MetagenoNets derives its name from two very common needs in microbial network biology:

1) Inferring 'Microbial associations/Networks' for an environment (e.g Disease state), given its abundance profile
2) Given a comprehensive 'Meta Data', stratify all group level (e.g Healthy, Control, Infected) networks; infer correlations with 'continous meta data categories' and all inter-omic associations (given a secondary inter-omic abundance profile for same environment).

1. Lengthy workflow for abundance profile to inference, insights and visualization:
A typical workflow involves (a) Data filtration to remove spurious or irrelevant features (b) Choosing from multiple Data Normalization and transformation strategies to account for inter-sample biases, confounding factors, compositionality etc (c) Choosing amongst plurality of correlation inference methods to derive network files (correlation matrix, adjacency matrix, edgelists, gmls, jsons) (d) Use graph theory algorithms to compute network characteristics (like global network properties, local centrality measures) (e) Use a visualization library to view the network

2. Meta Data introduces additional complexity:
Availability of comprehensive Meta Data associated with metagenomic studies adds an additional layer of complexity to the problem of inferring and probing microbial association networks. For a given metagenomic environment, there can be multiple levels of meta-data groups (like Geography as an environment can have countries as groups). This gives rise to a need for individually processing networks for each of such groups. In addition, quite often, continous meta-data (like BMI, Age) are also collected, and researchers are therefore interested in probing correlations of microbial abundances with such continous data points as well.

3. Inter-omic data further increases complexity:
It is not uncommon for metagenomic studies to have one or more 'associated' inter-omic abundance profile. For example, a WGS study can not only provide the researcher with microbial abundance profile, but also the abundances of various functional units (like Enzymes, GO, COG, Genes etc). Related inter-omic studies on same samples (like Gene expression profiling) can also become a closely associated inter-omic data. The inferred functions for 16S studies are another example of inter-omic profile associated with microbial abundance profiles. Availability of such secondary datasets often lead to a requirement of finding correlations of microbial profile(s) with such inter-omic units (like functions, genes etc). The outcomes of such correlations are often visualized in the form of 'Inter-omic integrated networks' and 'Bi-partitie networks'. Needless to point out, the process of achieving the same for each meta-data category (and corresponding group) is complex and hectic.

We present MetagenoNets web-server as an effort towards easing the process of inferring and analyzing correlation driven microbial association networks. Following features of MetagenoNets are expected to be of significant value addition to the space of microbiome network analysis:
(a) Accepts all types of small to large microbial feature tables along with multi-level meta-data. Provision for secondary feature tables allows deeper insights for an integrated analysis.
(b) Offers frequently used data normalization startegies (TSS, CSS, Quartile) and transformation strategies (DeSeq2, TMM, CLR)
(c) Provision for feature reduction through prevalence and occurence based filters
(d) Comprehensive offering of major state of art correlation driven network inference methods (i) SPARCC (ii) ReBoot/ CCREPE (iii) NAMAP (modified ReBoot), alongwith bootstrapped and classical versions of approaches based on Pearson and Spearman coefficient.
(e) Intelligent categorization of Meta-data into categorical and continous data types, thereby allowing the end-user to automatically correlate continous meta-data with primary feature set through integrated and bi-partite networks
(f) Three types of network generation options (i) class or group respective individual networks (ii) meta-data and/or secondary data integrated networks (iii) bi-partite networks, thereby enabling a comprehensive use of abundance datasets as well as associated meta-data
(g) Interactive visualizations for networks and network properties
(h) Set based comparisons of categorical meta-data driven network groups through interactive Venn Diagrams and network graphs of the intersections
(j) Easy to use user inter-face and modern web-design approach for seamless experience at the front-end

MetagenoNets facilitates end-users to concomitantly infer, statistically analyse, and compare correlation driven microbial association networks, and in the process generate a plethora of intuitive self-explanatory visual outputs in an automated fashion.