ArticleBioinformatics (Oxford, England)2020
BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- How error correction affects polymerase chain reaction deduplication: A survey based on unique molecular identifier datasets of short reads.Quantitative biology (Beijing, China) · 2025Review
- Bioinformatics tools for the sequence complexity estimates.Biophysical reviews · 2023Review
- LaRA 2: parallel and vectorized program for sequence-structure alignment of RNA sequences.BMC bioinformatics · 2022Article
- Fast-HBR: Fast hash based duplicate read remover.Bioinformation · 2022Article
- Immunological pathways of macrophage response toInnate immunity · 2020Article
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Authors and funding
5 authors.
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Abstract
motivationHigh-throughput next-generation sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically increases with coverage, collapsing such files into compact sets of non-redundant reads has the 2-fold advantage of reducing file size and speeding-up the alignment, avoiding to map the same sequence multiple times.
methodBioSeqZip generates compact and sorted lists of alignment-ready non-redundant sequences, keeping track of their occurrences in the raw files as well as of their quality score information. By exploiting a memory-constrained external sorting algorithm, it can be executed on either single- or multi-sample datasets even on computers with medium computational capabilities. On request, it can even re-expand the compacted files to their original state.
resultsOur extensive experiments on RNA-Seq data show that BioSeqZip considerably brings down the computational costs of a standard sequence analysis pipeline, with particular benefits for the alignment procedures that typically have the highest requirements in terms of memory and execution time. In our tests, BioSeqZip was able to compact 2.7 billion of reads into 963 million of unique tags reducing the size of sequence files up to 70% and speeding-up the alignment by 50% at least. AVAILABILITY AND IMPLEMENTATION: BioSeqZip is available at https://github.com/bioinformatics-polito/BioSeqZip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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