Virtual ChIP-seq Predicting transcription factor binding by learning from the transcriptome

Karimzadeh M, Hoffman MM. 2017. Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome. in prep; doi: https://doi.org/. (BibTeX)

The free Virtual ChIP-seq software package efficiently predicts binding of 40 TFs in any cell type with RNA-seq and ATAC-seq (or DNase-seq).

Predicting transcription factor binding

Virtual ChIP-seq uses multi-layer perceptron to predict binding of individual TFs. Virtual ChIP-seq uses data on chromatin accessibility, genomic conservation, and binding characteristics of TFs from previous experiments in other cell types. It also learns from the asso- ciation of gene expression and TF binding at different genomic regions. By incorporating existing ChIP-seq data, there is no longer a need to represent TF sequence preferences in form of position weight matrices. For a new cell type with data on chromatin accessibility and gene expression, Virtual ChIP-seq predicts indirect TF binding, as well as binding of TFs without known sequence preference.

Accuracy of predictions

To build a generalizable classifier that performs well on new cell types with only transcriptome and chromatin accessibility data, we train the multi-layer perceptron on training cell types (A549, GM12878, HCT-116, HepG2, HeLa-S3). We assess the performance of the model in validation cell types (IMR90 K562 MCF-7 NHEK H1 Ishikawa BJ T47D PANC-1 Jurkat). Below, we report median and standard deviation of performance among validation cell types.

Virtual ChIP-seq accepts chromatin accessibility data in narrowPeak format and RNA-seq data in format of a matrix where rows are human gene symbols and columns are cell types (Minimum of 1 column with your cell of interest). The RNA-seq measure must be normalized to length and library (accepts RPKM, FPKM, TPM, but not raw read counts). It takes an average of 6 CPU hours (depending on TF) and a minimum RAM of 8GB to generate the input tables for your TF of interest. Applying the trained model takes less than 20 minutes for most TFs and datasets.

Track hub, file access, and software

UCSC Genome Browser

View the Virtual ChIP-seq track hub in the UCSC genome browser.

Using the track hub

There are 40 supertracks corresponding to each transcription factor. Each supertrack contains to bigBed9 files, one showing genomic bins with TF binding in Cistrome DB datasets, and one showing Virtual ChIP-seq predictions in the Roadmap consortium datasets.

View the Virtual ChIP-seq track hub in UCSC genome browser.

Direct links

Download Virtual ChIP-seq predictions in the Roadmap datasets directly:

• Virtual ChIP-seq predictions in Roadmap datasets

Software and documentation

Read the documentation for Virtual ChIP-seq software, which begins with a quick start.

Support

Please ask questions about Virtual ChIP-seq on our mailing list. If you want to report a bug or request a feature, use Virtual ChIP-seq issue tracker. We are interested in all comments on the package, and the ease of use of installation and documentation.

Source code

• Bitbucket repository

Credits

Virtual ChIP-seq is developed by Mehran Karimzadeh during his PhD at Michael Hoffman Lab.