Biology subjects

Gupta, I.

Publications and source records attributed to Gupta, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Population-Scale Landscape of Mobile Element Insertions Across 82 Diverse Indian Populations

Mobile element insertions (MEIs) are a major source of structural variation in human DNA that can affect gene regulation and disrupt coding sequences. Population-scale MEI catalogs remain disproportionately skewed toward European-ancestry cohorts, with limited representation from India despite comprising nearly one-sixth of the global population and encompassing thousands of genetically distinct, largely endogamous communities. We characterized non-reference Alu and LINE1 insertions in 7,478 whole genomes from the GenomeIndia project, representing 82 populations selected to capture Indias geographic, linguistic, and social diversity. We cataloged 26,977 Alu and 7,075 LINE1 insertions, identifying 18,982 previously unreported insertions absent from existing publicly available MEI databases. Insertions bore signatures of purifying selection, depleted in exons and active chromatin and enriched in quiescent regions. MEI variants recapitulated the population structure seen in SNP data. Regulatory variation associated with MEIs was strongly population-structured, with a substantial fraction of European-defined MEI-eQTLs absent in the GenomeIndia cohort or carried at sharply different frequencies across communities, and MEIs in high linkage disequilibrium with GWAS variants. Sixty-nine insertions fell within exons, including a truncating LINE1 in CYP2J2 carried across five populations and a community-specific Alu in CABYR with a founder-effect signature. Collectively, these results reveal a layer of population-specific mobile element variation in Indian populations that is not captured by existing European-anchored reference resources. These variants represent potentially novel sources of genetic variation contributing to lack of transferability of disease associations across populations.

genomics↗

bioRxiv2026

AptBacterialDB: A Comprehensive, Manually Curated Database of Antibacterial Aptamers

In recent years, aptamers have transitioned from mere laboratory tools to highly potent molecular recognition agents capable of overcoming the strict limitations of conventional antibiotic therapies. We have developed AptBacterialDB, a manually curated, large, comprehensive database of experimentally validated antibacterial aptamers spanning 1996 to 2026. The database contains a total of 2131 aptamers targeting approx 75 different bacterial classes, and 124 aptamer targets with 95 entries found in UTexas databases, 97 in AptaDB, and 28 in Aptabase. It contains 1555 unique aptamer sequences, 189 unique modifications, 40 different selection approaches, and 44 different affinity methods. It integrates detailed annotations of about 20 fields, including sequence information, nucleic acid type, binding affinity, modifications, experimental and functional details. The secondary structure of the aptamers was predicted using ViennaRNA Package 2.0, demonstrating that they adopt mostly stable conformations, with a structured stem region. MySQL was implemented for database development, and a knowledge graph was integrated using ArcadeDB/openCypher for graphical visualization of aptamer-target-organisation relationships. Facilities such as different search modes, browsing, similarity search, REST API access, and entries linked to the existing database for a broader view of the aptamers have been provided. AptBacterialDB (https://webs.iiitd.edu.in/raghava/aptbacterialdb/) provides a user-friendly centralized platform to accelerate antibacterial aptamer research, therapeutic development, biosensor design, and computational modelling efforts. Authors BiographyO_LINisha Bajiya is currently working as Ph.D. Student in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIIshika Gupta is currently working as an M.Tech Student in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as a Professor in the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LI

bioinformatics↗

bioRxiv2026

NANOTAXI: A Shiny-Based GUI for Real-Time Classification and Analysis of 16S rRNA Nanopore Reads

