The SSRG pipeline was created as a simple and focused tool to investigate genetic diversity between genomes. The pipeline features two independent workflows:
- Read-mapping/variant calling
- Genetic distance estimation
People interested in point mutations should use the read-mapping/variant calling workflow. The unique feature of the SSRG pipeline resides in the creation with SSRG.pl of synthetic short reads from complete or draft genomes, which can then be fed to the read mapping/variant calling tools. Note that this approach works only for haploid genomes. Alternatively, users can select any FASTQ dataset to use with get_SNPs.pl.
People interested in genetic distances should use the genetic distance estimation workflow. This workflow is based on Mash, an excellent tool developed by Ondov et al., and focuses on data visualization. Genetic distances can be plotted as heatmaps, neighbor-joining trees, or clusters (using dimensionality reduction techniques).
Assessing the genetic diversity between genomes often involves the calculation of single nucleotide polymorphisms (SNPs) and insertions/deletions (indels). This is usually done by mapping short accurate sequencing reads from one or more species against a reference genome, from which variants are called. This approach works well when short read data from published genomes are available in public repositories such as NCBI's SRA but that is not always the case, especially now that genome sequencing is shifting towards the use of long read technologies. While genomes and/or long reads can be aligned against each other, the results are often suboptimal when the investigated chromosomes are highly reorganized, which can cause the mapping to fail. A simple solution to this problem is to deconstruct the genomes or long reads randomly into shorter fragments —much like DNA fragmentation protocols used in whole genome sequencing (WGS)— and to use these smaller synthetic reads as input for mapping. We have implemented this approach in SSRG.pl. Note that this approach is only valid for haploid genomes and should not be used in presence of heterozygosity.
- It enables comparisons between genomes for which sequencing datasets are not available in public repositories.
- It helps standardize datasets by providing reads with the exact same parameters. For example, genomes generated from Illumina, PacBio and/or Oxford Nanopore data can now be compared without fuss.
- Because bases from complete or draft genomes have been queried multiple times by the sequencing depth, the underlying confidence in the base being called is higher than from single sequencing reads (assuming of course that the corresponding loci in the assembled genomes are error-free). This in turn should lead to fewer problems caused by sequencing errors.
- Download genomes automatically from NCBI using CSV/Tab-delimited lists of operational taxonomic units (OTU)
- Calculate pairwise SNPs between FASTQ sequences and reference genomes using standard read mapping approaches.
- Run Mash and plot the estimated genetic distances as heatmaps, neighbor-joining trees, or clusters (using dimensionality reduction techniques).
git clone https://github.com/PombertLab/SSRG.git
cd SSRG/
export PATH=$PATH:$(pwd)/
export PATH=$PATH:$(pwd)/Tools/MASH
export PATH=$PATH:$(pwd)/Tools/NCBITo download the latest VarScan2 with curl, type:
curl \
-o VarScan.v2.4.4.jar \
-L https://github.com/dkoboldt/varscan/blob/master/VarScan.v2.4.4.jar?raw=trueIf desired, the default location of the VarScan2 jar file can be updated in get_SNPs.pl by modifying the following line:
my $varjar = 'VarScan.v2.4.4.jar';On a Fedora distribution, the following packages can be installed with the DNF package manager to facililate the installation/compilation of read mappers and other tools from the source code.
sudo dnf install \
perl \
R \
boost \
boost-devel \
zlib \
zlib-devel \
gsl \
gsl-devel \
autoconf \
automake \
libcurl-devel \
openssl-devel \
ncurses-devel \
java-1.?.?-openjdk \
java-1.?.?-openjdk-develTo install R data visualization packages, type:
RThen in R, type:
install.packages("ape")
install.packages("gplots")
install.packages("ggplot2")
install.packages("ggfortify")
install.packages("plotly")
install.packages("RColorBrewer")
install.packages("Rcpp")
install.packages("Rtsne")
quit()Comma-separated (.csv) lists of genomes for organisms of interest can be downloaded from NCBI.
