(Please refer test_files/run_test_files.sh to run the pipeline and to run MoSEA directly with SUPPA events).
Required Input:
--bedfile : Bed file in atleast 6 columns format, Default:strandness is TRUE
--genome : Genome Fasta File sequence in single line, eg. hg19.fa
Optional Input:
--output : Output file name, Default: STDOUT
--bedtoolspath : Bedtools bin path, Default: search for 'bedtools' in local path
Output: 1. Sequence Fasta file
Usage:
python mosea.py getfasta --bedfile sample.bed --genome /path/to/hg19.fa --output sample_output.fa
Usage (with optional parameter):
python mosea.py getfasta --bedfile sample.bed --genome /path/to/hg19.fa --output sample_output.fa --bedtoolspath /path/to/bedtools/bin/bedtools
Required Input:
--scan : Scans for given motif (this option is always true)
--pfm : If selected; Scans for motif using PFMs
--pfm_path : Path to folder where PFM matrices of motifs are stored
--kmer : If selected; Scans for motif using k-mers
--kmer_path: Path to k-mer(s) folder, (Note: kmers file should be saved with *.kmer extension, in fasta format or just one kmer in each line. Motif-label will be taken from file_name before ext (eg. for file 'SRSF1.kmer', label will be 'SRSF1').
--fasta : Input Fasta file (Sequences in Fasta file format to scan motifs)
Optional Input:
--count : Also creates count_file in tabular format for all motifs found on each sequences.
--out_dir : Output Directory (path to output directory, Default: creates fmo/ directory in current working path)
--fmo_path : Path to FIMO binary folder, Default: Search for 'fimo' in local path
Output: 1. fmo output files for each motif in out_dir/
2. Summary count_file table for all motifs and all sequences in one file (if --count)}
Usage:
python mosea.py scan --pfm --pfm_path /path/to/motif_pfm_dir/ --fasta sample_output.fa
or
python mosea.py scan --kmer --kmer_path /path/to/motif_kmer_dir/ --fasta sample_output.fa
Usage (with optional parameter):
python mosea.py scan --scan --pfm --pfm_path /path/to/motif_pfm_dir/ --fasta sample_output.fa --out_dir /path/my/fmo_output_dir/ --fmo_path /usr/local/bin/fimo --count
Required Input:
--reg_fa_file : Input file for regulated sequences in fasta format
--reg_count_file : Input file for regulated motif counts in tab seperated format(header=Motif_ids)
--bg_fa_file : Input file for background (control) sequences in fasta format
--bg_count_file : Input file for background (control) motif counts in tab seperated format(header=Motif_ids)
Optional Input:
--out_file : Creates output for enrichment test analysis, Default: output_MoSEA_enrichment.txt
--rand_count : Number of times randomization performed for each regulated sequence. Default:100
--match_len : Match length of background sequences to each regulated sequence(1=True, 0=False). Default: 0 (False)
--len_ext : Percent range of length extension on both side boundaries, to match with background sequences.
This option is valid only when '--match_len 1'.
Default: 15 (15% length extended on both sides)
Output: 1. Enrichment test output file for each motif with zscore
Usage:
python mosea.py enrich --reg_fa_file reg_sample_output.fa --reg_count_file reg_sample_output.space
--bg_fa_file bg_sample_output.fa --bg_count_file bg_sample_output.space
Usage (with optional parameter):
python mosea.py enrich --reg_fa_file reg_sample_output.fa --reg_count_file reg_sample_output.space
--bg_fa_file bg_sample_output.fa --bg_count_file bg_sample_output.space
--out_file my_output_file.tab --rand_count 200 --match_len 1 --len_ext 20
Requirements:
- Python 2.7 (numpy, bio, pandas, StringIO, collections, math)
- Bedtools
- Fimo (MEME-Suite version >version 4.11 or higher)
- Genome fasta sequence (\*.fa and \*.fai files)
- Motif PFMs or Kmers file or directory
Input files:
- Suppa event ids for regulated set (or Bedfile cordinates)
- Suppa event ids for control set (or Bedfile cordinates)
The analysis is divided into two steps, A) Motif Scan B) Enrichment
Input: 2 files:
- Regulated & Control event ids (Suppa events or Bedfile format)
Steps: Input_file -> Extract Sequence -> Scan sequences for Motif -> Create count table Requirement: Genome Fasta file (hg19.fa), Bedtools, FIMO from MEME suite
Input: 4 files: (All these files are generated in Analysis A)
- Regulated fasta sequences file
- Regulated motif count table
- Control(Background) fasta sequence file
- Control motif count file
Steps: For each regulated sequence -> create pools of Control seqs matching GC content and length of seq -> randomize 100 times -> count motifs on reg & control -> calculate z-score by observe(reg) vs expected (distribution from control) ((x-mean)/SD)
To test run call this script from MoSEA directory:
cd MoSEA/
./test_files/run_test_files.sh (Please point the paths to appropriate directories)
###Commands:
(This step is not necessary if cordinates are already in bedfile format. Go to step 2 directly)
$path_python ./mosealib/suppa_to_bed.py --ifile $reg_infile --event SE --ext 200 --ofile $bedfile_reg
(Ext : Extension is upstream and downstream regions from the center Exon)
$path_python mosea.py getfasta --bedfile $bedfile_reg --genome $path_genome --output $fafile_reg
fmopfm_outdir="fmo_pfm" #output dir for scanned motifs
$path_python mosea.py scan --pfm --pfm_path $path_pfms --fasta $fafile_reg --out_dir $fmopfm_outdir --fmo_path $path_fimo --count
Repeat above three steps for control files as well. Ideally, number of events in control set must be atleast 100x more than regulated set.
Required Input: 4 files (2 regulated files: fasta & count_table, 2 control files: fasta & count_table) generated in Analysis (A).
Important: Only in case of running SUPPA events, please seperate variable region types prior to each enrichment analysis. For eg.
- To seperate upstream region of SE event from fasta file (regulated and control):
grep -A1 ';up' regfile.fa >events_SE_up_reg.fa
grep -A1 ';up' controlfile.fa >events_SE_up_control.fa
- To seperate upstream region of SE event from count file (regulated and control):
cat reg_file_count | head -1 >up_reg_file_count; grep ';up' reg_file_count >>up_reg_file_count
cat control_file_count | head -1 >up_cont_file_count; grep ";up" control_file_count >>up_cont_file_count
Repeat it for all regions. (Please see run script test_files/run_script_test_all_events.sh for more information)
Regulated files : Fasta sequences and Motif count table (per region type)
- reg_file_fa
- reg_file_count
Control files : Fasta sequences and Motif count table (per region type)
- control_file_fa
- control_file_count
#Zscore Output file name
outfile="zscore_outfile.tab"
#perform enrichment
$path_python mosea.py enrich --reg_fa_file $reg_file_fa --reg_count_file $reg_file_count \
--bg_fa_file $control_file_fa --bg_count_file $control_file_count \
--out_file $outfile
To run the pipeline all at once, please run the dummy script in test_files/run_test_files.sh
To run extended version of all SUPPA events with variable region, please run script: test_files/run_script_test_all_events.sh
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