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Article ; Online: Environmental insults and compensative responses: when microbiome meets cancer.

Nagpal, Sunil / Mande, Sharmila S

Discover. Oncology

2023 Volume 14, Issue 1, Page(s) 130

Abstract: Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding ... ...

Abstract	Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding the functional consequence of the interface between host-microbiome and cancer. Given microbial communities have predominantly been explored through an ecological perspective, it is important that the foundational aspects of ecological stress and the fight to 'survive and thrive' are accounted for tumor-micro(b)environment as well. Building on existing evidence and classical microbial ecology, here we attempt to characterize the ecological stresses and the compensative responses of the microorganisms inside the tumor microenvironment. What insults would microbes experience inside the cancer jungle? How would they respond to these insults? How the interplay of stress and microbial quest for survival would influence the fate of tumor? This work asks these questions and tries to describe this underdiscussed ecological interface of the tumor and its microbiota. It is hoped that a larger scientific thought on the importance of microbial competition sensing vis-à-vis tumor-microenvironment would be stimulated.
Language	English
Publishing date	2023-07-15
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2730-6011
ISSN (online)	2730-6011
DOI	10.1007/s12672-023-00745-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Tracking mutational semantics of SARS-CoV-2 genomes.

Singh, Rohan / Nagpal, Sunil / Pinna, Nishal K / Mande, Sharmila S

Scientific reports

2022 Volume 12, Issue 1, Page(s) 15704

Abstract: Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while ... ...

Abstract	Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.
MeSH term(s)	COVID-19/virology ; Genome, Viral ; Humans ; Mutation ; SARS-CoV-2/genetics ; Semantics
Language	English
Publishing date	2022-09-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-20000-5
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Article ; Online: MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Nagpal, Sunil / Singh, Rohan / Taneja, Bhupesh / Mande, Sharmila S

Journal of molecular biology

2022 Volume 434, Issue 11, Page(s) 167589

Abstract: Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis ... ...

Abstract	Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.
MeSH term(s)	Datasets as Topic ; Humans ; Internet Use ; Machine Learning ; Metagenome ; Metagenomics ; Reproducibility of Results
Language	English
Publishing date	2022-04-18
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167589
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Article: Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

Nagpal, Sunil / Pinna, Nishal Kumar / Pant, Namrata / Singh, Rohan / Srivastava, Divyanshu / Mande, Sharmila S.

Journal of molecular biology. 2022 June 08,

2022

Abstract: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life ... ...

Abstract	Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a ‘temporal-modeling approach’ to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.
Keywords	Severe acute respiratory syndrome coronavirus 2 ; artificial intelligence ; epitopes ; genome ; genomics ; genotype ; health services ; models ; molecular biology ; mutation ; prognosis ; viruses
Language	English
Dates of publication	2022-0608
Publishing place	Elsevier Ltd
Document type	Article
Note	Pre-press version
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167684
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Article: MarkerML – Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning

Nagpal, Sunil / Singh, Rohan / Taneja, Bhupesh / Mande, Sharmila S.

Journal of molecular biology. 2022 Apr. 12,

2022

Abstract	Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.
Keywords	assets ; automation ; data collection ; metagenomics ; microbiome ; model validation ; models ; molecular biology ; prediction
Language	English
Dates of publication	2022-0412
Publishing place	Elsevier Ltd
Document type	Article
Note	Pre-press version
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167589
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: NetSets.js: a JavaScript framework for compositional assessment and comparison of biological networks through Venn-integrated network diagrams.

Nagpal, Sunil / Kuntal, Bhusan K / Mande, Sharmila S

Bioinformatics (Oxford, England)

2020 Volume 37, Issue 4, Page(s) 580–582

Abstract: Motivation: Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge- ... ...

