Biology subjects

Paul Medvedev

Publications and source records attributed to Paul Medvedev.

3 recordsLinked to original sources

bioRxiv2016

VarMatch: robust matching of small variant datasets using flexible scoring schemes

MotivationSmall variant calling is an important component of many analyses, and, in many instances, it is important to determine the set of variants which appear in multiple callsets. Variant matching is complicated by variants that have multiple equivalent representations. Normalization and decomposition algorithms have been proposed, but are not robust to different representation of complex variants. Variant matching is also usually done to maximize the number of matches, as opposed to other optimization criteria.\n\nResultsWe present the VarMatch algorithm for the variant matching problem. Our algorithm is based on a theoretical result which allows us to partition the input into smaller subproblems without sacrificing accuracy VarMatch is robust to different representation of complex variants and is particularly effective in low complexity regions or those dense in variants. VarMatch is able to detect more matches than either the normalization or decomposition algorithms on tested datasets. It also implements different optimization criteria, such as edit distance, that can improve robustness to different variant representations. Finally the VarMatch software provides summary statistics, annotations, and visualizations that are useful for understanding callers performance.\n\nAvailabilityVarMatch is freely available at: https://github.com/medvedevgroup/varmatch\n\nContactchensun@cse.psu.edu

Bioinformatics↗

bioRxiv2016

Computational Pan-Genomics: Status, Promises and Challenges

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic datasets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this paper, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies, and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Genomics↗

bioRxiv2015

Improving the Power of Structural Variation Detection by Augmenting the Reference

The uses of the Genome Reference Consortiums human reference sequence can be roughly categorized into three related but distinct categories: as a representative species genome, as a coordinate system for identifying variants, and as an alignment reference for variation detection algorithms. However, the use of this reference sequence as simultaneously a representative species genome and as an alignment reference leads to unnecessary artifacts for structural variation detection algorithms and limits their accuracy. We show how decoupling these two references and developing a separate alignment reference can significantly improve the accuracy of structural variation detection, lead to improved genotyping of disease related genes, and decrease the cost of studying polymorphism in a population.

Bioinformatics↗