Biology subjects

Chandra, O.

Publications and source records attributed to Chandra, O..

4 recordsLinked to original sources

bioRxiv2024

Explainable models using transcription factor binding and epigenome patterns at promoters reveal disease-associated genes and their regulators in the context of cell-types

Understanding genome-wide epigenetic regulation of diseases is important in establishing pathogenic factors and could aid in disease diagnosis, prognosis, and therapeutics. In this study, we have utilized transcription factors (TFs) and co-factor profiles (n=823) as features in machine learning models to link them to various diseases. Further, along with TFs and co-factor profiles, histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) as features allowed for the modeling of association of coding and non-coding genes to diseases. Such predicted associations could be independently validated using genome-wide association data and survival analysis. However, the unique aspect of our approach is that it highlights the link between TF binding patterns and diseases in the context of cell types. Besides highlighting relevant TF-binding in known cell-types associated with diseases, it also provided their surprising link with TFs expressed in immune cells and other seemingly non-related cells. Further investigation revealed such links to be genuine and potentially useful for prognosis, further revealing the need to deconvolve a set of known genes associated with diseases.

bioinformatics↗

bioRxiv2023

Deciphering the phenotypic heterogeneity and drug response in cancer cells using genome-wide activity and interaction of chromatin domains.

The effect of co-localization of genes in the topologically associated domains (TADs) and their activity as a regulatory unit in cancer samples and cells, together with drug-response, needs comprehensive analysis. Here, we analyzed the activity of TADs using cancer-cell transcriptomes along with chromatin-interaction and epigenome profiles to understand their relationship with drug-response. Our analysis of 819 cancer cell-line transcriptomes revealed that their response to multiple drugs was more correlated with the activity of individual TADs than genes. Applying our approach to 9014 cancer patients data (20 different cancer types) also revealed a higher association between survival and the activity of thousands of individual TADs in comparison to their genes. CRISPR-mediated knock-out of regulatory sites inside a TAD associated with cisplatin-response of oral cancer cells and discovery of primate-specific gain of synteny of genes within a TAD containing EGFR gene and its contribution towards cancer malignancy demonstrate greater utility of TAD-activity based analysis.

cancer biology↗

bioRxiv2022

Inferring functions of coding and non-coding genes using epigenomic patterns and deciphering the effect of combinatorics of transcription factors binding at promoters

The number of annotated genes in the human genome has increased tremendously, and understanding their biological role is challenging through experimental methods alone. There is a need for a computational approach to infer the function of genes, particularly for non-coding RNAs, with reliable explainability. We have utilized genomic features that are present across both coding and non-coding genes like transcription factor (TF) binding pattern, histone modifications, and DNase hypersensitivity profiles to predict ontology-based functions of genes. Our approach for gene function prediction (GFPred) made reliable predictions (>90% balanced accuracy) for 486 gene-sets. Further analysis revealed that predictability using only TF-binding patterns at promoters is also high, and it paved the way for studying the effect of their combinatorics. The predicted associations between functions and genes were validated for their reliability using PubMed abstract mining. Clustering functions based on shared top predictive TFs revealed many latent groups of gene-sets involved in common major biological processes. Available CRISPR screens also supported the inferred association of genes with the major biological processes of latent groups of gene-sets. For the explainability of our approach, we also made more insights into the effect of combinatorics of TF binding (especially TF-pairs) on association with biological functions.

bioinformatics↗

bioRxiv2020

Improving chromatin-interaction prediction using single-cell open-chromatin profile and making insight about the cis-regulatory landscape of the human brain

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell-type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin-interactions despite the fact that long-range interaction among genomic sites plays a significant role in gene regulation. Here, we propose a method that predicts both local and long-range interactions among genomic sites using single-cell open chromatin profiles. Using our methods better sensitivity, we could predict almost 0.7 million interactions among genomic sites across 7 cell-types in the human brain. The chromatin-interactions estimated in the human brain revealed surprising but useful insight about target genes of human-accelerated-elements and disease-associated mutations.

bioinformatics↗