Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes.

BACKGROUND: RNA-seq has become a standard technology to quantify mRNA. The measured values usually vary by several orders of magnitude, and while the detection of differences at high values is statistically well grounded, the significance of the differences for rare mRNAs can be weakened by the presence of biological and technical noise. RESULTS: We have developed a method for cleaning RNA-seq data, which improves the detection of differentially expressed genes and specifically genes with low to moderate transcription. Using a data modeling approach, parameters of randomly distributed mRNA counts are identified and reads, most probably originating from technical noise, are removed. We demonstrate that the removal of this random component leads to the significant increase in the number of detected differentially expressed genes, more significant pvalues and no bias towards low-count genes. CONCLUSION: Application of RNAdeNoise to our RNA-seq data on polysome profiling and several published RNA-seq datasets reveals its suitability for different organisms and sequencing technologies such as Illumina and BGI, shows improved detection of differentially expressed genes, and excludes the subjective setting of thresholds for minimal RNA counts. The program, RNA-seq data, resulted gene lists and examples of use are in the supplementary data and at https://github.com/Deyneko/RNAdeNoise .

Computational and Experimental Tools to Monitor the Changes in Translation Efficiency of Plant mRNA on a Genome-Wide Scale: Advantages, Limitations, and Solutions.

The control of translation in the course of gene expression regulation plays a crucial role in plants' cellular events and, particularly, in responses to environmental factors. The paradox of the great variance between levels of mRNAs and their protein products in eukaryotic cells, including plants, requires thorough investigation of the regulatory mechanisms of translation. A wide and amazingly complex network of mechanisms decoding the plant genome into proteome challenges researchers to design new methods for genome-wide analysis of translational control, develop computational algorithms detecting regulatory mRNA contexts, and to establish rules underlying differential translation. The aims of this review are to (i) describe the experimental approaches for investigation of differential translation in plants on a genome-wide scale; (ii) summarize the current data on computational algorithms for detection of specific structure⁻function features and key determinants in plant mRNAs and their correlation with translation efficiency; (iii) highlight the methods for experimental verification of existed and theoretically predicted features within plant mRNAs important for their differential translation; and finally (iv) to discuss the perspectives of discovering the specific structural features of plant mRNA that mediate differential translation control by the combination of computational and experimental approaches.

The features that distinguish lichenases from other polysaccharide-hydrolyzing enzymes and the relevance of lichenases for biotechnological applications.

The main specific features of ß-1,3-1,4-glucanases (or lichenases, EC 3.2.1.73), the enzymes that in a strictly specific manner hydrolyze ß-glucans of many cereal species and lichens containing ß-1,3 and ß-1,4 bonds, are reviewed as well as the current strategies used for their protein design, which have been successfully applied to make lichenases more attractive and promising for biocatalytic conversion of biomass, in particular, in the areas of their biotechnological application, such as brewing industry, animal feed manufacture, and biofuel production, which will in future allow these technologies to be economically and ecologically beneficial.

The Trametes hirsuta 072 laccase multigene family: Genes identification and transcriptional analysis under copper ions induction.

Laccases, blue copper-containing oxidases, ≿ an play an important role in a variety of natural processes. The majority of fungal laccases are encoded by multigene families that express closely related proteins with distinct functions. Currently, only the properties of major gene products of the fungal laccase families have been described. Our study is focused on identification and characterization of laccase genes, which are transcribed in basidiomycete Trametes hirsuta 072, an efficient lignin degrader, in a liquid medium, both without and with induction of laccase transcription by copper ions. We carried out production of cDNA libraries from total fungal RNA, followed by suppression subtractive hybridization and mirror orientation selection procedures, and then used Next Generation Sequencing to identify low abundance and differentially expressed laccase transcripts. This approach resulted in description of five laccase genes of the fungal family, which, according to the phylogenetic analysis, belong to distinct clusters within the Trametes genus. Further analysis established similarity of physical, chemical, and catalytic properties between laccases inside each cluster. Structural modeling suggested importance of the sequence differences in the clusters for laccase substrate specificity and catalytic efficiency. The implications of the laccase variations for the fungal physiology are discussed.