Real-time taxonomic classification of nanopore amplicon sequencing data enables rapid insights into microbial communities, with applications in clinical diagnostics, environmental monitoring, and outbreak surveillance. However, bridging the gap between long-read data and interpretable results often requires specialised bioinformatics expertise. There remains a need for integrated, user-friendly software that combines live data acquisition with downstream microbiome analysis. Here we present NANOTAXI, a fully automated Shiny-based GUI for the classification of barcoded 16S rRNA gene sequences generated by Oxford Nanopore sequencing. The platform supports four taxonomic classifiers, integrated with five reference databases, enabling flexible selection of classification strategies based on user requirements and available computational resources. In addition to real-time monitoring, NANOTAXI performs cohort-level analyses, including alpha and beta diversity, ordination, differential abundance testing, and functional inference using PICRUSt2. Validation using barcoded synthetic communities comprising pooled genomic DNA from clinically relevant bacterial species and the ZymoBIOMICS mock community demonstrated that NANOTAXI generated biologically coherent taxonomic and functional profiles. Benchmarking revealed clear trade-offs between computational performance and taxonomic specificity. Emu provided the lowest observed species-level false-positive rate, whereas Kraken2 offered the fastest classification and enabled continuous near-real-time monitoring across all tested databases. NANOTAXI is open source and freely available at https://github.com/Nirmal2310/NANOTAXI under the GPL version 3 license.

bioinformatics↗

bioRxiv2026

In silico investigation of alternative splicing of microexons in human peripheral tissues

Microexons are highly conserved fragments of exons ranging from 3 to 51 nucleotides (nts), representing a precise but poorly understood layer of post-transcriptional regulation outside the central nervous system. While their role in neuronal development is well documented, their behavior in peripheral tissues remains largely uncharacterized. In this study, we utilized VAST-TOOLS to perform a comprehensive meta-analysis of alternative microexon splicing across independent transcriptomic datasets spanning hepatic, pulmonary, renal, and colonic tissues. By comparing the diseased and wild-type (WT) profiles, we identified a robust set of differentially spliced microexons (DSMs) unique to each disease. Our findings suggest that microexon dysregulation in the liver, lung, kidney and colon may not be a primary driver of specific diseases, but rather a signature of a broader collapse in cellular splicing homeostasis. We propose that this phenomenon of differential splicing, particularly within critical hub proteins, fundamentally compromises protein interaction networks, thereby priming the cell for the diverse phenotypic failures observed across chronic disease states.

genomics↗

bioRxiv2025

Apparent failure and cryptic success of disease control via intermediate host population reduction: Density dependence in copepods has implications for Guinea worm disease transmission

All populations experience density dependence, lest they grow infinitely. However, elucidating what forms of density dependence act most strongly for a given species remains incredibly challenging. Identifying the mechanisms that regulate population density is particularly relevant to pest control, which often causes high mortality, reducing population density but enabling recovery when survivors are freed from intraspecific competition and density dependent vital rates (e.g., birth, death, and maturation rates). Understanding these demographic responses is critical for effective pest management, especially when specific life stages of pests differ in the harm they cause. Guinea worm disease (GWD) is a neglected tropical disease with an obligate copepod intermediate host, and importantly, only large-bodied copepods can be infected with GW parasites. GW disease has been the target of control for several decades, with main management strategies including the use of a chemical larvicide, Abate, which is applied to water bodies to cull copepod populations. Despite the wide application of Abate in GW endemic countries, little is known about long-term copepod population dynamics in response to Abate. Thus, we evaluated this mortality-based management of intermediate copepod hosts to control GW transmission, combining mathematical models with a population dynamics experiment designed to mimic Abate pesticide control methods across a range of intensities. Despite initial reductions in both total and stage specific population densities, copepod populations recovered so rapidly that control appeared to fail, enabled by extremely fast maturation rates in low-density populations. Much to our surprise, model simulations of GW transmission showed that although total and stage-specific densities rebounded, infectious adult copepods --the proximate cause of infections--were strongly suppressed, indicating cryptic success. This effect was enabled by the 15-day developmental period of GW larvae within copepods, which blocks the accumulation of infectious copepods between interventions. Ultimately, this study highlights the importance of understanding mechanisms of density dependence when designing and optimizing pest control interventions, as well as interpreting counterintuitive consequences of interventions.

ecology↗

bioRxiv2025

Primer Blind Spots: Underrepresentation of ESKAPE Pathogens from Developing Regions in 16S rRNA Targeted Sequencing

Targeted 16S rRNA sequencing is stalwart of microbial profiling and diagnostics, but its reliance on some "universal" primers introduces bias. In this in-silico study, we investigated how well these primers capture ESKAPE pathogens by screening multiple databases and found that a considerable fraction showed suboptimal primer binding, suggesting that amplification and identification may be compromised. Notably, these strains were disproportionately associated with isolates from developing countries, underscoring the risk of primer bias in global surveillance.

bioinformatics↗

bioRxiv2025

Unraveling Genomic Signatures of lncRNA Expression in Zebrafish Caudal Fin Regeneration: Bridging Regenerative Potential and Positional Memory.

