Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets

Abstract

With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorithms that can separate noise from the true variants. LoFreq is a state of the art algorithm for low-frequency variant detection but has a relatively long runtime compared to other tools. In addition to this, the interface for running in parallel could be simplified, allowing for multithreading as well as distributing jobs to a cluster. In this work we describe some specific contributions to LoFreq that remedy these issues.

Publication
In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 204-208. [DOI:10.1109/IPDPSW52791.2021.00038, arXiv:2105.03062]
Bryce Kille
Bryce Kille
PhD student

Bryce (1st year PhD student) received his MS in Bioinformatics and BS in Computer Science + Chemistry from the University of Illinois at Urbana-Champaign. As an undergraduate, he worked at Dow Agrosciences in both the computational biology and cheminformatics groups. His projects included developing software for phylogeny analysis and creating models for compound activity prediction. During his Master’s program, Bryce worked in a biochemistry lab developing software for genome mining as well as a on research project for creating bit-wise algorithms for the C++ STL. One of his main interests is casting biological and chemical problems into theoretical computer science questions.

Yunxi Liu
Yunxi Liu
PhD student

Louis (3rd year PhD student) obtained a B.S. degree in Computer Science from the University of Houston and a B.S. degree in Pharmacology from China Pharmaceutical University. During his undergraduate in UH, he did research in the Pattern Analysis Laboratory on image feature extraction. His current research interests include computational biology, metagenomics, and data science.

Nicolae Sapoval
Nicolae Sapoval
PhD student

Nick (3rd year PhD student) obtained a B.S. degree in Computer Science and a B.S. with Honors in Mathematics from the University of Chicago. At the University of Chicago Nick worked in wireless networks research and later in computational biophysics focusing on conformational transition modeling for insulin degrading enzyme. His current interests are in the areas of computational biology with a focus on genomic data.

Dr. Mike Nute
Dr. Mike Nute
Postdoctoral Scientist

Mike (Postdoctoral Scientist) received his Ph.D. in Statistics in 2019 from the University of Illinois at Urbana-Champaign where he was advised by Dr. Tandy Warnow in the Department of Computer Science and worked on algorithms related to multiple sequence alignment and phylogenetic tree estimation, in particular applying these methods to studying microbial communities. He was co-advised by Dr. Rebecca Stumpf in the Department of Anthropology where he and other lab members developed novel methods to compare the microbiomes of human and non-human primates. His research interest is in discovering a new applications for our understanding of microbial communities.

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