Biology subjects

Garai, S.

Publications and source records attributed to Garai, S..

8 recordsLinked to original sources

bioRxiv2026

AlphaGenome informed machine learning for pathogenicity prediction of germline mutations

Accurate interpretation of germline variants remains a central challenge in clinical genomics. Existing in silico pathogenicity predictors often provide incomplete coverage or restricted to missense variants from the coding region, leaving the variants from splice sites, -UTR, and intronic regions under-scored. We present AGVarPred, a gradient-boosted classifier designed to predict the pathogenicity of germline mutants, with a broad consequence-class coverage across SNVs and indels. Here, the population allele frequency from gnomAD, functional consequence annotations from Ensembl VEP, and cell-type-specific functional genomic scores from the AlphaGenome platform were integrated to develop the classifier. Using a gene-level split of 134,002 high-confidence ClinVar variants across 9,222 genes, AGVarPred achieved an AUC-ROC of 0.9753 (95% CI 0.9731-0.9775) and a AUC-PR of 0.9606 on 12,385 scored internal test variants. A paired DeLong analysis showed that the full model significantly outperformed an allele-frequency-plus-VEP model ({Delta}AUC = 0.1539, 95% CI 0.1489-0.1589; p < 1 x 10-300), supporting a substantial incremental contribution from AlphaGenome-derived features. Performance remained strong when restricted to SNVs (AUC-ROC 0.9558) and was also high for indels (AUC-ROC 0.9532). On five external benchmarks, AUC-ROCs ranged from 0.528 on a functional-assay-derived benchmark to 0.977 on a clinically curated deafness database, reflecting its dependence on benchmark composition and label source. A separate analysis of the VIP Database, whose pathogenic labels are ClinVar-derived, showed consistent performance (AUC-ROC of 0.892) in held-out genes. AGVarPred scored 100% of evaluated variants in the external benchmarks, whereas AlphaMissense and REVEL covered only small fractions of non-missense variants. Model interpretation indicated that gnomAD allele frequency was the dominant individual predictor; the 111 AlphaGenome features provided complementary functional signal, with splicing and chromatin assays contributing to the largest share of the AlphaGenome SHAP mass. A web-based interface for AGVarPred is available at https://pankaj357.github.io/AGVarPred/ to facilitate the submission of normalized GRCh38 germline variants and retrieval of pathogenicity predictions using the frozen AGVarPred model.

bioinformatics↗

bioRxiv2024

Quantifying Contributions from TopologicalCycles in the Brain Network towards Cognition

This study proposes a novel metric called Homological Vertex Importance Profile (H-VIP), utilizing topological data analysis tool persistent homology, to analyze human brain structural and functional connectomes. Persistent homology is a useful tool for identifying topological features such as cycles and cavities within a network. The salience of persistent homology lies in the fact that it offers a global view of the network as a whole. However, it falls short in precisely determining the relative relevance of the vertices of the network that contribute to these topological features. Our aim is to quantify the contribution of each individual vertex in the formation of homological cycles and provide insight into local connectivity. Our proposed H-VIP metric captures, quantifies, and compresses connectivity information from vertices even at multiple degrees of separation and projects back onto each vertex. Using this metric, we analyze two independent datasets: structural connectomes from the Human Connectome Project and functional connectomes from the Alzheimers Disease Neuroimaging Initiative. Our findings indicate a positive correlation between various cognitive measures and H-VIP, in both anatomical and functional brain networks. Our study also demonstrates that the connectivity in the frontal lobe has a higher correlation with cognitive performance compared to the whole brain network. Furthermore, the H-VIP provides us with a metric to easily locate, quantify, and visualize potentially impaired connectivity for each subject and may have applications in the context of personalized medicine for neurological diseases and disorders.

bioinformatics↗

bioRxiv2024

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

immunology↗

bioRxiv2024

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The datas innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

bioinformatics↗

bioRxiv2023

Homological landscape of human brain functional sub-circuits

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task (H0), ii) Motor task (H1), and iii) Working memory task (H2). At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject-domain which sheds light to subsequent investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.

neuroscience↗

bioRxiv2023

Integrated Molecular-Phenotypic Profiling Reveals Metabolic Control of Morphological Variation in Stembryos

