Evidence map›Paper›PMID 20529923›Full record

ArticleBioinformatics (Oxford, England)2010

A statistical method for the detection of variants from next-generation resequencing of DNA pools.

Vikas Bansal

Erratum issuedAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 103 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Whole Genome Resequencing Revealed the Effect of HelicaseInternational journal of molecular sciences · 2023
    Article
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  10. TILLING-by-SequencingInternational journal of molecular sciences · 2021
    Article
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  19. Article
  20. Competitive Growth Assay of MutagenizedFrontiers in plant science · 2020
    Article

43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Vikas BansalScripps Genomic Medicine, Scripps Translational Science Institute, La Jolla, CA 92037, USA. vbansal@scripps.edu

Funding

NCRR NIH HHS U54RR02504-01
6 · The paper itself

Abstract

motivationNext-generation sequencing technologies have enabled the sequencing of several human genomes in their entirety. However, the routine resequencing of complete genomes remains infeasible. The massive capacity of next-generation sequencers can be harnessed for sequencing specific genomic regions in hundreds to thousands of individuals. Sequencing-based association studies are currently limited by the low level of multiplexing offered by sequencing platforms. Pooled sequencing represents a cost-effective approach for studying rare variants in large populations. To utilize the power of DNA pooling, it is important to accurately identify sequence variants from pooled sequencing data. Detection of rare variants from pooled sequencing represents a different challenge than detection of variants from individual sequencing.

resultsWe describe a novel statistical approach, CRISP [Comprehensive Read analysis for Identification of Single Nucleotide Polymorphisms (SNPs) from Pooled sequencing] that is able to identify both rare and common variants by using two approaches: (i) comparing the distribution of allele counts across multiple pools using contingency tables and (ii) evaluating the probability of observing multiple non-reference base calls due to sequencing errors alone. Information about the distribution of reads between the forward and reverse strands and the size of the pools is also incorporated within this framework to filter out false variants. Validation of CRISP on two separate pooled sequencing datasets generated using the Illumina Genome Analyzer demonstrates that it can detect 80-85% of SNPs identified using individual sequencing while achieving a low false discovery rate (3-5%). Comparison with previous methods for pooled SNP detection demonstrates the significantly lower false positive and false negative rates for CRISP. AVAILABILITY: Implementation of this method is available at http://polymorphism.scripps.edu/~vbansal/software/CRISP/.

Indexed as

AlgorithmsBase SequenceData Interpretation, StatisticalGenetic VariationGenomePolymorphism, Single NucleotideSequence Analysis, DNA

Identifiers

PMID20529923
PMCPMC2881398

What OpenQuestion holds

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LicenceCC BY-NC
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.