16SIonTorrent

Bioinformatic analysis of IonTorrent data

Author

Affiliation

Olivier Rué

Migale bioinformatics facility

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 aim of these analyses is to obtain a BIOM file from 16S Ion Torrent data.

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 available in the Mathias working directory. We copied them, renamed them, compressed them and stored them in an archive.

cd /home/orue/work/16SIONTORRENT/
mkdir RAW_DATA
cp /home/mlavie/work/Limoges_IonT/RAW_DATA/Puce1/IonXpress_033_J0M1_rawlib.basecaller.fq RAW_DATA/
for i in *.fq ; do id=$(echo $i |cut -d '_' -f 2-3) ; mv $i ${id}.fastq ; done
pigz *.fastq
tar zcvf 16SIonTorrent.tar.gz *.fastq.gz

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

# seqkit
cd /home/orue/work/16SIONTORRENT/
qsub -cwd -V -N seqkit -q maiage.q -pe thread 4 -R y -b y "conda activate seqkit-2.0.0 && seqkit stats /home/orue/work/16SIONTORRENT/RAW_DATA/*.fastq.gz -j 4 > raw_data.infos && conda deactivate"

We can plot and display the number of reads to see if enough reads are present and if samples are homegeneous.

Figure 1: Raw reads number per sample.

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 /home/orue/work/16SIONTORRENT/
mkdir FASTQC LOGS
for i in /home/orue/work/16SIONTORRENT/RAW_DATA/*.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 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 discarded with bioinformatics.

Bioinformatics

Raw reads needed to be processed to build operational taxonomic units (OTUs). The FROGS [4] worfklow was used following authors guidelines.

tar zcvf 16SIonTorrent.tar.gz *.fastq.gz
preprocess.py illumina --min-amplicon-size 50 --max-amplicon-size 10000 --without-primers --already-contiged --input-archive 16SIonTorrent.tar.gz --nb-cpus 16

Preprocess

The options --already-contiged and without-primers are used to deal with IonTorrent data.

preprocess.py illumina --min-amplicon-size 50 --max-amplicon-size 10000 --without-primers --already-contiged --input-archive RAW_DATA/16SIonTorrent.tar.gz --nb-cpus 16

• Preprocess report

Clustering

clustering.py --input-fasta preprocess.fasta --input-count preprocess_counts.tsv --nb-cpus 24

• Preprocess report

10,531,600 OTUs were built with 50,166,330 sequences.

Remove chimera

remove_chimera.py --input-fasta clustering_seeds.fasta --input-biom clustering_abundance.biom --nb-cpus 24 --summary remove_chimera.html
otu_filters.py --input-fasta remove_chimera.fasta --input-biom remove_chimera_abundance.biom --output-fasta remove_chimera.fasta

• Remove chimera report

Few chimera are detected.

OTU filters

otu_filters.py --input-fasta clustering_seeds.fasta --input-biom clustering_abundance.biom --nb-cpus 16 --log-file filters.log --output-biom filters.biom --summary filters.html --excluded filters_excluded.tsv --contaminant /db/outils/FROGS/contaminants/phi.fa --min-sample-presence 1 --min-abundance 0.00005 --output-fasta filters.fasta

• OTU filters report

Warning

A lot of OTUs are removed but reducing the threshold to 1,000 for example would not change a lot.

Taxonomic affiliation

affiliation_OTU.py --input-biom filters.biom --input-fasta filters.fasta --reference /db/outils/FROGS/assignation/silva_138.1_16S_pintail100/silva_138.1_16S_pintail100.fasta --nb-cpus 16

• Affiliation OTU report
• Affiliation stats report

Warning

A lot of OTUs are not affiliated to Species level. There is not a lot of multi-affiliations but the affiliations are not precise in the databank (unknown species…)

Tree

tree.py --input-sequences filters.fasta --biom-file affiliation_abundance.biom --out-tree tree.nwk

Downloads

• BIOM file
• Tree file

References

1. Shen W, Le S, Li Y, Hu F. SeqKit: A cross-platform and ultrafast toolkit for FASTA/q file manipulation. PloS one. 2016;11:e0163962.

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. 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.