Evidence map›Paper›PMID 24465230›Full record

ReviewGenomics & informatics2013

A primer for disease gene prioritization using next-generation sequencing data.

Shuoguo Wang, Jinchuan Xing

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genomics & informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

2 authors.

Shuoguo WangDepartment of Genetics, The State University of New Jersey, Piscataway, NJ 08854, USA. ; Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Jinchuan XingDepartment of Genetics, The State University of New Jersey, Piscataway, NJ 08854, USA. ; Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

Funding

High-throughput Mobile Element Genotyping Using Next-generation SequencingR00HG005846 · NHGRI · RUTGERS, THE STATE UNIV OF N.J. · PI XING, JINCHUAN · 2012 to 2014
$736k
NHGRI NIH HHS R00 HG005846
6 · The paper itself

Abstract

High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. The challenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that are different from the reference genome, and to prioritize/rank the variants for the question of interest. The recent development of many computational algorithms and programs has vastly improved the ability to translate sequence data into valuable information for disease gene identification. However, the NGS data analysis is complex and could be overwhelming for researchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the most commonly used principles and tools for analyzing NGS data for disease gene identification.

Indexed as

disease gene prioritizationhigh-throughput DNA sequencinghuman genomesequence alignmentvariant discovery

Identifiers

PMID24465230
PMCPMC3897846

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.