Horizontal Gene Transfer (HGT) events, initially thought to be rare in Mycobacterium tuberculosis, have recentlybeen shown to be involved in the acquisition of virulence operons in M. tuberculosis. We have developed a newpartitioning framework based HGT prediction algorithm, called Grid3M, and applied the same for the prediction ofHGTs in Mycobacteria. Validation and testing using simulated and real microbial genomes indicated better performanceof Grid3M as compared with other widely used HGT prediction methods. Specific analysis of the genesbelonging to dormancy/reactivation regulons across 14 mycobacterial genomes indicated that horizontal acquisition isspecifically restricted to important accessory proteins. The results also revealed Burkholderia species to be a probablesource of HGT genes belonging to these regulons. The current study provides a basis for similar analyses investigatingthe functional/evolutionary aspects of HGT genes in other pathogens. A database of Grid3M predicted HGTs incompletely sequenced genomes is available at https://metagenomics.atc.tcs.com/Grid3M/.

Recent studies have highlighted the potential of ‘translational’ microbiome research in addressing real-world challengespertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positivelyimpacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation ofvarious ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic andprotein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challengefor computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions.In this review, we first describe various aspects of microbiomes that make them an attractive tool/target fordeveloping various translational applications. The challenge of deciphering signatures from an information-rich resourcelike the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome-based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with theimportance of a robust study design and the diligence required during every stage of analysis for achieving solutions withpotential translational value.

A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improvedunderstanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has beendeveloped to decipher microbial biomarkers for various diseases, cluster different ecosystems based on microbial content,and infer functional potential of the microbiome as well as analyze its temporal behavior. Development of user-friendlyvisualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patternscorresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance inrecent times pertaining to the huge volume of the generated microbiome data. In this review, we present a brief overview ofsome useful visualization techniques that have significantly enriched microbiome data analytics.

In the October 2019 Special Issue of the Journal of Biosciences on Current Trends in Microbiome Research, in theReview article titled ‘‘Visual exploration of microbiome data’’ by Bhusan K. Kuntal and Sharmila S. Mande (DOI:10.1007/s12038-019-9933-z; Vol. 44, Article No. 119), affiliation 3 for Bhusan K. Kuntal was incorrectlymentioned as ‘‘Academy of Scientific and Innovative Research, CSIR-National Chemical Laboratory Campus,Pune 411008, India’’. The correct affiliation should read as ‘‘Academy of Scientific and Innovative Research(AcSIR), Ghaziabad 201 002, India’’.