Update README.md

README.md

@@ -3,27 +3,27 @@
 [![Version](https://img.shields.io/github/v/release/gagneurlab/drop?include_prereleases)](https://github.com/gagneurlab/drop/releases)
 [![Version](https://readthedocs.org/projects/gagneurlab-drop/badge/?version=latest)](https://gagneurlab-drop.readthedocs.io/en/latest)
 
-The detection of RNA Outliers Pipeline (DROP) is an integrative workflow to detect aberrant expression, aberrant splicing, and mono-allelic expression from raw sequencing data. 
+The detection of RNA Outliers Pipeline (DROP) is an integrative workflow to detect aberrant expression, aberrant splicing, and mono-allelic expression from raw RNA sequencing data. 
 
 The manuscript is available in [Nature Protocols](https://www.nature.com/articles/s41596-020-00462-5).
 
-The website containing the different reports of the Geuvadis demo dataset described in the paper can be found [here](https://cmm.in.tum.de/public/paper/drop_analysis/webDir/drop_analysis_index.html).
+This [website](https://cmm.in.tum.de/public/paper/drop_analysis/webDir/drop_analysis_index.html) contains the different reports of the Geuvadis demo dataset described in the paper.
 
-This [video](https://www.youtube.com/watch?v=XvgjiFQClhM&t=2761s) can serve as an introduction to the tools used in DROP and their application to rare disease diagnostics.
+This [video](https://www.youtube.com/watch?v=XvgjiFQClhM&t=2761s) presents the tools used in DROP and their application to rare disease diagnostics.
 
 <img src="drop_sticker.png" alt="drop logo" width="200" class="center"/>
 
 
 ## Quickstart
 DROP is available on [bioconda](https://anaconda.org/bioconda/drop).
-We recommend using a dedicated conda environment (`drop_env` in this example). Installation time: ~ 10min.
+We recommend using a dedicated conda environment (`drop_env` in this example). Installation time: ~ 15 min.
 ```
 mamba create -n drop_env -c conda-forge -c bioconda drop --override-channels
 ```
 
-In the case of mamba/conda troubles we recommend using the fixed `DROP_<version>.yaml` installation file we make available on our [public server](https://www.cmm.in.tum.de/public/paper/drop_analysis/). Install the current version and use the full path in the following command to install the conda environment `drop_env`
+In the case troubles with mamba or conda, we recommend using the fixed `DROP_<version>.yaml` installation file we make available on our [public server](https://www.cmm.in.tum.de/public/paper/drop_analysis/). Install the current version and use the full path in the following command to install the conda environment `drop_env`
 ```
-mamba env create -f DROP_1.3.3.yaml
+mamba env create -f DROP_1.4.0.yaml
 ```
 
 Test installation with demo project
@@ -47,17 +47,16 @@ For more information on different installation options, refer to the
 
 ## What's new
 
-Versions 1.3.3, 1.3.2 and 1.3.1 fix some bugs.
+Version 1.4.0 fixes some bugs regarding the split reads counting for FRASER, which only affects cohorts containing samples with different strands (stranded forward, stranded reverse or unstranded). If your cohort contained samples with different strands, please rerun the AS module using version 1.4.0. In addition, due to snakemake updates affecting wBuild and the way we installed FRASER, installing DROP 1.3.3 no longer works. Version 1.3.4 fixes the FRASER version to ensure reproducibility and fixes certain scripts affected by the snakemake update. Running the pipeline with version 1.3.4 should provide the same outlier results as 1.3.3.
+
 Version 1.3.0 introduces the option to use FRASER 2.0 which is an improved version of FRASER that uses the Intron Jaccard Index metric instead of percent spliced in and splicing efficiency to quantify and later call aberrant splicing. To run FRASER 2.0, modify the `FRASER_version` parameter in the aberrantSplicing dictionary in the config file and adapt the `quantileForFiltering` and `deltaPsiCutoff` parameters. See the [config template](https://github.com/gagneurlab/drop/blob/master/drop/template/config.yaml) for more details. When switching between FRASER versions, we recommend running DROP in a
 separate folder for each version. Moreover, DROP now allows users to provide lists of genes to focus on and do the multiple testing correction instead of the usual transcriptome-wide approach. Refer to the [documentation](https://gagneurlab-drop.readthedocs.io/en/latest/prepare.html#limiting-fdr-correction-to-subsets-of-genes-of-interest).
 
 `Snakemake v.7.8` introduced some changes in which changes in parameters can cause rules to be re-executed. More info [here](https://github.com/snakemake/snakemake/issues/1694). This affects DROP and causes certain rules in the AS and QC modules to be triggered even if they were already completed and there were no changes in the sample annotation or scripts. The workaround is to run DROP by adding the parameter `--rerun-triggers mtime`, e.g. `snakemake -n --rerun-triggers mtime` or `snakemake --cores 10 --rerun-triggers mtime`. We will investigate the rules in DROP to fix this.
 
-Version 1.2.3 simplifies the plots in the AE Summary Script. In addition, there's a new heatmap in the sampleQC Summary that allows to better identify DNA-RNA mismatches.
-
 As of version 1.2.1 DROP has a new module that performs RNA-seq variant calling. The input are BAM files and the output either a single-sample or a multi-sample VCF file (option specified by the user) annotated with allele frequencies from gnomAD (if specified by the user). The sample annotation table does not need to be changed, but several new parameters in the config file have to be added and tuned. For more info, refer to the [documentation](https://gagneurlab-drop.readthedocs.io/en/latest/prepare.html#rna-variant-calling-dictionary).
 
-Also, as of version 1.2.1 the integration of external split and non-split counts to detect aberrant splicing is now possible. Simply specify in a new column in the sample annotation the directory containing the counts. For more info, refer to the [documentation](https://gagneurlab-drop.readthedocs.io/en/latest/prepare.html#external-count-examples).
+Also, as of version 1.2.1 the integration of external split and non-split counts to detect aberrant splicing is now possible. In a new column in the sample annotation, simply specify the directory containing the counts. For more info, refer to the [documentation](https://gagneurlab-drop.readthedocs.io/en/latest/prepare.html#external-count-examples).
 
 
 ## Set up a custom project