Biology subjects

Sethi, T.

Publications and source records attributed to Sethi, T..

3 recordsLinked to original sources

bioRxiv2021

Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning

The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases two months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.

bioinformatics↗

bioRxiv2021

Evaluating Sample Augmentation in Microarray Datasets with Generative Models: A Comparative Pipeline and Insights in Tuberculosis.

High throughput screening technologies have created a fundamental challenge for statistical and machine learning analyses, i.e., the curse of dimensionality. Gene expression data are a quintessential example, high dimensional in variables (Large P) and comparatively much smaller in samples (Small N). However, the large number of variables are not independent. This understanding is reflected in Systems Biology approaches to the transcriptome as a network of coordinated biological functioning or through principal Axes of variation underlying the gene expression. Recent advances in generative deep learning offers a new paradigm to tackle the curse of dimensionality by generating new data from the underlying latent space captured as a deep representation of the observed data. These have led to widespread applications of approaches such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), especially in domains where millions of data points exist, such as in computer vision and single cell data. Very few studies have focused on generative modeling of bulk transcriptomic data and microarrays, despite being one of the largest types of publicly available biomedical data. Here we review the potential of Generative models in recapitulating and extending biomedical knowledge from microarray data, which may thus limit the potential to yield hundreds of novel biomarkers. Here we review the potential of generative models and conduct a comparative analysis of VAE, GAN and gaussian mixture model (GMM) in a dataset focused on Tuberculosis. We further review whether previously known axes genes can be used as an effective strategy to employ domain knowledge while designing generative models as a means to further reduce biological noise and enhance signals that can be validated by standard enrichment approaches or functional experiments.

genomics↗

bioRxiv2020

lncRNA Mediated Hijacking of T-cell Hypoxia Response Pathway by Mycobacterium Tuberculosis Predicts Latent to Active Progression in Humans

Cytosolic functions of Long non-coding RNAs including mRNA translation masking and sponging are major regulators of biological pathways. Formation of T cell-bounded hypoxic granuloma is a host immune defence for containing infected Mtb-macrophages. Our study exploits the mechanistic pathway of Mtb-induced HIF1A silencing by the antisense lncRNA-HIF1A-AS2 in T cells. Computational analysis of in-vitro T-cell stimulation assays in progressors (n=119) versus non-progressor (n=221) tuberculosis patients revealed the role of lncRNA mediated disruption of hypoxia adaptation pathways in progressors. We found 291 upregulated and 227 downregulated DE lncRNAs that were correlated at mRNA level with HIF1A and HILPDA which are major players in hypoxia response. We also report novel lncRNA-AC010655 (AC010655.4 and AC010655.2) in cis with HILPDA, both of which contain binding sites for the BARX2 transcription factor, thus indicating a mechanistic role. Detailed comparison of infection with antigenic stimulation showed a non-random enrichment of lncRNAs in the cytoplasmic fraction of the cell in TB progressors. The lack of this pattern in non-progressors replicates indicates the hijacking of the lncRNA dynamics by Mtb. The in-vitro manifestation of this response in the absence of granuloma indicates pre-programmed host-pathogen interaction between T-cells and Mtb regulated through lncRNAs, thus tipping this balance towards progression or containment of Mtb. Finally, we trained multiple machine learning classifiers for reliable prediction of latent to the active progression of patients, yielding a model to guide aggressive treatment.

bioinformatics↗