Evidence map›Paper›PMID 35436292›Full record

ArticlePloS one2022

CHAPAO: Likelihood and hierarchical reference-based representation of biomolecular sequences and applications to compressing multiple sequence alignments.

Md Ashiqur Rahman, Abdullah Aman Tutul, Sifat Muhammad Abdullah, Md Shamsuzzoha Bayzid

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Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Md Ashiqur RahmanDepartment of Computer Science and Engineering/Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.ORCID 0000-0002-2933-2637
Abdullah Aman TutulDepartment of Computer Science and Engineering/Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Sifat Muhammad AbdullahDepartment of Computer Science and Engineering/Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Md Shamsuzzoha BayzidDepartment of Computer Science and Engineering/Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.ORCID 0000-0002-5640-0615

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-throughput experimental technologies are generating tremendous amounts of genomic data, offering valuable resources to answer important questions and extract biological insights. Storing this sheer amount of genomic data has become a major concern in bioinformatics. General purpose compression techniques (e.g. gzip, bzip2, 7-zip) are being widely used due to their pervasiveness and relatively good speed. However, they are not customized for genomic data and may fail to leverage special characteristics and redundancy of the biomolecular sequences.

resultsWe present a new lossless compression method CHAPAO (COmpressing Alignments using Hierarchical and Probabilistic Approach), which is especially designed for multiple sequence alignments (MSAs) of biomolecular data and offers very good compression gain. We have introduced a novel hierarchical referencing technique to represent biomolecular sequences which combines likelihood based analyses of the sequence similarities and graph theoretic algorithms. We performed an extensive evaluation study using a collection of real biological data from the avian phylogenomics project, 1000 plants project (1KP), and 16S and 23S rRNA datasets. We report the performance of CHAPAO in comparison with general purpose compression techniques as well as with MFCompress and Nucleotide Archival Format (NAF)-two of the best known methods especially designed for FASTA files. Experimental results suggest that CHAPAO offers significant improvements in compression gain over most other alternative methods. CHAPAO is freely available as an open source software at https://github.com/ashiq24/CHAPAO.

conclusionCHAPAO advances the state-of-the-art in compression algorithms and represents a potential alternative to the general purpose compression techniques as well as to the existing specialized compression techniques for biomolecular sequences.

Indexed as

Data CompressionAlgorithmsHigh-Throughput Nucleotide SequencingLikelihood FunctionsSequence AlignmentSequence Analysis, DNASoftware

Identifiers

PMID35436292
PMCPMC9015123

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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.