PaCBAM: fast and scalable processing of whole exome and targeted sequencing data

Abstract

BackgroundInterrogation of whole exome and targeted sequencing NGS data is rapidly becoming a preferred approach for the exploration of large cohorts in the research setting and importantly in the context of precision medicine. Single-base and genomic region level data retrieval and processing still constitute major bottlenecks in NGS data analysis. Fast and scalable tools are hence needed.ResultsPaCBAM is a command line tool written in C and designed for the characterization of genomic regions and single nucleotide positions from whole exome and targeted sequencing data. PaCBAM computes depth of coverage and allele-specific pileup statistics, implements a fast and scalable multi-core computational engine, introduces an innovative and efficient on-the-fly read duplicates filtering strategy and provides comprehensive text output files and visual reports. We demonstrate that PaCBAM exploits parallel computation resources better than existing tools, resulting in important reductions of processing time and memory usage, hence enabling an efficient and fast exploration of large datasets.ConclusionsPaCBAM is a fast and scalable tool designed to process genomic regions from NGS data files and generate coverage and pileup comprehensive statistics for downstream analysis. The tool can be easily integrated in NGS processing pipelines and is available from Bitbucket and Docker/Singularity hubs.

Cloud-native distributed genomic pileup operations

“…Several tools require additional input i.e. genomic intervals in case of GATK’s coverage and PaCBAM’s (Valentini et al ., 2019) pileup or the list of genomic positions in case of aseq’s (Romanel et al ., 2015) pileup that restrict processed data and affect algorithm’s computational complexity therefore the aforementioned solutions were not included in the final benchmark.…”

Integrating extracellular vesicle and circulating cell-free DNA analysis on a single plasma aliquot from breast cancer patients improves the detection of HER2 positivity

“…Furthermore, the combination of two diverse analytes, the cfDNA and EV-RNA in the context of non-metastatic early-stage BrCa patients represents an effective strategy for enabling liquid biopsies when the circulating tumor content is limited and restrains the sensitivity in detecting biomarkers and circulating oncogenic transcriptional variants [45].…”

Allele-specific genomic data elucidate the role of somatic gain and copy-number neutral loss of heterozygosity in cancer

“…After consistency checks related to the configuration setup, the pipeline verifies if the indices of the BAM files are present otherwise BAM indexing is run. The last step of the preparation phase is the computation of the SNP pileups ( Valentini et al., 2019 ) confined to regions covered by the sequencing kit, as utilized multiple times throughout the pipeline.…”

“…All tools were run on all the study samples, with the exception of MuTect2; in-house MuTect2 calls for 2,000 randomly selected patients were compared to those generated by the Genomic Data Commons (GDC), resulting in high concordance. The more time-consuming steps are those which process the entire BAMs namely PaCBAM ( Valentini et al., 2019 ) (SNP pileup, SNV pileup), Picard HSMetrics, and CNVKit.…”