Biology subjects

Shankar, R.

Publications and source records attributed to Shankar, R..

At least 19 recordsLinked to original sources

bioRxiv2026

In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion

Capturing the systemic effects of drugs in vivo remains a fundamental challenge for drug repurposing strategies. Here, we present an in vivo transcriptomics-based drug repurposing framework that profiles multi-organ RNA-seq from drug-treated rodents to model systemic drug responses and match tissue-specific drug signatures against a compendium of disease signatures for indication expansion. We applied this framework to semaglutide, a GLP-1 receptor agonist originally developed for diabetes and subsequently expanded for several other indications. By harmonizing more than 900 human disease transcriptomic signatures via agentic AI and using Z-transformed reversal of gene expression scoring to quantify drug-disease relationships, our approach successfully recovered established semaglutide indications while deprioritizing Alzheimers disease, an indication for which semaglutide was not effective. Top predictions for common diseases were supported by real-world electronic health record data from the Mount Sinai Health System and several predictions for rare indications are proposed for further investigation. This framework provides a scalable strategy for indication expansion across diverse therapeutic agents, especially investigational compounds for which real-world evidence remains sparse.

systems biology↗

bioRxiv2026

Automating scientific annotations for open transcriptomic profiles via multi-stage agents

Public transcriptomic repositories contain millions of samples, yet their large-scale reuse is hindered by heterogeneous and inconsistently reported metadata. In the Gene Expression Omnibus (GEO), key biological information is often distributed across study- and sample-level records, requiring context-dependent interpretation. Here we present GEOMeta, a large language model (LLM)-based multi-stage workflow with task-specialized agents for automated GEO metadata curation. The pipeline separates metadata retrieval, task-specific information extraction, field standardization, ontology mapping and quality control. Using GEOMeta, we generated standardized annotations for approximately 600,000 human bulk RNA-seq samples. To demonstrate its utility, we benchmarked transcriptome representation models for predicting sex, age, tissue and disease from transcriptome embeddings. We further prospectively annotated newly submitted GEO studies and evaluated 22 frontier LLMs. Recent open-source Flash models achieved annotation quality comparable to leading reasoning models while reducing costs by an order of magnitude. GEOMeta provides a scalable resource and reproducible framework for metadata curation.

bioinformatics↗

bioRxiv2026

GANGE: Achieving Sequencing Without Sequencing With Diffusion Guided Generative Genomic Transformer

The genome of a species is its book of life, but opening that book remains a costly affair due to the limitations the existing sequencing technologies pose. Short reads sequencers struggle to capture long and complex genomes, though have high fidelity rate. To counter that long reads from IIIrd generation sequencers are used, which are full of indel errors. Thus, reads from both approaches are collectively used with very high coverage, making the sequencing projects unreasonably high of cost and unapproachable to majority. Here we present a first of its kind generative deep-learning system, GANGE, which not just recovers the correct sequence with high accuracy from indel prone ONT reads at manifold lesser coverage but also extends it by 4kb, achieving sequencing without sequencing, horizontally as well as vertically while maintaining >92% accuracy consistently. This all makes it possible to drastically pull down sequencing project cost. GANGE was tested across A. thaliana, O. sativa genomes and Human chromosome 1 where it delivered outstanding assembly performance. Besides this, it was also used to accurately generate 2kb upstream promoters of all the genes from 12 different species, demonstrating that one can now also take up regulomics research just using RNA data alone when genome sequence is not available. With this all, GANGE brings a democratic turning point in the area of genomics and sequencing research.

bioinformatics↗

bioRxiv2026

ProMaya: a hierarchical universal Deep Learning framework for accurate and interpretable Protein-Protein interaction identification

Protein-protein interactions (PPIs) are molecular lego which define the physical states of cells. Accurately identifying PPIs remains challenging due to the interplay of several factors ranging from electrostatic to molecular geometry, topology, and physics. Existing computational approaches capture only fragments of this orchestra, limiting their generalizability across protein families and interaction types. Here, we present ProMaya, a hierarchical multi-scale Graph-transformer framework that integrates 3D atomic geometry, electronic distribution, residue-level structure and disorder, surface mass-density signatures, and large protein language-model embeddings of interacting proteins. Highly comprehensively benchmarked across nine species and 47 GB experimentally validated data, ProMaya achieved consistently >95% average accuracy, outperforming state-of-the-art tools by >12%. As driven by its explainability, the first time introduced atomic and protein language information dramatically boosted it to an outstanding level for PPI discovery in any species, potent to even bypass costly experiments. ProMaya system is freely accessible at https://scbb.ihbt.res.in/ProMaya/

bioinformatics↗

bioRxiv2025

Deep-Learning on condition specific expression profiles reveal critical cytosines in gene regulation

