Variabel

Variabel Algorithm and Pipeline

Background

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost, exemplified by well over a half of million ONT SARS-COV-2 datasets. Tracking low frequency intra-host variants has provided important insights with respect to elucidating within host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable computational solutions available.

Findings

In response to this need, we present Variabel, a novel variant call filtering tool that is able to recover intra-host variants from ONT data alone, for the first time, by exploiting the tendency of true variants to change in allele frequency across samples. We evaluated Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebolavirus, Norovirus, SARS-CoV-2); our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task.

Conclusions

Vulcan is the first method designed for rescuing low frequency intra-host variants from ONT data alone. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.

Collaborators

  • Dr. Fritz Sedlazeck (BCM HGSC)
  • Dr. Medhat Mahmoud (BCM HGSC)
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.

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.

Todd J. Treangen
Todd J. Treangen
Assistant Professor of Computer Science

My research interests include algorithms and data structures for efficient analysis of microbial genomes and metagenomes

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