recent changes

README.md

@@ -1,23 +1,22 @@
-<div align="center">
-    <h1>ScanNeo2 (working title)</h1>
+-<div align="left">
+    <h1>ScanNeo2</h1>
+    <img src="https://img.shields.io/badge/snakemake-≥6.4.1-brightgreen.svg">
     <img src="https://github.com/ylab-hi/ScanNeo2/actions/workflows/linting.yml/badge.svg" alt="Workflow status badge">
 </div>
 
-
 ## What is ScanNeo2
-`Scanneo2` is a snakemake workflow for the prediction of neoantigens from 
-multiple sources. In its current state, this includes 
+`Scanneo2` is a snakemake workflow for the prediction of neoantigens from multiple sources. In its current state, 
+this includes canonical-splicing, exitron-splicing, gene fusion, indels and snvs.
 
 ## Getting Started
 
-In principle, Scanneo2 aims to 
-
+In principle, Scanneo2 aims to resolve its dependencies automatically and requires only snakemake and snakedeploy.
 
 ## Quickstart
 
 Install `snakemake` and `snakedeploy`
 ```
-mamba env create --file https://
+mamba env create --file https://github.com/ylab-hi/ScanNeo2/blob/devel/environment.yml
 mamba activate scanneo2
 ```
 Deploy Scanneo2
@@ -26,17 +25,19 @@ mkdir -p /path/to/working/directory/
 cd /path/to/working/directory/
 snakedeploy deploy-workflow https://github.com/ylab-hi/scanneo2 . --tag v0.1.0
 ```
+Configure ScanNeo2 by modifying `config/config.yml`
+
 Run the workflow
 ```
+cd scanneo
 snakemake --cores all --use-conda
 ```
-
-Please consult the wiki for detailed instruction and explanations.
-
+Please consult the [wiki](https://github.com/ylab-hi/ScanNeo2/wiki) for detailed instruction and explanations.
 
 ### Docker
 
-We also provide a ready to use Docker Container that can be used 
+We also provide a ready-to-use [Docker Container](https://hub.docker.com/r/yanglabinfo/scanneo2) 
+that can be used to use Scanneo2.
 
 
 

config/config.yaml

@@ -1,11 +1,30 @@
-refgen: testdata/GRCh38.p13.genome.fa
-annotation: testdata/GRCh38_chr1.gtf  
+data:
+  name:  patient2
+  dnaseq: 
+    rep1: TESLA_testdata/patient2/WES/TESLA_9_1.fastq.gz TESLA_testdata/patient2/WES/TESLA_9_2.fastq.gz
+    rep2: TESLA_testdata/patient2/WES/TESLA_10_1.fastq.gz TESLA_testdata/patient2/WES/TESLA_10_2.fastq.gz
+  rnaseq:
+    rep1: TESLA_testdata/patient2/RNA/TESLA_11_1.fastq.gz TESLA_testdata/patient2/RNA/TESLA_11_2.fastq.gz
 
+
+
+
+sample: patient2  # naming of the sample (results/sample)
 dnaseq:
 rnaseq: LNCAP_bam/G28033.LNCaP_clone_FGC.1.bam
 
 
-preproc: true
+### pre-processing (only applied on fastq reads)
+preproc: 
+  activate: true  # whether (=true) or not (=false) to include pre-processing
+  minlen: 10  # discard reads which are less than <minlen> bases 
+  leading: 3  # remove leading low quality or N bases
+  trailing: 3  # remove trailing low quality or N bases
+  slidingwindow:
+    activate: true  # whether (=true) or not (=false) to include pre-processing
+    windowsize: 3  # number of bases to average across
+    quality: 20  # the average quality (Phred) required
+  adapters: TruSeq2-PE.fa    # path to fasta file containg adapters to be trimmed
 
 
 # if no readgroups file are provided