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The distribution of deleteriousness scores of genetic variants called by three different variant callers represented by boxplots.
Source publication
Genetic variants causing underlying pharmacogenetic and disease phenotypes have been used as the basis for clinical decision-making. However, due to the lack of standards for next-generation sequencing (NGS) pipelines, reproducing genetic variants among institutions is still difficult. The aim of this study is to show how many important variants fo...
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Accurate variant detection in the coding regions of the human genome is a key requirement for molecular diagnostics of Mendelian disorders. Efficiency of variant discovery from next-generation sequencing (NGS) data depends on multiple factors, including reproducible coverage biases of NGS methods and the performance of read alignment and variant ca...
Citations
Identification of genetic variations is a central part of population and quantitative genomics studies based on high-throughput sequencing data. Even though popular variant callers such as Bcftools mpileup and GATK HaplotypeCaller were developed nearly 10 years ago, their performance is still largely unknown for non-human species. Here, we showed by benchmark analyses with a simulated insect population that Bcftools mpileup performs better than GATK HaplotypeCaller in terms of recovery rate and accuracy regardless of mapping software. The vast majority of false positives were observed from repeats, especially for GATK HaplotypeCaller. Variant scores calculated by GATK did not clearly distinguish true positives from false positives in the vast majority of cases, implying that hard-filtering with GATK could be challenging. These results suggest that Bcftools mpileup may be the first choice for non-human studies and that variants within repeats might have to be excluded for downstream analyses.
The present world harbors several precarious diseases that are associated with mutations in genes. Cancer is one such disease and is often thought of as challenging to treat in the later stages. Identification of cancer-causing genes in the early stages provides better chances of survival. Therefore, the current study aimed to computationally study twenty cancer exomes belonging to five cancer types and identify the somatic variants that could point towards cancer prognosis. Twenty exome datasets were retrieved and a raw-data pre-processing check including FastQC check, adaptor trimming, gapped alignment, and refinement was performed to assess their qualities. The assessed exome datasets were then used to call the variants and analyze the mutational profiles. Identification of unique SNPs was carried out for each dataset and their functions were scrutinized to find out potential biomarkers. The outcomes of the study revealed that all twenty exome datasets passed the quality checks and 4181 variants were identified post data processing, filtration, and analysis of variants. A comprehensive analysis of the mutational profiles revealed the number of variants that were tolerated, deleterious, probably, and possible damaging as well as benign. CD82 (Cluster of differentiation 82), found in the human diffuse-type gastric cancer dataset and ANO1 (Anoctamine 1) in intrahepatic cholangiocarcinoma, were found to show good gene expression profiles in various cancer types including thyroid cancer, colorectal cancer, head, neck cancer, stomach cancer, and esophageal cancer for CD82 and colorectal cancer, head and neck cancer, stomach cancer, esophageal cancer, urinary bladder cancer, cancers of the kidneys and lungs for ANO1. A comparative analysis between the two potential markers revealed that CD82 was upregulated in a greater number of cancer types than ANO1. Therefore, the present computational study provides preliminary insights into using these potential biomarkers for the early detection and prognosis of varied cancer types.
