Publications of NIPGR Scientists

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    Validation of plant fusion peptides using proteomics data
    (Springer Nature Publishing AG, 2026) Hamid, Fiza; Aftab, Sahrish; Shree, Tanu; Kumar, Shailesh
    Fusion transcripts and their fused protein products are emerging as exciting entities in molecular biology, offering potential applications in diagnostics and therapeutics. These fusion proteins, derived from the translation of fusion transcripts, hold promise as unique biomarkers and targets for intervention. While numerous algorithms exist to identify fusion RNAs, the detection and validation of their protein counterparts through proteomics remains a growing area of research. This challenge is particularly intriguing in plant biology, where fusion events may affect stress responses, development, and adaptation. This chapter provides an accessible and practical workflow for validating plant fusion peptides using publicly available proteomics datasets.
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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.
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    A protocol for the detection of fusion transcripts using RNA-sequencing data
    (Springer Nature Publishing AG, 2024) Hamid, Fiza; Arora, Simran; Chitkara, Pragya; Kumar, Shailesh
    Fusion transcripts are formed when two genes or their mRNAs fuse to produce a novel gene or chimeric transcript. Fusion genes are well-known cancer biomarkers used for cancer diagnosis and as therapeutic targets. Gene fusions are also found in normal physiology and lead to the evolution of novel genes that contribute to better survival and adaptation for an organism. Various in vitro approaches, such as FISH, PCR, RT-PCR, and chromosome banding techniques, have been used to detect gene fusion. However, all these approaches have low resolution and throughput. Due to the development of high-throughput next-generation sequencing technologies, the detection of fusion transcript becomes feasible using whole genome sequencing, RNA-Seq data, and bioinformatics tools. This chapter will overview the general computational protocol for fusion transcript detection from RNA-sequencing datasets.
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    In silico identification of tRNA fragments, novel candidates for cancer biomarkers, and therapeutic targets
    (Springer Nature Publishing AG, 2024) Singh, Ankita; Zahra, Shafaque; Arora, Simran; Hamid, Fiza; Kumar, Shailesh
    The identification of a wide variety of RNA molecules using high-throughput sequencing techniques in the transcriptome pool of living organisms has revealed hidden regulatory insights in the cell. The class of non-coding RNA fragments produced from transfer RNA, or tRFs, is one such example. They are heterogeneously sized molecules with lengths ranging between 15 and 50 nt. They have a history of being dysregulated in human malignancies and other illnesses. The detection of these molecules has been made easier by a variety of bioinformatics techniques. The various types of tRFs and how they relate to cancer are covered in this chapter. It also provides a summary of the biological significance of tRFs reported in human cancer. Additionally, it emphasizes the utilities of databases and computational tools that have been created by different research teams for the investigation of tRFs. This will further aid the exploration and analysis of tRFs in cancer research and will support future advancement and a better comprehension of these molecules.