TEPIC 2-an extended framework for transcription factor binding prediction and integrative epigenomic analysis
1 May 2019·,,
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0 min read
Florian Schmidt
Fabian Kern
Peter Ebert
Nina Baumgarten
Marcel H. Schulz
Abstract
SUMMARY: Prediction of transcription factor (TF) binding from epigenetics data and integrative analysis thereof are challenging. Here, we present TEPIC 2 a framework allowing for fast, accurate and versatile prediction, and analysis of TF binding from epigenetics data: it supports 30 species with binding motifs, computes TF gene and scores up to two orders of magnitude faster than before due to improved implementation, and offers easy-to-use machine learning pipelines for integrated analysis of TF binding predictions with gene expression data allowing the identification of important TFs. AVAILABILITY AND IMPLEMENTATION: TEPIC is implemented in C++, R, and Python. It is freely available at https://github.com/SchulzLab/TEPIC and can be used on Linux based systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Type
Publication
Bioinformatics (Oxford, England)
Authors
Nina Baumgarten
(she/her)
Postdoc
I am a bioinformatician with a diverse background in analyzing genetic and epigenetic data. I have had the chance to work on a variety of projects since I joined the lab, like decoding the effect of non-coding variations, analysing GWAS data, and studying TF binding. Recently, my focus has been shifted toward spatial transcriptomics, which allows me to map genetic findings directly onto tissue architecture. I am motivated by the possibility that these data-driven insights might lead to a better understanding of complex diseases. In my free time, I love spending time in nature for hiking—it’s where the best ideas come to me!