TRUE)
# Name the derep-class objects by the sample names
names(derepFsR1) <- sample.names[outR1[,2] > 0]
# sample inference
dadaFsR1 <- dada(derepFsR1, err = errFR1, multithread = TRUE)
# make sequence table and distribution table for sequence lengths
seqtabR1 <- makeSequenceTable(dadaFsR1)
## SWR: If Collapse_overlapping=TRUE, collapses identical sequences together
# based on an overlap specified in 'Collapse_minOverlap'.
if (Collapse_overlapping == TRUE) {
seqtabR1 <- collapseNoMismatch(seqtabR1, minOverlap = Collapse_minOverlap,
orderBy = "abundance", identicalOnly = FALSE,
vec = TRUE, verbose = FALSE)
}
dim(seqtabR1)
table(nchar(getSequences(seqtabR1)))
# RDP can't assign taxonomy to sequences shorter than 50bp, so filter to > 50bp
seqtabR1 <- seqtabR1[,nchar(colnames(seqtabR1)) > 49]
## SWR: save RDS format seqtab to allow for merging of multiple runs and later
# analysis without rerunning the whole pipeline
saveRDS(seqtabR1, file.path(path, marker, "R01_only", "seqtab.rds"))
# remove chimeric sequences
seqtabR1.nochim <- removeBimeraDenovo(seqtabR1, method = "consensus",
multithread = TRUE, verbose = TRUE)
dim(seqtabR1.nochim)
# calculate percent of sequences that are non-chimeric
sum(seqtabR1.nochim)/sum(seqtabR1)
write.table(seqtabR1.nochim, file.path(path, marker,
"R01_only", "seqtabR1.nochim"))
## SWR: additional .RDS output table for easy downstream use in e.g. phyloseq
saveRDS(seqtabR1.nochim, file.path(path, marker,
"R01_only", "seqtab_nochim.rds"))
# output table for each sample
getN <- function(x) sum(getUniques(x))
if (min(outR1[,2]) > 0) {
trackR1 <- cbind(outR1, sapply(dadaFsR1,getN), rowSums(seqtabR1.nochim),
rowSums(seqtabR1.nochim)/outR1[,1]*100)
colnames(trackR1) <- c("input", "filtered", "denoisedF",
"nonchim","percent_retained")
rownames(trackR1) <- c(sample.names)
} else {
trackR1 <- cbind(outR1[outR1[,2] > 0,], sapply(dadaFsR1, getN),
rowSums(seqtabR1.nochim),
rowSums(seqtabR1.nochim)/outR1[,1][outR1[,2] > 0]*100)
trackR1 <- rbind(trackR1,cbind(outR1[,1][outR1[,2] == 0],
outR1[,2][outR1[,2] == 0],
NA,NA,NA))
colnames(trackR1) <- c("input", "filtered", "denoisedF",
"nonchim","percent_retained")
rownames(trackR1) <- c(sample.names[!outR1[,2] == 0],
sample.names[outR1[,2] == 0])
}
head(trackR1)
write.table(trackR1, file.path(path, marker, "R01_only", "trackR1.txt"))
if (assign_taxonomy == "TRUE") {
taxaR1 <- assignTaxonomy(seqtabR1.nochim, tax_database, multithread = TRUE,
minBoot = my_minBootstrap, outputBootstraps = TRUE)
sum(rownames(taxaR1[[1]]) == colnames(seqtabR1.nochim)) == nrow(taxaR1[[1]])
tmp <- cbind(rownames(taxaR1[[1]]), taxaR1[[1]], taxaR1[[2]])
rownames(tmp) <- paste0("ASV", 1:length(colnames(seqtabR1.nochim)))
tmp <- data.frame(tmp)
write.table(tmp, file.path(path, marker,
"R01_only", "taxonomy_ASVs_NC_R1.txt"))
## SWR: save RDS file omitting bootstrap values for downstream use in
# e.g phyloseq
saveRDS(taxaR1$tax, file.path(path, marker, "R01_only", "taxa.rds"))
