| Given the project timeline, C-WAP will no longer be under active development or maintenance come June 30, 2023. Please refer to the C-WAP successor Aquascope for an actively supported workflow. Freyja or Kallisto (two of the tools C-WAP incorporates) may also be of interest. Thank you for joining us on our analytic journey. |
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The CFSAN Wastewater Analysis Pipeline (C-WAP) uses a reference-based alignment to create a matrix of SNPs for a given set of samples and estimate the percentage of SARS-CoV-2 variants in the sample.
The process includes the following:
- Designating a reference and NGS data in fastq format
- Alignment of reads to the reference via Bowtie2
- Taxonomy check via Kraken2
- Processing of alignment results via Samtools
- Detection of variant positions with iVar
- Determine composition of variants via Kallisto, Linear Regression, Kraken2/Bracken and Freyja
- Generate an HTML and PDF formatted summary of results
C-WAP is a Nextflow, Python, and Bash-based bioinformatics pipeline for the analysis of either long-read (Oxford Nanopore Technologies or PacBio) or short-read (Illumina) whole genome sequencing data of DNA extracted from wastewater. It was developed for SARS-CoV-2 and its variants.
C-WAP was developed by the United States Food and Drug Administration, Center for Food Safety and Applied Nutrition.
Install Nextflow, Conda, and Micromamba. Afterwards, download c-wap repository and save. In addition, also obtain a copy of the Kraken2 standard DB (~50GB). You can either download a copy from here: https://benlangmead.github.io/aws-indexes/k2 or you can compile your own version following the instructions under the "Standard Kraken2 Database" section here: https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown
Afterwards, set $K2_STD_DB_PATH to your download location.
User provided:
Auto-fetched by C-WAP:
- Bowtie2 v2.4.5
- Minimap2 v2.24
- iVar v1.3.1
- Samtools v1.15
- Kraken2 v2.1.2
- Bracken v2.7
- Python v3.8.1
- Bcftools v1.15
- Pangolin v4.1.2
- Kallisto v0.48
- Entrez-direct v16.2
- Freyja v1.4.4
- Wkhtmltopdf v0.12.4
- Ghostscript v9.54
./startWorkflow.nf assumes that Conda, Micromamba, Nextflow and Ghostscript executables are available in the search path. All other dependencies are imported via Conda, with the exception of Freyja, which is imported via Micromamba. To trigger this installation, execute c-wap/prepare_envs.sh (no root privileges needed). The series of acquisition of the dependencies and creating of the environments might take a substantially long time (potentially hours). Subsequent analysis calls to C-WAP will make use of the cached environments stored under the c-wap/conda subdirectory and are expected to finish substantially faster. To update the dependencies (more often required for tools with a biological DB) delete the respective environment folder c-wap/conda/env-<toolName> and re-execute c-wap/prepare_envs.sh.
The driver script is startWorkflow.nf and a standard execution with paired-end Illumina reads would be:
startWorkflow.nf --platform i --primers path/to/bed --in path/to/fastq/ --out path/to/outputDir
Note that the input files need to be compressed fastq.gz formats only, and .sam, .bam or uncompressed .fastq files result in a fatal runtime error.
C-WAP produces a number of files from the various processing steps.
report - Standalone directory containing html and pdf summary report
absCounts.csv - Absolute counts table of uncovered and undercovered genes
breadthVcov.csv - Table of coverage depth and breadth
consensus.fa - Consensus fasta file generated by iVar
crdSNPs.csv - Table of common, rare, and diverse SNP coverages
freyja.demix - Lineage abundance estimate generated by Freyja
freyja_boot_lineages.csv - Freyja generated bootstraps
k2-majorCovid.out - Covid-specific kraken2 output with major lineages identified, against majorCovid DB
k2-majorCovid_bracken.out - Bracken lineage abundance estimates, against majorCovid DB
k2-std.out - Kraken2 output with standard database
kallisto.out - Python-parsed summary of the kallisto lineage abundance estimates
kallisto_abundance.tsv - Kallisto estimates of variant composition
lineage_report.csv - Pangolin lineage prediction for the consensus sequence
linearDeconvolution_abundance.csv - Linear deconvolution estimates of variant composition
mutationTable.csv - Lineage defining mutations
pos-coverage-quality.tsv - QC metrics on coverage and quality obtained from the pileup file
primer_hit_counts.tsv - Number of reads per primer
qc-flags - Suggested QC flags
resorted.stats - Samtools stats output from aligned and trimmed reads
scaledCounts.csv - Scaled counts table of uncovered and undercovered genes
seqSummary.csv - Average read quality, length, and coverage passing filter
sorted.stats - Samtools stats output from aligned but untrimmed reads
Variant composition analyses should be interpreted with caution where they should be treated as suspect if there are substantial gaps in coverage across the reference genome and/or a lack of sequencing depth. The Linear Deconvolution and Kraken2/Bracken COVID methods are internally developed methods and under testing and validation.
| Kayikcioglu T, Amirzadegan J, Rand H, Tesfaldet B, Timme RE, Pettengill JB. Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples. PeerJ. 2023 Jan 26;11:e14596. doi: 10.7717/peerj.14596. PMID: 36721781; PMCID: PMC9884472. |
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We gratefully acknowledge all data contributors, i.e., the Authors and their Originating laboratories responsible for obtaining the specimens, and their Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, some of which this research utlizes.
Khare, S., et al (2021) GISAID’s Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406
See the LICENSE.txt file included in the C-WAP Pipeline distribution.