![]() However, the accurate detection of INDELs is still difficult and remains a critical issue. Large number of tools are available for short-read alignment and searching for variants ( e. Therefore, the results of INDEL calling from individual WES can be used to predict the future health of individuals and to develop customized medical treatments. For example, cystic fibrosis (CF, MIM #219700), neurofibromatosis (NF1, MIM #162200), Charcot-Marie-Tooth neuropathy type 2A (CMT2A, MIM #118210), glycogen storage disease 2 (GSD2, MIM #23200), Huntington disease (HD, MIM #143100), and Duchenne muscular dystrophy (DMD, MIM #310200) are caused by INDELs in the coding regions of DNA. INDELs can cause or contribute to human genetic diseases. One of the most important aspects of genetics is to identify genetic variants in individuals. īy applying NGS on a large scale, WES is now possible at an individual level. This will provide an important resource for applications in medical sequencing, as INDELs have been implicated in a number of diseases. INDELs is a common and functionally important type of sequence polymorphism. Several types of natural genetic variations are present in patient samples, including single-nucleotide polymorphisms (SNPs), short insertions or deletions (INDELs) ranging from 1 base (bp) to 10 kilobases (kb) in length, and larger structural variants ranging from 10 kb to several megabases in length. WES has proven to be a valuable method for the discovery of the genetic causes of rare and complex diseases due to its moderate costs, the amount of manageable data, and straightforward interpretation of results. Specifically, whole-exome sequencing (WES) has been used to elucidate genetic variants underlying human diseases. Recent advances in next-generation sequencing (NGS) technologies have rapidly altered the research and routine work of human geneticists. does not affect our adherence to PLOS ONE policies on sharing data and materials. received no specific funding for this work, and this company did not play a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: Macrogen Inc. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This study was supported by the Korea National Institute of Health intramural research grant 4800-4861-312-210. Received: FebruAccepted: JPublished: August 9, 2017Ĭopyright: © 2017 Kim et al. PLoS ONE 12(8):Įditor: Obul Reddy Bandapalli, German Cancer Research Center (DKFZ), GERMANY Our study may also serve as a basis for understanding the accuracy and completeness of INDEL detection.Ĭitation: Kim B-Y, Park JH, Jo H-Y, Koo SK, Park M-H (2017) Optimized detection of insertions/deletions (INDELs) in whole-exome sequencing data. Herein we could suggest the accessible algorithms that selectively reduce error rates and thereby facilitate INDEL detection. We presented two key sources of difficulties in accurate INDEL detection: 1) the presence of repeat, and 2) heterozygous INDELs. Furthermore, we identified INDELs with high PPV (4 algorithms intersection: 98.7%, 3 algorithms intersection: 97.6%, and GATK and SAMtools intersection INDELs: 97.6%). We examined the sensitivity and PPV of GATK (90.2 and 89.5%, respectively), SAMtools (75.3 and 94.4%, respectively), Dindel (90.1 and 88.6%, respectively), and Freebayes (80.1 and 94.4%, respectively). GATK, SAMtools, Dindel, and Freebayes) for INDEL detection from the same sample. We compared the performance of the four algorithms ( i. To evaluate INDEL calling from whole-exome sequencing (WES) data, we performed Sanger sequencing for all INDELs called from the several calling algorithm. However, there are still many errors associated with INDEL variant calling, and distinguishing INDELs from errors in NGS remains challenging. The detection of INDELs through next-generation sequencing (NGS) is becoming more common due to the decrease in costs, the increase in efficiency, and sensitivity improvements demonstrated by the various sequencing platforms and analytical tools. Insertion and deletion (INDEL) mutations, the most common type of structural variance, are associated with several human diseases.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |