Evidence map›Paper›PMID 31999333›Full record

ArticleBioinformatics (Oxford, England)2020

BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis.

Gianvito Urgese, Emanuele Parisi, Orazio Scicolone, Santa Di Cataldo, Elisa Ficarra

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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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0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Gianvito UrgeseInteruniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino, Italy.
Emanuele ParisiDepartment of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.
Orazio ScicoloneDepartment of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.
Santa Di CataldoDepartment of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.
Elisa FicarraDepartment of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

High-Throughput Nucleotide SequencingSoftwareAlgorithmsExome SequencingRNA-SeqSequence Analysis, DNA

Identifiers

PMID31999333
PMCPMC7203750

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.