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Krismer, K.

Publications and source records attributed to Krismer, K..

2 recordsLinked to original sources

Transite: A computational motif-based analysis platform that identifies RNA-binding proteins modulating changes in gene expression

RNA-binding proteins (RBPs) play critical roles in regulating gene expression by modulating splicing, RNA stability, and protein translation. In response to various stimuli, alterations in RBP function contribute to global changes in gene expression, but identifying which specific RBPs are responsible for the observed changes in gene expression patterns remains an unmet need. Here, we present Transite a multi-pronged computational approach that systematically infers RBPs influencing gene expression changes through alterations in RNA stability and degradation. As a proof of principle, we applied Transite to public RNA expression data from human patients with non-small cell lung cancer whose tumors were sampled at diagnosis, or after recurrence following treatment with platinum-based chemotherapy. Transite implicated known RBP regulators of the DNA damage response and identified hnRNPC as a new modulator of chemotherapeutic resistance, which we subsequently validated experimentally. Transite serves as a generalizable framework for the identification of RBPs responsible for gene expression changes that drive cell-state transitions and adds additional value to the vast wealth of publicly-available gene expression data.

bioinformatics

High resolution discovery of chromatin interactions

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

bioinformatics