Machine Learning for Bioinformatics/Computational Biology

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, B.-X. Huo, A. Bhattacharjee, 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, B. J. Aronow, N. Salomonis. LungMAP portal ecosystem: Systems-level exploration of the lung. American Journal of Respiratory Cell and Molecular Biology. doi:10.1165/rcmb.2022-0165OC.

  • G. Li, B. Song, H. Singh, V. B. S. Prasath, H. L. Grimes, N. Salomonis. scTriangulate, decision-level integration of uni- and multimodal single-cell data. Nature Communications, 2022. In Revision. Biorxiv version 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, 23(5), September 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. PLOS Computational Biology, 2022. In Revision. Biorxiv version 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.