Zebrafish (Danio rerio) is an essential model organism for tissue regeneration, but the function of long non-coding RNAs (lncRNAs) in tissue regeneration is not well understood. Identifying important regeneration-associated lncRNAs may allow for cross-species applications, improving tissue repair in other organisms. However, few studies and a lack of functional validation complicate our understanding of their mechanistic functions. This research explores novel lncRNAs associated with caudal fin regeneration and positional memory, aiming to identify those that are evolutionarily important across species and may hold universal significance. RNA-seq data deposited in the NCBI database were compared at various important time points (0h post-amputation (hpa), 12 hpa, 1 day post-amputation (dpa), two dpa, three dpa, and seven dpa) and fin parts (proximal, middle, and distal) to uncover major regulatory lncRNAs. Using HISAT2, StringTie, FEELnc, Conservation Analysis and WGCNA (Weighted Gene Co-expression Network Analysis), we found 107 lncRNAs related to regeneration time points and 229 about positional memory during regeneration. Overlapping analysis identified 13 common genomic regions that are complete or partial lncRNAs, indicating a functional connection between regeneration and positional identity and expressed differently in each time point and each position. Additionally, a comparison with regeneration-associated mRNAs revealed that these 13 regions play critical roles in both processes, providing insights into the molecular mechanisms of regenerative precision. Following validation by RT-PCR, the results further suggest that these overlapping regions are differentially expressed across distinct positions within the same tissue, despite a consistent injury pattern. This positional variation in expression may indicate potential roles in both evolutionary adaptation and the regulation of regenerative processes across multiple tissues.

genomics↗

bioRxiv2025

K-MARVEL: K-Mer based Antimicrobial Resistance Virtual Exploration Lab

The rapid global spread of antimicrobial resistance (AMR) necessitates a new generation of computational tools for its surveillance. While next-generation sequencing offers unprecedented insight into the resistome, current methods face a trade-off: assembly-based approaches are computationally expensive and struggle with complex metagenomes, whereas direct-mapping of long reads is hampered by high error rates that obscure critical resistance-conferring mutations. Here, we present K-MARVEL (K-Mer based Antimicrobial Resistance Virtual Exploration Lab), a novel, open-source method to capture ARGs and resistance-conferring mutations from short and long-read sequencing datasets. It operates in protein k-mer space, providing inherent tolerance to nucleotide-level sequencing errors. On a comprehensive benchmark of 61 long and 49 short-read diverse datasets, K-MARVEL demonstrated superior accuracy, achieving F1-scores of 0.9783 and 0.9754 for short and long-read datasets, respectively. Its implementation in Rust enables high speed through parallelization while guaranteeing memory safety. Computationally, it demonstrated superior performance to conventional assembly-based methods, achieving an average speed up of 7x on short-read datasets and 5x on long-read datasets. In terms of memory footprint, it outperformed the assembly-based approaches for short-read datasets, but its memory footprint was comparable for long-read datasets. Notably, K-MARVEL accurately reconstructs functional genes from genomically fragmented evidence, providing a more comprehensive resistome assessment. In conclusion, K-MARVEL provides a scalable, flexible and memory-efficient solution for AMR surveillance. Its unique capabilities for handling noisy long-read data and complex genomic scenarios make it a powerful tool for researchers and public health scientists. K-MARVEL is open-source and freely available at https://bitbucket.org/amr-avenger/k-marvel under the GPL version 3 license.

bioinformatics↗

bioRxiv2025

Automated Eight-Stage Classification of Drosophila melanogaster Using Transfer-Learning CNNs with Mobile Live-Inference Deployment