Mammalian stem-cell-based models of embryo development (stembryos) hold great promise in basic and applied research. However, considerable phenotypic variation despite identical culture conditions limits their potential. The biological processes underlying this seemingly stochastic variation are poorly understood. Here, we investigate the roots of this phenotypic variation by intersecting transcriptomic states and morphological history of individual stembryos across stages modeling post-implantation and early organogenesis. Through machine learning and integration of time-resolved single-cell RNA-sequencing with imaging-based quantitative phenotypic profiling, we identify early features predictive of the phenotypic end-state. Leveraging this predictive power revealed that early imbalance of oxidative phosphorylation and glycolysis results in aberrant morphology and a neural lineage bias that can be corrected by metabolic interventions. Collectively, our work establishes divergent metabolic states as drivers of phenotypic variation, and offers a broadly applicable framework to chart and predict phenotypic variation in organoid systems. The strategy can be leveraged to identify and control underlying biological processes, ultimately increasing the reproducibility of in vitro systems. HighlightsO_LITime-resolved single-cell RNA-sequencing and imaging-based quantitative charting of hundreds of individual stembryos generates molecular and phenotypic fingerprints C_LIO_LIMachine learning and integration of molecular and phenotypic fingerprints identifies features and biological processes predictive of phenotypic end-state C_LIO_LIEarly imbalance of oxidative phosphorylation and glycolysis results in aberrant morphology and cellular composition C_LIO_LIMetabolic interventions tune stembryo end-state and can correct derailment of differentiation outcomes C_LI

developmental biology↗

bioRxiv2022

Identification of differentially expressed novel tau associated proteins in transgenic AD model of Drosophila - a proteomic and in silico Approach

Alzheimers disease (AD) is a memory related neurodegenerative disorder mainly associated with older adults. In this study, transgenic Drosophila AD model has been employed to investigate the tau associated proteome. Tau expression was specifically induced in the eye tissues and diseased fly heads were considered for proteomic studies with appropriate controls. We have identified 6 novel proteins from tau induced AD group by 2D and PD Quest analyses and further characterized them by in-vivo and in-silico approaches. The novel Tau interactors, [Obp44a Isoform A, Pglym Isoform A, IP15846p (Adh variant), RE45450p (mRpL2), Retinin, and Glob1 Isoform B], identified by MALDI-TOF/MS were validated through q-RT-PCR. The altered metabolic, behavioral and mitochondrial dynamics associated with Tau over expressing AD flies could be due to the altered expression of Odorant binding protein 44a (Obp44a) and mitochondrial ribosomal protein L2 (mRpL2), respectively. Further, we have showed for the first time the newly identified protein interaction with tau and other regulatory proteins through protein-protein docking, biocomputational classification and evolutionary relationship using in silico studies. Moreover, the study highlights the plausible role of these novel proteins in pathophysiology of phospho-tau induced AD flies and further help unraveling molecular pathways implicated in tauopathies.

neuroscience↗

bioRxiv2020

Plant Prionome maps reveal specific roles of prion-like proteins in stress and memory

Prions can be considered as molecular memory devices, generating reproducible memory of a conformational change. Prion-like proteins (PrLPs) have been demonstrated to be present in plants, but their role in plant stress and memory remains largely unexplored. In this work, we report the widespread presence of PrLPs in plants through a comprehensive analysis of 39 genomes representing major taxonomic groups. We find diverse functional roles associated with plant prionomes. Investigation of the rice transcriptome further delineated the role of PrLPs in stress and developmental responses, leading us to explore whether and to what extent PrLPs may build stress memory. The rice prionome is significantly enriched for Transposons/Retrotransposons (Ts/RTRs), and we derived transcriptional regulatory inferences from diurnal gene expression revealing a complex regulatory network between PrLPs, transcription factors and genes known to be involved in stress priming, as well as transient and trans-generational plant memory. Overall, our data suggest that plant memory mechanisms may rely upon protein-based signals embedded in PrLPs, in addition to chromatin-based epigenetic signals and provides important insights into the anticipated role of prions in stress and memory.

plant biology↗