ArticleJournal of biosciences2012
BIND - an algorithm for loss-less compression of nucleotide sequence data.
Article in Journal of biosciences, 2012. 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.
- A lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules.Briefings in functional genomics · 2025Article
- OReO: optimizing read order for practical compression.Bioinformatics advances · 2025Article
- Efficient DNA sequence compression with neural networks.GigaScience · 2020Article
- Algorithms designed for compressed-gene-data transformation among gene banks with different references.BMC bioinformatics · 2018Article
- FASTR: A novel data format for concomitant representation of RNA sequence and secondary structure information.Journal of biosciences · 2015Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in DNA sequencing technologies have enabled the current generation of life science researchers to probe deeper into the genomic blueprint. The amount of data generated by these technologies has been increasing exponentially since the last decade. Storage, archival and dissemination of such huge data sets require efficient solutions, both from the hardware as well as software perspective. The present paper describes BIND-an algorithm specialized for compressing nucleotide sequence data. By adopting a unique 'block-length' encoding for representing binary data (as a key step), BIND achieves significant compression gains as compared to the widely used general purpose compression algorithms (gzip, bzip2 and lzma). Moreover, in contrast to implementations of existing specialized genomic compression approaches, the implementation of BIND is enabled to handle non-ATGC and lowercase characters. This makes BIND a loss-less compression approach that is suitable for practical use. More importantly, validation results of BIND (with real-world data sets) indicate reasonable speeds of compression and decompression that can be achieved with minimal processor/ memory usage. BIND is available for download at http://metagenomics.atc.tcs.com/compression/BIND. No license is required for academic or non-profit use.
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Registered trials
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