Biology subjects

Tahir ul Qamar, M.

Publications and source records attributed to Tahir ul Qamar, M..

4 recordsLinked to original sources

bioRxiv2026

BioVix: An Integrated Large Language Model Framework for Data Visualization, Graph Interpretation, and Literature-Aware Scientific Validation

The application of Large Language Models (LLMs) for generating data visualizations through natural language interaction represents a promising advance in AI-assisted scientific analysis. However, existing LLM-based tools largely emphasize graph generation, while research workflows require not only visualization but also rigorous interpretation and validation against established scholarly evidence. Despite advances in visualization technologies, no single tool currently integrates literature references with visualization while also generating insights from graphical data. To address this gap, we present BioVix, a web-based LLM-driven framework that integrates interactive data visualization, natural-language querying, and automated retrieval of relevant academic literature. BioVix enables users to upload datasets, generate complex visualizations, interpret graphical patterns, and contextualize findings through literature references within a unified workflow. The system employs a multi-model architecture combining DeepSeek V3.1 for code and logic generation, Qwen2.5-VL-32B-Instruct for multimodal interpretation, and GPT-OSS-20B for conversational reasoning, coordinated through structured prompt engineering. BioVix was evaluated across diverse biological domains, including proteomic expression profiling, epigenomic peak annotation, and clinical diabetes data, demonstrating its flexibility in handling heterogeneous datasets and supporting exploratory, literature-aware analysis. While BioVix substantially streamlines exploratory research workflows, its LLM-generated outputs are intended to support, not replace, expert judgment, and users should independently verify results before scientific reporting. BioVix is openly available via public deployment on Hugging Face (https://huggingface.co/spaces/MuhammadZain10/BioVix), with source code provided through GitHub (https://github.com/MuhammadZain-Butt/BioVix).

bioinformatics↗

bioRxiv2025

XtractPAV: An Automated Pipeline for Identifying Presence-Absence Variations Across Multiple Genomes

MotivationPresence-absence variations (PAVs) significantly influence phenotypic diversity across and within species by modulating functional modules involved in stress responsiveness, adaptation, and developmental processes. This modulation ultimately contributes to genetic diversity at both inter- and intra-species levels. However, existing tools for detecting PAVs offer limitations in achieving optimal analysis because they lack scalable workflows for multi-genome comparisons and frequently necessitate manual integration. To address these challenges, we developed XtractPAV, an end-to-end pipeline that automates the extraction, annotation, and interactive visualization of PAVs across large-scale genomic datasets. ResultsXtractPAV was evaluated using assembled genomes of both eukaryotic and prokaryotic organisms, including Pyrus communis, Arabidopsis thaliana, Mus musculus, and Salmonella enterica, to assess its ability to detect the genomic variations across diverse species. The performance of XtractPAV was benchmarked against other established pipelines, demonstrating superior precision and a more comprehensive extraction of PAV segments. Notably, our pipeline not only identified the known PAVs from the reference set but also revealed novel variations in genes associated with various functions such as flowering time regulation and disease resistance. Furthermore, the automated report generation feature of XtractPAV produces publication-ready summaries of PAV distributions and related metrics. AvailabilityXtractPAV is freely accessible at https://github.com/SherazAhmadd/XtractPAV and on the XtractPAV webpage. The Package includes all requisite files, a user manual, test data, and a license permitting non-commercial use. Supplementary materialSupplementary data are accessible online at Bioinformatics.

bioinformatics↗

bioRxiv2021

Probing the Structural Basis of Citrus Phytochrome B using Computational Modelling and Molecular Dynamics Simulation Approaches

Phytochromes (Phys) are known as red/far-red light photoreceptors and are responsible for directing the photosensory responses across the species, majorly from fungal, bacterial and plant kingdoms. Such responses majorly include photosynthetic potential and pigmentation in bacteria, whereas in a plant, they are involved in chloroplast development and photomorphogenesis. Many prokaryotic Phys have been modelled for their structural and functional analysis, but their plant counterparts have not been explored yet. To date, only the crystal structures of the photo-sensing module (PSM) of PhyB isoform from Arabidopsis thaliana and Glycine max have been resolved experimentally. Thus, in this study, we elucidated the complete 3D structure of Citrus PhyB. Initially, the structure and organisation of the Citrus PhyB have been predicted computationally, which were found to have the same domain organisation as A. thaliana and G. max PhyBs, yet their considerable distinct structural difference indicated potential divergence in signaling and functioning. Therefore, to evaluate the structural and functional implications of Citrus PhyB, we compared its structure with A. thaliana and G. max PhyBs using molecular dynamics (MD) simulation approaches. The modeling studies revealed that the region of Citrus PhyB-GAF domain possibly contributes to the variations between Citrus, A. thaliana and G. max PhyBs structures/functions. Hence, structural and molecular insights into Citrus PhyB can help to discover the Phys signaling and thus, an essential framework can be designed for optogenetic reagents and various agricultural/horticulture benefits. One sentence summaryA complete Citrus PhyB structure together with photo-sensory and out-put modules provides significant information to evaluate its biological activities for agricultural benefits.

biophysics↗

bioRxiv2020

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

Coronavirus disease 2019 (COVID-19) associated pneumonia caused by severe acute respiratory coronavirus 2 (SARS-COV-2) was first reported in Wuhan, China in December 2019. Till date, no vaccine or completely effective drug is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven antigenic proteins were taken as targets and different epitopes (B-cell, T-cell and IFN-{gamma} inducing) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.29% coverage of the worlds population. Finally, 505 amino acids long MEV was designed by connecting sixteen MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. Linkers and adjuvant were added to enhance the immunogenicity response of the MEV. The antigenicity, allergenicity, physiochemical properties and structural details of MEV were analyzed in order to ensure safety and immunogenicity. MEV construct was non-allergenic, antigenic, stable and flexible. Molecular docking followed by molecular dynamics (MD) simulation analysis, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Codon optimization and in silico cloning of MEV ensured increased expression in the Escherichia coli K-12 system. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

immunology↗