In today’s big-data era, the exponential growth of information observed due to the latest advances of high-throughput technologies has been indisputable. Therefore, it is necessary for the scientific community to devise efficient algorithms and tools for the extraction, analysis, exploration, and representation of biological information. In this regard, we invite investigators to contribute original bioinformatics research and review articles describing novel methods, algorithms, software applications, web services, and workflows that are able to cope with larger datasets and additional complexity. Additionally, researchers should look to design new datasets or databases which integrate information from a variety of different sources. Submissions are encouraged from across the entire spectrum of life and biomedical sciences.
The most common approach in transcriptomics (RNA-seq and microarrays) is differential gene expression analysis. Genes identified as differentially expressed may be responsible for phenotype differences between various biological conditions. An alternative approach is gene co-expression analysis, which detects groups of genes with similar expression patterns across unrelated sets of transcriptomic data of the same organism. Co-expressed genes tend to be involved in similar biological processes. This Special Issue will include reviews and research articles on the topic differential gene expression and coexpression. The reviews will provide an overview of the methods available for transcriptomic analysis, while the research articles will provide an in-depth description of each state-of-the-art tool. Please send me an abstract prior to submission to make sure that your work falls within the scope of this Special Issue.