Compared to other nucleotides, the cytosines stand as the most expressive one for gene regulation in plants due to its status as methylation-based epigenetic switch. Methylation of some of these cytosines may have higher impact on downstream genes, making them critical ones. To this date not much has been done to decipher the criticality of such cytosines. This is first such pioneering study in decoding the critical Cs where a large volume of bisulfite and RNA-seq data, including 232 WGBS and 260 corresponding RNA-seq datasets from A. thaliana and rice, have been utilized. Using a deep learning system, strong relationship was established between methylation states of cytosines in contextual manner with respect to the downstream genes expression levels. Using the same system, all the 2kb upstream regions for Arabidopsis and rice genes have been annotated for critical cytosines. Experimental validation demonstrated specific methylation changes at critical cytosines under heat stress, affecting gene expression. The universal method developed may be applied to annotate other plant genomes for critical cytosine identification. GC% similarity, rather than homology, explains gene regulatory behavior under methylation control. The resource, CritiCal-C portal, is publicly accessible, paving the way for revolutionary strategies in gene regulation and expression with minimal interventions.

bioinformatics↗

bioRxiv2025

Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning

Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs), which interpret cis-regulatory DNA sequences to determine the timing and location of gene expression. The diversification of TFs and their interactions with cis-regulatory elements (CREs) underpins plant adaptation to stress through the formation of gene regulatory networks (GRNs). However, deciphering condition-specific GRNs and identifying transcription factor binding motifs (TFBMs) for spatio-temporal gene expression remain major challenges in plant biology. To decipher the conditional networks governing TF-Target gene interactions, we developed CTF-BIND, a novel computational framework designed to reason about the spatio-temporal dynamics of TF activity. Leveraging over [~]23TB of multi-omics data (ChIP-seq, RNA-seq, and protein-protein interaction data), we constructed Bayesian causal networks capable of explaining TF activity across diverse conditions. These networks, validated against extensive experimental data, were then integrated into a Graph Transformer deep learning system. This system uses expression information of network components to quantitatively determine TF activity levels. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, eliminating the need for separate ChIP-seq experiments. CTF-BIND achieved a high average accuracy of [~]93% when tested against experimentally established data from various conditions. It is implemented as an interactive, open-access web server, it not only provides TF binding profiles but also facilitates downstream functional analysis. Furthermore, we developed CTF-BIND-DB, (https://hichicob.ihbt.res.in/ctfbind/) a database capturing dynamic shifts in regulatory pathways, providing information on TGs, network ontology, and binding motifs. CTF-BIND and CTF-BIND-DB represent a transformative approach for understanding and determining TF activity in plant stress responses, offering a powerful tool for crop improvement and bypassing the limitations of traditional methods and extensive experimental validation.

bioinformatics↗

bioRxiv2025

DMRU: Generative Deep-Learning to unravel condition specific cytosine methylation in plants

Methylation at cytosines in plants influence spatio-temporal gene expression by regulating chromatin structure and accessibility. Some algorithms have been developed to identify DNA methylation but none of them are capable to tell the condition specific DNA methylation, making them hardly of any use. Here, we report a first of its kind an explainable Deep Encoders-Decoders generative system, DMRU, which learns the relationship between transcritpome status and DNA methylation states at any given time. It was also found that GC similarity is more relevant to the specificity of DNA methylation patterns than homology, concurring with reports of direct involvement of GC content in providing regulatory switches for DNA accessibility. Leveraging on which DMRU could perform with same level of accuracy in cross-species universal manner. In a comprehensive testing and benchmarking study across a huge volume of experimental data covering 85 different conditions, and multiple plant species, it has consistently achieved >90% accuracy. With this all, DMRU brings a completely new chapter in methylated cytosine discovery, giving a strong alternative to costly bisulfite sequencing experiments. DMRU may prove critical turning point in plant regulatory research and its acceleration.

