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README.md

BIRD: Big data Regression for predicting DNase I hypersensitivity

Overview

BIRD is a software to predict DNase I hypersensitivity (DNase-seq signal) based on gene expression data (support both human exon array and RNA-seq data). Using a pre-built model and input gene expression data, BIRD is capable to predict DNase-seq signal genome-wide (~1M genomic loci). BIRD provided two types of outputs: (1) data matrix format or (2) WIG format. Users can easily visualize the predicted DNase-seq signals in UCSC genome browser.

News

06/18/2020:

Add RNA-seq data (167 samples hg19 model) and an R script for calculating train-test distance.

03/26/2020:

An option (-wt) is added to output wig files with log-transformed values.

08/25/2019:

Application of BIRD using single-cell RNA-seq data

Our latest paper about applying BIRD prediction using single-cell RNA-seq data is online in Nucleic Acids Research :

Zhou W, Ji Z, Fang W, Ji H. Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq. Nucleic Acids Research, gkz716 (2019). (open access)

07/18/2019:

BIRD training data released

The training DNase-seq and RNA-seq data from 167 ENCODE samples for the latest BIRD model are release in https://github.com/WeiqiangZhou/BIRD-data

04/15/2019:

New RNA-seq prediction model

A new RNA-seq prediction model trained by 167 ENCODE samples is released in https://github.com/WeiqiangZhou/BIRD-model. We suggest users use this most up-to-date model for RNA-seq and single-cell RNA-seq data.

08/29/2018:

BIRD is updated to v1.1.1

Starting from this version, the prebuilt prediction models will not be included in this repo. Users can download the required models from https://github.com/WeiqiangZhou/BIRD-model.

Important updates:

  1. The quantile normalization function is updated to a more robust version (signfigantly boost prediction performance when there is a large number of tied values in the input data, e.g., single-cell RNA-seq data).

  2. Input data matching (gene id matching) is now included in the BIRD_predict program. Users don't have to prepare the input data matrix with the legacy R script match_input_matrix.r. Please read the How to use (for RNA-seq and single-cell RNA-seq) section for details.

  3. The predicted values are now bounded from 0 to 14. Users can use the -u option to change the upper bound when using their own prediction model. Users can also use -l option to perform prediction using the locus-level model rather than using the full model. This is useful when you build your own prediction model but you are not sure if the cluster-level model works or not.

Installation

Currently, BIRD supports Linux/Unix and macOS system.

First, download and install the latest version of BIRD:

Direct download from https://github.com/WeiqiangZhou/BIRD/releases/download/v1.1.1/BIRD_v1.1.1.zip or run:

wget https://github.com/WeiqiangZhou/BIRD/releases/download/v1.1.1/BIRD_v1.1.1.zip
unzip BIRD_v1.1.1.zip
cd BIRD_v1.1.1
make

Second, download and unzip the required prediction models (see https://github.com/WeiqiangZhou/BIRD-model):

For RNA-seq data:

Direct download from https://github.com/WeiqiangZhou/BIRD-model/releases/download/v1.3/human_hg19_model.bin.zip or run:

wget https://github.com/WeiqiangZhou/BIRD-model/releases/download/v1.3/human_hg19_model.bin.zip
unzip human_hg19_model.bin.zip

For exon array data:

Direct download from https://github.com/WeiqiangZhou/BIRD-model/releases/download/v1.1/Exonarray_model_file.bin.zip or run:

wget https://github.com/WeiqiangZhou/BIRD-model/releases/download/v1.1/Exonarray_model_file.bin.zip
unzip Exonarray_model_file.bin.zip

How to use (for RNA-seq and single-cell RNA-seq)

Prepare a data matrix with the gene ensembl ids as the first column and expression values for each sample as other columns (see FPKM_data_matrix.txt in the example folder for reference).

To get data matrix format output, run:

path_to_BIRD/BIRD_predict -b path_to_model/human_hg19_model.bin -i FPKM_data_matrix.txt -o output_file.txt

BIRD will first match the ensembl id of the genes used for building the prediction model with the ids in the input data matrix. The matched data matrix will be outputted as input_file.match.txt (e.g., FPKM_data_matrix.txt.match.txt). By default, the version section of the ensembl id will be ignored. For example, given an ensembl id ENSG00000227232.4, only ENSG00000227232 will be used for matching. Users can specify the -e flag to perform an exact match (e.g., ENSG00000227232.4 will be used for matching).

To get WIG format output, run:

path_to_BIRD/BIRD_predict -b path_to_model/human_hg19_model.bin -i FPKM_data_matrix.txt -o output_name -w

To get WIG format output with log-transformed values, run:

path_to_BIRD/BIRD_predict -b path_to_model/human_hg19_model.bin -i FPKM_data_matrix.txt -o output_name -wt

In this mode, BIRD will generate WIG file for each sample with prefix "output_name." followed by the column name in the input_file.txt.