Drosophila melanogaster is a foundational model organism whose rapid development and genetic tractability underpin research in genetics, development, and disease. Manual staging of its embryonic, larval, and pupal phases is slow and error-prone. An automated eight-class classifier is introduced to distinguish egg, first-, second-, and third-instar larvae, as well as white, brown, eye, and black pupae, from stereo-microscope images. By fine-tuning ImageNet-pretrained CNNs (ResNet-50, InceptionV3, ConvNeXtSmall) on a balanced dataset ([~]300 images per class), the best-performing model (ResNet-50) achieves 85% accuracy (F1 = 0.85) on a held-out validation set, significantly outperformed alternatives. Primary misclassifications align with subtle morphological transitions between adjacent stages. To facilitate broad adoption, the ResNet-50 model has been deployed in a lightweight Streamlit app offering live-camera inference ({approx}8 FPS on mobile). All code, pretrained weights, and data are publicly available, enabling scalable, high-throughput Drosophila staging for diverse experimental workflows.

bioinformatics↗

bioRxiv2025

QSProteome: A Community-Driven Interactive Platform for Large-Scale Exploration and Evaluation of Predicted Protein Complex Structures.

QSProteome (https://QSProteome.org) is a community-scale platform for modeling, evaluating, and refining quaternary protein structure. The resource hosts 35,528 unique modeled assemblies spanning over 42,000 genes, covering nearly all curated complexes in BioCyc and ComplexPortal databases. Each model is displayed on an interactive page with 3D visualization, chain-level confidence metrics, structural alignments, functional annotations, and automated stoichiometry checks against database expectations. A cloud-based server supports continuous user uploads and automated processing pipelines--enabling submission and validation of >54,000 models within 14 weeks. To promote iterative refinement, QSProteome includes a gamified re-curation workflow that transitions users from training modules into live curation, enabling the community-led assessment of 1,547 ABC transporter complexes. Together, these components form a dynamic, scalable infrastructure for proteome-scale structural biology. By unifying modeling, validation, and annotation in a reusable, searchable, and community-extensible framework, QSProteome enables proteome-scale structure accessibility and reuse--powering discovery, annotation, and collaborative refinement across the structural biology community.

bioinformatics↗

bioRxiv2025

SpliceCoord: A computational toolkit to identify coordinated exon splicing events from long-read transcriptome sequencing data

As long-read RNA sequencing technologies emerge and improve, they offer a greater depth and precision of the transcriptome of organisms, tissues, and cells, enabling deeper exploration of alternative splicing patterns. It has been observed that specific pairs of exons appear together in a higher or lower frequency than is expected by chance. Such a mutual inclusion or exclusion may reflect underlying structural or functional constraints; however, a systemic method for this analysis is lacking. Here, we develop an R-based computational toolkit, called SpliceCoord, to identify coordinated splicing of exons using a transcript-wise count matrix and a GTF file that maps the expressed transcripts and their exons to the genome.

bioinformatics↗

bioRxiv2025

Polystyrene and polyethylene terephthalate nanoplastics differentially impact mouse ovarian follicle function

Exposure to micro- and nanoplastics is unavoidable. Foods and beverages contain plastic particles from environmental contamination and processing and packaging materials, which are frequently made of polyethylene terephthalate (PET). Micro- and nanoplastics have been detected in human tissues such as the brain, liver, and placenta, as well as in ovarian follicular fluid, but little is known about the effects nanoplastics have on the female reproductive system. In addition, few studies on the health impacts of nanoplastics have been performed using environmentally relevant plastic types and concentrations. Thus, this research tested the hypothesis that nanoplastics made of spherical polystyrene (PS), a common model nanoplastic, would have different effects on cultured mouse ovarian follicles compared to secondary PET nanoplastics at environmentally relevant doses. The ovary is a highly sensitive reproductive organ responsible for the development of follicles, which contain the oocyte, and production of steroid hormones. Follicles were harvested from adult mouse ovaries and cultured for 96 h with vehicle, spherical commercially available 200 nm PS nanoplastics (1-100 {micro}g/mL), or lab-generated 240 nm PET nanoplastics (0.1-10 {micro}g/mL). PS and PET nanoplastic exposure inhibited follicle growth and altered expression of genes related to steroid synthesis, cell cycle, and oxidative stress. PET nanoplastics increased levels of pregnenolone and decreased expression of Cyp17a1. Overall, both plastic types altered ovarian function, but they impacted different genes in similar pathways. These findings suggest that nanoplastic exposure at environmentally relevant concentrations may pose a risk to female reproductive health by disrupting hormonal and molecular pathways. In addition, environmentally relevant plastic types and doses are necessary for studying health impacts of nanoplastics. HighlightsO_LINanoplastics disrupted ovarian function in vitro C_LIO_LISecondary PET and virgin PS differently inhibited follicle growth and ovarian gene expression C_LIO_LIPET exposure increased pregnenolone, impacting steroid hormone pathways C_LIO_LIEnvironmentally relevant plastics can harm female reproductive health C_LI