tmp2 <- cbind(t(seqtabR1.nochim),tmp)
rownames(tmp2) <- paste0("ASV", 1:length(colnames(seqtabR1.nochim)))
write.table(tmp2, file.path(path, marker,
"R01_only", "seqtabR1.nochim_withtax.txt"))
#write raw ASVs to a fasta file
seqsnochimR1 <- DNAStringSet(colnames(seqtabR1.nochim))
seqsnochimR1@ranges@NAMES <- paste0("ASV",1:length(
colnames(seqtabR1.nochim)))
writeXStringSet(seqsnochimR1, file.path(path, marker,
"R01_only", "ASVs_raw.fasta"),
format = "fasta")
#write ASVs with taxonomy in the header to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtabR1.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV",
1:length(colnames(seqtabR1.nochim)),
"|","|",
paste(tmp$Kingdom, tmp$Phylum, tmp$Class,
tmp$Order, tmp$Family, tmp$Genus,
tmp$Species, sep = ";"))
writeXStringSet(seqsnochim, file.path(path, marker,
"R01_only", "ASVs_withtax_R1.fasta"),
format = "fasta")
} else {
#write raw ASVs to a fasta file
seqsnochimR1 <- DNAStringSet(colnames(seqtabR1.nochim))
seqsnochimR1@ranges@NAMES <- paste0("ASV",1:length(
colnames(seqtabR1.nochim)))
writeXStringSet(seqsnochimR1, file.path(path, marker,
"R01_only", "ASVs_raw.fasta"),
format = "fasta")
}
}
#make paths available to subsequent chunks
dada2_dir <- outp
#If Linux, make 'dada2_folder'available globally.
if (os == "Linux") {
Sys.setenv(dada2Folder = dada2_dir)
}
```
## Make match list
Comment SWR: This is a short bash chunk to create the match list necessary for LULU using vsearch (must be installed). If you are on Windows or would rather use BLAST than vsearch check the (LULU Github)[<https://github.com/tobiasgf/lulu>] for additional info.
```{bash, eval=FALSE, tidy=FALSE}
# UNCOMMENT AND CHANGE ME (only if you're not running Linux as your OS) to the
# directory containing the dada2
#dada2Folder="/mnt/c/R/dada2results"
ASV="${dada2Folder}"/ASVs_raw.fasta
matchList="${dada2Folder}"/match_list.txt
vsearch --usearch_global "${ASV}" --db "${ASV}" --self --id .84 --iddef 1 \
--userout "${matchList}" -userfields query+target+id --maxaccepts 0 \
--query_cov .9 --maxhits 10
```
## LULU post-processing
Comment SWR: This chunk will run the LULU post-processing on the seqtab_nochim table and produce a new seqtab_nochim table, as well as a new raw ASV table. It requires the 'seqtab_nochim' and 'match_list' tables as input and will automatically get them when running the whole pipeline on a Linux OS. It also has the posibility to assign taxonomy to this new ASV table using the RDP classifier. All files will be saved to a subdirectory of the main output called 'lulu_output'. In the current form, the original ASV designations from the dada2 pipeline are not retained in the LULU output.
```{r lulu, eval=FALSE, tidy=FALSE}
# If you want to run this as a stand-alone chunk or are on Windows, uncomment
# and change the 'dada2_dir' variable. The directory must contain
# 'match_list.txt' and 'seqtab.nochim.rds'. If you run the whole pipeline on
# Linux, this will be set.
#dada2_dir = "C:/"
