Biology subjects

Narendra, T.

Publications and source records attributed to Narendra, T..

5 recordsLinked to original sources

bioRxiv2026

Tangerine: A Python framework for dynamic gene regulation analysis from transcriptomic time series

MotivationTime-series single-cell transcriptomics enables the study of dynamic gene regulation. However, standard computational tools frequently aggregate temporal data into static, dense topologies, obscuring the precise regulatory rewiring that drives developmental transitions. Further, navigating the inherent noise of statistical inference without losing biological interpretability remains an important bottleneck. ResultsWe present Tangerine, a Python framework for the dynamic reconstruction and interactive exploration of time-varying gene regulatory networks. Tangerine integrates time-constrained metacell aggregation with regularized linear modelling and non-parametric correlation to infer dynamic topologies. To solve the interpretability gap, it features a browser-based visual analytics engine. Tangerine empowers researchers to track macroscopic gene module evolution, interactively filter effect sizes, and link topological rewiring directly to raw transcriptomic evidence. Availability and implementationTangerine is implemented in Python and Plotly Dash. The code is available on Github at https://github.com/ntanmayee/tangerine.

bioinformatics↗

bioRxiv2025

Towards Personalized Epigenomics: Learning Shared Chromatin Landscapes and Joint De-Noising of Histone Modification Assays

Epigenetic mechanisms enable cellular differentiation and the maintenance of distinct cell-types. They enable rapid responses to external signals through changes in gene regulation and their registration over longer time spans. Consequently, the chromatin landscape, which is the overall organization and biochemical state of chromatin, exhibits both cell-type and individual specificity and contributes to phenotypic diversity. Genomic distributions of chromatin features are typically measured using ChIP-Seq and related methods. However, these measurements are subject to substantial biases introduced by the chromatin landscape itself. Here, we introduce DecoDen, which uses measurements of several different histone modifications, to simultaneously learn shared chromatin landscapes while de-biasing individual measurement tracks. We demonstrate DecoDens effectiveness on an integrative analysis of histone modification patterns across multiple tissues in personal epigenomes.

bioinformatics↗

bioRxiv2022

Multi-histone ChIP-Seq Analysis with DecoDen

Epigenetic mechanisms coordinate packaging, accessibility and read-out of the DNA sequence within the chromatin context. They significantly contribute to the regulation of gene expression. Thus, they play fundamental roles during differentiation on the one hand and maintenance and propagation of cell identity on the other. Epigenetic malfunctioning is associated with a large range of diseases, from neurodevelopmental disorders to cancer progression. In humans, hundreds of known epigenetic factors and complexes are involved in establishing covalent modifications on the DNA sequence itself and on associated histone proteins. Within the cellular context, the resulting combinatorial epigenomic patterns are neither established nor interpreted independently of each other and therefore exhibit high correlations in a region-specific manner. Post-translational modifications of histone proteins can be analysed using Chromatin Immunoprecipitation followed by sequencing (ChIP-Seq). Often, several assays for a number of different histone modifications are performed as part of the same experimental design. These measurements are, however, confounded by shared biases including chromatin accessibility and mappability. Existing computational methods analyse each histone modification separately. We introduce DecoDen, a new approach that leverages replicates and multi-histone ChIP-Seq experiments for a fixed cell type to learn and remove shared biases. DecoDen (Deconvolve and Denoise) consists of two major steps: We use non-negative matrix factorisation (NMF) to learn a joint cell-type specific background signal. Half-sibling regression (HSR) is then used to correct for these biases in the histone modification signals. We demonstrate that DecoDen is a robust and interpretable method that enables the unbiased discovery of subtle peaks, which are particularly important in an individual-specific context.

bioinformatics↗

bioRxiv2022

Getting Personal with Epigenetics: Towards Machine-Learning-Assisted Precision Epigenomics

Epigenetic modifications are dynamic control mechanisms involved in the regulation of gene expression. Unlike the DNA sequence itself, they vary not only between individuals but also between different cell types of the same individual. Exposure to environmental factors, somatic mutations, and ageing contribute to epigenomic changes over time, which may constitute early hallmarks or causal factors of disease. Epigenetic changes are reversible and, therefore, promising therapeutic targets. However, mapping efforts to determine an individuals cell-type-specific epigenome are constrained by experimental costs. We developed eDICE, an attention-based deep learning model, to impute epigenomic tracks. eDICE achieves improved overall performance compared to previous models on the reference Roadmap epigenomes. Furthermore, we present a proof of concept for the imputation of personalised epigenomic measurements on the ENTEx dataset, where eDICE correctly predicts individual- and cell-type-specific epigenetic patterns. This case study constitutes an important step towards robustly employing machine-learning-based approaches for personalised epigenomics.

bioinformatics↗

bioRxiv2020

ASPP2/PP1 complexes maintain the integrity of pseudostratified epithelia undergoing remodelling during morphogenesis

During development, pseudostratified epithelia undergo large scale morphogenetic events associated with increased mechanical stress. The molecular mechanisms that maintain tissue integrity in this context are poorly understood. Using a variety of genetic and imaging approaches, we uncover that the ASPP2/PP1 complex ensures proper epiblast and proamniotic cavity architecture via a mechanism that specifically prevents the most apical daughter cells from delaminating apically following cell division events. The ASPP2/PP1 complex achieves this by maintaining the integrity and organisation of the F-actin cytoskeleton at the apical surface of dividing cells. ASPP2/PP1 is also essential during gastrulation in the primitive streak, in somites and in the head fold region, suggesting that this complex is required across a wide range of pseudostratified epithelia during morphogenetic events that are accompanied by intense tissue remodelling and high cell proliferation. Finally, our study also suggests that the interaction between ASPP2 and PP1 is essential to the tumour suppressor function of ASPP2 which may be particularly relevant in the context of tissues that are subject to increased mechanical stress.

developmental biology↗