pharmacology and toxicology↗

bioRxiv2025

ANOMALY: A Snakemake pipeline for identifying NuMTs from Long-Read Sequencing Data

MotivationNuclear mitochondrial DNA segments (NuMT) can significantly affect cellular processes, including cancer development and disease progression. Current methods to call NuMTs rely on short-read sequencing data but struggle to resolve complex NuMTs. These limitations can be overcome by employing long-read sequencing data. However, no such workflow exists to capture NuMTs from long-read sequencing data. ResultsHere, we introduce ANOMALY, a novel, easy-to-use workflow for detecting NuMTs from long-read sequencing data. The pipeline takes raw sequencing data or aligned data and calls and visualizes sample NuMTs. On 50 simulated datasets, the pipeline demonstrated high accuracy, with a precision of 1.000, a recall of 0.989, and an F1-score of 0.994. The pipeline underscores the limitations of short-read data in resolving and capturing complex NuMTs while demonstrating that long-read data enables their accurate identification. Availability and ImplementationThe Snakemake pipeline employs Python, Bash and R and is published under an open-source GNU GPL v3 license. Detailed information about setting up and running the pipeline and the source code can be accessed at https://github.com/Nirmal2310/ANOMALY.

bioinformatics↗

bioRxiv2025

Deep Learning-Driven Gender Classification of Drosophila melanogaster: Advancing Real-Time Analysis with Mobile Integration

Researchers extensively use Drosophila melanogaster as a model organism in genetic research and developmental studies to better understand human diseases such as cancer and diabetes. For such investigations, it is necessary to identify and classify male and female flies. This study uses deep learning techniques for gender classification of Drosophila melanogaster via models YOLOv8, Detectron2, ResNet-50, InceptionV3, and MobileNet. Benchmarking revealed that YOLOv8 achieved a maximum accuracy of 98.0%. The classification and object detection models are integrated into a mobile application, facilitating real-time gender classification. This study demonstrates the possibility of integrating deep learning models with mobile technology while ensuring an efficient way for gender prediction, leading to better reproducibility and generating new opportunities in biological research.

scientific communication and education↗

bioRxiv2025

AmpC β-lactamases: A Key to Antibiotic Resistance in ESKAPE Pathogens

AmpC {beta}-lactamases (blaAmpC) are one of the important drivers for a high incidence of antimicrobial resistance (AMR) within ESKAPE pathogens, which are a group of bacteria that are the leading cause of hospital-acquired infections. Investigating the presence and characteristics of the blaAmpC is essential for understanding the molecular mechanism of resistance and developing effective strategies to combat AMR. We have investigated the presence of blaAmpC, distribution on plasmids/chromosomes, copy number, amino acid identities/variabilities, and evolutionary characteristics in ESKAPE pathogens. Our analysis indicates 1790 AmpC enzymes in 4713 completely assembled genomes, subdivided into nine different enzyme groups. AmpC is more common in A. baumannii, followed by P. aeruginosa, K. pneumoniae, and Enterobacter spp., but absent in E. faecium and S. aureus. Among the nine AmpC enzyme groups, ACC, ACT, CMH, and MIR are exclusively found in Enterobacter spp., while the largest enzyme group ADC is only present in A. baumannii; PDC and PIB are found only in P. aeruginosa. Phylogenetic analysis revealed significant divergence amongst few enzyme groups and closer evolutionary relationships within others. Functional motif analysis identified conserved catalytic residues across all enzyme groups, except PIB, which demonstrates structural and functional divergence. Because of these variations, PIBs ability to bind cephalosporins decreases while enhancing its activity against carbapenems, hence, we have excluded PIB from further analysis. This is the first detailed report on AmpC in ESKAPE pathogens, which also re-emphasizes the wise use of antibiotics to reduce the rise of AmpC cephalosporins resistance, a significant public health concern. HighlightsO_LIFirst detailed report on ESKAPE blaAmpC localization in chromosomes and plasmids C_LIO_LIblaAmpC is absent in E. faecium & S. aureus but prevalent in other four pathogens C_LIO_LIMost blaAmpC belong to ADC, exclusively present in A. baumannii chromosomes C_LIO_LIInterspecies gene exchange in K. pneumoniae, P. aeruginosa & Enterobacter spp. C_LIO_LIEvolutionary analysis shows recent divergence & possible horizontal gene transfer C_LI

