Biology subjects

Tanoli, Z.

Publications and source records attributed to Tanoli, Z..

18 recordsLinked to original sources

bioRxiv2026

CoTRA: an integrated R/Shiny framework for transparent bulk and single-cell RNA-seq analysis

Bulk RNA-seq and single-cell RNA-seq (scRNA-seq) are widely used to investigate gene-expression changes, but downstream analysis often requires multiple statistical,visualization, and reporting tools, creating fragmented workflows that are difficult to configure and reproduce. We developed CoTRA (Comprehensive Toolbox for RNA-seq Analysis), an open-source R/Shiny package providing independent bulk and scRNA-seq workflows within a common graphical environment. CoTRA supports quality control, differential expression, annotation, enrichment, dimensionality reduction, clustering, marker detection, cell-type annotation, differential abundance, trajectory inference, pathway activity, cell-cell communication, and reporting while exposing key analytical parameters. Compared with 14 other platforms across 49 predefined criteria, CoTRA fully supported 46 and partially supported three. Under matched inputs and parameters, CoTRA reproduced direct DESeq2, edgeR, and Seurat implementations, including identical significant bulk gene sets and scRNA-seq clustering (ARI = 1.000; NMI = 1.000). Retinal case studies recapitulated degeneration-associated transcriptional changes and demonstrated cell-type-resolved analysis. Synthetic scRNA-seq benchmarking scaled to 50,000 cells with 5.10 GB peak memory. CoTRA v1.0.0 requires R [≥] 4.4.0, has been tested on Linux, Windows, and macOS, is GPL-3 licensed, and is available at https://github.com/UmairSeemab/CoTRA, and support is provided through GitHub Issues.

bioinformatics↗

bioRxiv2026

Functional genomics guided multi-omics framework identifies aldehyde metabolism as a therapeutic vulnerability in Fanconi anaemia

Understanding the molecular vulnerabilities associated with Fanconi anemia (FA) is essential for identifying therapeutic opportunities and elucidating the mechanisms underlying disease progression and cancer predisposition. However, progress in this area remains constrained by limited availability of representative FA cellular models. To address this challenge, we defined an FA-like cellular state by identifying cancer cell lines exhibiting high-dependency on core FA pathway genes, and integrated CRISPR-Cas9 gene essentiality data at multiple molecular layers, including mutation, copy number alterations, mRNA expression, and independent patient-derived transcriptomic datasets. Functional enrichment analyses highlighted biological pathways previously implicated in FA pathogenesis, most notably aldehyde detoxification, cholesterol/fatty acid metabolism, and androgen signaling. Analysis of LINCS-L1000 perturbational transcriptomics resource identified compounds, capable of reversing the FA-associated transcriptional signature, further supporting the pharmacological tractability of the identified molecular vulnerabilities. In addition, drug-target affinity analysis prioritized aldehyde-metabolizing enzymes, including ALDH1A1 and ALDH2, as potentially druggable candidates. Notably, disulfiram demonstrated predicted high-affinity interactions with multiple proteins involved in aldehyde and lipid metabolism, including ALDH1A1, ALDH2, and MGLL, supporting its potential for further investigation in FA-related settings. Although additional validations are required, the identified vulnerabilities and candidate targets provide a foundation for future mechanistic and therapeutic investigations in FA and FA-associated malignancies.

cancer biology↗

bioRxiv2026

PrimeKG-Plus: a literature-derived expansion of a multimodal precision medicine knowledge graph

