scholarly journals Bayesian multistudy factor analysis for high-throughput biological data

2021 ◽  
Vol 15 (4) ◽  
Author(s):  
Roberta De Vito ◽  
Ruggero Bellio ◽  
Lorenzo Trippa ◽  
Giovanni Parmigiani
Keyword(s):  
Factor Analysis ◽  
High Throughput ◽  
Biological Data

scholarly journals iSEE: Interactive SummarizedExperiment Explorer

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 741 ◽  
Author(s):  
Kevin Rue-Albrecht ◽  
Federico Marini ◽  
Charlotte Soneson ◽  
Aaron T.L. Lun
Keyword(s):  
High Throughput ◽  
Software Package ◽  
Biological Data ◽  
Data Exploration ◽  
Data Sets ◽  
Proteomics Data ◽  
Code Tracking ◽  
Dynamic Linking ◽  
Interactive Visualisation ◽  
Visual Interface

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.

scholarly journals Bioinformatics in otolaryngology research. Part one: concepts in DNA sequencing and gene expression analysis

2014 ◽  
Vol 128 (10) ◽  
pp. 848-858 ◽  
Author(s):  
T J Ow ◽  
K Upadhyay ◽  
T J Belbin ◽  
M B Prystowsky ◽  
H Ostrer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Storage ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Expression Analysis ◽  
Biological Data ◽  
Biological Research ◽  
Nucleotide Sequencing ◽  
New Era ◽  
Crucial Component

AbstractBackground:Advances in high-throughput molecular biology, genomics and epigenetics, coupled with exponential increases in computing power and data storage, have led to a new era in biological research and information. Bioinformatics, the discipline devoted to storing, analysing and interpreting large volumes of biological data, has become a crucial component of modern biomedical research. Research in otolaryngology has evolved along with these advances.Objectives:This review highlights several modern high-throughput research methods, and focuses on the bioinformatics principles necessary to carry out such studies. Several examples from recent literature pertinent to otolaryngology are provided. The review is divided into two parts; this first part discusses the bioinformatics approaches applied in nucleotide sequencing and gene expression analysis.Conclusion:This paper demonstrates how high-throughput nucleotide sequencing and transcriptomics are changing biology and medicine, and describes how these changes are affecting otorhinolaryngology. Sound bioinformatics approaches are required to obtain useful information from the vast new sources of data.

GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
Batch Correction ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

scholarly journals Systems Analysis of Biliary Atresia Through Integration of High-Throughput Biological Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Jun Min ◽  
Mylarappa Ningappa ◽  
Juhoon So ◽  
Donghun Shin ◽  
Rakesh Sindhi ◽  
...  
Keyword(s):  
Biliary Atresia ◽  
High Throughput ◽  
Systems Analysis ◽  
Biological Data

scholarly journals jFuzzyMachine – An Open–source Fuzzy Logic–based Regulatory Inference Engine for High–throughput Biological Data

2020 ◽  
Author(s):  
Paul Aiyetan
Keyword(s):  
Fuzzy Logic ◽  
High Throughput ◽  
Regulatory Network ◽  
Network Inference ◽  
Hct116 Cell ◽  
Inference Engine ◽  
Biological Data ◽  
Inference System ◽  
Apparent Lack ◽  
Link Type

AbstractElucidating mechanistic relationships between and among intracellular macromolecules is fundamental to understanding the molecular basis of normal and diseased processes. Here, we introduce jFuzzyMachine – a fuzzy logic-based regulatory network inference engine for high-throughput biological data. We describe its design and implementation. We demonstrate its functions on a sampled expression profile of the vorinostat-resistant HCT116 cell line. We compared jFuzzyMachine’s inferred regulatory network to that inferred by the ARACNe (an Algorithm for the Reconstruction of Gene Regulatory Networks) tool. Potentially more sensitive, jFuzzyMachine showed a slight increase in identified regulatory edges compared to ARACNe. A significant overlap was also observed in the identified edges between the two inference methods. Over 70 percent of edges identified by ARACNe were identified by jFuzzyMachine. Beyond identifying edges, jFuzzyMachine shows direction of interactions, including bidirectional interactions – specifying regulatory inputs and outputs of inferred relationships. jFuzzyMachine addresses an apparent lack of freely available community tool implementing a fuzzy logic regulatory network inference method – mitigating a limitation to applying and extending benefits of the fuzzy inference system to understanding biological data. jFuzzyMachine’s source codes and precompiled binaries are freely available at the Github repository locations:https://github.com/paiyetan/jfuzzymachine andhttps://github.com/paiyetan/jfuzzymachine/releases/tag/v1.7.21.

scholarly journals Exploratory Gene Ontology Analysis with Interactive Visualization

2018 ◽  
Author(s):  
Junjie Zhu ◽  
Qian Zhao ◽  
Eugene Katsevich ◽  
Chiara Sabatti
Keyword(s):  
Gene Ontology ◽  
High Throughput ◽  
Association Studies ◽  
Biological Data ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Ontology Structure ◽  
Comprehensive Picture ◽  
Power Studies ◽  
Gene Expression Studies

AbstractThe Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

2019 ◽  
Vol 21 (3) ◽  
pp. 815-835 ◽  
Author(s):  
Zhongjie Liang ◽  
Gennady M Verkhivker ◽  
Guang Hu
Keyword(s):  
Protein Dynamics ◽  
High Throughput ◽  
Molecular Mechanisms ◽  
Normal Mode Analysis ◽  
Allosteric Regulation ◽  
Genomic Medicine ◽  
Biological Data ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Evolutionary Analysis

Abstract Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

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