To install the software you will need:

• g (>= 4.8)

• automake (>= 2.68)

• boost libraries (>= 1.48): boost-filesystem, boost-program-options, boost-test, boost-regex sudo aptitude install libboost-dev libboost-program-options-dev libboost-test-dev libboost-filesystem-dev libboost-regex-dev

• libconfig -dev sudo aptitude install libconfig -dev

• pip

1. Dependencies

• Linux: - boost libraries sudo aptitude install sudo aptitude install libboost1.49-dev libboost-program-options1.49-dev libboost-test1.49-dev libboost-filesystem1.49-dev libboost-regex1.49-dev - libconfig -dev sudo aptitude install libconfig -dev
• Mac OS (you will need MacPorts, can be downloaded from here: https://www.macports.org/): - boost libraries sudo port install boost - libconfig -dev: git clone //github.com/hyperrealm/libconfig; cd libconfig; autoreconf; ./configure; make; sudo make install

2. KMAD KMAD can be compiled and installed from the kmad directory (you will need the automake package to do this): ./configure; make; sudo make install
3. ELM update script (scripts/update_elm.py) To be able to use the script for updating the ELM database run: pip install -r requirements.txt
4. convert script To be able to annotate predicted phosphorylations you will need to install NetPhos. You can download it here: http://www.cbs.dtu.dk/services/NetPhos/ Install it according to the provided instructions - make sure you can run it simply by calling netphos. The script for converting the input will run anyway without NetPhos, only you won't have the predicted phosphorylations. All the other features will be annotated anyway.

The simplest alignment of fasta sequences can be performed with the command:

kmad -i input.fasta -o output_prefix -c 1

To perform alignment with features you need to first create a .7c file with convert.py input_filename output_filename. The convert.py script is in the 'scripts' directory.

KMAD command for aligning with features is: (you have to use the '-c 7' flag when running on the '.7c' input)

kmad -i input.7c -o output_prefix -p 10 -d 3 -m 3 -c 7

For more detailed information try kmad --help

For the user defined features you need to provide a configuration file. If you run it on a fasta file kmad -i input_filename.fasta -o output_filename --conf config_filename -c 1 If you run it on a '.7c' file: kmad -i input_filename.fasta -o output_filename --conf config_filename -c 1 An example of a conf file is in the root directory (conf_file_example.cfg)

To update the elm database (which you don't need to run very often) simply execute: update_elm.py

You can define your own features to be included in the alignment. This can be done with a configuration file (--conf flag). An example of a configuration file is provided in the KMAD directory ("conf_file_example").

Description

All of the possible fields are present in the example file, but only some of them are opbligatory. Here's a detailed decsription of the field meanings:

name - a unique feature name; type = string; OBLIGATORY

tag - features can be assigned various categories, this is the category's name; type = string; optional

add_score - score for aligning features that add points to the alignment score; type = positive double; optional

subtract_score - score for aligning features that subtract points from the alignment score; type = positive double; optional

add_features - names of features that add points to the alignment; type = list of strings; OBLIGATORY (it can be an empty list)

add_tags - names of feature categories that add points to the alignment; type = list of strings; OBLIGATORY (it can be an empty list)

add_exceptions - names of exceptions to the add_tags field; type = list of strings; OBLIGATORY (it can be an empty list)

subtract_features - names of features that subtract points from the alignment; type = list of strings; OBLIGATORY (it can be an empty list)

subtract_tags - names of feature categories that subtract from the alignment; type = list of strings; OBLIGATORY (it can be an empty list)

subtract_exceptions - names of exceptions to the subtract_tags field; type = list of strings; OBLIGATORY (it can be an empty list) pattern - regular expression by which feature X will be assigned to all sequences positions - positions on which the feature is present: seq - sequence number (starting at 1); type = integer, pos - position numbers (starting at 1); type = list of integers; OBLIGATORY

In case you need to process large amounts of data, you might want to faster annotate your sequences. The process will be a little bit faster if you use your local copy of swissprot database. To be able to do that you need to:

1. Download swissprot files: ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz
2. Unpack them gunzip filename
3. Process the files with scripts from the 'scripts' directory (OUT_FASTA_DIR and OUT_TXT_DIR are directories where you want the fasta and txt files to be located) PATH_TO_KMAD/scripts/split_swiss_txt.py uniprot_sprot.dat OUT_FASTA_DIR PATH_TO_KMAD/scripts/split_swiss_fasta.py uniprot_sprot.fasta OUT_TXT_DIR
4. Specify the paths to these dirrectories when you run the convert.py script convert.py input output --swiss_fasta FASTA_DIR --swiss_txt TXT_DIR