Abstract	Motivation: Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge-lists. An edge-list and the corresponding generated network are, however, composed of two elements, namely, edges (e.g. protein-protein interactions) and nodes (e.g. proteins). Researchers often use individual lists of edges and nodes to compare composition of biological networks using existing Venn diagram tools. However, specialized analysis workflows are required for comparison of nodes as well as edges. Apart from this, different tools or graph libraries are needed for visualizing any specific edges of interest (e.g. protein-protein interactions which are present across all networks or are shared between subset of networks or are exclusively present in a selected network). Further, these results are required to be exported in the form of publication worthy network diagram(s), particularly for small networks. Results: We introduce a (server independent) JavaScript framework (called NetSets.js) that integrates popular Venn and network diagrams in a single application. A free to use intuitive web application (utilizing NetSets.js), specifically designed to perform both compositional comparisons (e.g. for identifying common/exclusive edges or nodes) and interactive user defined visualizations of network (for the identified common/exclusive interactions across multiple networks) using simple edge-lists is also presented. The tool also enables connection to Cytoscape desktop application using the Netsets-Cyapp. We demonstrate the utility of our tool using real world biological networks (microbiome, gene interaction, multiplex and protein-protein interaction networks). Availabilityand implementation: http://web.rniapps.net/netsets (freely available for academic use). Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Protein Interaction Maps ; Proteins/genetics ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2020-08-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa723
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Article: Attenuation of the BTLA/HVEM Regulatory Network in the Circulation in Primary Sjögren's Syndrome.

Small, Annabelle / Cole, Suzanne / Wang, Jing J / Nagpal, Sunil / Hao, Ling-Yang / Wechalekar, Mihir D

Journal of clinical medicine

2022 Volume 11, Issue 3

Abstract: Primary Sjögren's syndrome (SjS) is an inflammatory autoimmune disorder which targets the lacrimal and salivary glands, resulting in glandular dysfunction. Currently, the immune drivers of SjS remain poorly understood and peripheral biomarkers of disease ...

Abstract	Primary Sjögren's syndrome (SjS) is an inflammatory autoimmune disorder which targets the lacrimal and salivary glands, resulting in glandular dysfunction. Currently, the immune drivers of SjS remain poorly understood and peripheral biomarkers of disease are lacking. The present study therefore sought to investigate the immune cell constituents of the SjS peripheral blood, and to assess the role of the BTLA/HVEM/CD160 co-stimulatory network by characterizing expression within the periphery. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood of
Language	English
Publishing date	2022-01-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm11030535
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Article ; Online: Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

Nagpal, Sunil / Pinna, Nishal Kumar / Pant, Namrata / Singh, Rohan / Srivastava, Divyanshu / Mande, Sharmila S

Journal of molecular biology

2022 Volume 434, Issue 15, Page(s) 167684

Abstract: Motivation: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to ... ...

Abstract	Motivation: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). Results: We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.
MeSH term(s)	COVID-19/virology ; Genome, Viral/genetics ; Genotype ; Humans ; Machine Learning ; Mutation ; SARS-CoV-2/genetics ; SARS-CoV-2/pathogenicity ; Severity of Illness Index
Language	English
Publishing date	2022-06-11
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167684
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Article ; Online: NetConfer: a web application for comparative analysis of multiple biological networks.

Nagpal, Sunil / Baksi, Krishanu Das / Kuntal, Bhusan K / Mande, Sharmila S

BMC biology

2020 Volume 18, Issue 1, Page(s) 53

Abstract: Background: Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling ... ...

Abstract	Background: Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited. Results: Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data. Conclusion: NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.
MeSH term(s)	Biology/methods ; Computer Communication Networks ; Data Analysis ; Software
Language	English
Publishing date	2020-05-19
Publishing country	England
Document type	Journal Article
ISSN	1741-7007
ISSN (online)	1741-7007
DOI	10.1186/s12915-020-00781-9
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Article ; Online: MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks.

Nagpal, Sunil / Singh, Rashmi / Yadav, Deepak / Mande, Sharmila S

Nucleic acids research

2020 Volume 48, Issue W1, Page(s) W572–W579

Abstract: Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy ... ...

Abstract	Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group 'meta-data' and 'inter-omic' functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates 'continuous and categorical' meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.
MeSH term(s)	Algorithms ; Humans ; Inflammatory Bowel Diseases/microbiology ; Metagenomics ; Microbiota ; Software
Language	English
Publishing date	2020-04-27
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkaa254
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