danko-lab / rtfbs_db

Parse TF motifs from public databases, read into R, and scan using 'rtfbs'.

rtfbs_db

Transcription factors (TFs) regulate complex programs of gene transcription by binding to short DNA sequence motifs. Here we introduce rtfbsdb, a unified framework that integrates a database of more than 65,000 TF binding motifs with tools to easily and efficiently scan target genome sequences. Rtfbsdb clusters motifs with similar DNA sequence specificities and optionally integrates RNA-seq or PRO-seq data to restrict analyses to motifs recognized by TFs expressed in the cell type of interest. Our package allows common analyses to be performed rapidly in an integrated environment.

Uses: Parse TF motifs from public databases, read into R, and scan using 'rtfbs'.

Sources of Position Weight Matrices (PWMs).

• CIS-BP: http://cisbp.ccbr.utoronto.ca/bulk.php Weirauch MT, et. al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell. 2014 Sep 11;158(6):1431-43. doi: 10.1016/j.cell.2014.08.009.

• Jolma: Downloaded from the supplementary data of this paper: http://www.nature.com/nature/journal/vaop/ncurrent/full/nature15518.html Jolma A, et. al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature. 2015 Nov 19;527(7578):384-8.

• Jolma: Downloaded from the supplementary data of this paper: http://www.cell.com/retrieve/pii/S0092867412014961 Jolma A, et. al. DNA-binding specificities of human transcription factors. Cell. 2013 Jan 17;152(1-2):327-39. doi: 10.1016/j.cell.2012.12.009.

• Jaspar: http://129.177.120.189/cgi-bin/jaspar2010/jaspar_db.pl Mathelier A, et. al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 2014 Jan 1;42(1):D142-7. doi: 10.1093/nar/gkt997. Epub 2013 Nov 4.

• HOCOMOCO: http://hocomoco.autosome.ru/ Kulakovskiy IV, et. al. HOCOMOCO: a comprehensive collection of human transcription factor binding sites models. Nucleic Acids Res. 2013 Jan;41(Database issue):D195-202. doi: 10.1093/nar/gks1089. Epub 2012 Nov 21.

• Neph: URL: ftp://ftp.ebi.ac.uk/pub/databases/ensembl/encode/supplementary/integration_data_jan2011/byDataType/footprints/jan2011/de.novo.pwm Neph S, et. al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature. 2012 Sep 6;489(7414):83-90. doi: 10.1038/nature11212.

• UniProbe: http://the_brain.bwh.harvard.edu/uniprobe/ References: http://the_brain.bwh.harvard.edu/uniprobe/references.php

Requires

• rtfbs: In R, type: "install.packages('rtfbs')"

• bedops:

  • Get the latest version of the bedops binaries here: https://bedops.readthedocs.org/en/latest/
  • Install, and add them to your path.

• The twoBitToFa program from the Kent libraries. Download it here: http://hgdownload.cse.ucsc.edu/admin/exe/

• 2bit files for your genome of interest. Find links to these here: http://hgdownload.cse.ucsc.edu/downloads.html

Installation

• For more information about the installation, please check the tutorial document (Page 9 in 'rtfbsdb-tutorial.pptx').

• If the package has been installed in R, please use the following codes to update the package.

library("devtools");
install_github("Danko-Lab/rtfbs_db/rtfbsdb")

Documents

• Tutorial, including installation and usage. https://github.com/Danko-Lab/rtfbs_db/blob/master/rtfbsdb-tutorial.pptx

• R vignette: https://github.com/Danko-Lab/rtfbs_db/blob/master/rtfbsdb-vignette.pdf

• R manual: https://github.com/Danko-Lab/rtfbs_db/blob/master/rtfbsdb-manual.pdf

• Paper: https://doi.org/10.1093/bioinformatics/btw338

How to cite?

• Wang, Z., Martins, A. L., & Danko, C. G. (2016). RTFBSDB: an integrated framework for transcription factor binding site analysis. Bioinformatics, 32(19), 3024-3026.

rtfbs_db's Issues

One more TFBS database (HOCOMOCO)

Hello.
Please consider inclusion of motifs from the biggest and very well performing human&mouse motif database Hocomoco-10 (http://hocomoco.autosome.ru/). It contains motifs for 601 human TFs (and where possible these motifs are non-redundant).
On downloads page (http://hocomoco.autosome.ru/downloads) you can get motifs in several formats.
Hope you will find our database interesting and useful for your project.
Best, Ilya.

