No abstract

BackgroundMost of the existing in silico phosphorylation site prediction systems use machine learning approach that requires preparing a good set of classification data in order to build the classification knowledge. Furthermore, phosphorylation is catalyzed by kinase enzymes and hence the kinase information of the phosphorylated sites has been used as major classification data in most of the existing systems. Since the number of kinase annotations in protein sequences is far less than that of the proteins being sequenced to date, the prediction systems that use the information found from the small clique of kinase annotated proteins can not be considered as completely perfect for predicting outside the clique. Hence the systems are certainly not generalized. In this paper, a novel generalized prediction system, PPRED (Phosphorylation PREDictor) is proposed that ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites.ResultsExperimental results based on cross validations and an independent benchmark reveal the significance of using the evolutionary information alone to classify phosphorylation sites from protein sequences. The prediction performance of the proposed system is better than those of the existing prediction systems that also do not incorporate kinase information. The system is also comparable to systems that incorporate kinase information in predicting such sites.ConclusionsThe approach presented in this paper provides an efficient way to identify phosphorylation sites in a given protein primary sequence that would be a valuable information for the molecular biologists working on protein phosphorylation sites and for bioinformaticians developing generalized prediction systems for the post translational modifications like phosphorylation or glycosylation. PPRED is publicly available at the URL http://www.cse.univdhaka.edu/~ashis/ppred/index.php.

Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Over the past decade, researchers reported a large number of human disease associations with the lncRNAs, both intergenic lncRNAs (lincRNAs) and non-intergenic lncRNAs. Thanks to the next generation sequencing platform, RNA-seq, through which researchers also were able to quantify expression profiles of each of the lncRNAs in human tissue samples. In this article we adapted the non-negative matrix factorization method to develop a low-rank computational model that can describe the existing knowledge about both non-intergenic and intergenic lncRNA-disease associations represented in a two dimensional association matrix as well as convey a way of ranking disease causing lncRNAs. We proposed several NMF formulations for the problem and we found that the sparsity-constrained NMF obtained the best model among all the other models. By exploiting the inherent biclustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance. Moreover, we proposed an integrative NMF formulation where we incorporated along with the coding gene and lincRNA disease association data, prior knowledge about relationship networks among the coding genes and lincRNAs, and the RNA-seq expression profile data to identify potential lincRNA-coding gene co-modules with which we further enhanced the lincRNA-disease associations and untangled mysteries about functional chemistry of the intergenic lncRNAs. Experimental results show the superiority of our proposed method over two state-of-theart clustering algorithms-k-means and hierarchical clustering.

No abstract

No abstract