fq2bam_meth - NVIDIA Docs

Generate BAM/CRAM output given one or more pairs of FASTQ files from bisulfite sequencing (BS-Seq). Can also optionally generate a BQSR report.

What is fq2bam_meth?

The tool fq2bam_meth is a fast, accurate algorithm for mapping methylated DNA sequence reads to a reference genome, performing local alignment, and producing alignment for different parts of the query sequence. It implements the baseline tool bwa-meth ^[1] ^[2] in a performant method using fq2bamfast (BWA-MEM + GATK) as a backend for processing on GPU.

Why fq2bam_meth?

fq2bam_meth is the Parabricks wrapper for bwa-meth, which will sort the output and can mark duplicates and recalibrate base quality scores in line with GATK best practices.

The Parabricks fq2bam_meth tool is capable of handling longer reads and is less sensitive to errors than other alignment algorithms. We enable fast and accurate whole-genome bisulfite sequencing (WGBS) to detect DNA-methylation at the single base pair level ^[3].

Some of the advantages of using fq2bam_meth over similar tools include:

It is faster than many other BS-Seq alignment algorithms, making it the ideal choice for high-throughput analysis.
It maintains compatibility with existing CPU-based tools.

How should I use fq2bam_meth?

fq2bam_meth uses an accelerated version of BWA-MEM to generate BAM/CRAM output given one or more pairs of FASTQ files from BS-Seq. The user can turn-off marking of duplicates by adding the --no-markdups option. The BQSR step is only performed if the --knownSites input and --out-recal-file output options are provided; doing so will also generate a BQSR report.

Prior to running alignment, the reference genome must be converted using baseline bwa-meth. The bwa-meth indexing step produces a reference fasta file with a name formatted as fasta.bwameth.c2t. The indexing preparation step requires running bwameth.py index $REF.fasta. Baseline bwa-meth requires baseline BWA-MEM to be in the user’s path for indexing functionality. Note that indexing is a time-consuming prerequisite that should only need to be completed once per reference genome. The bwameth.py script can be found here.

Quick Start

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            # This command assumes all the inputs are in INPUT_DIR and all the outputs go to OUTPUT_DIR.
docker run --rm --gpus all --volume INPUT_DIR:/workdir --volume OUTPUT_DIR:/outputdir \
    --workdir /workdir \
    nvcr.io/nvidia/clara/clara-parabricks:4.3.1-1 \
    pbrun fq2bam_meth \
    --ref /workdir/${REFERENCE_FILE} \
    --in-fq /workdir/${INPUT_FASTQ_1} /workdir/${INPUT_FASTQ_2}  \
    --knownSites /workdir/${KNOWN_SITES_FILE} \
    --out-bam /outputdir/${OUTPUT_BAM} \
    --out-recal-file /outputdir/${OUTPUT_RECAL_FILE}

Compatible CPU-based bwa-meth, GATK4 Commands

The commands below are the bwa-meth-0.2.7, bwa-0.7.15, and GATK4 counterpart of the Parabricks command above. The output from these commands will be identical to the output from the above command. See the Output Comparison page for comparing the results.

Note

Set --bwa-options="-K 10000000" in fq2bam_meth and -K 10000000 in baseline to produce compatible pair-ended results.

Note

fq2bam_meth will not strip _R1 and _R2 from read names during preprocessing like baseline bwa-meth.

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            # Run bwa-meth and pipe the output to create a sorted BAM.
$ python bwa-meth.py \
    --read-group '@RG\tID:sample_rg1\tLB:lib1\tPL:bar\tSM:sample\tPU:sample_rg1' \
    --reference <INPUT_DIR>/${REFERENCE_FILE} <INPUT_DIR>/${INPUT_FASTQ_1} <INPUT_DIR>/${INPUT_FASTQ_2} \
    -t 32 -K 10000000 | \
  gatk SortSam \
    --java-options -Xmx30g \
    --MAX_RECORDS_IN_RAM 5000000 \
    -I /dev/stdin \
    -O cpu.bam \
    --SORT_ORDER coordinate

# Mark duplicates.
$ gatk MarkDuplicates \
    --java-options -Xmx30g \
    -I cpu.bam \
    -O mark_dups_cpu.bam \
    -M metrics.txt

# Generate a BQSR report.
$ gatk BaseRecalibrator \
    --java-options -Xmx30g \
    --input mark_dups_cpu.bam \
    --output <OUTPUT_DIR>/${OUTPUT_RECAL_FILE} \
    --known-sites <INPUT_DIR>/${KNOWN_SITES_FILE} \
    --reference <INPUT_DIR>/${REFERENCE_FILE}

fq2bam_meth Reference

Run GPU-accelerated bwa-meth compatible alignment, co-ordinate sorting, marking duplicates, and Base Quality Score Recalibration to convert bisulfite reads from FASTQ to BAM/CRAM.

Input/Output file options

--ref REF
--in-fq [IN_FQ ...]
--in-se-fq [IN_SE_FQ ...]
--in-fq-list IN_FQ_LIST
--knownSites KNOWNSITES
--interval-file INTERVAL_FILE
--out-recal-file OUT_RECAL_FILE
--out-bam OUT_BAM
--out-duplicate-metrics OUT_DUPLICATE_METRICS
--out-qc-metrics-dir OUT_QC_METRICS_DIR

Tool Options:

--max-read-length MAX_READ_LENGTH
--min-read-length MIN_READ_LENGTH
-L INTERVAL, --interval INTERVAL
--bwa-options BWA_OPTIONS
--no-warnings
--filter-flag FILTER_FLAG
--skip-multiple-hits
--align-only
--no-markdups
--fix-mate
--markdups-assume-sortorder-queryname
--markdups-picard-version-2182
--monitor-usage
--optical-duplicate-pixel-distance OPTICAL_DUPLICATE_PIXEL_DISTANCE
--read-group-sm READ_GROUP_SM
--read-group-lb READ_GROUP_LB
--read-group-pl READ_GROUP_PL
--read-group-id-prefix READ_GROUP_ID_PREFIX
-ip INTERVAL_PADDING, --interval-padding INTERVAL_PADDING
--standalone-bqsr
--set-as-failed SET_AS_FAILED
--do-not-penalize-chimeras

Performance Options:

--bwa-nstreams BWA_NSTREAMS
--bwa-cpu-thread-pool BWA_CPU_THREAD_POOL
--gpuwrite
--gpuwrite-deflate-algo GPUWRITE_DEFLATE_ALGO
--gpusort
--use-gds
--memory-limit MEMORY_LIMIT
--low-memory

Common options:

--logfile LOGFILE
--tmp-dir TMP_DIR
--with-petagene-dir WITH_PETAGENE_DIR
--keep-tmp
--no-seccomp-override
--version

GPU options:

--num-gpus NUM_GPUS

Note

The --in-fq option takes the names of two FASTQ files, optionally followed by a quoted read group. The FASTQ filenames must not start with a hyphen.

Note

When using the --in-fq-list option a read group is required on each line of the input file.

[1]

Baseline bwa-meth: https://github.com/brentp/bwa-meth/

[2]

Bwa-meth manuscript: http://arxiv.org/abs/1401.1129

[3]

https://doi.org/10.1038/s41587-022-01336-9