Biology subjects

Tomer, R.

Publications and source records attributed to Tomer, R..

24 records · Page 2Linked to original sources

bioRxiv2022

Prediction of celiac disease associated epitopes and motifs in a protein

Celiac disease (CD) is an autoimmune gastrointestinal disorder which causes immune-mediated enteropathy against gluten. The gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2 and HLA-DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. In addition, prediction of CD associated epitope/peptide is also required for developing antigen-based immunotherapy against celiac disease. In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used in this study for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated which achieve maximum AUROC 0.98. We discovered CD associated motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package "CDpred". We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/). Key PointsO_LICeliac disease is one of the prominent autoimmune diseases C_LIO_LIGluten immunogenic peptides are responsible for celiac disease C_LIO_LIMapping of celiac disease associated epitopes and motifs on a proteins C_LIO_LIIdentification of proline and glutamine rich regions C_LIO_LIA web server and software package for predicting CD associate peptides C_LI Authors BiographyO_LIRitu Tomer is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. 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 Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2022

Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

There are a number of antigens that induce autoimmune response against {beta}-cells, leading to Type 1 diabetes mellitus (T1DM). Recently several antigen-specific immunotherapies have been developed to treat T1DM. Thus identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g., immunotherapeutic). In this study, for the first time an attempt has been made to develop a method for predicting, designing and scanning of T1DM associated peptides with high precision. We analyzed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. Firstly, we developed a similarity/alignment based method using BLAST and achieved a high probability of correct hits with poor coverage. Secondly, we developed an alignment free method using machine learning techniques and got maximum AUROC 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment based methods and achieve maximum AUROC 0.95 with MCC 0.81 on independent dataset. We developed a webserver "DMPPred" and standalone server, for predicting, designing and scanning of T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/). Key PointsO_LIPrediction of peptides responsible for inducing immune system against {beta}-cells C_LIO_LICompilation and analysis of Type 1 diabetes associated HLA binders C_LIO_LIBLAST based similarity search against Type 1diabetes associated peptides C_LIO_LIAlignment free method using machine learning techniques and composition C_LIO_LIA hybrid method using alignment free and alignment based approach C_LI Authors BiographyO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIRitu Tomer is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIAnjali Dhall is currently working as Ph.D. 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 Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2021

Nfeature: A platform for computing features of nucleotide sequences

In the past few decades, public repositories on nucleotides have increased with exponential rates. This pose a major challenge to researchers to predict the structure and function of nucleotide sequences. In order to annotate function of nucleotide sequences it is important to compute features/attributes for predicting function of these sequences using machine learning techniques. In last two decades, several software/platforms have been developed to elicit a wide range of features for nucleotide sequences. In order to complement the existing methods, here we present a platform named Nfeature developed for computing wide range of features of DNA and RNA sequences. It comprises of three major modules namely Composition, Correlation, and Binary profiles. Composition module allow to compute different type of compositions that includes mono-/di-tri-nucleotide composition, reverse complement composition, pseudo composition. Correlation module allow to compute various type of correlations that includes auto-correlation, cross-correlation, pseudo-correlation. Similarly, binary profile is developed for computing binary profile based on nucleotides, mono-nucleotides, di-/tri-nucleotide properties. Nfeature also allow to compute entropy of sequences, repeats in sequences and distribution of nucleotides in sequences. In addition to compute feature in whole sequence, it also allows to compute features from part of sequence like split, start, end, and rest. In a nutshell, Nfeature amalgamates existing features as well as number of novel features like nucleotide repeat index, distance distribution, entropy, binary profile, and properties. This tool computes a total of 29217 and 14385 features for DNA and RNA sequence, respectively. In order to provide, a highly efficient and userfriendly tool, we have developed a standalone package and web-based platform (https://webs.iiitd.edu.in/raghava/nfeature).

bioinformatics↗

bioRxiv2021

Mapping morphological malformation to genetic dysfunction in blood vessel organoids with 22q11.2 Deletion Syndrome

DiGeorge Syndrome, or 22q11.2 deletion syndrome (22q11.2 DS), is a genetic disorder caused by microdeletions in chromosome 22, impairing the function of endothelial cells (EC) and/or mural cells and leading to deficits in blood vessel development such as abnormal aortic arch morphology, tortuous retinal vessels, and tetralogy of Fallot. The mechanism by which dysfunctional endothelial cells and pericytes contribute to the vasculopathy, however, remains unknown. In this study, we used human blood vessel organoids (VOs) generated from iPSC of 22q11.2 DS patients to model the vascular malformations and genetic dysfunctions. We combined high-resolution lightsheet imaging and single-cell transcriptome analysis to link the genetic profile and vascular phenotype at the single-cell level. We developed a comprehensive analytical methodology by integrating deep learning-mediated blood vessel segmentation, network graph construction, and tessellation analysis for automated morphology characterization. We report that 22q11.2DS VOs demonstrate a smaller size with increased angiogenesis/sprouting, suggesting a less stable vascular network. Overall, clinical presentations of smaller vascular diameter, less connected vasculature, and increased branch points were recapitulated in 22q11.2DS VOs. Single-cell transcriptome profiling showed heterogeneity in both 22q11.2DS and control VOs, but the former demonstrated alterations in endothelial characteristics that are organ-specific and suggest a perturbation in the vascular developmental process. Intercellular communication analysis indicated that the vascular dysfunctions in 22q11.2 deletion were due to a lower cell-cell contact and upregulated extracellular matrix organization involving collagen and fibronectin. Voronoi diagram-based tessellation analysis also indicated that the colocalization of endothelial tubes and mural cells was different between control and 22q11.2 VOs, indicating that alterations in EC and mural interactions might contribute to the deficits in vascular network formation. This study illustrates the utility of VO in revealing the pathogenesis of 22q11.2DS vasculopathy.

bioengineering↗

bioRxiv2019

Light-sheet microscopy with isotropic, sub-micron resolution and solvent-independent large-scale imaging

We present cleared tissue Axially Swept Light-Sheet Microscopy (ctASLM), which achieves sub-micron isotropic resolution, high optical sectioning capability, and large field of view imaging (870x870 m2) over a broad range of immersion media. ctASLM can image live, expanded, and both aqueous and organic chemically cleared tissue preparations and provides 2- to 5-fold better axial resolution than confocal or other reported cleared tissue light-sheet microscopes. We image millimeter-sized tissues with sub-micron 3D resolution, which enabled us to perform automated detection of cells and subcellular features such as dendritic spines.

bioengineering↗

bioRxiv2017

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

neuroscience↗