EGGTOMEAT

Evaluation of the impact of chicken farming practices on microbial flux ( feces, cecal contents and carcasses)

closed
collaboration
Authors
Affiliation

Olivier Rué

Migale bioinformatics facility

Mahendra Mariadassou

Migale bioinformatics facility

Published

February 13, 2023

Modified

February 19, 2025

Note

This document is a report of the analyses performed. You will find all the code used to analyze these data. The version of the tools (maybe in code chunks) and their references are indicated, for questions of reproducibility.

Aim of the project

The aims of these analyses are to build amplicon sequence variants (ASVs) from raw reads and to affiliate ASVs sequences to obtain the taxonomic composition of the samples. Reads were provided by the @BRIDGE from an Illumina Miseq instrument (2x251 bp). The targeted amplicon is the V3-V4 region of the 16S rRNA. 3 different runs were performed.

Data management

Important

All data is managed by the migale facility for the duration of the project. Once the project is over, the Migale facility does not keep your data. We will provide you with the raw data and associated metadata that will be deposited on public repositories before the results are used. We can guide you in the submission process. We will then decide which files to keep, knowing that this report will also be provided to you and that the analyses can be replayed if needed.

Sequencing data

Data were downloaded from Filesender, then deposited on the Front server (Bruyères-le-Châtel) and a copy was sent to the abaca server (Toulouse datacenter). First, we renamed FASTQ files to remove useless informations in filenames and correct duplicated ids.

cd /save_projet/eggtomeat/
mkdir RAW_DATA
mkdir RAW_DATA/RUN1
mkdir RAW_DATA/RUN2
mkdir RAW_DATA/RUN3
# Rename files
cd EGTM\ run\ 1_dec22/
rm -f Undetermined_S0_L001_R*
for i in *_R1_*.fastq.gz ; do id=$(echo $i |cut -d '_' -f 1) ; cp $i ../RAW_DATA/RUN1/${id}_R1.fastq.gz ; done
for i in *_R2_*.fastq.gz ; do id=$(echo $i |cut -d '_' -f 1) ; cp $i ../RAW_DATA/RUN1/${id}_R2.fastq.gz ; done

cd ../EGTM\ run\ 2_dec22/
rm -f Undetermined_S0_L001_R*
for i in *_R1_*.fastq.gz ; do id=$(echo $i |cut -d '_' -f 1) ; cp $i ../RAW_DATA/RUN2/${id}_R1.fastq.gz ; done
for i in *_R2_*.fastq.gz ; do id=$(echo $i |cut -d '_' -f 1) ; cp $i ../RAW_DATA/RUN2/${id}_R2.fastq.gz ; done

cd ../EGTM\ run\ 3_jan23/
for i in *_R1_*.fastq.gz ; do id=$(echo $i |cut -d '_' -f 1) ; cp $i ../RAW_DATA/RUN3/${id}_R1.fastq.gz ; done
for i in *_R2_*.fastq.gz ; do id=$(echo $i |cut -d '_' -f 1) ; cp $i ../RAW_DATA/RUN3/${id}_R2.fastq.gz ; done

cd /save_projet/eggtomeat/RAW_DATA/RUN1
mv H2O_R1.fastq.gz H2ORUN1_R1.fastq.gz
mv H2O_R2.fastq.gz H2ORUN1_R2.fastq.gz
mv H2Ob_R1.fastq.gz H2ObRUN1_R1.fastq.gz
mv H2Ob_R2.fastq.gz H2ObRUN1_R2.fastq.gz
cd /save_projet/eggtomeat/RAW_DATA/RUN2
mv H2O_R1.fastq.gz H2ORUN2_R1.fastq.gz
mv H2O_R2.fastq.gz H2ORUN2_R2.fastq.gz
mv H2Obis_R1.fastq.gz H2ObisRUN2_R1.fastq.gz
mv H2Obis_R2.fastq.gz H2ObisRUN2_R2.fastq.gz
cd /save_projet/eggtomeat/RAW_DATA/RUN3
mv H2O_R1.fastq.gz H2ORUN3_R1.fastq.gz
mv H2O_R2.fastq.gz H2ORUN3_R2.fastq.gz
mv H2Oter_R1.fastq.gz H2OterRUN3_R1.fastq.gz
mv H2Oter_R2.fastq.gz H2OterRUN3_R2.fastq.gz
mv H2Obis_R1.fastq.gz H2ObisRUN3_R1.fastq.gz
mv H2Obis_R2.fastq.gz H2ObisRUN3_R2.fastq.gz
mv H2O4_R1.fastq.gz H2O4RUN3_R1.fastq.gz
mv H2O4_R2.fastq.gz H2O4RUN3_R2.fastq.gz

cd ../RUN1
tar zcvf EGTM_run2.tar.gz *.fastq.gz
cd ../RUN2
tar zcvf EGTM_run2.tar.gz *.fastq.gz
cd ../RUN3
tar zcvf EGTM_run3.tar.gz *.fastq.gz

seqkit [1] was used to get informations from FASTQ files.

