Bioinformatics

AI/ML/DL 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, Gene regulatory network (GRN) inference, antimicrobial resistance (AMR) prediction, metabolomics, lipidomics, CyTOF, flow and mass cytometry. 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. 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, CellDrift, scTriangulate, MaxATAC

3. Metagenomics: MGS2AMR, AI4AMR

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] (ScienceBlog, Featured-Research @ CCHMC 2022)

• 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]

• G. Li, B. Song, H. Singh, V. B. S. Prasath, H. L. Grimes, N. Salomonis. Decision level integration of unimodal and multimodal single cell data with scTriangulate. Nature Communications, 14, 406, January 2023. doi:10.1038/s41467-023-36016-y  Biorxiv version doi:10.1101/2021.10.16.464640 [scTriangulate]

• 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, 19(1), e1010863, January 2023. doi:10.1371/journal.pcbi.1010863. Code [maxATAC]

• 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.

• P.-J. Van Camp, V. B. S. Prasath, D. B. Haslam, A. Porollo. MGS2AMR: A gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile. Microbiome, 11, Article number 223, October 2023. doi:10.1186/s40168-023-01674-z Code [MGS2AMR]

• P. Rajdeo, B. Aronow, V. B. S. Prasath. Deep learning-based multimodal spatial transcriptomics analysis for cancer. Advances in Cancer Research, vol 163, July 2024. doi:10.1016/bs.acr.2024.08.001. [SpaTra]

MultiModal Bioinformatics

Next-generation sequencing (NGS) is becoming commonplace and within a few years it will be routinely used like standard medical imaging modalities (CT, MRI, pathology,...). Hence personal genomes will be increasingly utilized for precision medicine. It is therefore very important to develop new approaches using latest data science tools for solving problems in bioinformatics. Prasath Lab is interested in leveraging AI/ML/DL to solve challenges in large-scale data processing in the computational biology domain. Given the expertise and experience with the multidisciplinary projects and the proven track-record in bringing quantitative approaches from mathematics, computer science, and statistics we are well-poised to be a connector among different domains. We are also part of multiple consortia (e.g. LungMAP, Rare Diseases (RDCRN),...) and our research interests span the full spectrum of bioinformatics:

• Understanding the Genomics at the Single-Cell Level

• Disease Genomics (Oncology, Neuro, Blood)

• Systems-Level Analysis of Biological Networks

• Generative AI for Bioinformatics

• Interpretable AI Tools

• Foundation Models  

Oncology Bioinformatics

Prasath Lab is interested in improving our ability to detect and prevent cancer - from early to late-stage - by using mathematical/computational models to better understand the correlations between multiscale and multimodal (imaging, genomics, EHR,...) cancer data.

• Image-driven Biomarkers

• Combined Multimodal Networks

• Leveraging State-of-the-art Tools and Large-scale Cancer Data

• Elucidating Risk Stratification for Clinical Decision Support