Evidence map›Paper›PMID 23212387›Full record

ArticleNature communications2012

A fast and accurate SNP detection algorithm for next-generation sequencing data.

Feng Xu, Weixin Wang, Panwen Wang, Mulin Jun Li, Pak Chung Sham, Junwen Wang

Abstract read
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In one paragraph

Article in Nature communications, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
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  17. Review
  18. Rising Strengths Hong Kong SAR in Bioinformatics.Interdisciplinary sciences, computational life sciences · 2017
    Review
  19. Article
  20. 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.

Feng XuDepartment of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Weixin Wang
Panwen Wang
Mulin Jun Li
Pak Chung Sham
Junwen Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various methods have been developed for calling single-nucleotide polymorphisms from next-generation sequencing data. However, for satisfactory performance, most of these methods require expensive high-depth sequencing. Here, we propose a fast and accurate single-nucleotide polymorphism detection program that uses a binomial distribution-based algorithm and a mutation probability. We extensively assess this program on normal and cancer next-generation sequencing data from The Cancer Genome Atlas project and pooled data from the 1,000 Genomes Project. We also compare the performance of several state-of-the-art programs for single-nucleotide polymorphism calling and evaluate their pros and cons. We demonstrate that our program is a fast and highly accurate single-nucleotide polymorphism detection method, particularly when the sequence depth is low. The program can finish single-nucleotide polymorphism calling within four hours for 10-fold human genome next-generation sequencing data (30 gigabases) on a standard desktop computer.

Indexed as

AlgorithmsChromosomes, Human, Pair 21Chromosomes, Human, Pair 22GenomeGenomicsGenotypeHumansNeoplasmsPolymorphism, Single NucleotideSequence Analysis, DNATime Factors

Identifiers

PMID23212387

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.