期刊
BIOINFORMATICS
卷 35, 期 9, 页码 1608-1609出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty856
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资金
- German Federal Ministry of Education and Research in Germany (BMBF) [01DP17005, 01KU1216A]
- Cluster of Excellence on Multimodal Computing and Interaction (DFG) [EXC248]
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.
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