diff --git a/README.md b/README.md
index 4cc3d07e9f4b1a03d4ea1a0e10d56cff3c8d14c7..21e234a65748b2fa7cc33ddd1e303b6747d0e125 100755
--- a/README.md
+++ b/README.md
@@ -19,9 +19,9 @@ BAM files from Strand-seq experiments and the final output are SV predictions in
a tabular format as well as in a graphical representation. To get to this point,
the workflow goes through the following steps:
- 1. Read binning in fixed-width genomic windows of 50kb or 100kb via [mosaicatcher](https://github.com/friendsofstrandseq/mosaicatcher)
- 2. Normalization of coverage in respect to a reference sample (included)
- 3. Strand state detection ([mosaicatcher](https://github.com/friendsofstrandseq/mosaicatcher))
+ 1. Read binning in fixed-width genomic windows of 100kb via [mosaicatcher](https://github.com/friendsofstrandseq/mosaicatcher)
+ 2. Normalization of coverage with respect to a reference sample (included)
+ 3. Strand state detection (included)
4. Haplotype resolution via [StrandPhaseR](https://github.com/daewoooo/StrandPhaseR)
5. Multi-variate segmentation of cells ([mosaicatcher](https://github.com/friendsofstrandseq/mosaicatcher))
6. Bayesian classification of segmentation to find SVs using mosaiClassifier (included)
@@ -64,56 +64,7 @@ below `bam/`. The same settings from the `Snake.config.json` config files are
applied to all samples.
-## Installation / Execution
-
-> A Snakemake version of at least 4.8.0 is required for Singularity support.
-> When only an old Snakemake version is available, remove the `singularity`
-> line in `Snakefile` and go for option 2 or 3.
-
-### Option 1: Singularity/Docker image
-
-We provide a [Docker image](https://hub.docker.com/r/smei/mosaicatcher-pipeline/)
-of this pipeline, which can be used in Snakemake together with
-[Singularity](https://singularity.lbl.gov/). This image contains all software
-(but no data) required to run MosaiCatcher.
-
- 1. **Singularity required.** We tested this with version 2.5.1.
-
- 2. **Provide SNP call set (optional).** External VCF files (if available) should be
- *copied* into a subfolder of the pipeline, which can be read from within the image.
- Accordingly, you need to specify a relative path in `Snake.config-singularity.json`.
-
- 3. **Run Snakemake with `--use-singularity` option.** The software inside the
- Singularity image need to access external data, such as the reference genome.
- These are specified in a separate config file.
-
- We also stripped off the content of the R package
- [BSgenome.Hsapiens.UCSC.hg38](http://www.bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg38.html)
- (find it in your local R installation), which need to be made available inside
- the image by binding these files during execution.
- This is how the command looks like:
-
- ```
- # paths on the host system
- REF="~/data/refGenomes/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna"
- R_REF="~/R-lib/3.4.0/BSgenome.Hsapiens.UCSC.hg38/extdata/single_sequences.2bit"
-
- snakemake \
- --use-singularity \
- --singularity-args \
- "-B ${REF}:/reference.fa:ro \
- -B ${REF}.fai:/reference.fa.fai:ro \
- -B ${R_REF}:/usr/local/lib/R/site-library/BSgenome.Hsapiens.UCSC.hg38/extdata/single_sequences.2bit:ro" \
- --configfile Snake.config-singularity.json
- ```
-
- > **Note:** Currently only hg38 is supported within the singularity inmage.
-
-
-
-### Option 2: Bioconda environment
-
-To install the correct environment, you can use Bioconda.
+## Installation using the Bioconda environment
1. **Install MiniConda:**
In case you do not have Conda yet, it is easiest to just install
@@ -133,63 +84,6 @@ In case you do not have Conda yet, it is easiest to just install
4. **Run** `snakemake`
-
-### Option 3: Manual setup
-
-1. **Install required software:**
-
- * Install [mosaicatcher](https://github.com/friendsofstrandseq/mosaicatcher)
- (*currently you will need the `develop` branch*)
- * Install *BSgenome.Hsapiens.UCSC.hg38* from [Bioconductor](http://www.bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg38.html):
-
- ```
- source("https://bioconductor.org/biocLite.R")
- biocLite('BSgenome.Hsapiens.UCSC.hg38')
- ```
-
- * Install [Strand-Phaser](https://github.com/daewoooo/StrandPhaseR).
- This is no longer installed automatically
- * Install other required R packages
-
-2. **Set up the configuration of the snakemake pipeline**
-
- * Open `Snake.config.json` and specify the path to the executatables
- (such as Mosaicatcher) and to the R scripts.
-
-3. Run `snakemake`
-
-
-## Cluster support (experimental)
-
-You can ask Snakemake to submit your jobs to a HPC cluster. We provided a config
-file (`cluster.json`) for this purpose, yet it might need to be adapted to your
-infrastructure. Here is an example command:
-
- ```
- snakemake -j 100 \
- --cluster-config Snake.cluster.json \
- --cluster "sbatch --cpus-per-task {cluster.n} --time {cluster.time} --mem {cluster.mem}"
- ```
-
- Further, it is often advisable to increase the time Snakemake waits for the
- file system via this flag:
-
- ```
- --latency-wait 60
- ```
-
- In the HPC system this was tested (based on SLURM), Snakemake sometimes does not
- recognize if a job was killed on the cluster and hangs up waiting for it to finish.
- To overcome this, we provide a small script called `cluster_status.py` which can
- be passed to Snakemake as shown below. Note that this script might need to be adapted.
-
- ```
- --cluster-status cluster_status.py
- ```
-
- Finally, of course the cluster mode can be combined with `--use-singularity`.
-
-
## SNP calls
The pipeline will run simple SNV calling using [samtools](https://github.com/samtools/samtools) and [bcftools](https://github.com/samtools/bcftools) on Strand-seq. If you **already have