# seqkit
cd /work_projet/eggtomeat
mkdir RUN1 RUN2 RUN3
qsub -cwd -V -N seqkit -q maiage.q -pe thread 4 -R y -b y "conda activate seqkit-2.0.0 && seqkit stats /save_projet/eggtomeat/RAW_DATA/RUN1/*.fastq.gz -j 4 > RUN1/raw_data.infos && conda deactivate"
qsub -cwd -V -N seqkit -q maiage.q -pe thread 4 -R y -b y "conda activate seqkit-2.0.0 && seqkit stats /save_projet/eggtomeat/RAW_DATA/RUN2/*.fastq.gz -j 4 > RUN2/raw_data.infos && conda deactivate"
qsub -cwd -V -N seqkit -q maiage.q -pe thread 4 -R y -b y "conda activate seqkit-2.0.0 && seqkit stats /save_projet/eggtomeat/RAW_DATA/RUN3/*.fastq.gz -j 4 > RUN3/raw_data.infos && conda deactivate"

We can plot and display the number of reads (Figure 1) to see if the amount of reads by sample and if sequencing depth is homegeneous.

Figure 1: Raw reads number per sample.

27 samples have less than 1,000 reads (Table 1)

raw_data %>% filter(num_seqs < 1000) %>% select(Sample, Run, num_seqs) %>% arrange(num_seqs) %>% kbl() %>%   kable_styling(full_width = F)
Table 1: List of samples with less than 1000 reads
Sample Run num_seqs
19950J61 Run1 157
H2ObisRUN2 Run2 194
H2ORUN1 Run1 203
18894rJ61 Run2 214
18826J61 Run2 229
H2ObisRUN3 Run3 235
18884J61 Run2 238
18852J61 Run2 301
19942J47 Run2 301
19956J2 Run1 311
18892J61 Run2 322
H2O4RUN3 Run3 336
H2ORUN3 Run3 338
H2OterRUN3 Run3 348
H2ObRUN1 Run1 364
18900J61 Run2 373
19938J47 Run2 377
19918J47 Run2 470
H2ORUN2 Run2 533
19954J2 Run1 572
19906J61 Run2 582
18852J47 Run2 662
18840J61 Run2 668
12771J47 Run2 707
19974J47 Run2 738
Pq9Tneg Run3 779
18832J61 Run2 931

Quality control

FastQC [2] is a program designed to spot potential problems in high througput sequencing datasets. It runs a set of analyses on one or more raw sequence files in fastq or bam format and produces a report which summarises the results. MultiQC [3] aggregates results from bioinformatics analyses across many samples into a single report.

cd /work_projet/eggtomeat/RUN1
mkdir FASTQC LOGS
for i in /save_projet/eggtomeat/RAW_DATA/RUN1/*.fastq.gz ; do echo "conda activate fastqc-0.11.9 && fastqc $i -o FASTQC && conda deactivate" >> fastqc.sh ; done
qarray -cwd -V -N fastqc -o LOGS -e LOGS fastqc.sh

cd /work_projet/eggtomeat/RUN2
mkdir FASTQC LOGS
for i in /save_projet/eggtomeat/RAW_DATA/RUN2/*.fastq.gz ; do echo "conda activate fastqc-0.11.9 && fastqc $i -o FASTQC && conda deactivate" >> fastqc.sh ; done
qarray -cwd -V -N fastqc -o LOGS -e LOGS fastqc.sh

cd /work_projet/eggtomeat/RUN3
mkdir FASTQC LOGS
for i in /save_projet/eggtomeat/RAW_DATA/RUN3/*.fastq.gz ; do echo "conda activate fastqc-0.11.9 && fastqc $i -o FASTQC && conda deactivate" >> fastqc.sh ; done
qarray -cwd -V -N fastqc -o LOGS -e LOGS fastqc.sh

qsub -cwd -V -N multiqc -o LOGS -e LOGS -b y "conda activate multiqc-1.11 && multiqc RUN1/FASTQC RUN2/FASTQC/ RUN3/FASTQC -o MULTIQC && conda deactivate"
Note

Quality control shows heterogeneous metrics between samples. Some samples are very poorly sequenced (controls but also samples of interest). The sequencing quality of some samples is also poor after 150 base pairs. There are still some N’s in a few reads, we also notice the presence of Illumina adapters, indicating very small fragments. All of these poor quality reads will be dicarded with bioinformatics.

