Application of stable isotope probing to identify temperature responsive bacterial taxa at the level of 3-hydroxy fatty acid synthesis
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.
The aim of the project is to provide a better understanding of how soil bacteria respond to temperature variations at the membrane level. Bacterial lipids can be used in paleoclimatology to reconstruct temperature of past climates in marine and terrestrial archives. This involves the use of proxies and calculations that are based on the relative amounts of these lipids. In soil, only one organic proxy is available, mainly due to the difficulty to work on soil, which is much more heterogeneous in structure than aquatic environments. A drawback of this sungle proxy is the error which is associated with it as well as the lack of confirmation of the result by a different approach. A new proxy based on 3-hydroxy fatty acids has been proposed in 2016. To better understand and validate this proxy, as well as to investigate new more precise proxies, we aim at understanding what soil bacterial species contribute to the proposed proxy by investigating their response te temperature at the taxonomic and lipid levels. We have used a DNA- and lipid-SIP approach during a time-course experiment and are now at the stage of analyzing what species have used the 13C labelled substrate in a temperature dependent manner. The profile of the enriched taxa in the 13C fraction will be compared in the different samples (different temperature, different incubation time) and will be compared with the 13C-enriched lipid profiles in the same samples.
Deliverables agreed at the preliminary meeting (Table 1).
| Definition | |
|---|---|
| 1 | HTML report |
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.
Raw data were sent on November 15 and deposited on the front server and a copy was sent to the abaca server.
cd /home/orue/work/PROJECTS/TEMPO/
mkdir RAW_DATA
# Rename FASTQ files
for i in DiversiteTEMPO/donneesbrutes/*_R1.fastq.gz ; do id=$(echo basename $i |cut -d '.' -f 5 ) ;cp $i RAW_DATA/${id}.fastq.gz ; done
for i in DiversiteTEMPO/donneesbrutes/*_R2.fastq.gz ; do id=$(echo basename $i |cut -d '.' -f 5 ) ;cp $i RAW_DATA/${id}.fastq.gz ; done
scp -r RAW_DATA/ orue@abaca.maiage.inrae.fr:/backup/partage/migale/TEMPO/# seqkit
cd /home/orue/work/PROJECTS/TEMPO/
qsub -cwd -V -N seqkit -pe thread 4 -R y -b y "conda activate seqkit-2.0.0 && seqkit stats /home/orue/work/PROJECTS/TEMPO/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 homogeneous.
cd /home/orue/work/PROJECTS/TEMPO/
mkdir FASTQC LOGS
for i in /home/orue/work/PROJECTS/TEMPO/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"The MultiQC report shows expected metrics for Illumina Miseq sequencing data.
The quality control is good enough to go further. Only the PCR blank sample has too few reads. We will remove it later after having checked its composition.
We used FROGS
The first tool, called preprocess allows to clean reads. From FASTQ files, we overlap R1 and R2 reads, then look at primers and remove them, filter on length and finally dereplicate remaining sequences.
mkdir FROGS4
cd FROGS4
conda activate frogs-4.1.0
preprocess.py illumina --merge-software pear --min-amplicon-size 250 --max-amplicon-size 580 --five-prim-primer CCTACGGGNGGCWGCAG --three-prim-primer GGATTAGATACCCBDGTAGTC --R1-size 300 --R2-size 300 --nb-cpus 16 --input-archive ../RAW_DATA/Tempo.tar.gzThe preprocess report shows that almost all reads are kept (7,591,113 from 7,776,650), indicating that there were no problems during sequencing.
Now we cluster sequences with swarm
clustering.py --input-fasta preprocess.fasta --input-count preprocess_counts.tsv --nb-cpus 16 --distance 1 --fastidiousThen, we removed chimera with vsearch
remove_chimera.py --input-fasta clustering.fasta --input-biom clustering.biom --non-chimera remove_chimera.fasta --nb-cpus 8 --log-file remove_chimera.log --out-abundance remove_chimera.biom --summary remove_chimera.htmlThe remove chimera report shows that 77.5% of the clusters (but 30% of the sequences) are considered as chimera. It is expected for 16S data.
Finally, we filter ASVs to remove low-abundant ASVs (composed of few sequences). We use the threshold of 0.005% of total abundance. We check also if phiX sequences are remaining.
cluster_filters.py --input-fasta remove_chimera.fasta --input-biom remove_chimera_abundance.biom --output-fasta filters.fasta --nb-cpus 8 --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 0.00005The filters report is very informative. The filters have removed 99.6% of clusters. 1,901 ASVs are remaining. However, only 24.8% of the sequences were deleted. The smallest ASV is composed of 267 sequences. No phiX contamination is observed.
Now, it’s time to affiliate our ASVs to know what they are. We used blast
taxonomic_affiliation.py --reference /db/outils/FROGS/assignation/silva_138.1_16S_pintail100/silva_138.1_16S_pintail100.fasta --input-biom filters.biom --input-fasta filters.fasta --output-biom affiliation.biom --nb-cpus 8 --taxonomy-ranks 'Domain', 'Phylum', 'Class', 'Order', 'Family', 'Genus', 'Species'The affiliation report shows that all ASVs have been affiliated. 17.6% of the ASVs are multi-affiliated at Species level. It means that the ASV sequences are shared with several species. It is expected for 16S sequences.
The affiliations stats report allows us to see the taxonomic compositon for each sample. An other interesting information is the alignment distribution. We show that the alignments are good (% identity and coverage). It indicates that our ASVs sequences were present in our databank.
Here are the informations of the ASVs:
Finally, we can build a phylogenetic tree from the sequences. It will allow us to use some diversity indices that need a phylogenetic tree.
ree.py --biom-file affiliation.biom --input-sequences filters.fasta --out-tree tree.nwk --log-file tree.log --html tree.htmlHere is the tree report.
Reports on diversity analyses for each phyloseq object
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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
