Special Issue "Systems Analytics and Integration of Big Omics Data"
Deadline for manuscript submissions: closed (31 August 2018).
A printed edition of this Special Issue is available here.
2. Department of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
Interests: computational biology; genomics and genetics; big data; endocrine disruption; contaminants of emerging concern; bisphenol A; impacts of MNPs on terrestrial and aquatic systems; bioplastics
Special Issues and Collections in MDPI journals
The emergence and global utilization of high-throughput (HT) technologies, including deep sequencing technologies (genomics) and mass spectrometry (proteomics, metabolomics, lipidomics), has allowed geneticists, biologists, and biostatisticians to bridge the gap between genotype and phenotype on a scale that was not previously possible. The adoption of a novel technology is met by a paradigm shift in how biological assays are designed and executed.
Throughput is increased typically by an order of magnitude and accompanied by an exponential cost reduction compared to older traditional approaches. The economic benefit and efficacy of nascent technologies is often realized by process-miniaturization combined with the multiplexing of millions of reactions.
Big data encompasses the collection of data sets derived from technologies. They are so large and complex that their processing is impractical using traditional data processing applications. Instead, challenges arise in collection: analysis, mining, sharing, transfer, visualization, archival and integration of big data.
Analogous to the impact of high-throughput DNA sequencing on genomics and transcriptomics, mass spectrometry has revolutionized proteomics studies in a similar manner providing independent draft maps of the human proteome. Large-scale interrogation of biological systems using mass-spectrometry based proteomics provides insights not available from genomics data, namely information on protein abundance, cell-type and time-dependent expression patterns, post-translational modifications and protein–protein interactions.
As observed with DNA microarray analysis pipelines over a decade ago, and more recently with HT sequencing, better analytical tools are emerging primarily from open source efforts permitting additional analyses and enhanced information mining from raw data sets compared to the tool kits provided with the instruments themselves.
Various statistical pipelines require different types of compute structure: large database storage arrays for query intensive data analysis, high throughput sequencing requiring a high-speed data networks with a hierarchical type compute core, statistical modeling methods requiring a modular type closely coupled compute infrastructure
Administration and development strategies must take into account the ever-growing size of data, public accessibility of analyzed data, software deprecations, software upgrades, hardware failures, user interface improvements, user account management, long term storage as well as security of systems.
In this Special Issue, we will focus on integration strategies for systems level analysis of omics data, big data infrastructure, rigor and transparency in big data research, best practices for sharing omics data with public repositories, recent developments in pathway and network algorithm development, and integration of omics data with clinical and biomedical data.
Prof. Dr. Gary Hardiman
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- systems level analysis
- high-throughput sequencing
- mass spectrometry
- bioinformatics pipelines
- rigor and transparency in big data research
- omics data management
- analysis provenance
- algorithm development for pathway/network integration