Bioinformatics

A combination of dada2 [4] and FROGS [5] was used to build amplicon sequence variants (ASVs). The detail is shown in Figure 2. The script is deposited on the ForgeMIA (https://forgemia.inra.fr/migale/dada2_frogs/).

flowchart TB
    style FROGS stroke:#5f999d
    style dada2 stroke:#5f999d
    database[(Databank)]-->affiliation
    fastq[[Fastq]]
    biom[[BIOM]]
    tsv[[TSV]]
    
    fastq-->seqkit
    seqkit---plotQualityProfiles
    subgraph dada2
        direction TB
        plotQualityProfiles-->filterAndTrim1[filterAndTrim]-->filterAndTrim2[filterAndTrim]-->learnErrors-->dada-->mergePairs-->makeSequenceTable
    end
    filterAndTrim1-->cutadapt-->filterAndTrim2
    makeSequenceTable-->rc
    subgraph FROGS
        direction TB
        rc[remove chimera]-->filters-->affiliation
    end
    
    
    affiliation-->biom
    affiliation--> tsv
Figure 2: Bioinformatics workflow 
cd /work_projet/eggtomeat/
cp /work_home/orue/GIT/dada2_frogs/dada2_FROGS.Rmd .
R -e "rmarkdown::render('dada2_FROGS.Rmd', params=list(
  author='Olivier Rué', 
  reference='/db/outils/FROGS/assignation/silva_138.1_16S_pintail100/silva_138.1_16S_pintail100.fasta', 
  region='16S',
  min_reads=1000,
  expname='EGGTOMEAT_RUN1',
  input_directory='/save_projet/eggtomeat/RAW_DATA/RUN1/',
  forward_primer='ACGGRAGGCWGCAG',
  reverse_primer='TACCAGGGTATCTAATCCT',
  output_directory='/work_projet/eggtomeat/RUN1/out_dada2_FROGS/',
  min_abundance=0.00005,
  its=FALSE,
  threads=24), 
  output_file = '/work_projet/eggtomeat/RUN1/out_dada2_FROGS/report_run1.html')"
  
R -e "rmarkdown::render('dada2_FROGS.Rmd', params=list(
  author='Olivier Rué', 
  reference='/db/outils/FROGS/assignation/silva_138.1_16S_pintail100/silva_138.1_16S_pintail100.fasta', 
  region='16S',
  min_reads=1000,
  expname='EGGTOMEAT_RUN2',
  input_directory='/save_projet/eggtomeat/RAW_DATA/RUN2/',
  forward_primer='ACGGRAGGCWGCAG',
  reverse_primer='TACCAGGGTATCTAATCCT',
  output_directory='/work_projet/eggtomeat/RUN2/out_dada2_FROGS/',
  min_abundance=0.00005,
  its=FALSE,
  threads=24), 
  output_file = '/work_projet/eggtomeat/RUN2/out_dada2_FROGS/report_run2.html')"

R -e "rmarkdown::render('dada2_FROGS.Rmd', params=list(
  author='Olivier Rué', 
  reference='/db/outils/FROGS/assignation/silva_138.1_16S_pintail100/silva_138.1_16S_pintail100.fasta', 
  region='16S',
  min_reads=1000,
  expname='EGGTOMEAT_RUN3',
  input_directory='/save_projet/eggtomeat/RAW_DATA/RUN3/',
  forward_primer='ACGGRAGGCWGCAG',
  reverse_primer='TACCAGGGTATCTAATCCT',
  output_directory='/work_projet/eggtomeat/RUN3/out_dada2_FROGS/',
  min_abundance=0.00005,
  its=FALSE,
  threads=24), 
  output_file = '/work_projet/eggtomeat/RUN3/out_dada2_FROGS/report_run3.html')"

Biostatistics

The phyloseq package [6] is a tool to import, store, analyze, and graphically display complex metabarcoding data, especially when there is associated sample data, phylogenetic tree, and/or taxonomic assignment of the OTUs or ASVs. Various customs functions written to enhance the base functions of phyloseq are available in the phyloseq-extended package [7].

if(!file.exists("html/physeq.rds")){
  for(run in c("RUN1","RUN2","RUN3")){
    biomfile <- paste0("html/",run,"/FROGS/affiliations.biom")
    frogs <- import_frogs(biomfile, taxMethod = "blast")
    metadata <- read.table("data/metadata.tsv", row.names = 1, header = TRUE, sep = "\t", stringsAsFactors = FALSE)
    sample_data(frogs) <- metadata
    saveRDS(frogs, paste0("html/",run,"/",run,".rds"))
  }
}

All samples are merged…

if(!file.exists("html/physeq.rds")){
  physeq <- merge_phyloseq(readRDS("html/RUN1/RUN1.rds"),readRDS("html/RUN2/RUN2.rds"),readRDS("html/RUN3/RUN3.rds"))  
  saveRDS(physeq, "html/physeq.rds")
}else{
  physeq <- readRDS("html/physeq.rds")
}
physeq
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 2256 taxa and 651 samples ]
sample_data() Sample Data:       [ 651 samples by 9 sample variables ]
tax_table()   Taxonomy Table:    [ 2256 taxa by 7 taxonomic ranks ]

