Evidence map›Paper›PMID 23758828›Full record

ArticleBMC bioinformatics2013

QualComp: a new lossy compressor for quality scores based on rate distortion theory.

Idoia Ochoa, Himanshu Asnani, Dinesh Bharadia, Mainak Chowdhury, Tsachy Weissman, Golan Yona

Abstract read
In one paragraph

Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

23 citing papers in PubMed.

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  10. Review
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  12. QVZ: lossy compression of quality values.Bioinformatics (Oxford, England) · 2015
    Article
  13. Article
  14. Article
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  18. Aligned genomic data compression via improved modeling.Journal of bioinformatics and computational biology · 2014
    Article
  19. Traversing theResearch in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2014
    Article
  20. Data compression for sequencing data.Algorithms for molecular biology : AMB · 2013
    Article
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

6 authors.

Idoia OchoaDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu
Himanshu Asnani
Dinesh Bharadia
Mainak Chowdhury
Tsachy Weissman
Golan Yona

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNext Generation Sequencing technologies have revolutionized many fields in biology by reducing the time and cost required for sequencing. As a result, large amounts of sequencing data are being generated. A typical sequencing data file may occupy tens or even hundreds of gigabytes of disk space, prohibitively large for many users. This data consists of both the nucleotide sequences and per-base quality scores that indicate the level of confidence in the readout of these sequences. Quality scores account for about half of the required disk space in the commonly used FASTQ format (before compression), and therefore the compression of the quality scores can significantly reduce storage requirements and speed up analysis and transmission of sequencing data.

resultsIn this paper, we present a new scheme for the lossy compression of the quality scores, to address the problem of storage. Our framework allows the user to specify the rate (bits per quality score) prior to compression, independent of the data to be compressed. Our algorithm can work at any rate, unlike other lossy compression algorithms. We envisage our algorithm as being part of a more general compression scheme that works with the entire FASTQ file. Numerical experiments show that we can achieve a better mean squared error (MSE) for small rates (bits per quality score) than other lossy compression schemes. For the organism PhiX, whose assembled genome is known and assumed to be correct, we show that it is possible to achieve a significant reduction in size with little compromise in performance on downstream applications (e.g., alignment).

conclusionsQualComp is an open source software package, written in C and freely available for download at https://sourceforge.net/projects/qualcomp.

Indexed as

SoftwareAlgorithmsAnimalsData CompressionGenomeGenomicsHigh-Throughput Nucleotide SequencingMicePolymorphism, Single Nucleotide

Identifiers

PMID23758828
PMCPMC3698011

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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.