SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

Wiewiórka, Marek; Szmurło, Agnieszka; Kuśmirek, Wiktor; Gambin, Tomasz

doi:10.1101/494468

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…InDels were analyzed for frameshift, intragenics, intronics, as well as ‘ splice site donor’ and ‘ splice site acceptor ’. After the depth coverage analysis, defined as the total number of reads overlapping a given genomic position [ 51 ], 68 SNPs and 12 InDels were selected to design specific primers to perform their validation using High Resolution Melting Analysis (qPCR-HRM).…”

Section: Resultsmentioning

confidence: 99%

Identification of SNPs and InDels associated with berry size in table grapes integrating genetic and transcriptomic approaches

et al. 2020

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Background: Berry size is considered as one of the main selection criteria in table grapes breeding programs, due to the consumer preferences. However, berry size is a complex quantitive trait under polygenic control, and its genetic determination of berry weight is not yet fully understood. The aim of this work was to perform marker discovery using a transcriptomic approach, in order to identify and characterize SNP and InDel markers associated with berry size in table grapes. We used an integrative analysis based on RNA-Seq, SNP/InDel search and validation on table grape segregants and varieties with different genetic backgrounds. Results: Thirty SNPs and eight InDels were identified using a transcriptomic approach (RNA-Seq). These markers were selected from SNP/InDel found among segregants from a Ruby x Sultanina population with contrasting phenotypes for berry size. The set of 38 SNP and InDel markers was distributed in eight chromosomes. Genotypephenotype association analyses were performed using a set of 13 RxS segregants and 41 table grapes varieties with different genetic backgrounds during three seasons. The results showed several degrees of association of these markers with berry size (10.2 to 30.7%) as other berry-related traits such as length and width. The co-localization of SNP and /or InDel markers and previously reported QTLs and candidate genes associated with berry size were analysed. Conclusions: We identified a set of informative and transferable SNP and InDel markers associated with berry size. Our results suggest the suitability of SNPs and InDels as candidate markers for berry weight in seedless table grape breeding. The identification of genomic regions associated with berry weight in chromosomes 8, 15 and 17 was achieved with supporting evidence derived from a transcriptome experiment focused on SNP/InDel search, as well as from a QTL-linkage mapping approach. New regions possibly associated with berry weight in chromosomes 3, 6, 9 and 14 were identified.

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Section: Resultsmentioning

confidence: 99%

Identification of SNPs and InDels associated with berry size in table grapes integrating genetic and transcriptomic approaches

et al. 2020

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“…Megadepth processes BAMs one chromosome at a time, allocating a chromosome-length array in memory. It scans alignments in the BAM -possibly looking only within user-specified regions -and tallies base coverage in the array, either via the increment/decrement approach (Pedersen et al, 2018;Wiewiórka et al, 2019) or by storing explicit counts, depending on the operation. Megadepth uses the same general approach for BigWig files, scanning them base-by-base.…”

Section: Methodsmentioning

confidence: 99%

Megadepth: efficient coverage quantification for BigWigs and BAMs

Wilks

Ahmed

Baker

et al. 2020

Preprint

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MotivationA common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types.ResultsMegadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19,000 GTExV8 BigWig files in approximately one hour using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package.Availabilityhttps://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth.Contactchris.wilks@jhu.edu

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“…Supplementary information on benchmarking procedure as well as test data are publicly accessible at the project documentation site http://biodatageeks.org/sequila/benchmarking/benchmarking.html#depth-of-coverage. An archival copy of the code and supporting data is also available via the GigaScience database GigaDB [19].…”

Section: Availability Of Supporting Data and Materialsmentioning

confidence: 99%

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

et al. 2019

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Background Depth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next-generation sequencing pipelines, including the analysis of RNA-sequencing data, detection of copy number variants, or quality control procedures. Results Building upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides efficient depth of coverage calculations, reaching >100× speedup over the state-of-the-art tools. The performance and scalability of our solution allow for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface. Conclusions SeQuiLa-cov provides significant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines.

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SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

Cited by 3 publications

References 15 publications

Identification of SNPs and InDels associated with berry size in table grapes integrating genetic and transcriptomic approaches

Identification of SNPs and InDels associated with berry size in table grapes integrating genetic and transcriptomic approaches

Megadepth: efficient coverage quantification for BigWigs and BAMs

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

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