ArticleBMC medical genomics2020
Comparative assessments of indel annotations in healthy and cancer genomes with next-generation sequencing data.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
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- CardioVar: a machine learning framework for pathogenicity prediction of cardiomyopathy genetic variants.Bioinformatics advances · 2026Article
- Genome-Wide Identification and Characterization of SNPs and InDels ofPlants (Basel, Switzerland) · 2024Article
- Association between INDELs in MicroRNAs and Susceptibility to Gastric Cancer in Amazonian Population.Genes · 2022Article
- Optimizing Insertion and Deletion Detection Using Next-Generation Sequencing in the Clinical Laboratory.The Journal of molecular diagnostics : JMD · 2022Article
- A mechanistic mathematical model of initiation and malignant transformation in sporadic vestibular schwannoma.British journal of cancer · 2022Article
- scAllele: A versatile tool for the detection and analysis of variants in scRNA-seq.Science advances · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundInsertion and deletion (indel) is one of the major variation types in human genomes. Accurate annotation of indels is of paramount importance in genetic variation analysis and investigation of their roles in human diseases. Previous studies revealed a high number of false positives from existing indel calling methods, which limits downstream analyses of the effects of indels on both healthy and disease genomes. In this study, we evaluated seven commonly used general indel calling programs for germline indels and four somatic indel calling programs through comparative analysis to investigate their common features and differences and to explore ways to improve indel annotation accuracy.
methodsIn our comparative analysis, we adopted a more stringent evaluation approach by considering both the indel positions and the indel types (insertion or deletion sequences) between the samples and the reference set. In addition, we applied an efficient way to use a benchmark for improved performance comparisons for the general indel calling programs
resultsWe found that germline indels in healthy genomes derived by combining several indel calling tools could help remove a large number of false positive indels from individual programs without compromising the number of true positives. The performance comparisons of somatic indel calling programs are more complicated due to the lack of a reliable and comprehensive benchmark. Nevertheless our results revealed large variations among the programs and among cancer types.
conclusionsWhile more accurate indel calling programs are needed, we found that the performance for germline indel annotations can be improved by combining the results from several programs. In addition, well-designed benchmarks for both germline and somatic indels are key in program development and evaluations.
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