This can be done by entering a query in the search box, then by clicking on the lineage link (e.g. Prokaryotes) next to Overview. This links redirects to the corresponding list of genomes in NCBI.
Filters can be applied, then the list of corresponding genomes downloaded. Note that while the tooltip displays TSV Download, the file downloaded will be in CSV format.
- To download FASTA and GenBank files from a small .csv list of 3 Streptococcus pneumoniae genomes from NCBI with queryNCBI.pl, type:
queryNCBI.pl \
-l Examples/S_pneumoniae_3.csv \
-o DATASETS \
-fa \
-gbOptions for queryNCBI.pl are:
-l (--list) TAB/CSV-delimited list from NCBI
-o (--outdir) Output directory [Default: ./]
-fa (--fasta) Retrieve fasta files
-gb (--genbank) Retrieve GenBank annotation files (.gbk; if available)
-gff (--gff3) Retrieve GFF3 annotation files (.gff; if available)
-p (--protein) Retrieve protein sequences (.faa; if available)
-cds Retrieve protein coding sequences (.fna; if available)
- FASTQ datasets can be generated from the downloaded genomes with SSRG.pl.
SSRG.pl \
-f DATASETS/*.fasta \
-o FASTQ \
-r 250Options for SSRG.pl are:
-f (--fasta) Fasta/multifasta file(s)
-o (--outdir) Output directory [Default: ./]
-l (--list) List of fasta file(s), one per line
-r (--readsize) Desired read size [default: 150]
-t (--type) Read type: single (SE) or paired ends (PE) [default: PE]
-i (--insert) PE insert size [default: 350]
-s (--sdev) PE insert size standard deviation (in percentage) [default: 10]
-c (--coverage) Desired sequencing depth [default: 50]
-qs (--qscore) Quality score associated with each base [default: 30]
-q64 Use the old Illumina Q64 FastQ format instead of the default Q33 Sanger/Illumina 1.8+ encoding
- To test the read-mapping step with get_SNPs.pl but skip variant calling, type:
## Running get_SNPs.pl with 8 threads, 16 Gb RAM (change according to your settings)
get_SNPs.pl \
-threads 8 \
-mem 16 \
-fa DATASETS/*.fasta \
-pe1 FASTQ/*R1.fastq \
-pe2 FASTQ/*R2.fastq \
-o RESULTS \
-mapper minimap2 \
-preset sr \
-rmo \
-bam- To test the read-mapping and variant calling steps with get_SNPs.pl, type:
## Running get_SNPs.pl with 8 threads, 16 Gb RAM (change according to your settings)
get_SNPs.pl \
-threads 8 \
-mem 16 \
-fa DATASETS/*.fasta \
-pe1 FASTQ/*R1.fastq \
-pe2 FASTQ/*R2.fastq \
-o RESULTS \
-mapper minimap2 \
-preset sr \
-bam \
-caller varscan2 \
-var VarScan.v2.4.4.jar ## Replace jar file location accordinglyOptions for get_SNPs.pl are:
-h (--help) Display this list of options
-v (--version) Display script version
-o (--outdir) Output directory [Default: ./]
# Mapping options
-fa (--fasta) Reference genome(s) in fasta file
-fq (--fastq) Fastq reads (single ends) to be mapped against reference(s)
-pe1 Fastq reads #1 (paired ends) to be mapped against reference(s)
-pe2 Fastq reads #2 (paired ends) to be mapped against reference(s)
-mapper Read mapping tool: bowtie2, minimap2, ngmlr or hisat2 [default: minimap2]
-threads Number of processing threads [default: 16]
-mem Max total memory for samtools (in Gb) [default: 16] ## mem/threads = memory per thread
-bam Keeps BAM files generated
-idx (--index) Type of bam index generated (bai or csi) [default = csi] ## .bai not compatible with -mem
-sam Keeps SAM files generated; SAM files can be quite large
-rmo (--read_mapping_only) Do not perform variant calling; useful when only interested in bam/sam files and/or mapping stats
-ns (--no_stats) Do not calculate stats; stats can take a while to compute for large eukaryote genomes
# Mapper-specific options
-preset MINIMAP2 - Preset: sr, map-ont, map-pb or asm20 [default: sr]
-X BOWTIE2 - Maximum paired ends insert size [default: 750]
