Evidence map›Paper›PMID 25407965›Full record

ArticleBMC bioinformatics2014

Vindel: a simple pipeline for checking indel redundancy.

Zhiyi Li, Xiaowei Wu, Bin He, Liqing Zhang

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Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. SPDI: data model for variants and applications at NCBI.Bioinformatics (Oxford, England) · 2020
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5 · Who and what money

Authors and funding

4 authors.

Zhiyi LiDepartment of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. zli04@vt.edu.
Xiaowei WuDepartment of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA. xwwu@vt.edu.
Bin HeDepartment of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. binhe@vt.edu.
Liqing ZhangDepartment of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. lqzhang@vt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the advance of next generation sequencing (NGS) technologies, a large number of insertion and deletion (indel) variants have been identified in human populations. Despite much research into variant calling, it has been found that a non-negligible proportion of the identified indel variants might be false positives due to sequencing errors, artifacts caused by ambiguous alignments, and annotation errors.

resultsIn this paper, we examine indel redundancy in dbSNP, one of the central databases for indel variants, and develop a standalone computational pipeline, dubbed Vindel, to detect redundant indels. The pipeline first applies indel position information to form candidate redundant groups, then performs indel mutations to the reference genome to generate corresponding indel variant substrings. Finally the indel variant substrings in the same candidate redundant groups are compared in a pairwise fashion to identify redundant indels. We applied our pipeline to check for redundancy in the human indels in dbSNP. Our pipeline identified approximately 8% redundancy in insertion type indels, 12% in deletion type indels, and overall 10% for insertions and deletions combined. These numbers are largely consistent across all human autosomes. We also investigated indel size distribution and adjacent indel distance distribution for a better understanding of the mechanisms generating indel variants.

conclusionsVindel, a simple yet effective computational pipeline, can be used to check whether a set of indels are redundant with respect to those already in the database of interest such as NCBI's dbSNP. Of the approximately 5.9 million indels we examined, nearly 0.6 million are redundant, revealing a serious limitation in the current indel annotation. Statistics results prove the consistency of the pipeline on indel redundancy detection for all 22 chromosomes. Apart from the standalone Vindel pipeline, the indel redundancy check algorithm is also implemented in the web server http://bioinformatics.cs.vt.edu/zhanglab/indelRedundant.php .

Indexed as

INDEL MutationSoftwareAlgorithmsGenomeHigh-Throughput Nucleotide SequencingHumansSequence AlignmentSequence Analysis, DNA

Identifiers

PMID25407965
PMCPMC4245841

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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.