Special Issue "Impact of Parallel and High-Performance Computing in Genomics"
Deadline for manuscript submissions: closed (20 October 2019) | Viewed by 19684
Interests: large-scale genome sequence analyses, next-generation transcriptomics, metagenomics, machine learning, application of cloud computing and high-performance computing in genomics
Interests: dynamics and transmission of prion proteins in yeast; identification of structural variation from high throughput sequencing data; genome evolution and population dynamics
Interests: large-scale genomics and metagenomics; cloud computing; high-performance computing
Interests: large-scale parallel computer system architecture; systems for storing and processing big data; reconfigurable computing for cognitive problems; parallel programming environment and tools; high-performance computing
Interests: bioinformatics; computational genomics; computational epidemiology; analysis of high-throughput sequencing data; statistical inference; discrete algorithms
Special Issues, Collections and Topics in MDPI journals
In the past two decades, massive parallel next-generation sequencing techniques have also parallelized genomics projects. Human genome sequencing has evolved from sequencing a few individuals to the massive parallel sequencing of 100,000 individual people or single cells. Culturing and sequencing single microbes has been replaced by culture-independent, metagenomics sequencing. Genomic data generated from single projects has grown from a few megabases to hundreds of gigabases or even terabases. Analyzing these datasets can reveal robust links between genotypes and phenotypes, illustrate the precise mechanisms of cellular changes in cancer, uncover novel taxa with unprecedented metabolic capabilities, and the list goes on and on. However, effectively and efficiently analyzing these massive datasets poses a significant challenge not only to the underlying computing infrastructures and programming models, but also to the algorithms that drill out insights and visualize them.
This Special Issue focuses on various “big data genomics” strategies that employ parallel programming paradigms to analyze extremely large genomics datasets. Its scope includes, but is not limited to, traditional task parallelism (MP, MPI, GPU and FPGA), data parallelism (MapReduce, Spark), or the recent model parallelism (deep learning). We welcome submissions of reviews, research articles, and short communications. We also encourage the submission of manuscripts describing new ideas, in the form of “concept papers”.Dr. Zhong Wang
Prof. Suzanne Sindi
Dr. Alexander Sczyrba
Prof. Hong An
Prof. Alex Zelikovsky
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.
- massive parallel next-generation sequencing
- metagenomics sequencing
- human genome sequencing
- big data genomics
- task parallelism
- data parallelism
- model parallelism
- deep learning