update docs

README.md

@@ -57,51 +57,17 @@ Furthermore, steps can be skipped (to avoid re-running steps unnecessarily). Use
 
     ./run.py -h
 
-## Obtaining and preparing data
+## Further reading
 
-Scripts to download and prepare data from the [Sequence Read Archive](https://www.ncbi.nlm.nih.gov/sra) are included in
-LSTrAP in the folder **helper**. Furthermore, it is recommended to remove splice variants from the GFF3 files, a script
-to do that is included there as well. Detailed instructions for each script provided to obtain and prepare data can be
-found [here](docs/helper.md)
+  * [LSTrAP output](docs/example_output.md)
+  * [Quality statistics](docs/quality.md): How to check the quality of samples and remove problematic samples
+  * [Helper Scripts](docs/helper.md): To acquire data from the [Sequence Read Archive](https://www.ncbi.nlm.nih.gov/sra)
+  and process results.
 
-## Quality report
-
-After running LSTrAP a log file (*lstrap.log*) is written, in which samples which failed a quality measure
-are reported. Note that __no samples are excluded from the final network__. In case certain samples need to be excluded
-from the final network remove the htseq file for the sample you which to exclude and re-run the pipeline skipping all
-steps prior to building the network.
-
-    ./run.py config.ini data.ini --skip-interpro --skip-orthology --skip-bowtie-build --skip-trim-fastq --skip-tophat --skip-htseq --skip-qc
-
-More information on how the quality of samples is determined can be found [here](docs/quality.md).
-
-## Output
-
-Apart from the output all tools included generate, LSTrAP will generate raw and normalized expression matrices, a 
-co‑expression network and co‑expression clusters.
-
-A detailed overview of files produces, including examples, can be found [here](docs/example_output.md).
-
-## Helper Scripts
-
-LSTrAP comes with a few additional scripts to assist users to download and process data from the [Sequence Read Archive](http://www.ncbi.nlm.nih.gov/sra),
-repeat analyses and the case study reported in the manuscript (Proost et al., *under preparation*).
-
-Details for each script can be found [here](docs/helper.md)
-
-## Running LSTrAP on transcriptome data
-
-To use LSTrAP on a *de novo* assembled transcriptome a little pre-processing is required. Instead of the genome a fasta 
-file containing **coding** sequences can be used (remove UTRs). Using the helper script fasta_to_gff.py a gff file suited
-for LSTrAP can be generated.
-
-    python3 fasta_to_gff.py /path/to/transcript.cds.fasta > output.gff
-
-
 
 ## Contact
 
-LSTrAP was developed by [Sebastian Proost](mailto:proost@mpimp-golm.mpg.de) and [Marek Mutwil](mailto:mutwil@mpimp-golm.mpg.de) at the [Max-Planck Institute for Molecular Plant Physiology](http://www.mpimp-golm.mpg.de/2168/en)
+LSTrAP was developed by [Sebastian Proost](mailto:proost@mpimp-golm.mpg.de) and [Marek Mutwil](mailto:mutwil@gmail.com) at the [Max-Planck Institute for Molecular Plant Physiology](http://www.mpimp-golm.mpg.de/2168/en)
 
 ## Acknowledgements and Funding
 

docs/helper.md

@@ -18,6 +18,11 @@ Script to convert sra files into fastq. Sratools is required.
 
     python3 sra_to_fastq.py /sra/files/directory /fastq/output/directory
 
+## Running LSTrAP on transcriptome data
+
+To use LSTrAP on a *de novo* assembled transcriptome a little pre-processing is required. Instead of the genome a fasta 
+file containing **coding** sequences can be used (remove UTRs). Using the helper script fasta_to_gff.py a gff file suited
+for LSTrAP can be generated.
 
 ### parse_gff.py