Himanshu Sinha

Himanshu Sinha
Indian Institute of Technology Madras | IIT Madras · Department of Biotechnology

PhD

About

83
Publications
4,885
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1,238
Citations
Additional affiliations
June 2016 - present
Indian Institute of Technology Madras
Position
  • Professor (Associate)
August 2008 - June 2016
Tata Institute of Fundamental Research
Position
  • Professor (Full)
April 2005 - August 2008

Publications

Publications (83)
Preprint
Full-text available
Background: The prevalence of preterm birth (PTB) is high in lower and middle-income countries (LMIC) such as India. In LMIC, since a large proportion seeks antenatal care for the first time beyond 14-weeks of pregnancy, accurate estimation of gestational age (GA) using measures derived from ultrasonography scans in the second and third trimesters...
Preprint
Full-text available
The emergence of large-scale transcriptomic data provides the opportunity for identifying novel putative targets of microRNAs (miRNAs). In this study, we followed a computational pipeline to predict the candidate gene targets of the miR-34 family. This approach integrates the expressions of miR-34 with genes of heterogeneous primary cervical epithe...
Article
Full-text available
Background: Different formulae have been developed globally to estimate gestational age (GA) by ultrasonography in the first trimester of pregnancy. In this study, we develop an Indian population-specific dating formula and compare its performance with published formulae. Finally, we evaluate the implications of the choice of dating method on pret...
Preprint
Full-text available
Background Different methods and formulae have been developed for different populations for estimation of GA in the first trimester of pregnancy. In this study, we develop an Indian population-specific GA dating formula and compare its performance with the previously published formulae. Finally, we evaluate the implications of the choice of dating...
Article
Full-text available
Biological networks catalog the complex web of interactions happening between different molecules, typically proteins, within a cell. These networks are known to be highly modular, with groups of proteins associated with specific biological functions. Human diseases often arise from the dysfunction of one or more such proteins of the biological fun...
Article
Full-text available
Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organis...
Article
Full-text available
Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competiti...
Article
One of the fundamental question in biology is how the genotype regulates the phenotype. An increasing number of studies indicate that in most cases, the effect of a genetic locus on the phenotype is context-dependent, i.e. it is influenced by the genetic background and the environment in which the phenotype is measured. Still, the majority of the s...
Article
Full-text available
Integrating network theory approaches over time-resolved genome-wide gene expression data, we proposed a network-based framework, which considered intricate dynamic regulatory relationships of transcription factors and target genes, for assessing the molecular underpinnings underlying extreme phenotypic differences between two strains of the yeast,...
Article
Full-text available
Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest pheno-typically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture o...
Article
Full-text available
The ribosome is an ancient machine, performing the same function across organisms. Although functionally unitary, recent experiments suggest specialized roles for some ribosomal proteins. Our central thesis is that ribosomal proteins function in a modular fashion to decode genetic information in a context dependent manner. We show through large dat...
Data
Phenotypic variation of yeast ribosomal proteins. (A) Distribution of variance of normalized growth of all non-essential genes in yeast (4,769) in 293 different environments. The genes are on the x-axis, arranged in increasing order of the variance of normalized growth in 293 environments (y-axis) due to their deletion. The 191 genes to the right o...
Data
Gene Ontology Enrichment for various groups and clusters (XLS)
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ribosomal proteins in Clusters 1, 2, 3 and Clusters A, B, C (XLS)
Data
Distribution of signal to noise in human and mouse data from ENCODE. ENCODE data was normalized by median subtraction per array and then log transformed (see Methods). The mean and standard deviation (sd) over replicates was computed to obtain x = log10(sd/mean). The distribution of x for human (A) and mouse (B) ENCODE data and (C) human GTEx data...
Data
Networks of transcription factors that bind uniquely to ribosomal proteins in Cluster A, B and C (see Fig 2B). These network clusters were identified using the STRING database (http://string-db.org). The thickness of blue lines connecting two transcription factors indicates the strength of experimental evidence for their interaction. Gene enrichmen...
Data
Phenotype of ribosomal protein deletions in SK1 strain. Ten-fold spot dilutions series (starting with 108 cells/ml) of wild type and ribosomal protein deletion strains of SK1 background phenotyped in rich medium YPD and an oxidative stress, Cadmium chloride (CdCl2 500 μM) (PDF)
Data
