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Identification of active transcriptional regulatory elements from GRO-seq data.

Nature methods
Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detec...

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns.

Oncotarget
Accumulating evidences suggest that long non-coding RNAs (lncRNAs) perform important functions. Genome-wide chromatin-states area rich source of information about cellular state, yielding insights beyond what is typically obtained by transcriptome pr...

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BMC bioinformatics
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often ...

Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.

Environmental and molecular mutagenesis
Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium io...

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

Nucleic acids research
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the te...