WIG file can be visualized in UCSC genome browser by adding custom tracks:

http://genome.ucsc.edu/cgi-bin/hgGateway

Or in IGV:

http://software.broadinstitute.org/software/igv/

For help information, run:

path_to_BIRD/BIRD_predict -h
Usage:                                                                                                      
Standard output: BIRD_predict -b model_file.bin -i input_file.txt -o output_file.txt
Standard output will save a matrix contained all predited value in log scale (log2(x+1) transformed).
WIG output: BIRD_predict -b model_file.bin -i input_file.txt -o output_name -w
WIG output will save each sample as a WIG file.
Options:
-b   Specify library file. If not sepecified,the program will search for model_file.bin in the current directory.
-i   Specify input file (gene expression matrix).
-o   Specify output file.
-u   Set upper bound for predicted values (default:14).
-w   Output WIG file for each sample.
-wt  Output WIG file for each sample with log-transformed value.
-l   Use locus-level model for prediction.  
-e   Use exact id match for matching the gene expression data.

How to use (for exon array)

BIRD accepts gene expression output file from GeneBASE (see Exon_K562_lab.txt in the example folder for reference). If you have the raw exon array data (CEL file), use GeneBASE to get the gene expression.

To download and install GeneBASE, see http://web.stanford.edu/group/wonglab/GeneBASE/

How to use GeneBASE, see https://github.com/WeiqiangZhou/BIRD/blob/master/exonarray_instruction.md

After running GeneBASE, you will get the gene expression data file (e.g. input_file.txt).

To get data matrix format output, run:

path_to_BIRD/BIRD_predict -b path_to_model/Exonarry_model_file.bin -i input_file.txt -o output_file.txt

To get WIG format output, run:

path_to_BIRD/BIRD_predict -b path_to_model/Exonarry_model_file.bin -i input_file.txt -o output_name -w

For large dataset

It could be memory intensive to run BIRD on dataset with sample size larger than 100 (memory usage > 1G). In such cases, it is recommended to run BIRD via the bash script BIRD_bash.sh. It is required that R is installed and make sure the Rscript command is executable. The bash script will partition the input file into N files according to partition_size (e.g. 100, the number of samples in a partitioned input file).

To get data matrix format output, run:

bash path_to_BIRD/BIRD_bash.sh path_to_BIRD path_to_model/human_hg19_model.bin input_file.txt output_name partition_size 0

The output files should be output_name_part1.txt, ..., output_name_partN.txt.

To get WIG format output, run:

bash path_to_BIRD/BIRD_bash.sh path_to_BIRD path_to_model/human_hg19_model.bin input_file.txt output_name partition_size 1

Example

Run BIRD:

path_to_BIRD/BIRD_predict -b path_to_model/human_hg19_model.bin -i path_to_BIRD/example/FPKM_data_matrix.txt -o GM12878_DNase.txt
path_to_BIRD/BIRD_predict -b path_to_model/human_hg19_model.bin -i path_to_BIRD/example/FPKM_data_matrix.txt -o GM12878_DNase -w

Run BIRD_bash.sh:

bash path_to_BIRD/BIRD_bash.sh path_to_BIRD path_to_model/human_hg19_model.bin path_to_BIRD/example/FPKM_data_matrix.txt GM12878_DNase 1 0
bash path_to_BIRD/BIRD_bash.sh path_to_BIRD path_to_model/human_hg19_model.bin path_to_BIRD/example/FPKM_data_matrix.txt GM12878_DNase 1 1

How to get gene expression from RNA-seq

Use Tophat and Cufflinks to obtain the gene expression (i.e. FPKM) for the input sample.

To download and install Tophat and Cufflinks, see https://ccb.jhu.edu/software/tophat/tutorial.shtml and http://cole-trapnell-lab.github.io/cufflinks/getting_started/

How to use Tophat and Cufflinks, see https://github.com/WeiqiangZhou/BIRD/blob/master/RNAseq_instruction.md

The most updated software to process RNA-seq data are HISAT2 and StringTie. see https://ccb.jhu.edu/software/hisat2/index.shtml and https://ccb.jhu.edu/software/stringtie/

How to build the prediction model

The training data for all pre-built BIRD models are available in: https://github.com/WeiqiangZhou/BIRD-data

If you would like to know how to build a prediction model, see https://github.com/WeiqiangZhou/BIRD/blob/master/build_prediction_model.md for details.

Note:

Change path_to_BIRD to the path where you install BIRD.

Chagne path_to_model to the path where you store the prebuilt models.

Genomic information in the current version of BIRD is based on human genome hg19.

PDDB: Predicted DNase I hypersensitivity database

A database of the predicted DNase I hypersensitivity for 2,000 GEO exon array samples is available at http://jilab.biostat.jhsph.edu/~bsherwo2/bird/index.php

Interpretation of the prediction models

The predictor genes for each genomic locus and each DHS cluster, motif enrichment analysis results, GO analysis results, and the active cell types for each DHS cluster in the prediction models are provided as an online resource which is available at https://zhiji.shinyapps.io/CABS/.

Users can input a list of interested genomic regions and explore the overlapped DHSs from the prediction models.

Contact

Weiqiang Zhou: wzhou14@jhu.edu

References

Zhou W, Sherwood B, Ji Z, Xue Y, Du F, Bai J, Ying M, Ji H. Genome-wide Prediction of DNase I Hypersensitivity Using Gene Expression. Nature Communications 8, 1038 (2017). (open access)

Zhou W, Ji Z, Fang W, Ji H. Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq. Nucleic Acids Research, gkz716 (2019). (open access)

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