El Diario de Pontevedra : periódico liberal: Ano XXI Número 3478 - 1904 setembro 13

Initial and tissue-specific candidate mRNAs with expression levels ≥1 FPKM for the prediction of TINCR-mRNA interactions. Expression levels were derived from RNA-seq data of GTEx consortium (Expression Atlas ID: E-MTAB-2919). One-tailed Fisher’s exact test was applied for comparing initial dataset and tissue-specific dataset. P-values were adjusted for multiple testing with Bonferroni correction. Tissue-specific expression of TINCR was also detected by ROKU [12]. (PDF 19 kb

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Initial and tissue-specific candidate mRNAs with expression levels ≥1 FPKM for the prediction of TINCR-mRNA interactions. Expression levels were derived from RNA-seq data of GTEx consortium (Expression Atlas ID: E-MTAB-2919). One-tailed Fisher’s exact test was applied for comparing initial dataset and tissue-specific dataset. P-values were adjusted for multiple testing with Bonferroni correction. Tissue-specific expression of TINCR was also detected by ROKU [12]. (PDF 19 kb

Additional file 6 of Computational prediction of lncRNA-mRNA interactions by integrating tissue specificity in human transcriptome

Initial and tissue-specific candidate mRNAs with expression levels ≥1 FPKM for the prediction of TINCR-mRNA interactions. Expression levels were derived from RNA-seq data of Human Protein Atlas project (Expression Atlas ID: E-MTAB-2836). One-tailed Fisher’s exact test was applied for comparing initial dataset and tissue-specific dataset. P-values were adjusted for multiple testing with Bonferroni correction. Tissue-specific expression of TINCR was also detected by ROKU [12]. (PDF 15 kb

Additional file 5 of Computational prediction of lncRNA-mRNA interactions by integrating tissue specificity in human transcriptome

Number of tissue-specific lncRNA and mRNAs detected as outlier expression by applying ROKU [12] to RNA-seq data derived from NIH Epigenomics Roadmap project [15]. All expression levels were obtained from Expression Atlas (ID: E-MTAB-3871). In total, 4973 lncRNA and 16,164 protein-coding genes with expression level ≥1 FPKM were analyzed in this dataset. The values in parenthesses indicate the ratio of tissue-specific genes to total. (PDF 14 kb

Improved Accuracy in RNA–Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA–protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA–protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA–protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA–protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein–protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA–protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA–protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA–protein complexes