Visual exploration of microbiome data
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Ó Indian Academy of Sciences ( 01234567 89().,-volV)( 01234567 89().,-volV)
Review Visual exploration of microbiome data BHUSAN K. KUNTAL1,2,3, and SHARMILA S. MANDE1* 1
Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B Hadapsar Industrial Estate, Pune 411 013, India
2
3
Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pune 411 008, India
Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory Campus, Pune 411 008, India *Corresponding author (Email, [email protected])
Enrolled as industry sponsored PhD candidate.
A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improved understanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has been developed 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-friendly visualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patterns corresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance in recent times pertaining to the huge volume of the generated microbiome data. In this reveiw, we present a brief overview of some useful visualization techniques that have significantly enriched microbiome data analytics. Keywords.
Association network; data; microbial community; microbiome; multivariate statistics software; visualization
1. Introduction Microbiome studies aim to estimate the taxonomic and functional composition of the samples under a study using either cross-sectional or temporal experiments. The study population may be segregated into groups corresponding to a particular physiological condition, ethnicity, ecological features, geographical distribution, etc. One of the aims in such experiments is to understand the microbial diversity in samples as well as decipher the differentiating biomarkers between selected groups (Parks et al. 2014). The changes in mutual associations between the microbes within each of these groups also play an important role in understanding the community structure (Kuntal et al. 2018). Various analytical methods have been developed to achieve the above-mentioned objectives (Caporaso et al. 2010; McMurdie and Holmes 2013; Roumpeka et al. 2017). Visual representations of these analytical outputs help in obtaining insights from the results in a user-friendly as well as visually appealing manner (Galloway-Pen˜a and Guindani 2018). In this review, we briefly summarize the important techniques employed in http://www.ias.ac.in/jbiosci
microbiome data visualization and discuss some web-based tools which provide microbiome researchers a one stop platform for analyzing any microbiome data without complex installations.
2. Overview of mi
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