A survey of tools for variant analysis of next-generation genome sequencing data

By Stephan Pabinger, Andreas Dander, Maria Fischer, René Snajder, Michael Sperk, Mirjana Efremova, Birgit Krabichler, Michael R. Speicher, Johannes Zschocke, Zlatko Trajanoski. Briefings Bioinform., 2013

589 citations recorded by the source corpus.

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b14655fe4b80d5e2ee063e5d405ef6c51e95204e

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Citations in the corpus

SVIM: structural variant identification using mapped long reads (2018, background): “There exists a wide variety of tools for SV calling from short reads [10] but despite ongoing efforts, the discovery of SVs from short-read data remains challenging [11].”
Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data (2016, methodology): “Methods have been developed for matched DNA samples, where germline and somatic tumor tissues are both available for an individual Pabinger et al. [2014], Wang et al. [2013].”
The Ensembl Variant Effect Predictor (2016, methodology): “To facilitate this, we developed the Ensembl Variant Effect Predictor (VEP) (www.ensembl.org/vep), which differs significantly from other tools (Pabinger et al. 2014) (see Table 1 and discussion below) and from the previously published Ensembl SNP Effect Predictor (McLaren et al. 2010).”
Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement (2014, methodology): “genome [11], and particularly small events (SNPs and small indels)”
HemI: A Toolkit for Illustrating Heatmaps (2014, background): “Recent progress in high-throughput techniques, such as DNA microarray, nextgeneration sequencing (NGS) and quantitative proteomics, has increased the demand for the visualization of multi-dimensional and numeric data [1–3].”
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins (2017, methodology): “This copious data stream requires efficient processing and annotation to be useful, and in the case of human genetic diseases, efficient computational methods are necessary to reduce the number of observed variants to a few that are likely causative for the phenotype under scrutiny (2,3,7).”
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing (2019, methodology): “Although the sequencing-based methods can in theory detect any type of SV, no single computational algorithm can accurately and sensitively detect all types and all sizes of SVs [23].”