Evidence map›Paper›PMID 26615213›Full record

ArticleBioinformatics (Oxford, England)2016

Comment on: 'ERGC: an efficient referential genome compression algorithm'.

Sebastian Deorowicz, Szymon Grabowski, Idoia Ochoa, Mikel Hernaez, Tsachy Weissman

Abstract readLetterComment
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Sebastian DeorowiczInstitute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100 Poland.
Szymon GrabowskiInstitute of Applied Computer Science, Lodz University of Technology, Al. Politechniki 11, 90-924 Łódź, Poland and.
Idoia OchoaDepartment of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.
Mikel HernaezDepartment of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.
Tsachy WeissmanDepartment of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.

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

motivationData compression is crucial in effective handling of genomic data. Among several recently published algorithms, ERGC seems to be surprisingly good, easily beating all of the competitors.

resultsWe evaluated ERGC and the previously proposed algorithms GDC and iDoComp, which are the ones used in the original paper for comparison, on a wide data set including 12 assemblies of human genome (instead of only four of them in the original paper). ERGC wins only when one of the genomes (referential or target) contains mixed-cased letters (which is the case for only the two Korean genomes). In all other cases ERGC is on average an order of magnitude worse than GDC and iDoComp. CONTACT: sebastian.deorowicz@polsl.pl, iochoa@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Data CompressionSequence Analysis, DNAAlgorithmsGenomeGenome, HumanGenomicsHumans

Identifiers

PMID26615213
PMCPMC4907388

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.