BackgroundDisease-centered knowledge graphs (KGs) support drug repurposing and precision medicine research, yet many remain static after release while primary databases and literature continue to expand. PrimeKG (Precision Medicine Knowledge Graph) is a widely used multimodal KG providing a holistic view of diseases. However, its public release reflects a June 2021 data cutoff, omitting several years of subsequent data growth. This lag is especially consequential for rare diseases, where mechanistic and therapeutic evidence often remains scattered across publications rather than structured resources. FindingWe present PrimeKG-Plus, a refreshed, rare-disease-enriched release of PrimeKG, rebuilt from all twenty original data resources updated to their December 2025 releases and three additional resources: OpenTargets, RepurposeDrugs, and nSIDES. Beyond synchronizing biomedical databases, PrimeKG-Plus captures approximately five years of previously unavailable rare-disease knowledge from the biomedical literature, curated from 637 PubMed abstracts and PubMed Central full-text articles using a language-model-assisted workflow focused on four rare neurological disorders: Canavan disease, Niemann-Pick disease type C, Tay-Sachs disease, and Batten disease. Extracted relations were refined through entity normalization, UMLS synonym mapping, embedding-based similarity ranking, and human expert review. Network topology analysis showed improved indirect drug-disease connectivity across three to six hops and added 447,288 drug-protein-disease paths linking previously unreachable drug-disease pairs. Temporal validation using drug approval records identified 55 molecular entities approved after the original PrimeKG June 2021 cutoff, 46 of which were absent from the original graph. ConclusionPrimeKG-Plus restores the temporal relevance of PrimeKG, providing an updated resource for drug repurposing, rare-disease research, and downstream machine-learning applications.

bioinformatics↗

bioRxiv2025

Off-target mapping enhances selectivity of machine learning-predicted CK2 inhibitors

A key challenge in drug development is identification of druggable targets, the modulation of which attenuates disease progression, while avoiding inhibition of proteins that lead to dose-limiting toxicities. Here, we investigate a drug target casein kinase 2 (CK2) - a serine/threonine kinase implicated in cancer, for which existing inhibitors have so far failed in clinical trials. Using molecular and pharmacoepidemiology approaches, we show that small molecules targeting cyclin-dependent kinase (CDK) family members CDK1/2/7/9, including the existing CK2 inhibitors, have a higher risk to induce adverse effects or fail in clinical trials. Based on this finding, we establish a machine learning (ML) assisted discovery pipeline to redesign more specific and allosteric lead compounds against CK2, with a more selective on-target binding and favourable off-target profile. Importantly, we show that such selective design is possible when standard molecular docking and ML algorithms are combined with an error prediction model. In conclusion, our study reports a simple yet efficient ML-powered drug discovery pipeline and novel submicromolar inhibitors targeting clinically relevant CK2 kinase with no clinically approved antagonists available. Our prediction pipeline was able to achieve a 90% hit-rate, significantly reducing the need for subsequent wet-lab validation.

pharmacology and toxicology↗

bioRxiv2025

ODDB: Ocular Disease Database for integrated analysis of ocular disease gene drug relationships

Ocular diseases such as age-related macular degeneration, glaucoma, diabetic retinopathy, and inherited retinal dystrophies are leading causes of vision loss worldwide, yet existing databases often address only limited aspects of these disorders. To fill this gap, we developed the Ocular Disease Database (ODDB), a web-based resource that integrates genes, biomarkers, variants, and drugs associated to ocular diseases. Data were systematically collected through literature mining of PubMed-indexed journals, the NCBI Gene Expression Omnibus (GEO), and drug regulatory agency datasets. Multi-omics, experimental, and clinical information were harmonized using standardized integration workflows. The database is organized according to two complementary ontologies: one based on the anatomical site of pathology (cornea, retina, optic nerve) and another on gene inheritance pattern. ODDB currently covers over 170 ocular diseases, more than 1190 genes, 2400+ variants, and 386 drugs, including both approved and investigational compounds. Each record includes detailed annotations of associated genes, variants, therapeutic targets, and mechanisms of action. The platform supports interactive querying and network-based visualization of disease-gene-drug relationships. All data was internally validated for accuracy and are compliant with FAIR principles, ensuring accessibility and interoperability. ODDB (https://www.oculardiseases.fi/) provides a comprehensive and standardized reference for exploring molecular mechanisms and therapeutic opportunities in ocular diseases.

