Assessing reproducibility of inherited variants detected with short-read whole genome sequencing.

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers.

Author Correction: Best practices for benchmarking germline small-variant calls in human genomes.

In the version of this article initially published online, two pairs of headings were switched with each other in Table 4: "Recall (PCR free)" was switched with "Recall (with PCR)," and "Precision (PCR free)" was switched with "Precision (with PCR)." The error has been corrected in the print, PDF and HTML versions of this article.

Best practices for benchmarking germline small-variant calls in human genomes.

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling.

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data.

The analysis of whole-genome or exome sequencing data from trios and pedigrees has been successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyzes data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees.

Data mining in bioinformatics using Weka.

Unlabelled: The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table.