bioinformatics↗

bioRxiv2025

Comparative immuno-biology at clinical recognition of early multiple organ dysfunction syndrome in pediatric and adult patients using single-cell transcriptomics

Globally, sepsis remains a major health issue, with Multiple Organ Dysfunction Syndrome (MODS) being a leading cause of mortality. MODS, a severe condition often seen in intensive care units, typically results from infections or trauma and involves complex pathophysiological processes requiring various clinical interventions. Although infections are the main triggers, the mechanisms driving MODS remain unclear. To investigate the transition of sepsis to MODS, we generated a single cell RNA sequencing dataset comprising 86,839 immune cells from pediatric sepsis patients at the clinical onset of MODS patients and age-matched controls, identifying 22 distinct cell types. A cluster of S100 genes, located in the same genomic region, was highly expressed in neutrophils in MODS patients, demonstrating strong diagnostic potential across cohorts (AUC=0.94- 0.99) and potential as therapeutic targets. We found that many B and T cells showed heightened inflammation and increased apoptotic activity during early MODS. Additionally, specific transcription regulators and surface proteins associated with inflammation and S100 regulations were uniquely expressed in MODS. Pseudotime analysis revealed distinct S100 gene expression patterns between controls and MODS. Cell-cell interaction analysis highlighted dendritic cells as key mediators, enhancing communication between plasma cells and V{delta} T cells while activating inflammatory and immunosuppressive pathways. We also analyzed 116,803 immune cells from adult MODS patients, revealing stronger immune dysregulation compared to pediatric MODS, including altered S100 gene expression, and enhanced cell-cell interactions. These findings suggest that S100 genes may serve as a marker for MODS. Furthermore, insights gained from adult MODS could improve our understanding of rare pediatric MODS and contribute to the development of better therapeutics for all MODS patients.

bioinformatics↗

bioRxiv2024

PTF-Vac: Ab-initio discovery of plant transcription factors binding sites using deep co-learning encoders-decoders

Discovery of transcription factors (TFs) binding sites (TFBS) and their motifs in plants pose significant challenges due to high cross-species variability. The interaction between TFs and their binding sites is highly specific and context dependent. Most of the existing TFBS finding tools are not accurate enough to discover these binding sites in plants. They fail to capture the cross-species variability, interdependence between TF structure and its TFBS, and context specificity of binding. Since they are coupled to predefined TF specific model/matrix, they are highly vulnerable towards the volume and quality of data provided to build the motifs. All these software make a presumption that the user input would be specific to any particular TF which renders them of very limited use for practical applications like genomic annotations of newly sequenced species. Here, we report an explainable Deep Encoders-Decoders generative system, PTF-V[a]c, founded on a universal model of deep co-learning on variability in binding sites and TF structure, PTFSpot, making it completely free from the bottlenecks mentioned above. It has successfully decoupled the process of TFBS discovery from the prior step of motif finding and requirement of TF specific motif models. Due to the universal model for TF:DNA interactions as its guide, it can discover the binding motifs in total independence from data volume, species and TF specific models. In a comprehensive benchmarking study across a huge volume of experimental data, it has outperformed most advanced motif finding deep learning (DL) algorithms. With this all, PTF-V[a]c brings a completely new chapter in ab-initio TFBS discovery through generative AI. Short SummaryThe discovery of transcription factor binding sites (TFBS) in plants is challenging due to high variability across species and context-specific interactions. Traditional tools rely on predefined models and often fail in their cross-species applications. PTF-V[a]c has implemented generative a universal deep-learning model that decouples TFBS discovery from predefined motifs, enabling accurate, species-independent TFBS/motif identification while outperforming existing methods by huge leads.

bioinformatics↗

bioRxiv2023

Deep Co-learning on transcription factors and their binding sites attains impeccable universality in plants

Unlike animals, variability in transcription factors (TF) and their binding regions (TFBR) across the plants species is a major problem which most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitation has resulted into underdevelopment of plant regulatory research and rampant use of Arabidopsis like model species, generating misleading results. Here we report a revolutionary transformers based deep-learning approach, PTFSpot, which learns from TF structures and their binding regions co-variability to bring a universal TF-DNA interaction model to detect TFBR with complete freedom from TF and species specific models limitations. During a series of extensive benchmarking studies over multiple experimentally validated data, it not only outperformed the existing software by >30% lead, but also delivered consistently >90% accuracy even for those species and TF families which were never encountered during model building process. PTFSpot makes it possible now to accurately annotate TFBRs across any plant genome even in the total lack of any TF information, completely free from the bottlenecks of species and TF specific models.