bioinformatics↗

bioRxiv2025

DLRNA-BERTa: A transformer approach for RNA-drug binding affinity prediction

RNA-based therapies are a rapidly expanding field, offering treatments for a wide range of diseases, including many rare conditions. To date, 24 RNA therapeutics have received FDA approval, with 131 more in clinical trials, underscoring RNAs growing role in modern medicine. In this context, Bidirectional Encoder Representations from Transformers (BERT) models provide a cost-effective and accurate virtual screening strategy for accelerating RNA-targeted drug discovery. These models take RNA FASTA sequences and compound SMILES strings as inputs and generate predicted binding affinities in nanomolar units. In this study, we introduce DLRNA-BERTa, a RoBERTa-based framework combining RNA-BERTa, pretrained on 9.76 million RNA sequences, with ChemBERTa-v2 for predicting small molecule-RNA interactions. The framework includes six class-specific models, aptamers, repeats, ribosomal RNAs, riboswitches, microRNAs (miRNAs), and viral RNAs, plus a general model for cases where the RNA class is unknown. Proposed DLRNA-BERTa consistently outperforms existing RNA-drug interaction prediction methods. Pearson correlation coefficients achieved are: 0.94 (aptamers), 0.95 (repeats), 0.93 (ribosomal RNAs), 0.94 (riboswitches), 0.95 (viral RNAs), 0.98 (miRNAs), and 0.92 (general model), demonstrating robust performance across RNA classes. Benchmarking against four independent datasets from the ROBIN repository further confirms generalizability. Application of DLRNA-BERTa to 3,492 approved drugs from the ChEMBL database identified 2,859 compounds with predicted affinities (pKd [≥] 6) across 294 RNA targets. As proof of concept, bleomycin is highlighted, supported by literature evidence of RNA-binding activity. A publicly accessible web application is available at https://huggingface.co/spaces/IlPakoZ/DLRNA-BERTa, in alignment with FAIR principles.

molecular biology↗

bioRxiv2025

Data-driven strategies for drug repurposing

Drug discovery is a complex, time-intensive, and costly process, often requiring more than a decade and substantial financial investment to bring a single therapeutic to market. Drug repurposing, the systematic identification of new indications for existing approved drugs, offers a cost-effective and expedited alternative to traditional pipelines, with the potential to address unmet clinical needs. In this study, we present a comparative analysis of drug-target interaction data from three extensively curated resources: ChEMBL, BindingDB, and GtoPdb, evaluating their release histories, curation methodologies, and coverage of approved and investigational compounds and targets. To facilitate therapeutic interpretation, we manually classified ChEMBL targets into 12 high-level biological families and mapped 817 clinically approved drug indications into 28 broader therapeutic groups. This structured framework enabled a systematic profiling of physicochemical properties among approved drugs across therapeutic categories. Our analyses revealed associations between physicochemical characteristics and therapeutic groups, providing practical guidance for indication-specific compound prioritization and refining the repurposing studies. We also examined cross-indication drug approvals to identify areas with high repurposing potential. Finally, we implemented a pathway-based computational pipeline to predict repositioning opportunities for FDA-approved drugs across ten major cancer types, demonstrating its adaptability to other disease contexts. Overall, this work consolidates drug-target data and computational repurposing into a data-driven framework that advances drug discovery and translational applications.

molecular biology↗

bioRxiv2025

From Library to Landscape: Integrative Annotation Workflows for Compound Libraries in Drug Repurposing

In the rapidly advancing landscape of drug discovery and repurposing, efficient access and integration of chemical and bioactivity data from public repositories has become essential. We implemented two complementary annotation pipelines (KNIME- and Python-based) designed to automate the extraction and integration of curated chemical and bioactivity data from public repositories. These pipelines are adaptable to any user-provided compound library, allowing reproducible workflows to integrate data from heterogeneous sources (e.g., ChEMBL and PubChem). As part of the REMEDi4ALL project, which aims to establish a European platform for drug repurposing, we validated our framework on a harmonized subset of the Specs repurposing collection (over 5000 compounds, available in-house). Additionally, we developed two interactive dashboards that support multilayered analyses and visualization by integrating chemical properties, bioactivity profiles, and relational data. We show how this framework streamlines the collection of harmonized data and facilitates analyses that are critical in drug repurposing efforts, while remaining versatile for broader applications in drug discovery. Both pipeline protocols are publicly available online, and the dashboards are open access.

