Biology subjects

Singh, S.

Publications and source records attributed to Singh, S..

40 records · Page 3Linked to original sources

bioRxiv2017

Genetic polymorphism of Cytochrome-P450-2C9 (CYP2C9) in Indian populations

Cytochrome-P450-2C9 (CYP2C9) metabolizes wide range of drugs and highly express in human liver. Various mutations of CYP2C9 (R144C, I359L etc.), associated with drug-response, are highly diverse. We aimed to investigate the genetic diversity of CYP2C9 in Indian-subcontinent, using 1278 subjects from 36 populations. High frequency of CYP2C9*3 (0-0.179) was observed, comparative to other populations, including Europeans. Subjects having CYP2C9*3/*3 requires lower dose of warfarin, comparative to CYP2C9*1/*3 or CYP2C9*1/*1. Since, Indians are practicing marriage among their caste system, we predicted and observed high frequency (0-0.05) of CYP2C9*3/*3. Out of 21 populations, living outside of Indian subcontinent, only Toscani and Southern Han-Chinese have 0.009 and 0.01 CYP2C9*3/*3, respectively, lower than Indians. We found a non-synonymous mutation (L362V), observed only in Indian-subcontinent, and have 0-0.056 allelic, 0-0.037 L/V and 00.037 V/V genotype frequency. We observed unfavorable interatomic interactions between hydroxylation sites of warfarin and reactive oxyferryl heme in mutant, comparative to wild-type CYP2C9, in molecular dynamic simulations; and predict lower kinetic activity.

evolutionary biology↗

bioRxiv2016

Systematic morphological profiling of human gene and allele function reveals Hippo-NF-κB pathway connectivity

We hypothesized that human genes and disease-associated alleles might be systematically functionally annotated using morphological profiling of cDNA constructs, via a microscopy-based Cell Painting assay. Indeed, 50% of the 220 tested genes yielded detectable morphological profiles, which grouped into biologically meaningful gene clusters consistent with known functional annotation (e.g., the RAS-RAF-MEK-ERK cascade). We used novel subpopulation-based visualization methods to interpret the morphological changes for specific clusters. This unbiased morphologic map of gene function revealed TRAF2/C-REL negative regulation of YAP 1/WWTR1-responsive pathways. We confirmed this discovery of functional connectivity between the O_SCPCAPNF-C_SCPCAP{kappa}O_SCPCAPBC_SCPCAP pathway and Hippo pathway effectors at the transcriptional level, thereby expanding knowledge of these two signaling pathways that critically regulate tumor initiation and progression. We make the images and raw data publicly available, providing an initial morphological map of major biological pathways for future study.

systems biology↗

bioRxiv2016

Automating Morphological Profiling with Generic Deep Convolutional Networks

Morphological profiling aims to create signatures of genes, chemicals and diseases from microscopy images. Current approaches use classical computer vision-based segmentation and feature extraction. Deep learning models achieve state-of-the-art performance in many computer vision tasks such as classification and segmentation. We propose to transfer activation features of generic deep convolutional networks to extract features for morphological profiling. Our approach surpasses currently used methods in terms of accuracy and processing speed. Furthermore, it enables fully automated processing of microscopy images without need for single cell identification.

bioinformatics↗

bioRxiv2016

Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks

In the human genome, distal enhancers are involved in regulating target genes through proxi-mal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Here we report a new computational method (named \"SPEID\") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.

bioinformatics↗