Browsing by Author "Shukla, Jagriti"

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    AquaaG: A comprehensive pipeline for quality assessment and annotation of genomes
    (Elsevier B.V., 2026) Shukla, Jagriti; Mukherjee, Kanka; Sahu, Namrata; Kumar, Shailesh
    The rapid expansion of publicly available genome assemblies has made genome annotation an increasingly challenging task, particularly for large-scale analyses across prokaryotic and eukaryotic organisms. While several tools exist for assembly evaluation and annotation, their use often involves fragmented workflows that require extensive manual coordination. To overcome this limitation, we introduce AquaaG, an automated and reproducible genome annotation pipeline. AquaaG integrates genome assembly retrieval from NCBI, assembly quality assessment using QUAST, organism-specific annotation using Prokka for prokaryotes and BRAKER3 for eukaryotes, gene-space completeness evaluation using BUSCO, and functional annotation using EggNOG-mapper. The pipeline is configured through simple YAML files and supports species-level, kingdom-level, and custom assembly-based analyses with optional submitter-based filtering. AquaaG therefore provides a practical and reproducible framework for high-throughput genome annotation and assessment.
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    The intersection of AI and genomics in health and disease: Advancements and applications
    (Elsevier B.V., 2026) Kaushik, Love; Vivek, A T; Arora, Simran; Hamid, Fiza; Mukherjee, Kanka; Bisht, Niyati; Chaudhary, Sakshi; Shukla, Jagriti; Nawani, Sakshi; Kumar, Shailesh
    AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug discovery and enhances variant detection, while in cancer genomics, AI enables early detection via liquid biopsies and refines treatment by integrating multi-omics data to improve therapeutic precision. However, challenges such as data biases in underrepresented populations, limited model interpretability, and ethical concerns regarding privacy and algorithmic inequity hinder clinical adoption and demand robust governance. Efforts to diversify datasets also face standardization hurdles, although explainable AI and federated learning provide promising solutions for improving transparency and privacy. In this chapter, we discuss the role of AI in advancing genomics from diagnostics to novel therapies and emphasize the need for equitable frameworks to ensure responsible implementation, thereby paving the way for breakthroughs in personalized medicine.