Evidence map›Paper›PMID 22922203›Full record

ArticleJournal of biosciences2012

BIND - an algorithm for loss-less compression of nucleotide sequence data.

Tungadri Bose, Monzoorul Haque Mohammed, Anirban Dutta, Sharmila S Mande

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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.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tungadri BoseBio-Sciences R&D Division, TCS Innovation Labs, 54B Hadapsar Industrial Estate, Tata Consultancy Services Limited, Hadapsar, Pune 411 013, India.
Monzoorul Haque Mohammed
Anirban Dutta
Sharmila S Mande

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsInformation Storage and RetrievalSequence Analysis, DNABase SequenceComputing MethodologiesData CompressionSoftware

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

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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.