Abstract
The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen.
Publication
Genome Biology
PhD student
Advait (5th year PhD student) obtained a dual degree, B.E Computer Science and MS Biological Sciences from BITS, Pilani in India. During his undergraduate degree, he received the Khorana Scholarship (2016) from the Indo-US Science and Technology Forum and also a thesis fellowship (2017-18) to work at Icahn School of Medicine, Mount Sinai, NY. At Mount Sinai, he worked on creating a Sub-cellular process-based ontology that predicts whole cell function using Natural Language Processing. His research interests are at the intersection of genomic data science and designing efficient algorithms to analyze genomic data.
PhD student
Bryce (2nd 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.