bioinformatics↗

bioRxiv2025

Spatial artefact detection improves reproducibility of drug screening experiments

Reliable and reproducible drug screening experiments are essential for drug discovery and personalized medicine. Here, we demonstrate how systematic experimental errors negatively impact reproducibility, and that conventional quality control (QC) methods based on plate controls fail to detect these errors. To address this limitation, we developed a control-independent QC approach using normalized residual fit error (NRFE) to identify systematic errors in drug screening experiments. Comprehensive analysis of >100,000 duplicate measurements from the PRISM pharmacogenomic study revealed that the NRFE-flagged experiments show three-fold lower reproducibility between technical replicates. By integrating NRFE with existing QC methods to analyze 41,762 matched drug-cell line pairs between two datasets from the Genomics of Drug Sensitivity in Cancer project, we improved the cross-dataset correlation from 0.66 to 0.76. Available as an R package at https://github.com/IanevskiAleksandr/plateQC, plateQC provides a robust toolset for enhancing drug screening data reliability and consistency for basic research and translational applications.

bioinformatics↗

bioRxiv2024

Attention-based approach to predict drug-target interactions across seven target superfamilies

MotivationDrug-target interactions (DTIs) hold pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary, and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets. Yet, most FDA-approved drugs bind with only a small fraction of disease targets. ResultsThis study introduces an attention-based method to predict drug-target bioactivities for all human proteins across seven superfamilies. Nine different descriptor sets were meticulously examined to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P<0.001) for six out of seven superfamilies. The proposed method outperformed nine state-of-the-art deep learning and graph-based methods, and importantly, maintained relatively high performance for most target superfamilies when tested in independent sources of bioactivity data. We computationally validated 185,676 drug-target pairs from ChEMBL-V33, that were not available in model training, with a reasonable performance of Spearman correlation > 0.57 (P<0.001) for most superfamilies. This justifies the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing activities among 3,492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications. Availability and implementationThe codes and training datasets to reproduce and extend the results are available at https://github.com/AronSchulman/MMAtt-DTA.

systems biology↗

bioRxiv2023

Mining drug-target interactions from biomedical literature using chemical and gene descriptions based ensemble transformer model

Drug-target interactions (DTIs) play a pivotal role in drug discovery, as it aims to identify potential drug targets and elucidate their mechanism of action. In recent years, the application of natural language processing (NLP), particularly when combined with pre-trained language models, has gained considerable momentum in the biomedical domain, with the potential to mine vast amounts of texts to facilitate the efficient extraction of DTIs from the literature. In this article, we approach the task of DTIs as an entity-relationship extraction problem, utilizing different pretrained transformer language models, such as BERT, to extract DTIs. Our results indicate that an ensemble approach, by combining gene descriptions from the Entrez Gene database with chemical descriptions from the Comparative Toxicogenomics Database (CTD), is critical for achieving optimal performance. The proposed model achieves an F1 score of 80.6 on the hidden DrugProt test set, which is the top-ranked performance among all the submitted models in the official evaluation. Furthermore, we conduct a comparative analysis to evaluate the effectiveness of various gene textual descriptions sourced from Entrez Gene and UniProt databases to gain insights into their impact on the performance. Our findings highlight the potential of NLP-based text mining using gene and chemical descriptions to improve drug-target extraction tasks. Datasets utilized in this study are accessible at https://dtis.drugtargetcommons.org/.