bioinformatics↗

bioRxiv2023

Comprehensive evaluation of plant transcription factors binding sites discovery tools

Transcription factors (TFs) are regulatory proteins which bind to a specific DNA region known as the transcription factor binding regions (TFBRs) to regulate the rate of transcription process. The identification of TFBRs has been made possible by a number of experimental and computational techniques established during the past few years. The process of TFBR identification involves peak identification in the binding data, followed by the identification of motif characteristics. Using the same binding data attempts have been made to raise computational models to identify such binding regions which could save time and resources spent for binding experiments. These computational approaches depend a lot on what way they learn and how. These existing computational approaches are skewed heavily around human TFBRs discovery, while plants have drastically different genomic setup for regulation which these approaches have grossly ignored. Here, we provide a comprehensive study of the current state of the matters in plant specific TF discovery algorithms. While doing so, we encountered several software tools issues rendering the tools not usable to researches. We fixed them and have also provided the corrected scripts for such tools. We expect this study to serve as a guide for better understanding of software tools approaches for plant specific TFBRs discovery and the care to be taken while applying them, especially during cross-species applications. The corrected scripts of these software tools are made available at https://github.com/SCBB-LAB/Comparative-analysis-of-plant-TFBS-software.

bioinformatics↗

bioRxiv2023

Computational discovery of co-expressed antigens as dual targeting candidates for cancer therapy through bulk, single-cell, and spatial transcriptomics

MotivationBispecific antibodies (bsAbs) that bind to two distinct surface antigens on cancer cells are emerging as an appealing therapeutic strategy in cancer immunotherapy. However, considering the vast number of surface proteins, experimental identification of potential antigen pairs that are selectively expressed in cancer cells and not in normal cells is both costly and time-consuming. Recent studies have utilized large bulk RNA-seq databases to propose bispecific targets for various cancers. But, co-expressed pairs derived from bulk RNA-seq do not necessarily indicate true co-expression of both markers in the same cell. Single-cell RNA-seq (scRNA-seq) can circumvent this issue but the issues in dropouts and low-coverage of transcripts impede the large-scale characterization of co-expressed pairs. ResultsWe present a computational pipeline for bsAbs identification which combines the advantages of bulk and scRNA-seq while minimizing the issues associated with using these approaches separately. We select Hepatocellular Carcinoma (HCC) as a case study to demonstrate the utility of the approach. First, using the bulk RNA-seq samples in the OCTAD database, we identified target pairs that most distinctly differentiate tumors cases from healthy controls. Next, we confirmed our findings on the scRNA-seq database comprising 39,361 healthy cells from vital organs and 18,000 malignant cells from HCC. The top pair was GPC3[~]MUC13, where both genes are co-expressed on the surface of over 30% of malignant HCCs and have very low expression in other cells. Finally, we leveraged the emerging spatial transcriptomic to validate the co-expressed pair in situ. Availability and ImplementationA standalone R package for bsAbs identification in bulk data is available via GitHub (https://github.com/Lionir/bsAbsFinder).

bioinformatics↗

bioRxiv2023

Detection of viral infection in cell lines using ViralCellDetector

Cell lines are commonly used in research to study biology, including gene expression regulation, cancer progression, and drug responses. However, cross-contaminations with bacteria, mycoplasma, and viruses are common issues in cell line experiments. Detection of bacteria and mycoplasma infections in cell lines is relatively easy but identifying viral infections in cell lines is difficult. Currently, there are no established methods or tools available for detecting viral infections in cell lines. To address this challenge, we developed a tool called ViralCellDetector that detects viruses through mapping RNA-seq data to a library of virus genome. Using this tool, we observed that around 10% of experiments with the MCF7 cell line were likely infected with viruses. Furthermore, to facilitate the detection of samples with unknown sources of viral infection, we identified the differentially expressed genes involved in viral infection from two different cell lines and used these genes in a machine learning approach to classify infected samples based on the host response gene expression biomarkers. Our model reclassifies the infected and non-infected samples with an AUC of 0.91 and an accuracy of 0.93. Overall, our mapping- and marker-based approaches can detect viral infections in any cell line simply based on readily accessible RNA-seq data, allowing researchers to avoid the use of unintentionally infected cell lines in their studies.

