Biology subjects

Meena, N.

Publications and source records attributed to Meena, N..

4 recordsLinked to original sources

bioRxiv2025

The mouse pangenome reveals the structural complexity of the murine protein coding landscape

We present the first mouse pangenome consisting of 17 high-quality inbred mouse strain genomes with complete annotation. This collection includes 12 widely used classical laboratory strains and 5 wild-derived strains. We have fully resolved previously incomplete genomic regions, including the major histocompatibility complex (MHC), the defensin cluster, T-cell receptor, and Ly49 complexes. Hundreds of non-reference genes identified in previous publications not found in GRCm39, like Defa1, Raet1a, and Klra20 (Ly49T), were localised in the new reference genomes. We conducted the first genome-wide scan of variable number tandem repeats (VNTRs) within the coding regions of mice, identifying over 400 genes with VNTR polymorphisms up to more than 600 repeat copies and repeat units reaching 990 nucleotides. Our strain-specific annotations enhance RNA-Seq analyses, as demonstrated in PWK/PhJ, where we observed a 5.1% improvement in read mapping and expression level differences in 2.1% of coding genes compared to using GRCm39.

genomics↗

bioRxiv2019

Translational products encoded by novel ORFs may form protein-like structures and have biological functions

Translation products encoded by non canonical or novel open reading frame (ORF) genomic regions are generally considered too small to play any significant biological role, and dismissed as inconsequential. In this study, we show that mutations mapping to novel ORFs have significantly higher pathogenicity scores than mutations in protein-coding regions. Importantly, novel ORFs can translate into protein-like structures with putative independent biological functions that can be of relevance in disease states, including cancer. We thus provide strong evidence to support the systematic study of novel ORFs to gain new insights into normal biological and disease processes.\n\nOne Sentence SummaryNon coding regions may encode protein-like products that are important to understand diseases.

genetics↗

bioRxiv2019

Proteins encoded by Novel ORFs have increased disorder but can be biochemically regulated and harbour pathogenic mutations

Recent evidence has suggested that protein or protein-like products can be encoded by previously uncharacterized Open Reading Frames (ORFs) that we define as Novel Open Reading Frames (nORFs)1,2. These nORFs are present in both coding and non coding regions of the human genome and the novel proteins that they encode have increased the number and complexity of cellular proteome from bacteria to humans. It is a conundrum whether these protein or protein-like products could play any significant functional biological role. But hopes have been raised to target them for anticancer and antimicrobial therapy3,4. To infer whether these novel proteins can perform biological functions, we used computational predictions to systematically investigate whether their amino acid sequences can form ordered or disordered structures. Our results indicated that that these novel proteins have significantly higher predicted disorder structure compared to all known proteins, yet we do not find any correlation between the pathogenicity of the mutations and whether they are present in the ordered and disordered regions of these novel proteins. This study reveals that we should investigate these novel proteins more systematically as they may be important to understand complex diseases.

genetics↗

bioRxiv2017

A Bioinformatics Pipeline for Whole Exome Sequencing: Overview of the Processing and Steps from Raw Data to Downstream Analysis

Recent advances in next generation sequencing (NGS) technologies have given an impetus to find causality for rare genetic disorders. Since 2005 and aftermath of the human genome project, efforts have been made to understand the rare variants of genetic disorders. Benchmarking the bioinformatics pipeline for whole exome sequencing (WES) has always been a challenge. In this protocol, we discuss detailed steps from quality check to analysis of the variants using a WES pipeline comparing them with reposited public NGS data and survey different techniques, algorithms and software tools used during each step. We observed that variant calling performed on exome and whole genome datasets have different metrics generated when compared to variant callers, GATK and VarScan with different parameters. Furthermore, we found that VarScan with strict parameters could recover 80-85% of high quality GATK SNPs with decreased sensitivity from NGS data. We believe our protocol in the form of pipeline can be used by researchers interested in performing WES analysis for genetic diseases and by large any clinical phenotypes.

bioinformatics↗