Nucleotide diversity of ribosomal proteins across the SGRP strains. (A) Nucleotide diversity of coding and promoter sequences of ribosomal proteins and a control set of genes using Tukey’s multiple comparisons’ test (P < 0.05). The bars with the same letter code do not differ significantly. (B) Normalized Shannon Entropy of coding region and 5’UTRs...
Data
Pearson correlation heatmap (P < 0.05) of 110 human cell types and tissues based on rank order expression of 66 ribosomal proteins in ENCODE data. (PDF)
Data
Variable ribosomal proteins across tissues in mouse. (A) Hierarchical clustering of 18 tissues in mice based on expression rank orders of 14 variable ribosomal proteins (1,000 bootstraps). (B) Hierarchical clustering of the 14 variable ribosomal proteins based on their expression rank orders in 18 tissues in mice (1,000 bootstraps). The ribosomal p...
Data
Variance analysis of various pathways and genetic (XLS)
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Nucleotide diversity and Shannon Entropy comparisons of ribosomal proteins in Clusters A, B and C. (XLS)
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Expression ranks and classes of 66 ribosomal proteins in 110 human cell types and tissues downloaded from ENCODE (XLS)
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Yeast deletion collection phenotyped in 293 environments. (XLS)
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Double deletion genetic interactions of the 65 ribosomal proteins with 121 ribosomal and non-ribosomal proteins. (XLS)
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Strains used in this study and Primer sequences (XLS)
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Transcription Factors binding to ribosomal proteins in Clusters A, B and C downloaded from YEASTRACT (XLS)
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Expression ranks and classes of 42 ribosomal proteins in 18 mouse tissues downloaded from ENCODE (XLS)
Data
Expression ranks and classes of 79 ribosomal proteins in 54 human tissues downloaded from GTEx portal. (XLS)
Article
Full-text available
The ability of a genotype to show diverse phenotypes in different environments is called phenotypic plasticity. Phenotypic plasticity helps populations to evade extinctions in novel environments, facilitates adaptation and fuels evolution. However, most studies focus on understanding the genetic basis of phenotypic regulation in specific environmen...
Data
Normal distribution of environmental variance (VarE) phenotype. (A) Histogram showing the normal distribution of environmental variance across all environments. x-axis shows classes of variance with an interval size of VarE = 1.0 and y-axis shows the number of segregants showing a particular variance value. (B) QQ plot comparing the observed varian...
Data
LOD score distribution plots of environmental variance in Hv_subgroup1 (A) and Hv_subgroup2 (B). The dashed line represent the LOD cut off of 1.0, permutation P < 0.05. (EPS)
Data
Comparison of mean of segregants across different groups of environments. (A) Comparison of the mean values of each segregant across 24 Hv environments (x-axis) with that across Lv environments (y-axis). (B) Comparison of the mean values of each segregant across two mutually exclusive sets of 10 environments each, chosen from the 24 Lv environments...
Data
Plasticity QTL identified using environmental variance (VarE) in Hv and Lv environments. (XLSX)
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Comparison of QTL identified in each environment independently. (XLSX)
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Plasticity QTL identified using sum of slopes in 10 randomly generated orders of environment in Hv and Lv environments. (XLSX)
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Plasticity QTL identified using sum of slopes in allele specific orders of environment in Hv and Lv environments. (XLSX)
Article
Full-text available
Using network theory on an integrated time-resolved genome-wide gene expression data, we investigated the intricate dynamic regulatory relationships of transcription factors and target genes to unravel signatures that contribute to extreme phenotypic differences in yeast, Saccharomyces cerevisiae. We performed a comparative analysis of the gene exp...
Article
Full-text available
Studying the molecular consequences of rare genetic variants has the potential of identifying novel and hereto uncharacterized pathways causally contributing to phenotypic variation. Here we characterize the functional consequences of a rare coding variant of TAO3, previously reported to significantly contribute to sporulation efficiency variation...
Article
Full-text available
Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants' effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is...
Article
Full-text available
Antagonistic pleiotropy (AP), the ability of a gene to show opposing effects in different phenotypes, has been identified in various life history traits and complex disorders, indicating its fundamental role in balancing fitness over the course of evolution. It is intuitive that natural selection might maintain AP to allow organisms phenotypic flex...
Article
Reciprocal hemizygosity analysis is a genetic technique that allows phenotypic determination of allelic effects of a gene in a genetically uniform background. Expanding this single gene technique to generate a genome-wide collection is termed as reciprocal hemizygosity scanning (RHS). The RHS collection should circumvent the need for linkage mappin...
Article
Full-text available