# Variant calling options
-caller [default: varscan2] ## Variant caller: varscan2 or bcftools
-type [default: snp] ## snp, indel, or both
-ploidy [default: 1] ## BCFtools option; change ploidy (if needed)
# VarScan2 parameters - see http://dkoboldt.github.io/varscan/using-varscan.html
-var [default: VarScan.v2.4.4.jar] ## Which varscan jar file to use
-mc (--min-coverage) [default: 15] ## Minimum read depth at a position to make a call
-mr (--min-reads2) [default: 15] ## Minimum supporting reads at a position to call variants
-maq (--min-avg-qual) [default: 28] ## Minimum base quality at a position to count a read
-mvf (--min-var-freq) [default: 0.7] ## Minimum variant allele frequency threshold
-mhom (--min-freq-for-hom) [default: 0.75] ## Minimum frequency to call homozygote
-pv (--p-value) [default: 1e-02] ## P-value threshold for calling variants
-sf (--strand-filter) [default: 0] ## 0 or 1; 1 ignores variants with >90% support on one strand
- To check for synonymous/non-synonymous SNPs against a reference genome (e.g. Streptococcus pneumoniae R6) with synonymy.pl, type:
REF=Streptococcus_pneumoniae_R6.fasta
synonymy.pl \
-gcode 11 \
-fa DATASETS/$REF \
-ref DATASETS/Streptococcus_pneumoniae_R6.gbk \
-format gb \
-vcf RESULTS/minimap2.varscan2.VCFs/*$REF*.vcf \
-o SYNONYMY \
-vOptions for synonymy.pl are:
-fa (--fasta) Reference genome in fasta format
-r (--ref) Reference genome annotation in GBK or GFF format
-f (--format) Reference file format; gb or gff [default: gff]
-vcf (--vcf) SNPs in Variant Calling Format (VCF)
-o (--outdir) Output directory [Default: ./]
-p (--prefix) Table prefix [Default: synonymy]
-gc (--gcode) NCBI genetic code [Default: 1]
1 - The Standard Code
2 - The Vertebrate Mitochondrial Code
3 - The Yeast Mitochondrial Code
4 - The Mold, Protozoan, and Coelenterate Mitochondrial Code and the Mycoplasma/Spiroplasma Code
11 - The Bacterial, Archaeal and Plant Plastid Code
# For complete list, see https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi
-v (--verbose) Adds verbosity
- To download FASTA and GenBank files from a CSV list of 75 Streptococcus pneumoniae genomes, type:
queryNCBI.pl \
-l Examples/S_pneumoniae_75.csv \
-o DATASETS \
-faOptions for queryNCBI.pl are:
-l (--list) TAB/CSV-delimited list from NCBI
-o (--outdir) Output directory [Default: ./]
-fa (--fasta) Retrieve fasta files
-gb (--genbank) Retrieve GenBank annotation files (.gbk; if available)
-gff (--gff3) Retrieve GFF3 annotation files (.gff; if available)
-p (--protein) Retrieve protein sequences (.faa; if available)
-cds Retrieve protein coding sequences (.fna; if available)
- To run Mash on the downloaded FASTA files with run_Mash.pl, type:
run_Mash.pl \
-f DATASETS/*.fasta \
-o MASH \
-n S_pneumoniae_75.mashOptions for run_Mash.pl are:
-f (--fasta) Reference genome(s) in fasta file
-o (--outdir) Output directory [Default: ./]
-n (--name) Output file name [Default: Mash.mash]
-s (--sort) Sort Mash output by decreasing order of similarity
- To convert the Mash output to a distance matrix with MashToDistanceMatrix.pl, type:
MashToDistanceMatrix.pl \
-i MASH/S_pneumoniae_75.mash \
-o MASH/ \
-p S_pneumoniae_75 \
-f tsvOptions for MashToDistanceMatrix.pl are:
-i (--input) Mash output file
-o (--outdir) Output folder [Default: ./]
-p (--prefix) Output file prefix [Default: Mash]
-f (--format) Output format; csv or tsv [Default: csv]
- To generate a quick neighbor-joining tree with MashR_plotter.pl, type:
MashR_plotter.pl \
-i MASH/S_pneumoniae_75.tsv \
-if tsv \
-t tree \
-newick S_pneumoniae_75.tre \
-f pdf \
-o PLOTS \
-n S_pneumoniae_75_NJ_tree \
-he 20The PDF generated should be similar to S_pneumoniae_75_NJ_tree.pdf from the Images/ directory.
