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Update readme: specify dependency stage + docker #203

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Update readme: specify dependency stage + docker
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19 changes: 12 additions & 7 deletions README.md
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@@ -1,7 +1,7 @@
[![Build Status](https://travis-ci.org/debbiemarkslab/EVcouplings.svg?branch=master)](https://travis-ci.org/debbiemarkslab/EVcouplings)
# EVcouplings

Predict protein structure, function and mutations using evolutionary sequence covariation.
Predict protein structure, function and mutations using evolutionary sequence covariation. Runs online at [evcouplings.org](http://evcouplings.org)

## Installation and setup

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*After installation and before running compute jobs, the paths to the respective binaries of the following external tools have to be set in your EVcouplings job configuration file(s).*

#### plmc (required)
#### plmc (required | couplings stage)

Tool for inferring undirected statistical models from sequence variation. Download and install plmc to a directory of your choice from the [plmc github repository](https://github.com/debbiemarkslab/plmc) according to the included documentation.

Expand All @@ -41,31 +41,31 @@ For compatibility with evcouplings, please compile using
make all-openmp32


#### jackhmmer (required)
#### jackhmmer (required | align+compare stages)

Download and install HMMER from the [HMMER webpage](http://hmmer.org/download.html) to a directory of your choice.

#### HHsuite (optional)
#### HHsuite (optional | align stage)

evcouplings uses the hhfilter tool to filter sequence alignments. Installation is only required if you need this functionality.

Download and install HHsuite from the [HHsuite github repository](https://github.com/soedinglab/hh-suite) to a directory of your choice.

#### CNSsolve 1.21 (optional)
#### CNSsolve 1.21 (optional | fold stage)

evcouplings uses CNSsolve for computing 3D structure models from coupled residue pairs. Installation is only required if you want to run the *fold* stage of the computational pipeline.

Download and unpack a compiled version of [CNSsolve 1.21](http://cns-online.org/v1.21/) to a directory of your choice. No further setup is necessary, since evcouplings takes care of setting the right environment variables internally without relying on the included shell script cns_solve_env
(you will have to put the path to the cns binary in your job config file however, e.g. cns_solve_1.21/intel-x86_64bit-linux/bin/cns).

#### PSIPRED (optional)
#### PSIPRED (optional | fold stage)

evcouplings uses PSIPRED for secondary structure prediction, to generate secondary structure distance and dihedral angle restraints for 3D structure computation.
Installation is only required if you want to run the *fold* stage of the computational pipeline, and do not supply your own secondary structure predictions.

Download and install [PSIPRED](http://bioinfadmin.cs.ucl.ac.uk/downloads/psipred/) according to the instructions in the included README file.

#### maxcluster (optional)
#### maxcluster (optional | fold stage)

evcouplings uses maxcluster to compare predicted 3D structure models to experimental protein structures, if there are any for the target protein or one
of its homologs. Installation is only required if you want to run the *fold* stage of the computational pipeline.
Expand Down Expand Up @@ -109,6 +109,11 @@ Relevant PDB structures for comparison of ECs and 3D structure predictions will
Uniprot to PDB index mapping files will be automatically generated by EVcouplings based on the SIFTS database.
You can either generate the files by running *evcouplings_dbupdate* (see above, preferred), or by pointing the sifts_mapping_table and sifts_sequence_db configuration parameters to file paths inside an already existing directory. If these files do not yet exist, they will be created by fetching and integrating data from the web (this may take a while) when the pipeline is first run and saved under the given file paths.

## Community efforts

- **Docker installation**
@nselem has compiled a set of dockerfiles to build the pipeline (including most dependencies) via Docker: https://github.com/nselem/evcouplings

## Documentation and tutorials

Please refer to the Jupyter notebooks in the [notebooks subdirectory](https://github.com/debbiemarkslab/EVcouplings/tree/master/notebooks) on how to
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