bioinformatics↗

bioRxiv2023

Peptriever: A Bi-Encoder approach for large-scale protein-peptide binding search

MotivationIn peptide therapeutics, the successful interaction between a designed peptide and a specific receptor is crucial, while minimizing interactions with other receptors is equally essential. Current computational methods excel at estimating the probability of the former but estimating the latter requires excessive computational resources, making it challenging. ResultsIn this study, we propose transformers-based protein embeddings that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73. AvailabilityTraining data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. A live demonstration of the application can be found at https://peptriever.app/. Contacttomermrsn@gmail.com and zia.rehman@helsinki.fi

bioinformatics↗

bioRxiv2022

A gene essentiality signature for studying the mechanism of action of drugs

Cancer drugs often kill cells independent of their putative targets, suggesting the limitation of existing knowledge on the mechanisms of action. In this study, we explored whether the integration of loss-of-function genetic and drug sensitivity screening data can define a gene essentiality signature to better understand the drug target interactions. We showed that our gene essentiality signature can predict drug targets more accurately than chemical fingerprints and drug-perturbated gene expression signatures. We further showed how gene essentiality signature can help identify mechanisms of action of drugs de novo, including the EGFR inhibitor lapatinib, and drugs associated with DNA mismatch repair. Finally, we established gene essentiality signatures for noncancer drugs and used them to predict their anticancer targets. We have successfully validated the target predictions for multiple noncancer drugs, using cell-based drug target deconvolution by the proteome integral solubility alteration assay. Our study provides a novel signature of drugs that may facilitate the rational design of drug repurposing.

systems biology↗

bioRxiv2022

DrugRepo: A novel approach to repurpose a huge collection of compounds based on chemical and genomic features

The drug development process consumes 9-12 years and approximately one billion US dollars in terms of costs. Due to high finances and time costs required by the traditional drug discovery paradigm, repurposing the old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data types to repurpose vast collection of compounds for 674 cancer types and other diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurpose scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. More than 100 of our repurposed compounds can be matched with ongoing studies at clinical trials (https://clinicaltrials.gov/). Our analysis is supported by a web tool available at: http://drugrepo.org/.

bioinformatics↗

bioRxiv2021

Using BERT to identify drug-target interactions from whole PubMed

BackgroundDrug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and they are collected in large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of studies providing this data (~0.1 million) likely constitutes only a fraction of all studies on PubMed that contain experimental DTI data. Finding such studies and extracting the experimental information is a challenging task, and there is a pressing need for machine learning for the extraction and curation of DTIs. To this end, we developed new text mining document classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format. ResultsOur novel method identified and extracted DTIs from 2.1 million studies not previously included in public DTI databases. Using 10-fold cross-validation, we obtained ~99% accuracy for identifying studies containing drug-target pairs. The accuracy for the prediction of assay format is ~90%, which leaves room for improvement in future studies. ConclusionThe BERT model in this study is robust and the proposed pipeline can be used to identify new and previously overlooked studies containing DTIs and automatically extract the DTI data points. The tabular output facilitates validation of the extracted data and assay format information. Overall, our method provides a significant advancement in machine-assisted DTI extraction and curation. We expect it to be a useful addition to drug mechanism discovery and repurposing.

bioinformatics↗

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2020

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

bioinformatics↗

bioRxiv2020

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

A caveat of cancer cell line models is that their molecular and functional profiling is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge is how to make an integrated use of omics profiles of cancer cell lines for reliable discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics profiling studies across 12 research laboratories for 2018 cell lines. To account for relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. Extension of the approach identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient cells on RNA helicases. HighlightsO_LIA comprehensive meta-analysis of 53 multi-modal omics profiles of >2000 cancer cell lines from 12 research laboratories C_LIO_LIAn unexpected lack of consistency between TMT-labelled and non-labelled global proteomic profiles C_LIO_LIA non-parametric approach to integrate omics profiles from multiple laboratories and to identify robust molecular patterns in individual cell lines C_LIO_LIThe multi-modal data integration reveals novel drivers and potential therapeutic targets, including ECHDC1 in breast cancers and DDX27 in PTEN mutant cancers. C_LI

cancer biology↗