Biology subjects

Chowdhury, H. M. A. M.

Publications and source records attributed to Chowdhury, H. M. A. M..

3 recordsLinked to original sources

bioRxiv2025

Robin: An Advanced Tool for Comparative Loop Caller Result Analysis Leveraging Large Language Models

BackgroundThere has been significant interest in genomics research, leading to the development of numerous new methods. One notable area of progress is in chromosome looping detection algorithms (also known as loop callers). However, despite these advancements, there is no available platform to analyze, compare, or benchmark current tools results on the go. Developing such a platform is crucial to accelerate research and ensure the reliability and effectiveness of new methods in the field. ResultsHence, in this work, we propose Robin, an advanced ready-to-go platform for comparative loop caller result analysis leveraging Large Language Models (LLMs). Robin is a web server designed to analyze loop caller results, offering a comprehensive range of analysis metrics such as recovery and overlap. It is integrated with HiGlass and incorporates LLMs to enable users to generate plots simply by providing instructions. ConclusionsOverall, Robin is a robust and comprehensive loop caller result analysis and visualization tool. It is publicly accessible at http://hicrobin.online, with a comprehensive documentation available at http://documentation.hicrobin.online/.

bioinformatics↗

bioRxiv2024

ScHiCAtt: Enhancing Single-Cell Hi-C Resolution Using Attention-Based Models

The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures; however, limited resolution, data sparsity, and incomplete coverage in single-cell Hi-C data pose significant challenges for comprehensive analysis. Traditional CNN-based models often suffer from blurring and loss of fine details, while GAN-based methods encounter difficulties in maintaining diversity and generalization. Moreover, existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance high-resolution data in another cell type. To address these limitations, we propose ScHiCAtt (Single-cell Hi-C Attention-Based Model), which leverages attention mechanisms to capture both long-range and local dependencies in Hi-C data, significantly enhancing resolution while preserving biologically meaningful interactions. We implement this mechanism and check its validity on data from different cells of the same organisms and data of different organisms. By dynamically focusing on regions of interest, attention mechanisms effectively mitigate data sparsity and enhance model performance in low-resolution contexts. Extensive experiments on Human and Drosophila single-cell Hi-C data demonstrate that ScHiCAtt consistently outperforms existing methods in terms of computational and biological reproducibility metrics across different downsampling ratios, especially under extreme downsampling conditions. The model is publicly available at https://github.com/OluwadareLab/ScHiCAtt.

bioinformatics↗

bioRxiv2023

Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness

The chromosome is a fundamental component of cell biology, housing DNA that encapsulates hierarchical genetic information. DNA compresses its size by forming loops, and these loop regions contain numerous protein particles, including CTCF, SMC3, H3 histone, and Topologically Associating Domains (TADs). In this study, we conducted a comprehensive study of 22 loop calling methods. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the primary and replicate GM12878 Hi-C datasets at 5KB and 10KB resolutions. Our evaluation criteria encompassed various factors, including loop count, reproducibility, overlap, running time, Aggregated Peak Analysis (APA), and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII. This analysis offers insights into the loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score (BCCscore) to measure their overall robustness ensuring a comprehensive evaluation of their performance.

bioinformatics↗