Tejasvi Matam's research while affiliated with Johns Hopkins University and other places

Publications (6)

Article
Full-text available
Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, in...
Conference Paper
The three-prime untranslated region (3'-UTR) of a mRNA influences its biological behavior, from stability, post-transcriptional control through miRNAs, and availability for translation. Alternative polyadenylation (APA) can modulate 3' end site selection, and approximately 50% of coding genes are subject to it. Global transcript shortening has been...
Preprint
Full-text available
Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including disease. We present hereFC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive...
Article
In recent years, in depth exploration of genomes structure and function has revealed a central role for non-coding RNAs (ncRNAs) in orchestrating key biological and cellular processes through the fine tuning of gene expression regulation. Most importantly a role for ncRNAs has also started to emerge in human disease pathogenesis. This further speak...
Article
Full-text available
Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods i...

Citations

... The advantage of diverse annotations is illustrated by the FC-R2 study [27], which quantified recount2's bigWigs using the FANTOM-CAT annotation, which includes a large number of non-coding RNAs [20]. The study reported the tissue specificity of different classes of RNA: coding mRNA, divergent promoter lncRNA, intergenic promoter lncRNA, and enhancer lncRNA. ...
... Due to the immense complexity of the study of expression in the genome, many computational methods, reviewed in [5] are necessary. The use of computational methods, where complex and voluminous amounts of data is involved, is seemingly the only approach for conclusive study, as discussed in [6] (differential analysis), [7] (signature study), [8] (expression profiling), [9] (cancer-specific correlations) and other studies mentioned in [10]. For our own study of diverse pathways, there was no substitution to the application of automated methods. ...
... We extend STPs from a network property to a sample property (like the existence of individual aberrations) by declaring an STP to be "aberrant" in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the target gene is RNA-aberrant, meaning its expression level is "divergent" (i.e., outside the normal, baseline range [14]). This defines one binary random variable per STP, of which there are typically hundreds of thousands, most of which have a very small probability to be realized in a sample. ...