Setting lowest.reads.RPKM=5 in tfbs.selectExpressedMotifs() results in NULL values which causes clustering to fail

Following the package vignette exactly but changing lowest.reads.RPKM=5 causes this error:

> tfs2 <- tfbs.selectExpressedMotifs(tfs,
+                                   file.hg19.twobit.chr19, 
+                                   file.gencode.gtf.chr19, 
+                                   file.bigwig.plus.chr19, 
+                                   file.bigwig.minus.chr19, 
+                                   pvalue.threshold=0.001,
+                                   include.DBID.missing=TRUE,
+                                   lowest.reads.RPKM=5,
+                                   seq.datatype="PRO-seq");
  12758 transcripts are selected from GENCODE dataset for PRO-seq .
  13591245 Reads in /home/gvillafano/R/x86_64-pc-linux-gnu-library/3.3/rtfbsdb/extdata/GSM1480327_K562_PROseq_chr19_plus.bw and /home/gvillafano/R/x86_64-pc-linux-gnu-library/3.3/rtfbsdb/extdata/GSM1480327_K562_PROseq_chr19_minus.bw 
* 1851 motifs did not find DBID in the Gencode file.
* 1957 expressed TFs are selected from 1964 motifs after filtering by the gene expression.
> tfs <- tfbs.clusterMotifs(tfs2, method="agnes", pdf.heatmap="heatmap.pdf", ncores=11)
Error in 1 - mat : non-numeric argument to binary operator
In addition: Warning message:
In mclapply(1:tfbs@ntfs, function(i) { :
  all scheduled cores encountered errors in user code

The reason is include.DBID.missing=TRUE here results in NULL values, and any NULL values in the tfs.filt@pwm list are problematic because they then cause tfbs.clusterMotifs() to fail.

> sum(sapply(tfs2@pwm, is.null))
[1] 1851

As a workaround one can set include.DBID.missing=FALSE

Not sure what the best solution here would be ... perhaps subset the pwm matrix list to remove the NULL values and store the problematic DBID indices in another S4 slot for later inspection?

Additional info

Using git version 5dbf18d of the rtfbs_db package

> sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils     datasets 
 [9] methods   base     

other attached packages:
 [1] rGADEM_2.22.0        seqLogo_1.40.0       BSgenome_1.42.0      rtracklayer_1.34.2  
 [5] TFBSTools_1.12.2     rtfbsdb_0.4.0        Biostrings_2.42.1    XVector_0.14.1      
 [9] GenomicRanges_1.26.4 GenomeInfoDb_1.10.3  IRanges_2.8.2        S4Vectors_0.12.2    
[13] BiocGenerics_0.20.0 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.10                lattice_0.20-34             GO.db_3.4.0                
 [4] png_0.1-7                   Rsamtools_1.26.2            gtools_3.5.0               
 [7] digest_0.6.12               R6_2.2.0                    plyr_1.8.4                 
[10] RSQLite_1.1-2               httr_1.2.1                  ggplot2_2.2.1              
[13] zlibbioc_1.20.0             lazyeval_0.2.0              annotate_1.52.1            
[16] rtfbs_0.3.5                 R.utils_2.5.0               R.oo_1.21.0                
[19] Matrix_1.2-8                apcluster_1.4.3             splines_3.3.2              
[22] BiocParallel_1.8.2          readr_1.1.0                 stringr_1.2.0              
[25] CNEr_1.10.2                 bigWig_0.2-9                RCurl_1.95-4.8             
[28] munsell_0.4.3               DirichletMultinomial_1.16.0 SummarizedExperiment_1.4.0 
[31] KEGGREST_1.14.1             tibble_1.3.0                XML_3.98-1.6               
[34] TFMPvalue_0.0.6             GenomicAlignments_1.10.1    bitops_1.0-6               
[37] R.methodsS3_1.7.1           xtable_1.8-2                gtable_0.2.0               
[40] DBI_0.6-1                   magrittr_1.5                scales_0.4.1               
[43] stringi_1.1.5               reshape2_1.4.2              latticeExtra_0.6-28        
[46] vioplot_0.2                 RColorBrewer_1.1-2          tools_3.3.2                
[49] Biobase_2.34.0              poweRlaw_0.70.0             hms_0.3                    
[52] rphast_1.6.5                AnnotationDbi_1.36.2        colorspace_1.3-2           
[55] cluster_2.0.6               caTools_1.17.1              memoise_1.0.0              
[58] VGAM_1.0-3                 
>