- To generate a quick heatmap with MashR_plotter.pl, type:
MashR_plotter.pl \
-i MASH/S_pneumoniae_75.tsv \
-if tsv \
-t heatmap \
-f pdf \
-o PLOTS \
-n S_pneumoniae_75_heatmap \
-colors white cyan magenta \
-he 20 \
-wd 20The PDF generated should be similar to S_pneumoniae_75_heatmap.pdf from the Images/ directory.
- To generate a quick t-SNE multidimensional reduction plot with MashR_plotter.pl, type:
MashR_plotter.pl \
-i MASH/S_pneumoniae_75.tsv \
-if tsv \
-t cluster \
-m tsne \
-pe 15 \
-cmode terrain \
-f pdf \
-o PLOTS \
-n S_pneumoniae_75_tSNE \
-he 20 \
-wd 20 \
-lb \
-fs 25The PDF generated should be similar, but not identical, to S_pneumoniae_75_tSNE.pdf from the Images/ directory. t-SNE graphs are generated using random seeds, which affect how the distances are represented in 2D.
The script queryNuccore.pl retrieves genomes/proteins from the NCBI Nucleotide database using a list of accession numbers (one per line). This script uses efetch from NCBI's E-utilities suite.
To download FASTA and GenBank files with queryNuccore.pl, type:
queryNuccore.pl \
-l Examples/list_example_queryNuccore.txt
-o DATASETS \
-fa \
-gbOptions for queryNuccore.pl are:
-l (--list) Accession numbers list, one accession per line
-o (--outdir) Output folder [Default: ./]
-db (--database) NCBI database to be queried [Default: nuccore]
-fa (--fasta) Reference genome(s) in fasta format
-gb (--genbank) Reference genome(s) in GenBank format
-sqn (--sequin) Reference genome(s) in Sequin ASN format
-p (--proteins) Protein sequences (amino acids)
-c (--cds) Protein sequences (nucleotides)
The script get_SRA.pl downloads data from the NCBI Sequence Read Archive and converts it to FASTQ format using fasterq-dump from the NCBI SRA toolkit.
To download NCBI SRA datasets with get_SRA.pl using 10 threads (-t 10), type:
get_SRA.pl \
-t 10 \
-o FASTQ \
-l Examples/list_example_get_SRA.txt \
-p \
-vNote that this process can take quite a while depending on the size of the requested datasets and the available bandwidth.
Options for get_SRA.pl are:
-t (--threads) Number of CPU threads to use [Default: 10]
-o (--outdir) Output directory [Default: ./]
-l (--list) List(s) of SRA accesssion numbers, one accession number per line
-p (--progess) Show progess
-v (--verbose) Add verbosity
-f (--force) Force to overwrite existing file(s)
This work was supported in part by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number R15AI128627) to Jean-Francois Pombert. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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