ViralConsensus

ViralConsensus is a fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data. ViralConsensus is orders of magnitude faster and more memory-efficient than existing methods. Further, unlike existing methods, ViralConsensus can pipe data directly from a read mapper via standard input and performs viral consensus calling on-the-fly, making it an ideal tool for viral sequencing pipelines.

Note that, although the tool's name implies that it is primarily meant for viral genomics, ViralConsensus can be used to build consensus genome sequences from arbitrary genomes (e.g. bacterial, mitochondrial, etc.), as long as there is a single reference sequence that is being used for reconstruction. The caveat is that memory consumption scales linearly with reference genome length, meaning ultra-long reference genomes (e.g. human chromosomes) may require prohibitively large amounts of memory (though they may be feasible in large-memory systems).

To run ViralConsensus, you can either install the command-line tool (instructions below), or you can try out the web app created by my student, Daniel Ji, which is a complete WebAssembly port of ViralConsensus (meaning it runs fully client-side in your own web browser!). The web app works well for reasonably small datasets, but for larger datasets, you will want to use the command-line tool.

Installation

ViralConsensus is written in C++ and depends on htslib. First, you need to install all dependencies (if you haven't already):

bash sudo apt-get update sudo apt-get -y upgrade sudo apt-get install -y automake bzip2 gcc g++ git libbz2-dev libcurl4-openssl-dev liblzma-dev make wget zlib1g-dev wget -qO- "https://github.com/samtools/htslib/releases/download/1.18/htslib-1.18.tar.bz2" | tar -xj cd htslib-* autoreconf -i ./configure make sudo make install

In order to compile and run ViralConsensus, htslib needs to be installed in your environment's library path. If you followed the instructions above (so htslib was installed in /usr/local), you may need to add the following to your .bashrc file:

bash export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib"

Then, you can simply download the latest release tarball (or clone this repo) and compile with make:

bash git clone https://github.com/niemasd/ViralConsensus.git cd ViralConsensus make sudo mv viral_consensus /usr/local/bin/ # optional; install executable globally

You can also install ViralConsensus via conda, and you can find a Docker container of ViralConsensus on DockerHub (niemasd/viral_consensus).

Usage

viral_consensus -i IN_READS -r REF_GENOME -o OUT_CONSENSUS [-op OUT_POS_COUNTS] [-oi OUT_INS_COUNTS] [-q MIN_QUAL] [-d MIN_DEPTH] [-f MIN_FREQ] [-a AMBIG] [-p PRIMER_BED] [-po PRIMER_OFFSET] -i/--in_reads IN_READS Input reads file (CRAM/BAM/SAM), or '-' for standard input -r/--ref_genome REF_GENOME Input reference genome (FASTA) -o/--out_consensus OUT_CONSENSUS Output consensus genome (FASTA), or '-' for standard output -op/--out_pos_counts OUT_POS_COUNTS Output position counts (TSV), or '-' for standard output (default: don't output) -oi/--out_ins_counts OUT_INS_COUNTS Output insertion counts (JSON), or '-' for standard output (default: don't output) -q/--min_qual MIN_QUAL Minimum base quality to count base in counts (default: 20) -d/--min_depth MIN_DEPTH Minimum depth to call base/insertion in consensus (default: 10) -f/--min_freq MIN_FREQ Minimum frequency to call base/insertion in consensus (default: 0.5) -a/--ambig AMBIG Ambiguous symbol (default: N) -p/--primer_bed PRIMER_BED Primer file (BED) -po/--primer_offset PRIMER_OFFSET Number of bases after primer to also trim (default: 0) -v/--version Print version number -h/--help Print this usage message

Piping from Read Mapper

Because ViralConsensus does not require reads to be sorted, data can be piped directly from the read mapper to ViralConsensus to avoid unnecessary disk I/O (and thus improve speed):

```bash

ViralConsensus is single-threaded, so if running many samples, best to run mapper single-threaded as well and parallelize across samples

minimap2 -t 1 -a -x sr reference.fas reads.fastq.gz | viral_consensus -i - -r reference.fas -o consensus.fas ```

Users will likely want to keep the output of the read mapper (e.g. as a compressed BAM file) for other downstream analyses, but rather than writing the output SAM/BAM/CRAM to disk and then having ViralConsensus read from that file, tee should be used instead to send the stream down two (or more) paths:

bash minimap2 -t 1 -a -x sr reference.fas reads.fastq.gz | tee >(viral_consensus -i - -r reference.fas -o consensus.fas) | samtools view -b -@ 1 > reads.bam

Output Files

ViralConsensus produces 3 output files: the consensus sequence (FASTA), base counts at every position (TSV), and insertion counts before every position (JSON). You can visualize the position base counts TSV file in SamBamViz to look at coverage across the genome as well as to look at base counts at each position.

Citing ViralConsensus

If you use ViralConsensus in your work, please cite:

Moshiri N (2023). "ViralConsensus: A fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data." Bioinformatics. btad317. doi:10.1093/bioinformatics/btad317

If you use the ViralConsensus web application (rather than the command-line tool), please also cite:

Ji D, Aboukhalil R, Moshiri N (2023). "ViralWasm: a client-side user-friendly web application suite for viral genomics." Bioinformatics. btae018. doi:10.1093/bioinformatics/btae018