# CHANGE ME to the path for the taxonomy database you will be using for
# identification (if not already specified above)
tax_database = tax_database
# CHANGE ME to specify the minimum confidence interval for RDP assignment of
# taxonomy.
my_minBootstrap = 80
# CHANGE ME to "TRUE" or "FALSE" if you want to use settings that differ from
# the setup chunk.
assign_taxonomy = assign_taxonomy
### NO CHANGES NEEDED BELOW
# load libraries (including dada2 so it can be run standalone)
library(lulu)
library(dada2)
library(Biostrings)
# Create output directory, read and parse input files
lulu_output <- file.path(dada2_dir, "lulu_output")
dir.create(lulu_output)
knitr::opts_chunk$set(root.dir = lulu_output)
matchlist <- paste0(dada2_dir, "/match_list.txt")
matchlist <- read.table(matchlist, header = FALSE, as.is = TRUE,
stringsAsFactors = FALSE)
ASV_table <- readRDS(file.path(dada2_dir, "seqtab_nochim.rds"))
ASV_table <- as.data.frame(t(ASV_table))
ASV_list <- paste0("ASV", seq(nrow(ASV_table)))
seq_list <- as.list(row.names(ASV_table))
names(seq_list) <- ASV_list
row.names(ASV_table) <- ASV_list
# Perform lulu post-processing
curated_ASVs <- invisible(lulu(ASV_table, matchlist))
# Parse and save curated seqtab_nochim
seqtab.nochim.lulu <- curated_ASVs$curated_table
seqtab.nochim.lulu$order <- row.names(seqtab.nochim.lulu)
seqtab.nochim.lulu <- seqtab.nochim.lulu[match(ASV_list,
seqtab.nochim.lulu$order),]
seqtab.nochim.lulu <- seqtab.nochim.lulu[complete.cases(seqtab.nochim.lulu),]
seqtab.nochim.lulu$order <- NULL
delASVs <- curated_ASVs$discarded_otus
delASVs <- which(names(seq_list) %in% delASVs)
seq_list <- seq_list[-delASVs]
row.names(seqtab.nochim.lulu) <- seq_list
seqtab.nochim.lulu <- t(seqtab.nochim.lulu)
saveRDS(seqtab.nochim.lulu, file = file.path(lulu_output, "seqtab_nochim.rds"))
# Optional RDS taxonomy and create seqtab_nochim_withtax and ASV_withtax,
# otherwise just output a new raw ASV table
if (assign_taxonomy == "TRUE") {
taxa <- assignTaxonomy(seqtab.nochim.lulu, tax_database, multithread = TRUE,
minBoot = my_minBootstrap, outputBootstraps = TRUE)
sum(rownames(taxa[[1]]) == colnames(seqtab.nochim.lulu)) == nrow(taxa[[1]])
tmp <- cbind(rownames(taxa[[1]]),taxa[[1]],taxa[[2]])
rownames(tmp) <- paste0("ASV",1:length(colnames(seqtab.nochim.lulu)))
tmp <- data.frame(tmp)
write.table(tmp, file.path(lulu_output, "taxonomy_ASVs_NC.txt"))
saveRDS(taxa$tax, file.path(lulu_output, "taxa.rds"))
tmp2 <- cbind(t(seqtab.nochim.lulu),tmp)
rownames(tmp2) <- paste0("ASV",1:length(colnames(seqtab.nochim.lulu)))
write.table(tmp2, file.path(lulu_output, "seqtab.nochim_withtax.txt"))
# write raw ASVs to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim.lulu))
seqsnochim@ranges@NAMES <- paste0("ASV", 1:length(colnames(
seqtab.nochim.lulu)))
writeXStringSet(seqsnochim, file.path(lulu_output, "ASVs_raw.fasta"),
format = "fasta")
#write ASVs containing taxonomy in the header to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim.lulu))
seqsnochim@ranges@NAMES <- paste0("ASV",
1:length(colnames(seqtab.nochim.lulu)),
"|","|",
paste(tmp$Kingdom, tmp$Phylum, tmp$Class,
tmp$Order, tmp$Family, tmp$Genus,
tmp$Species, sep = ";"))
writeXStringSet(seqsnochim, file.path(lulu_output, "ASVs_withtax.fasta"),
format = "fasta")
} else {
#write raw ASVs to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim.lulu))
seqsnochim@ranges@NAMES <- paste0("ASV",1:length(
colnames(seqtab.nochim.lulu)))
writeXStringSet(seqsnochim, file.path(lulu_output, "ASVs_raw.fasta"),
format = "fasta")
}
```
## SWR: Extra chunk to reanalyse
Reanalyse e.g. with a different taxonomy database or pool runs. This chunk can be run completely independently (all libraries load again), given that the seqtab.RDS files were generated before
```{r Reanalysis/Pooling, eval=FALSE}
#set knitr chunk options
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(root.dir = path)
#load libraries for R session
library(dada2)
library(Biostrings)
# reset paths and marker name and reload libraries to make this chunk standalone
# CHANGE ME to the directory containing the file seqtab.rds for run1
path1 = "C:/Transfer_zhzhlin/Bioimmigrants/OITS1/R1_R2_pooled_final"
# CHANGE ME to the directory containing the file seqtab.rds for run2
# (if applicable)
path2 = "C:/Transfer_zhzhlin/Bioimmigrants/Run2/Qual_filtered"
# CHANGE ME to the directory containing the file seqtab.rds for run2
# (if applicable)
path3 = "C:/Users/mada/Documents/dada2_test/test/"
# CHANGE ME to the directory containing the file seqtab.rds for run2
# (if applicable)
path4 = "C:/Users/mada/Documents/dada2_test/test/"
