-
Bioscience Reports Aug 2017Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational... (Review)
Review
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
Topics: Animals; Computational Biology; Drug Discovery; Humans
PubMed: 28487472
DOI: 10.1042/BSR20160180 -
Experimental & Molecular Medicine Sep 2020Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently,... (Review)
Review
Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.
Topics: Animals; Biotechnology; Computational Biology; Epigenomics; Gene Expression Profiling; Genomics; Humans; Organ Specificity; Proteomics; Single-Cell Analysis; Transcriptome
PubMed: 32929225
DOI: 10.1038/s12276-020-0420-2 -
Nature Methods Jul 2012For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and...
For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
Topics: Computational Biology; History, 20th Century; History, 21st Century; Image Processing, Computer-Assisted; National Institutes of Health (U.S.); Software; United States
PubMed: 22930834
DOI: 10.1038/nmeth.2089 -
PLoS Biology Mar 2021Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of...
Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of Data Science, what we do suddenly looks different.
Topics: Computational Biology; Curriculum; Data Science; Humans; Information Dissemination; Schools; Students
PubMed: 33735179
DOI: 10.1371/journal.pbio.3001165 -
Computational and Mathematical Methods... 2014
Topics: Algorithms; Brain; Cluster Analysis; Computational Biology; Humans; Neurosciences; Software; Systems Analysis
PubMed: 24738006
DOI: 10.1155/2014/120280 -
TheScientificWorldJournal 2013
Topics: Chromosome Mapping; Computational Biology; Gene Expression Profiling; Oligonucleotide Array Sequence Analysis
PubMed: 23818827
DOI: 10.1155/2013/591976 -
Methods (San Diego, Calif.) Jul 2018The structure of RNA has been a natural subject for mathematical modeling, inviting many innovative computational frameworks. This single-stranded polynucleotide chain... (Review)
Review
The structure of RNA has been a natural subject for mathematical modeling, inviting many innovative computational frameworks. This single-stranded polynucleotide chain can fold upon itself in numerous ways to form hydrogen-bonded segments, imperfect with single-stranded loops. Illustrating these paired and non-paired interaction networks, known as RNA's secondary (2D) structure, using mathematical graph objects has been illuminating for RNA structure analysis. Building upon such seminal work from the 1970s and 1980s, graph models are now used to study not only RNA structure but also describe RNA's recurring modular units, sample the conformational space accessible to RNAs, predict RNA's three-dimensional folds, and apply the combined aspects to novel RNA design. In this article, we outline the development of the RNA-As-Graphs (or RAG) approach and highlight current applications to RNA structure prediction and design.
Topics: Algorithms; Computational Biology; Databases, Nucleic Acid; Models, Molecular; Nucleic Acid Conformation; RNA
PubMed: 29621619
DOI: 10.1016/j.ymeth.2018.03.009 -
Digestive Diseases and Sciences Mar 2020Understanding how health-promoting microbiota are established and their beneficial interactions with the host is of critical biomedical importance. The current high... (Review)
Review
Understanding how health-promoting microbiota are established and their beneficial interactions with the host is of critical biomedical importance. The current high throughput data acquisition technologies allow for integrating components of the gut ecosystem. The richness of data types and large number of measured variables involved underscores the critical importance of the appropriate choice of analytical and computational methods that can be used to model this complex ecosystem. This review outlines currently used approaches to perform analyses of data obtained as a result of interrogation of the gut-microbiota ecosystem and the challenges associated with these methodological and computational efforts. The problem of large dimensionality versus small numbers of samples is explained with discussions of clustering, dimensionality reduction, and statistical testing. Predictive modeling and data integration specific to the gut ecosystem are also discussed.
Topics: Animals; Computational Biology; Gastrointestinal Microbiome; Gastrointestinal Tract; Host Microbial Interactions; Humans; Metabolomics
PubMed: 32016821
DOI: 10.1007/s10620-020-06105-9 -
Cytometry. Part a : the Journal of the... Jun 2015
Topics: Computational Biology; Image Processing, Computer-Assisted; Systems Biology
PubMed: 26033857
DOI: 10.1002/cyto.a.22663 -
Neurology Apr 2013In the last few years, there has been an overwhelming influx of advanced genomics and brain imaging research data. The evolution of computational infrastructures needed...
In the last few years, there has been an overwhelming influx of advanced genomics and brain imaging research data. The evolution of computational infrastructures needed to accompany neuroscience data growth eventually led to the birth of neuroinformatics. Neuroinformatics combines neuroscience and informatics research to develop innovative tools for organization of large-volume, high-dimensional neuroscience data. It also applies computational models to integrate and analyze these data to eventually understand brain structure and function.
Topics: Brain Diseases; Computational Biology; Humans; Neurology
PubMed: 23569003
DOI: 10.1212/WNL.0b013e31828c2f2e