bioinformatics↗

bioRxiv2024

The conserved landscape of RNA modifications and transcript diversity across mammalian evolution

Gene expression programs underpin the development of shared phenotypes, yet the importance of transcript complexity in shaping mammalian evolution remains unclear. Here we present a comprehensive long-read direct RNA sequencing atlas of full-length transcripts and their m6A modifications across six tissues (hippocampus, frontal cortex, cerebellum, testes, skeletal muscle, and liver) in five mammals (human, mouse, rat, dog, and cow) and a non-mammalian out-group (chicken). Our analysis reveals that 29% of genes have multiple mammalian-conserved alternative transcripts, with 31% of these genes showing tissue-specific switching of the major transcript isoforms. We uncover extensive conservation of coordinated splicing events, primarily driven by tissue-specific mutually associated exon splicing, particularly in neural tissues and cytoskeletal genes. At the epitranscriptome level, we find that 14.2% of m6A RNA modifications are conserved across mammals, with 39% of analysed genes containing a conserved m6A RNA modification. Our work provides unprecedented insight into the evolution of transcript complexity and the epitranscriptome, highlighting their potential roles in shaping mammalian phenotypic diversity and providing a valuable resource for understanding post-transcriptional regulation across mammalian evolutionary time.

genomics↗

bioRxiv2024

In Vivo Optical Clearing of Mammalian Brain

Established methods for imaging the living mammalian brain have, to date, taken the brains optical properties as fixed; we here demonstrate that it is possible to modify the optical properties of the brain itself to significantly enhance at-depth imaging while preserving native physiology. Using a small amount of any of several biocompatible materials to raise the refractive index of solutions superfusing the brain prior to imaging, we could increase several-fold the signals from the deepest cells normally visible and, under both one-photon and two-photon imaging, visualize cells previously too dim to see. The enhancement was observed for both anatomical and functional fluorescent reporters across a broad range of emission wavelengths. Importantly, visual tuning properties of cortical neurons in awake mice, and electrophysiological properties of neurons assessed ex vivo, were not altered by this procedure.

bioengineering↗

bioRxiv2024

Unraveling lncRNA Diversity at a Single Cell Resolution and in a Spatial Context across Different Cancer Types

Long non-coding RNAs (lncRNAs) play pivotal roles in gene regulation and disease, including cancer. Overcoming the limitations of lncRNA analysis with bulk data, we analyzed single-cell and spatial transcriptomics data to uncover 354937 novel lncRNAs and their functions across 13 cancer types. LncRNA functions were assessed by identifying their cell-type specificity and distinct spatial distributions across different tissue regions. First, lncRNAs were computationally validated by comparing to existing databases, and experimentally validated using spatial long read sequencing methods. Further, genome-wide computation of spatial-autocorrelation identified coexpression of lncRNAs with cancer-associated protein coding genes across the tissue. Additionally, genomic co-localization of lncRNAs with regulatory features and disease-associated genetic variants suggest possible functional association. The identified lncRNAs were analyzed for responses to immunotherapy and prognostic value, revealing cancer-outcome associated lncRNAs. We have made this novel resource available as an open website SPanC-Lnc hosted on AWS cloud to serve as a pan-cancer atlas of single cell- and spatially-resolved lncRNAs. These can complement established biomarkers because they reflect the unique characteristics of specific cell populations within tumors, offering new insights into disease progression and treatment response.

cancer biology↗