… and samples with less than 1000 reads are removed

physeq_final <- subset_samples(physeq, sample_sums(physeq) > 1000) 
saveRDS(physeq_final,"html/physeq_final.rds")

The available variables describing the samples are

names(sample_data(physeq_final))
[1] "QCbannks"      "ClusterRaw"    "ClusterPF"     "run_number"   
[5] "breeding_type" "chicken_sex"   "chicken_ID"    "breeding_time"
[9] "sample_type"  
p <- plot_composition(physeq = physeq_final, taxaRank1 = "Kingdom", taxaSet1 = "Bacteria", taxaRank2 = "Genus", numberOfTaxa = 20L, x = "Sample")
Problematic taxa
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     taxa
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC         TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC     TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC       TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC       TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC
                                                                                                                                                                                                                                                                                                                                                                                                                      Kingdom
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC     Bacteria
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC   Bacteria
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC Bacteria
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC    Bacteria
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC    Bacteria
                                                                                                                                                                                                                                                                                                                                                                                                                         Phylum
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC     Firmicutes
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC   Firmicutes
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC Firmicutes
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC    Firmicutes
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC    Firmicutes
                                                                                                                                                                                                                                                                                                                                                                                                                          Class
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC     Clostridia
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC   Clostridia
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC Clostridia
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC    Clostridia
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC    Clostridia
                                                                                                                                                                                                                                                                                                                                                                                                                                                   Order
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC     Peptostreptococcales-Tissierellales
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC                    Clostridia UCG-014
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC          Clostridia vadinBB60 group
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC                         Lachnospirales
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC                        Oscillospirales
                                                                                                                                                                                                                                                                                                                                                                                                                                    Family
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC     Peptostreptococcaceae
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC                 Unknown
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC               Unknown
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC          Lachnospiraceae
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC          Ruminococcaceae
                                                                                                                                                                                                                                                                                                                                                                                                                                 Genus
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC     Multi-affiliation
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC             Unknown
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC           Unknown
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC              Unknown
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC              Unknown
                                                                                                                                                                                                                                                                                                                                                                                                                     rank
TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGCGATGAAGGCCTTCGGGTCGTAAAGCTCTGTCCTCAAGGAAGATAATGACGGTACTTGAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAAC        3
TCGGGAATATTGCGCAATGGAGGAAACTCTGACGCAGTGACGCCGCGTATAGGAAGAAGGTTTTCGGATTGTAAACTATTGTCGTTAGGGAAGATAAAAGACAGTACCTAAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGATATTAAGTTAGTTGTGAAATCCCTCGGCTTAACTGAGGAACTGCAACTAAAACTGGTATTCTTGAGTGTTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGACTTTCTGGACAATAACTGACGTTGAGGCACGAAAGTGTGGGGAGCAAAC      9
TGGGGAATATTGGGCAATGGGCGAAAGCCTTACCCAGCAATGCCGCGTGAGTGAAGAAGGTCTTCGGATTGTAAAGCTCTTTGATTGGGGACGAGTAGAAGACGGTACCCAAGGAACAAGCCCCGGCTAACTATGTGCCAGCAGCCGCGGTAATACATAGGGGGCGAGCGTTGTCCGGAATGACTGGGCGTAAAGGGTGTGTAGGCGGTTTGGCAAGTTAGAAGTGTAATACCCAGGGCTTAACTCGGGTGCTGCTTCTAAAACTACCTGACTTGAGTGTCGGAGAGGAAAATGGAATTCCCAGTGTAGCGGTAGAATGCACAGATATTGGGAGGAACACCGGAGGCGAAAGCGATTTTCTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAAC   13
TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAACCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAAC      14
TGAGGGATATTGGTCAATGGGGGAAACCCTGAACCAGCAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTCCTCTGTGAAGATAATGACGGTAGCAGAGGAGGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTATCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGTAGCAAAC      15
p + facet_grid(". ~ run_number", scales = "free_x", space = "free")

References

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2. Andrews S. FastQC a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
3. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8.
4. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from illumina amplicon data. Nature methods. 2016;13:581.
5. Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics. 2018;34:1287–94. doi:10.1093/bioinformatics/btx791.
6. McMurdie PJ, Holmes S. Phyloseq: An r package for reproducible interactive analysis and graphics of microbiome census data. PloS one. 2013;8:e61217.
7. Mariadassou M. Phyloseq-extended: Various customs functions written to enhance the base functions of phyloseq. 2018. https://github.com/mahendra-mariadassou/phyloseq-extended.

Reuse

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A work by Migale Bioinformatics Facility
Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France
Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, 78350, Jouy-en-Josas, France