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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 -
Microbial Biotechnology Sep 2016Microbial bioinformatics in 2020 will remain a vibrant, creative discipline, adding value to the ever-growing flood of new sequence data, while embracing novel... (Review)
Review
Microbial bioinformatics in 2020 will remain a vibrant, creative discipline, adding value to the ever-growing flood of new sequence data, while embracing novel technologies and fresh approaches. Databases and search strategies will struggle to cope and manual curation will not be sustainable during the scale-up to the million-microbial-genome era. Microbial taxonomy will have to adapt to a situation in which most microorganisms are discovered and characterised through the analysis of sequences. Genome sequencing will become a routine approach in clinical and research laboratories, with fresh demands for interpretable user-friendly outputs. The "internet of things" will penetrate healthcare systems, so that even a piece of hospital plumbing might have its own IP address that can be integrated with pathogen genome sequences. Microbiome mania will continue, but the tide will turn from molecular barcoding towards metagenomics. Crowd-sourced analyses will collide with cloud computing, but eternal vigilance will be the price of preventing the misinterpretation and overselling of microbial sequence data. Output from hand-held sequencers will be analysed on mobile devices. Open-source training materials will address the need for the development of a skilled labour force. As we boldly go into the third decade of the twenty-first century, microbial sequence space will remain the final frontier!
Topics: Computational Biology; Databases, Nucleic Acid; Genomics; Internet
PubMed: 27471065
DOI: 10.1111/1751-7915.12389 -
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 -
GigaScience Aug 2018With the rapid development of next-generation sequencing technology, ever-increasing quantities of genomic data pose a tremendous challenge to data processing.... (Review)
Review
With the rapid development of next-generation sequencing technology, ever-increasing quantities of genomic data pose a tremendous challenge to data processing. Therefore, there is an urgent need for highly scalable and powerful computational systems. Among the state-of-the-art parallel computing platforms, Apache Spark is a fast, general-purpose, in-memory, iterative computing framework for large-scale data processing that ensures high fault tolerance and high scalability by introducing the resilient distributed dataset abstraction. In terms of performance, Spark can be up to 100 times faster in terms of memory access and 10 times faster in terms of disk access than Hadoop. Moreover, it provides advanced application programming interfaces in Java, Scala, Python, and R. It also supports some advanced components, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for computing graphs, and Spark Streaming for stream computing. We surveyed Spark-based applications used in next-generation sequencing and other biological domains, such as epigenetics, phylogeny, and drug discovery. The results of this survey are used to provide a comprehensive guideline allowing bioinformatics researchers to apply Spark in their own fields.
Topics: Animals; Computational Biology; Genomics; High-Throughput Nucleotide Sequencing; Humans; Mice; Software
PubMed: 30101283
DOI: 10.1093/gigascience/giy098 -
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