Bioinformatics

Machine Learning for Bioinformatics/Computational Biology - To Be Added

Application of machine/deep learning to single cell RNA sequence (scRNAseq), Assay for Transposase-Accessible Chromatin using sequence (ATAC-seq), etc. Analysis of high throughput sequencing genomics data (ChIP-Seq, DNase-Seq, and/or ATAC-Seq). Deep learning, explainable AI for computational biology problems - quality control (QC) clustering, denoising, imputation, trajectory inference, immunogenicity prediction, RNA binding protein prediction, transcription factor binding prediction, antimicrobial resistance prediction. Analysis of multiplexed imaging, mass spectrometry imaging (MSI), CODEX imaging, spatial transcriptomics in tandem with single-cell genomics data and in general multi-omics, and multimodal analysis. Exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data. Automated cell population discovery, exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data.

Project pages:


  1. Imaging-based: Clustering, Spatial Transcriptomics

  2. Single-cell: DeepImmuno, scTriangulate, CellDrift, MaxATAC

  3. Metagenomics: MGS2AMR/GLOPS/ADAG


Selected publications:


  • J. Zhang, Q. Wu, C. B. Johnson, G. Pham, J. M. Kinder, A. Olsson, A. Slaughter, M. May, B. Weinhaus, A. D'Alessandro, J. D. Engel, J. X. Jiang, J. M. Kofron, L. F. Huang, V. B. S. Prasath, S. S. Way, N. Salomonis, H. L. Grimes, D. Lucas. In situ mapping identifies distinct vascular niches for myelopoiesis. Nature, 590, 457-462, February 2021. doi:10.1038/s41586-021-03201-2 [Clustering]

  • G. Li, B. Iyer, V. B. S. Prasath, Y. Ni, N. Salomonis. DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Briefings in Bioinformatics, 22(6), November 2021. doi:10.1093/bib/bbab160. Code, online app. [DeepImmuno]

  • N. Gaddis, J. Fortriede, M. Guo, E. E. Bardes, M. Kouril, S. Tabar, K. Burns, M. E. Ardini-Poleske, S. Loos, D. Schnell, K. Jin, B. Iyer, Y. Du, J. Korte, R. Munshi, V. Smith, A. Herbst, J. A. Kitzmiller, G. C. Clair, J. Carson, J. Adkins, E. E. Morrisey, G. S. Pryhuber, R. Misra, J. A. Whitsett, X. Sun, T. Heathorn, B. Paten, V. B. S. Prasath, Y. Xu, T. Tickle, Bruce J. Aronow, N. Salomonis. LungMAP portal ecosystem: Systems-level exploration of the lung. American Journal of Respiratory Cell and Molecular Biology, 2022. Biorxiv, December 2021. doi:10.1101/2021.12.05.471312

  • G. Li, B. Song, H. Singh, V. B. S. Prasath, H. L. Grimes, N. Salomonis. scTriangulate, a game-theory based framework for optimal solutions of multimodal single-cell data. Nature Communications, 2022. In Revision. Biorxiv, October 2021.doi:10.1101/2021.10.16.464640 [scTriangulate]

  • K. Jin, D. Schnell, G. Li, N. Salomonis, S. Prasath, R. Szczesniak, B. J. Aronow. CellDrift: Inferring perturbation responses in temporally-sampled single cell data. Briefings in Bioinformatics, 2022. doi:10.1093/bib/bbac324. Code [CellDrift]

  • T. A Cazares, F. W. Rizvi, B. Iyer, X. Chen, M. Kotliar, J. A. Wayman, A. Bejjani, O. Donmez, B. Wronowski, S. Parameswaran, L. C. Kottyan, A. Barski, M. T. Weirauch, V. B. S. Prasath, E. R. Miraldi. maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. Biorxiv, January 2022. doi:10.1101/2022.01.28.478235. Code [maxATAC]

  • P.-J. Van Camp et al. Antimicrobial resistance evaluation of individual bacteria in metagenomic sequencing data. 2022.