CaReAl: capturing read alignments in a BAM file rapidly and conveniently

Yoomi Park, Heewon Seo, Kyunghun Yoo, Ju Han Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Some of the variants detected by high-throughput sequencing (HTS) are often not reproducible. To minimize the technical-induced artifacts, secondary experimental validation is required but this step is unnecessarily slow and expensive. Thus, developing a rapid and easy to use visualization tool is necessary to systematically review the statuses of sequence read alignments. Here, we developed a high-performance alignment capturing tool, CaReAl, for visualizing the read-alignment status of nucleotide sequences and associated genome features. CaReAl is optimized for the systematic exploration of regions of interest by visualizing full-depth read-alignment statuses in a set of PNG files. CaReAl was 7.5 times faster than IGV ‘snapshot’, the only stand-alone tool which provides an automated snapshot of sequence reads. This rapid user-programmable capturing tool is useful for obtaining read-level data for evaluating variant calls and detecting technical biases. The multithreading and sequential wide-genome-range-capturing functionalities of CaReAl aid the efficient manual review and evaluation of genome sequence alignments and variant calls. CaReAl is a rapid and convenient tool for capturing aligned reads in BAM. CaReAl facilitates the acquisition of highly curated data for obtaining reliable analytic results.

Original languageEnglish
Article number23
JournalJournal of Big Data
Volume8
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

  • Data visualization
  • High‐throughput sequencing
  • Variant evaluation

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