Aug 30, 2020

Public workspaceallele.variability V.3

DOI
dx.doi.org/10.17504/protocols.io.bkhqkt5w
  • Sara Beier1,
  • Sara Beier2
  • 1Lebniz Institute for Baltic Sea Research;
  • 2Leibniz Institute for Baltic Sea Research
Sara Beier
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DOI: dx.doi.org/10.17504/protocols.io.bkhqkt5w
Protocol Citation: Sara Beier, Sara Beier 2020. allele.variability. protocols.io https://dx.doi.org/10.17504/protocols.io.bkhqkt5w
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: August 30, 2020
Last Modified: August 30, 2020
Protocol Integer ID: 41232
Abstract
A prerequisite to improve the predictability of microbial community dynamics is to understand their assembly mechanisms. To study factors that contribute to microbial community assembly, we examined temporal dynamics of genes in five aquatic metagenome time series, originating from marine off-shore or coastal sites and one lake, while focusing on a trait-based data evaluation.
We expected to find gene-specific patterns for the temporal allele variability depending on the metacommunity size of carrier-taxa and variability of the milieu and the substrates that the resulting enzymes are exposed to. In more detail we hypothesized that a larger metacommunity would cause increased temporal variability of functional units, as shown previously for taxonomic units. Furthermore, we hypothesized that multi-copy genes feature higher temporal variability then single-copy genes, because gene multiplication is often the consequence of increased variability in (subtil) changes of substrate quality and quantity. Finally, we hypothesized that direct exposure of proteins to the extracellular environment would result in increased temporal variability of the respective gene compared to intracellular proteins as they would be exposed to highly variable conditions. The first two hypotheses were confirmed in all, while an effect of the subcellular location of gene-products was only seen in three out of the five time series.
The gene with highest allele variability throughout all datasets was an iron transporter, which also represents a target for phage infections. This finding points to the general importance of iron transporter mediated phage infections on the assembly and maintenance of diversity of aquatic prokaryotes.
removal of nextera adaptors (cutadapt v1.8.3)



Command
Run for all raw data sequence revers and forward read-files from BATS and HOT
cutadapt -a CTGTCTCTTATA -o ca.file.forward.fastq.gz file.forward.fastq.gz
cutadapt -a CTGTCTCTTATA -o ca.file.reverse.fastq.gz file.reverse.fastq.gz



Command
Run on BATS and HOT output files from step 1 and on LMO rawdata sequence files
sickle pe \
-f /data/sara/LTG/pacific/BAT1/cutadapt/ca.file.forward.fastq.gz \
-r /data/sara/LTG/pacific/BAT1/cutadapt/ca.file.reverse.fastq.gz \
-t sanger \
-o /data/sara/LTG/pacific/BAT1/sickle/qtrim.file.forward.fastq \
-p /data/sara/LTG/pacific/BAT1/sickle/qtrim.file.reverse.fastq \
-s /data/sara/gesifus.strains/sickle/qtrim.file.unpaired.fastq \
-q 20 -l 50


Command
Run for SOLA (output files step 2). The presonal referenece database (personal_default_rRNA_DBs.fna) contains the silve rRNA sequences (downloaded 2013)
#interleave reads
merge-paired-reads.sh qtrim.file.forward.fastq qtrim.file.reverse.fastq file.inter.fastq
#sortmerna
sortmerna --I file.inter.fastq --paired-in -n 2 --db personal_default_rRNA_DBs.fna --other file.inter.protein -a 20
#unmerge protein data
unmerge-paired-reads.sh file.inter.protein.fastq file.forward.protein.fastq file.reverse.protein.fastq


Command
Run for BATS, HOT (using output from step 2, only data from 10 sample days considered in this study) and SOLA (using output from step 3, data fro all available sample days)
fq2fa --merge --filter qtrim.file.1.fastq qtrim.file.2.fastq file.merged.fa #interleave paired reads
cat file*.merged.fa > all.merged.fa #concatenate all individual merged read files of a time series into one file
idba_ud --mink=25 --maxk=99 --step=4 -l all.merged.fa -o IDBAoutput #run assembly


Command
Run for BATS, HOT and SOLA using the outpute file from step 4
prodigal -i IDBA.contig.fa -a prod.pep -d prod.fas -o prod.gff -f gff


Command
run for SOLA (predicted genes from step 5) and LMO (predicted genes from BARM assembly that were different from the public available data translated with table 11). The reference KEGG database (KEGG.faa) was downloaded on 15.05.2016.
makeblastdb -in KEGG.faa -parse_seqids -dbtype prot #create database for blastp
blastp -db KEGG.faa -query prod.pep -outfmt 6 -num_alignments 1 -num_threads 16 -out blastout.tab #run blastp


Command
Diamond-Blast annotation (DIAMOND v0.8.22.84)
diamond makedb --in KEGG.faa -d KEGG #create diamond database
diamond blastp -d KEGG -q prod.pep  --more-sensitive -k 1 -o diamond.tab  -p 26 #run diamond blast


Command
run for BATS, HOT, LMO (quality trimmed reads, step 3), SOLA (protein-coding reads, step 4) and MENDOTA (quality trimmed reads provided by collaborators). Assembled contigs were used as reference (BATS, HOT, SOLA: output from step 4; LMO: BARM assembly, MENDOTA: assembly provided by collaborators)
bowtie2-build contig.fa contigs #build indexed reference file
bowtie2 --very-sensitive-local --no-unal -x contigs -1 qtrim.file.forrward.fastq -2 qtrim.file.reverse.fastq -S file.sam #for LMO, SOLA, BATS, HOT with paired-end reads
bowtie2 --very-sensitive-local --no-unal -x contigs -U qtrim.file.fastq -S file.sam #for MENDOTA with single-end reads


Command
the prod.gff outputfile from step 5 was reformatted using a personal script (convert_prodigal_GFF_to_subread_featureCount_SAF.pl) to prod.saf concerning the requirements of the featureCounts software. For BATS, HOT, LMO, SOLA and MENDOTS sam-files produced during step 7 were used.
convert_prodigal_GFF_to_subread_featureCount_SAF.pl -g prod.gff -s prod.saf #converts prodigal output into prod.saf
featureCounts -p -a prod.saf -T 30 -F SAF -o feature.tab *sam #for LMO, SOLA, BATS, HOT with paired-end reads
featureCounts -a prod.saf -T 12 -F SAF -o feature.tab *sam #for MENDOTA with single-end reads


Command
Run for prokaryotic amino acid sequences from the KEGG database (downloaded Nov 2015) representing each KEGG ortrholog (K*.prokaryotes.faa) with the alternative settings -n, -p or -a for sequences affiliating with gram negatives, gram positives or archaea, respectively
psort -n/p/a K*.prokaryotes.faa -o terse |awk -F'\t' '{$1="XXX" FS $1;}1' OFS='\t'|sed 1d > psort.K*.prok.tab #create psortoutput for each KEGG ortholog
cat psort.K* >psort.all.prok.tab #concatenate results


Command
run for BATS, HOT, LMO, MENDOTA, SOLA: input files amino acid sequence files obtained after gene calling (prod.pep)
cd-hit -i prod.pep -o out.clust -c 1.00 -n 5 -M 16000 -T 22