scholarly journals NetConfer: a web application for comparative analysis of multiple biological networks

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Sunil Nagpal ◽  
Krishanu Das Baksi ◽  
Bhusan K. Kuntal ◽  
Sharmila S. Mande
Keyword(s):  
Comparative Analysis ◽  
Biological Networks ◽  
Web Application ◽  
Biological Systems ◽  
Batch Process ◽  
Biological Data ◽  
Data Intensive ◽  
Multiple Networks ◽  
Biological Data Analysis ◽  
Multiple Network

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.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

scholarly journals Systems Biology in the Context of Big Data and Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Altaf-Ul-Amin ◽  
Farit Mochamad Afendi ◽  
Samuel Kuria Kiboi ◽  
Shigehiko Kanaya
Keyword(s):  
Big Data ◽  
Systems Biology ◽  
Biological Networks ◽  
Network Biology ◽  
Biological Data ◽  
The Other ◽  
System Level ◽  
Data Handling ◽  
Data Intensive ◽  
Cellular Components

Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

scholarly journals Correction to: NetConfer: a web application for comparative analysis of multiple biological networks

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Sunil Nagpal ◽  
Krishanu Das Baksi ◽  
Bhusan K. Kuntal ◽  
Sharmila S. Mande
Keyword(s):  
Comparative Analysis ◽  
Biological Networks ◽  
Web Application

An amendment to this paper has been published and can be accessed via the original article.

Practical R for biologists: an introduction

2021 ◽  
Keyword(s):  
Statistical Tests ◽  
Statistical Modelling ◽  
Biological Data ◽  
Early Years ◽  
Main Text ◽  
Biological Data Analysis ◽  
Base Functions ◽  
Almost All ◽  
Selection Of ◽  
R Functions

Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.

Graph Cutting in Image Processing Handling with Biological Data Analysis

2019 ◽  
pp. 203-216
Author(s):  
Mária Ždímalová ◽  
Tomáš Bohumel ◽  
Katarína Plachá-Gregorovská ◽  
Peter Weismann ◽  
Hisham El Falougy
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Biological Data ◽  
Biological Data Analysis

scholarly journals Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

2019 ◽  
Author(s):  
Alessandro Greco ◽  
Jon Sanchez Valle ◽  
Vera Pancaldi ◽  
Anaïs Baudot ◽  
Emmanuel Barillot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Biological Data Analysis ◽  
Specific Factors ◽  
Molecular Bases

AbstractMatrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology.We here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimer’s disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities.To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through a pancancer analysis. Additionally, new candidate molecular players, such as Estrogen Receptor (ER), CDH1 and HDAC, are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, suggesting the existence of heterogeneity across patients also in the context of inverse comorbidity.

scholarly journals SPDE: A Multi-functional Software for Sequence Processing and Data Extraction

2020 ◽  
Author(s):  
Dong Xu ◽  
Zhuchou Lu ◽  
Kangming Jin ◽  
Wenmin Qiu ◽  
Guirong Qiao ◽  
...  
Keyword(s):  
Data Extraction ◽  
Single Gene ◽  
Biological Data ◽  
Sequence Processing ◽  
Reverse Complement ◽  
Biological Data Analysis ◽  
Basic Functions ◽  
Functional Software ◽  
Genome Information ◽  
Ncbi Blast

AbstractEfficiently extracting information from biological big data can be a huge challenge for people (especially those who lack programming skills). We developed Sequence Processing and Data Extraction (SPDE) as an integrated tool for sequence processing and data extraction for gene family and omics analyses. Currently, SPDE has seven modules comprising 100 basic functions that range from single gene processing (e.g., translation, reverse complement, and primer design) to genome information extraction. All SPDE functions can be used without the need for programming or command lines. The SPDE interface has enough prompt information to help users run SPDE without barriers. In addition to its own functions, SPDE also incorporates the publicly available analyses tools (such as, NCBI-blast, HMMER, Primer3 and SAMtools), thereby making SPDE a comprehensive bioinformatics platform for big biological data analysis.AvailabilitySPDE was built using Python and can be run on 32-bit, 64-bit Windows and macOS systems. It is an open-source software that can be downloaded from https://github.com/simon19891216/[email protected]

scholarly journals Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

2021 ◽  
Author(s):  
Roozbeh Manshaei
Keyword(s):  
Unique Solution ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Biological Systems ◽  
Network Reconstruction ◽  
Biological Data ◽  
Tangible Interaction ◽  
Survival Prediction ◽  
New Approach ◽  
Gene Regulatory

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can inform and reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

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