In budding yeast, Saccharomyces cerevisiae, the phosphate signalling and response pathway, known as PHO pathway, monitors phosphate cytoplasmic levels by controlling genes involved in scavenging, uptake and utilization of phosphate. Recent attempts to understand the phosphate starvation response in other ascomycetes have suggested the existence of...
Article
Full-text available
For a unicellular, non-motile organism like Saccharomyces cerevisiae, carbon sources act both as nutrients and as signaling molecules, and consequently affect various fitness parameters including growth. It is therefore advantageous for yeast strains to adapt their growth to carbon source variation. The ability of a given genotype to manifest diffe...
Article
Full-text available
Yeast sporulation efficiency is a quantitative trait and is known to vary among experimental populations and natural isolates. Some studies have uncovered the genetic basis of this variation and have identified the role of sporulation genes (IME1, RME1) and sporulation-associated genes (FKH2, PMS1, RAS2, RSF1, SWS2), as well as non-sporulation path...
Data
List of 397 sporulation and sporulation associated genes used in this study (from refs. [3]–[5], [16]). (XLSX)
Data
List of top 69 SNPs identified by the LOD score cutoff of 2.50 and validated by the binomial analysis. (XLSX)
Data
Sporulation efficiency kinetics data for all S. cerevisiae strains in SGRP collection. (XLSX)
Data
List of all significant SNPs with genome coordinates, their LOD score (LOD >3.50 and Bonferroni corrected p-value <0.03) and corresponding synonymous, non-synonymous or regulatory SNP changes. All of these SNPs were also not associated with population structure using EMMAX-KLA at 95% confidence (from Diao and Chen [18]). (XLSX)
Article
Full-text available
The acquisition of new genes, via horizontal transfer or gene duplication/diversification, has been the dominant mechanism thus far implicated in the evolution of microbial pathogenicity. In contrast, the role of many other modes of evolution-such as changes in gene expression regulation-remains unknown. A transition to a pathogenic lifestyle has r...
Data
Transcript expression levels, confidence intervals and differential expression FDR
Data
32 PHO pathway genes (Ogawa et al, 2000; Wykoff et al, 2007) covered by our dataset (in alignment of Y and S genome and with at least 20 probes)
Data
Full-text available
Supplementary information, Supplementary table VI, Supplementary figures S1-6
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Transcription factor target sets enrichment for differentially expressed genes between S and Y
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Allelic expression ratios from sequence traces
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List of sense-antisense pairs and their expression levels
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Growth phenotypes in Arsenate containing media for 184 genotyped Y/S segregants
Data
Asymptotic standard deviation parameters per hybridization
Article
Full-text available
Recent reports have shown that most of the genome is transcribed and that transcription frequently occurs concurrently on both DNA strands. In diploid genomes, the expression level of each allele conditions the degree to which sequence polymorphisms affect the phenotype. It is thus essential to quantify expression in an allele- and strand-specific...
Article
Full-text available
Several quantitative trait loci (QTL) mapping strategies can successfully identify major-effect loci, but often have poor success detecting loci with minor effects, potentially due to the confounding effects of major loci, epistasis, and limited sample sizes. To overcome such difficulties, we used a targeted backcross mapping strategy that genetica...
Article
Full-text available
Synopsis Most of the differences in phenotype between unrelated members of a species are polygenic in nature. Because of their ubiquity and importance, these polygenic (or quantitative) traits have been intensively studied, and a variety of techniques have been proposed to identify and characterize quantitative trait genes (QTGs). Indeed, the main...
Data
Restriction Map of Plasmids pHS09/pHS10 Showing the Cloned Fragment and Restriction Enzymes Used to Obtain RHO2 3′UTR Fragments pHS09 is CEN URA3 RHO2–145 and pHS10 is CEN URA3 RHO2–288 (see Materials and Methods and Table S2). (1.5 MB PSD)
Data
Restriction Map of Plasmid pBN49 pBN49 is a pFA6-based [34] plasmid containing a novel HygMX4/T-urf13 cassette. Primers JM41 and JM42 were used to amplify the cassette. (2.0 MB PSD)
Data
Restriction Map of Plasmid pBN33 pBN33 is a pAG26-based [35] plasmid containing a novel FCY1-NatMX4 cassette. Primers JM41 and JM42 were used to amplify the cassette. (1.8 MB PSD)
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Restriction Map of Plasmids pHS07/pHS08 Showing the Cloned Fragment and Restriction Enzymes Used to Obtain MKT1 Fragments pHS07 is CEN URA3 MKT1–288 END3–288 and pHS08 is CEN URA3 MKT1–145 END3–145 (see Materials and Methods and Table S2). MKT1 coding polymorphisms are shown at 30 and 453 amino acid residues. (1.8 MB PSD)
Data
Map of Plasmid pSA41 with CEN and NatMX4 Cassette pSA41 is a pAG36-based [35] plasmid. (1.1 MB PSD)
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Map of Plasmid pBN98 pBN98 is a pXP45-based [49] plasmid with CEN and KanMX4 cassettes and a BsrG1 site engineered upstream of Kan start codon. (348 KB TIF)
Data
MKT1, END3, and RHO2 RHA Data (67 KB DOC)