# CHANGE ME to the path you want to save the output to. If reanalyzing one run,
# output will automatically reset to be equal to 'path1'.
output = path
# CHANGE ME to the path for the taxonomy database you will be using for
# identification (if not already specified above)
tax_database = tax_database
# CHANGE ME to specify the minimum confidence interval for RDP assignment of
# taxonomy.
my_minBootstrap = 80
#CHANGE ME "TRUE" to pool multiple runs (add more paths if more than 4 runs are to be analysed), "FALSE" to reanalyse a single run
Poolruns = "FALSE"
#CHANGE ME to "TRUE" or "FALSE" if you want to use settings that differ from the setup chunk.
assign_taxonomy = assign_taxonomy
if (Poolruns == "TRUE") {
#could implement for loop here, if ever used widely
st1 <- readRDS(file.path(path1, "seqtab.rds"))
st2 <- readRDS(file.path(path2, "seqtab.rds"))
#st3 <- readRDS(file.path(path3, "seqtab.rds"))
#st4 <- readRDS(file.path(path4, "seqtab.rds"))
#CHANGE ME according to the number of runs to be pooled)
st.all <- mergeSequenceTables(st1, st2)
}else{
#If Poolruns=FALSE, only the first path will be reanalysed.
seqtab.nochim <- readRDS(file.path(path1, "seqtab_nochim.rds"))
output <- path1
}
# remove chimeric sequences
if (Poolruns == "TRUE") {
seqtab.nochim <- removeBimeraDenovo(st.all, method = "consensus",
multithread = TRUE, verbose = TRUE)
dim(seqtab.nochim)
# calculate percent of sequences that are non-chimeric
sum(seqtab.nochim)/sum(st.all)
write.table(seqtab.nochim, file.path(output, "seqtab.nochim.txt"))
saveRDS(seqtab.nochim, file.path(output, "seqtab_nochim.rds"))
}
if (assign_taxonomy == "TRUE") {
taxa <- assignTaxonomy(seqtab.nochim, tax_database, multithread = TRUE,
minBoot = my_minBootstrap, outputBootstraps = TRUE)
sum(rownames(taxa[[1]]) == colnames(seqtab.nochim)) == nrow(taxa[[1]])
tmp <- cbind(rownames(taxa[[1]]), taxa[[1]], taxa[[2]])
rownames(tmp) <- paste0("ASV", 1:length(colnames(seqtab.nochim)))
tmp <- data.frame(tmp)
write.table(tmp, file.path(output, "taxonomy_ASVs_NC.txt"))
saveRDS(taxa$tax, file.path(output, "taxa.rds"))
tmp2 <- cbind(t(seqtab.nochim), tmp)
rownames(tmp2) <- paste0("ASV", 1:length(colnames(seqtab.nochim)))
write.table(tmp2, file.path(output, "seqtab.nochim_withtax.txt"))
#write raw ASVs to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV",1:length(colnames(seqtab.nochim)))
writeXStringSet(seqsnochim, file.path(output, "ASVs_raw.fasta"),
format = "fasta")
#write ASVs containing taxonomy in the header to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV",
1:length(colnames(seqtabR1.nochim)),
"|","|",
paste(tmp$Kingdom, tmp$Phylum, tmp$Class,
tmp$Order, tmp$Family, tmp$Genus,
tmp$Species, sep = ";"))
writeXStringSet(seqsnochim, file.path(output, "ASVs_withtax.fasta"),
format = "fasta")
} else {
#write raw ASVs to a fasta file
seqsnochim <- DNAStringSet(colnames(seqtab.nochim))
seqsnochim@ranges@NAMES <- paste0("ASV",1:length(colnames(seqtab.nochim)))
writeXStringSet(seqsnochim, file.path(output, "ASVs_raw.fasta"),
format = "fasta")
}
dada2_dir <- output
#If Linux, make 'dada2_folder'available globally.
if (os == "Linux") {
Sys.setenv(dada2Folder = dada2_dir)
}
```
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