Evidence map›Paper›PMID 29046896›Full record

ArticleProceedings. Data Compression Conference2017

GeneComp, a new reference-based compressor for SAM files.

Reggy Long, Mikel Hernaez, Idoia Ochoa, Tsachy Weissman

Abstract read
In one paragraph

Article in Proceedings. Data Compression Conference, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Reggy LongDepartment of Electrical Engineering, Stanford University, CA.
Mikel HernaezDepartment of Electrical Engineering, Stanford University, CA.
Idoia OchoaDepartment of Electrical Engineering, Stanford University, CA.
Tsachy WeissmanDepartment of Electrical Engineering, Stanford University, CA.

Funding

Genomic Compression: From Information Theory to Parallel AlgorithmsU01CA198943 · NCI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MILENKOVIC, OLGICA, WEISSMAN, TSACHY · 2015 to 2017
$1.4M
NCI NIH HHS U01 CA198943
6 · The paper itself

Abstract

The affordability of DNA sequencing has led to unprecedented volumes of genomic data. These data must be stored, processed, and analyzed. The most popular format for genomic data is the SAM format, which contains information such as alignment, quality values, etc. These files are large (on the order of terabytes), which necessitates compression. In this work we propose a new reference-based compressor for SAM files, which can accommodate different levels of compression, based on the specific needs of the user. In particular, the proposed compressor GeneComp allows the user to perform lossy compression of the quality scores, which have been proven to occupy more than half of the compressed file (when losslessly compressed). We show that the proposed compressor GeneComp overall achieves better compression ratios than previously proposed algorithms when working on lossless mode.

Identifiers

PMID29046896
PMCPMC5641594

What OpenQuestion holds

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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.