bioinformatics↗

bioRxiv2022

Sentences, Words, Attention: A "Transforming" Aphorism of miRNA Discovery

Discovering pre-miRNAs is the core of miRNA discovery. Using traditional sequence/structural features many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performance has been far away from acceptable. This becomes more grave in plants where unlike animals pre-miRNAs are much more complex and difficult to identify. This is reflected by the huge gap between the available software for miRNA discovery and species specific miRNAs information for animals and plants. Here, we present miWords, an attention based genomic language processing transformer and context scoring deep-learning approach, with an optional sRNA-seq guided CNN module to accurately identify pre-miRNA regions in plant genomes. During a comprehensive bench-marking the transformer part of miWords alone significantly outperformed the compared published tools with consistent performance while breaching accuracy of 98% across a large number of experimentally validated data. Performance of miWords was also evaluated across Arabidopsis genome where also miWords, even without using its sRNA-seq reads module, outperformed those software which essentially require sRNA-seq reads to identify miRNAs. miWords was run across the Tea genome, reporting 803 pre-miRNA regions, all validated by sRNA-seq reads from multiple samples, and 10 randomly selected cases re-validated by qRT-PCR.

bioinformatics↗

bioRxiv2021

DeepPlnc: Discovering plant lncRNAs through multimodal deep learning on sequential data

We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During benchmarking it consistently outperformed all the compared tools and maintained a highly significant lead in the range of 4.6%-10.3% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.

bioinformatics↗

bioRxiv2021

RBPSpot: Learning on Appropriate Contextual Information for RBP Binding Sites Discovery

Identifying RBP binding sites and mechanistic factors determining the interactions remain a big challenge. Besides the sparse binding motifs across the RNAs, it also requires a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites while using an ultra-fast BWT/FM-indexing coupled inexact k-mer spectrum search for statistically significant seeds. The seed works as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network (DNN). Contextual features based on pentamers/dinucloetides which also capture shape and structure properties appeared critical. Contextual CG distribution pattern appeared important. The developed models also got support from MD-simulation studies and the implemented software, RBPSpot, scored consistently high for the considered performance metrics including average accuracy of [~]90% across a large number of validated datasets while maintaining consistency. It clearly outperformed some recently developed tools, including some with much complex deep-learning models, during a highly comprehensive bench-marking process involving three different data-sets and more than 50 RBPs. RBPSpot, has been made freely available, covering most of the human RBPs for which sufficient CLIP-seq data is available (131 RBPs). Besides identifying RBP binding spots across RNAs in human system, it can also be used to build new models by user provided data for any species and any RBP, making it a valuable resource in the area of regulatory system studies.

bioinformatics↗

bioRxiv2020

Various RNA-binding proteins and their conditional networks explain miRNA biogenesis and help to reveal the potential SARS-CoV-2 host miRNAome system

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

bioinformatics↗

bioRxiv2020

Reversal of Infected Host Gene Expression Identifies Repurposed Drug Candidates for COVID-19

The repurposing of existing drugs offers the potential to expedite therapeutic discovery against the current COVID-19 pandemic caused by the SARS-CoV-2 virus. We have developed an integrative approach to predict repurposed drug candidates that can reverse SARS-CoV-2-induced gene expression in host cells, and evaluate their efficacy against SARS-CoV-2 infection in vitro. We found that 13 virus-induced gene expression signatures computed from various viral preclinical models could be reversed by compounds previously identified to be effective against SARS- or MERS-CoV, as well as drug candidates recently reported to be efficacious against SARS-CoV-2. Based on the ability of candidate drugs to reverse these 13 infection signatures, as well as other clinical criteria, we identified 10 novel candidates. The four drugs bortezomib, dactolisib, alvocidib, and methotrexate inhibited SARS-CoV-2 infection-induced cytopathic effect in Vero E6 cells at < 1 {micro}M, but only methotrexate did not exhibit unfavorable cytotoxicity. Although further improvement of cytotoxicity prediction and bench testing is required, our computational approach has the potential to rapidly and rationally identify repurposed drug candidates against SARS-CoV-2. The analysis of signature genes induced by SARS-CoV-2 also revealed interesting time-dependent host response dynamics and critical pathways for therapeutic interventions (e.g. Rho GTPase activation and cytokine signaling suppression).

genomics↗