Discovering the gene expression signature associated with a cellular state is one of the basic quests in majority of biological studies. For most of the clinical and cellular manifestations, these molecular differences may be exhibited across multiple layers of gene regulation like genomic variations, gene expression, protein translation and post-translational modifications. These system wide variations are dynamic in nature and their crosstalk is overwhelmingly complex, thus analyzing them separately may not be very informative. This necessitates the integrative analysis of such multiple layers of information to understand the interplay of the individual components of the biological system. Recent developments in high throughput RNA sequencing and mass spectrometric (MS) technologies to probe transcripts and proteins made these as preferred methods for understanding global gene regulation. Subsequently, improvements in "big-data" analysis techniques enable novel conclusions to be drawn from integrative transcriptomic-proteomic analysis. The unified analyses of both these data types have been rewarding for several biological objectives like improving genome annotation, predicting RNA-protein quantities, deciphering gene regulations, discovering disease markers and drug targets. There are different ways in which transcriptomics and proteomics data can be integrated; each aiming for different research objectives. Here, we review various studies, approaches and computational tools targeted for integrative analysis of these two high-throughput omics methods.

Survival in a given environment requires specific functions, so genomic variation is anticipated within in individual taxonomic groups that exhibit a large diversity in lifestyles. In this study, we sequence and assemble the genome of Oceanobacillus faecalis strain HM6, a resident of the human gut. Using the genus Oceanobacillus and the HM6 draft genome sequence, we explore the functional requirements for survival in a symbiotic arrangement within the human gut, in contrast to free living in the environment. Comparative genomics of seven available Oceanobacillus complete genomes highlight a genomically heterogeneous group. Our analysis did not find strict phylogenetic separation between free-living and host–symbiont Oceanobacillus members. By comparing functional gene content between host-associated and free-living species, we identified candidate genes that are potentially involved in symbiotic lifestyles, including phosphotransferase genes, transporters and two component response regulators. This study summarizes genomic and phylogenetic differences in the Oceanobacillus genus. Additionally, we highlight functions that may be key for survival in the human gut community.

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Personalized medicine relies on successful identification of genome-wide variations that governs inter-individual differences in phenotypes and system level outcomes. In Ayurveda, assessment of composite constitution types "Prakriti" forms the basis for risk stratification, predicting health and disease trajectories and personalized recommendations. Here, we report a novel method for identifying pleiotropic genes and variants that associate with healthy individuals of three extreme and contrasting "Prakriti" constitutions through exome sequencing and state-of-the-art computational methods. Exome Seq of three extreme Prakriti types from 108 healthy individuals 54 each from genetically homogeneous populations of North India (NI, Discovery cohort) and Western India (VADU, Replication cohort) were evaluated. Fisher's Exact Test was applied between Prakriti types in both cohorts and further permutation based p-value was used for selection of exonic variants. To investigate the effect of sample size per genetic association test, we performed power analysis. Functional impact of differentiating genes and variations were inferred using diverse resources -Toppfun, GTEx, GWAS, PheWAS, UK Biobank and mouse knockdown/knockout phenotype (MGI). We also applied supervised machine learning approach to evaluate the association of exonic variants with multisystem phenotypes of Prakriti. Our targeted investigation into exome sequencing from NI (discovery) and VADU (validation) cohorts datasets provide ~7,000 differentiating SNPs. Closer inspection further identified a subset of SNPs (2407 (NI) and 2393 (VADU)), that mapped to an overlapping set of 1181 genes. This set can robustly stratify the Prakriti groups into three distinct clusters with distinct gene ontological (GO) enrichments. Functional analysis further strengthens the potential pleiotropic effects of these differentiating genes/variants and multisystem phenotypic consequences. Replicated SNPs map to some very prominent genes like FIG4, EDNRA, ANKLE1, BCKDHA, ATP5SL, EXOCS5, IFIT5, ZNF502, PNPLA3 and IL6R. Lastly, multivariate analysis using random forest uncovered rs7244213 within urea transporter SLC14A2, that associate with an ensemble of features linked to distinct constitutions. Our results reinforce the concept of integration of Prakriti based deep phenotypes for risk stratification of healthy individuals and provides markers for early actionable interventions.