Tran, D.-T. | Search bioRxiv

bioRxiv2018

HeartBioPortal: an internet-of-omics for human cardiovascular disease data

Cardiovascular disease (CVD) is the leading cause of death worldwide, responsible for over 17 million deaths annually, a rate which outpaces even that related to cancer. Despite these sobering statistics, the state-of-the-art in computational infrastructure for the study of contemporary datasets related to CVD lags substantially behind that widely available in oncology, where improved data science and visualization methods have delivered publicly available comprehensive cancer genomics resources like Memorial Sloan Kettering Cancer Centers cBioPortal1,2 and the National Cancer Institutes Genomic Data Commons (GDC) Portal3,4. In our view, such portals do an outstanding job of transforming data from The Cancer Genome Atlas (TCGA) into logical data visualizations that provide additional biological insight. Developing a similar user-friendly computational platform for CVD could signi ...

bioinformatics↗

bioRxiv2018

A graph-based algorithm for RNA-seq data normalization

The use of RNA-sequencing has garnered much attention in the recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Current normalization methods are based on assumptions that fail to hold when RNA-seq profiles become more abundant and heterogeneous. We present a normalization procedure that does not rely on these assumptions, or on prior knowledge about the reference transcripts in those conditions. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our benchmark data showed that it can recover the reference transcripts with high precision, thus resulting in high-quality normalization. As demonstrated on a real data set, this algorithm gives good results and is efficient enough to be applicable to real-life data.\n\n2012 ACM Subject ClassificationApplied computing [->] Computational transcriptomics, Applied computing [->] Bioinformatics\n\nDigital Object Identifier10.4230/LIPIcs.WABI.2018.xxx\n\nFundingThis material was based on research supported by the National Heart, Lung, and Blood Institute (NHLBI)-NIH sponsored Programs of Excellence in Glycosciences [grant number HL107152 to B.K.], and partially by NSF [CAREER grant 1350344 to M.M.]. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

bioinformatics↗