Commit 7c9c5b7a authored by Christian Arnold's avatar Christian Arnold

Version 1.2.3, see Changelog for details

parent 061c149c
......@@ -438,9 +438,9 @@ Details
For user convenience, we provide such sorted files as described in the publication as a separate download:
- hg19: For a pre-compiled list of 620 human TF with in-silico predicted TFBS based on the *HOCOMOCO 10* database and *PWMScan* for hg19, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_hg19_PWMScan_HOCOMOCOv10.tar.gz>`__
- hg38: For a pre-compiled list of 771 human TF with in-silico predicted TFBS based on the *HOCOMOCO 11* database and *FIMO* from the MEME suite1 for hg38, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_hg38_FIMO_HOCOMOCOv11.tar.gz>`_
- mm10: For a pre-compiled list of 423 mouse TF with in-silico predicted TFBS based on the *HOCOMOCO 10* database and *PWMScan* for mm10, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_mm10_PWMScan_HOCOMOCOv10.tar.gz>`__
- hg19: For a pre-compiled list of 638 human TF with in-silico predicted TFBS based on the *HOCOMOCO 10* database and *PWMScan* for hg19, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_hg19_PWMScan_HOCOMOCOv10.tar.gz>`__
- hg38: For a pre-compiled list of 767 human TF with in-silico predicted TFBS based on the *HOCOMOCO 11* database and *FIMO* from the MEME suite for hg38, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_hg38_FIMO_HOCOMOCOv11.tar.gz>`_. For a pre-compiled list of 768 human TF with in-silico predicted TFBS based on the *HOCOMOCO 11* database and *PWMScan* for hg38, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_hg38_PWMScan_HOCOMOCOv11.tar.gz>`_
- mm10: For a pre-compiled list of 422 mouse TF with in-silico predicted TFBS based on the *HOCOMOCO 10* database and *PWMScan* for mm10, `download this file: <https://www.embl.de/download/zaugg/diffTF/TFBS/TFBS_mm10_PWMScan_HOCOMOCOv10.tar.gz>`__
However, you may also manually create these files to include additional TF of your choice or to be more or less stringent with the predicted TFBS. For this, you only need PWMs for the TF of interest and then a motif prediction tool such as *FIMO* or *MOODS*.
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......@@ -53,6 +53,11 @@ We also put the paper on *bioRxiv*, please read all methodological details here:
Change log
============================
Version 1.2.3 (2019-02-27)
- Added a pre-compiled list of 768 human TF with in-silico predicted TFBS based on the *HOCOMOCO 11* database and *PWMScan* for hg38 as well as updating the other pre-compiled lists to account for recent changes and retractions in the *HOCOMOCO* database. See section :ref:`_parameter_dir_TFBS` for details.
- added an additional filtering in the binning step for a rare corner case due to changes in the number of samples during an analysis
Version 1.2.2 (2019-02-01)
- Minor code fixed. Removed the creation of the circular plot, which has been replaced with the Volcano plot over time. Fixed a bug that could have led to wrong log2 fold-change values for the RNA-Seq data under special circumstances. We recommend rerunning the ``summaryFinal`` rule. Ask us for more details if you are concerned about this.
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......@@ -265,11 +265,12 @@ for (fileCur in par.l$files_input_TF_allMotives) {
# MERGE #
#########
# TODO: full join necessary?
TF.motifs.all = TF.motifs.ori %>%
full_join(TF.motifs.CG, by = c("CG.identifier")) %>%
mutate(CG.bins = cut(CG, breaks = CGBins, labels = paste0(round(CGBins[-1] * 100,0),"%"), include.lowest = TRUE)) %>%
dplyr::select(-one_of("CG.identifier", "CG"))
dplyr::select(-one_of("CG.identifier", "CG")) %>%
dplyr::filter(!is.na(CG.bins)) # for rare cases of NA for CG:bins (which can happen if the number of samples is changed)
# Not needed anymore, delete
......
......@@ -269,7 +269,8 @@ if (file_peaks != "") {
nRowsFiltered = rowsBefore - nrow(peaks.df)
if (par.l$verbose & nRowsFiltered > 0) flog.info(paste0("Filtered ", nRowsFiltered, " non-unique positions out of ", rowsBefore, " from peaks."))
write_tsv(peaks.df, path = par.l$output_peaksClean, col_names = FALSE)
peaks.df.transf = dplyr::mutate_if(peaks.df, is.numeric, as.character)
write_tsv(peaks.df.transf, path = par.l$output_peaksClean, col_names = FALSE)
} else {
......
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