Identification of streptococcal small RNAs that are putative targets of RNase III through bioinformatics analysis of RNA sequencing data.

Background: Small noncoding regulatory RNAs (sRNAs) are post-transcriptional regulators, regulating mRNAs, proteins, and DNA in bacteria. One class of sRNAs, trans-acting sRNAs, are the most abundant sRNAs transcribed from the intergenic regions (IGRs) of the bacterial genome. In Streptococcus pyogenes, a common and potentially deadly pathogen, many sRNAs have been identified, but only a few have been studied.

Identification of potential antimicrobials against Salmonella typhimurium and Listeria monocytogenes using Quantitative Structure-Activity Relation modeling.

The shelf-life of fresh carcasses and produce depends on the chemical and physical properties of antimicrobials currently used for treatment. For many years the gold standard of these antimicrobials has been Cetylpyridinium Chloride (CPC) a quaternary ammonium compound (QAC). CPC is very effective at removing bacterial pathogens from the surface of chicken but has not been approved for other products due to a toxic residue left behind after treatment.

Identification of genome-wide non-canonical spliced regions and analysis of biological functions for spliced sequences using Read-Split-Fly.

Background: It is generally thought that most canonical or non-canonical splicing events involving U2- and U12 spliceosomes occur within nuclear pre-mRNAs. However, the question of whether at least some U12-type splicing occurs in the cytoplasm is still unclear. In recent years next-generation sequencing technologies have revolutionized the field.

Comparison of Alternative Splicing Junction Detection Tools Using RNA-Seq Data.

Background: Alternative splicing (AS) is a posttranscriptional process that produces differ-ent transcripts from the same gene and is important to produce diverse protein products in response to environmental stimuli. AS occurs at specific sites on the mRNA sequence, some of which have been de-fined. Multiple bioinformatics tools have been developed to detect AS from experimental data.

Dissecting the biological relationship between TCGA miRNA and mRNA sequencing data using MMiRNA-Viewer.

Background: MicroRNAs (miRNA) are short nucleotides that interact with their target genes through 3' untranslated regions (UTRs). The Cancer Genome Atlas (TCGA) harbors an increasing amount of cancer genome data for both tumor and normal samples. However, there are few visualization tools focusing on concurrently displaying important relationships and attributes between miRNAs and mRNAs of both cancer tumor and normal samples.