Evidence map›Paper›PMID 26286629›Full record

ArticleHuman genomics2015

Performance evaluation of indel calling tools using real short-read data.

Mohammad Shabbir Hasan, Xiaowei Wu, Liqing Zhang

Open access · goldAbstract read
In one paragraph

Article in Human genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
8.4field-weighted citation impact, top 2% of its field
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

49 citing papers in PubMed, 110 citations in OpenAlex.

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  9. Journal of clinical microbiology · 2023
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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

3 authors at 1 institution in 1 country.

Mohammad Shabbir HasanDepartment of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. shabbir5@vt.edu.
Xiaowei WuDepartment of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA. xwwu@vt.edu.
Liqing ZhangDepartment of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. lqzhang@vt.edu.
Virginia Tech · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInsertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. With the advance of next-generation sequencing technology, many indel calling tools have been developed; however, evaluation and comparison of these tools using large-scale real data are still scant. Here we evaluated seven popular and publicly available indel calling tools, GATK Unified Genotyper, VarScan, Pindel, SAMtools, Dindel, GTAK HaplotypeCaller, and Platypus, using 78 human genome low-coverage data from the 1000 Genomes project.

resultsComparing indels called by these tools with a known set of indels, we found that Platypus outperforms other tools. In addition, a high percentage of known indels still remain undetected and the number of common indels called by all seven tools is very low.

conclusionAll these findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results.

Indexed as

SoftwareAlgorithmsGenome, HumanHigh-Throughput Nucleotide SequencingHuman Genome ProjectHumansINDEL MutationSequence Analysis, DNA

Identifiers

PMID26286629
PMCPMC4545535
OpenAlexW1882576502

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.