Dataset Information

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

ABSTRACT: MOTIVATION:High-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. METHOD:BioSeqZip 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. RESULTS:Our 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.

SUBMITTER: Urgese G

PROVIDER: S-EPMC7203750 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Urgese Gianvito G Parisi Emanuele E Scicolone Orazio O Di Cataldo Santa S Ficarra Elisa E

Bioinformatics (Oxford, England) 20200501 9

<h4>Motivation</h4>High-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 spee ...[more]

PMID: 31999333

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Project description:Next generation sequencing (NGS) technologies are now widely used in many biological studies. In NGS, sequence reads are randomly sampled from the genome sequence of interest. Most computational approaches for NGS data first map the reads to the genome and then analyze the data based on the mapped reads. Since many organisms have unknown genome sequences and many reads cannot be uniquely mapped to the genomes even if the genome sequences are known, alternative analytical methods are needed for the study of NGS data. Here we suggest using word patterns to analyze NGS data. Word pattern counting (the study of the probabilistic distribution of the number of occurrences of word patterns in one or multiple long sequences) has played an important role in molecular sequence analysis. However, no studies are available on the distribution of the number of occurrences of word patterns in NGS reads. In this article, we build probabilistic models for the background sequence and the sampling process of the sequence reads from the genome. Based on the models, we provide normal and compound Poisson approximations for the number of occurrences of word patterns from the sequence reads, with bounds on the approximation error. The main challenge is to consider the randomness in generating the long background sequence, as well as in the sampling of the reads using NGS. We show the accuracy of these approximations under a variety of conditions for different patterns with various characteristics. Under realistic assumptions, the compound Poisson approximation seems to outperform the normal approximation in most situations. These approximate distributions can be used to evaluate the statistical significance of the occurrence of patterns from NGS data. The theory and the computational algorithm for calculating the approximate distributions are then used to analyze ChIP-Seq data using transcription factor GABP. Software is available online (www-rcf.usc.edu/?fsun/Programs/NGS_motif_power/NGS_motif_power.html). In addition, Supplementary Material can be found online (www.liebertonline.com/cmb).

Dataset Information

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

Publications

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

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