Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches.

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.

A comprehensive survey of models for dissecting local ancestry deconvolution in human genome.

Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations. In this article, we survey existing ancestry deconvolution methods, with special emphasis on multiway admixture, and compare these methods based on simulation results reported by different studies, computational approaches used, including mathematical and statistical models, and biological challenges related to each method. This should orient users on the choice of an appropriate method or tool for given population admixture characteristics and update researchers on current advances, challenges and opportunities behind existing ancestry deconvolution methods.

A multi-scenario genome-wide medical population genetics simulation framework.

MOTIVATION: Recent technological advances in high-throughput sequencing and genotyping have facilitated an improved understanding of genomic structure and disease-associated genetic factors. In this context, simulation models can play a critical role in revealing various evolutionary and demographic effects on genomic variation, enabling researchers to assess existing and design novel analytical approaches. Although various simulation frameworks have been suggested, they do not account for natural selection in admixture processes. Most are tailored to a single chromosome or a genomic region, very few capture large-scale genomic data, and most are not accessible for genomic communities. RESULTS: Here we develop a multi-scenario genome-wide medical population genetics simulation framework called 'FractalSIM'. FractalSIM has the capability to accurately mimic and generate genome-wide data under various genetic models on genetic diversity, genomic variation affecting diseases and DNA sequence patterns of admixed and/or homogeneous populations. Moreover, the framework accounts for natural selection in both homogeneous and admixture processes. The outputs of FractalSIM have been assessed using popular tools, and the results demonstrated its capability to accurately mimic real scenarios. They can be used to evaluate the performance of a range of genomic tools from ancestry inference to genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: The FractalSIM package is available at http://www.cbio.uct.ac.za/FractalSIM. CONTACT: emile.chimusa@uct.ac.za. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A broad survey of DNA sequence data simulation tools.

In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to date and evaluated 20 state-of-art DNA sequence simulation tools on their ability to produce accurate reads based on their implemented sequence error model. We provide a succinct description of each tool and suggest which tool is most appropriate for the given different scenarios. Given the multitude of similar yet non-identical tools, researchers can use this review as a guide to inform their choice of DNA sequence simulation tool. This paves the way towards assessing existing tools in a unified framework, as well as enabling different simulation scenario analysis within the same framework.