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help message fix
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renewiegandt committed Dec 10, 2018
1 parent 037b2bf commit 15aa68a
Showing 1 changed file with 35 additions and 36 deletions.
71 changes: 35 additions & 36 deletions pipeline.nf
Original file line number Diff line number Diff line change
@@ -1,10 +1,4 @@
//!/usr/bin/env nextflow

Channel.fromPath(params.input).map {it -> [it.simpleName, it]}.set {bigwig_input}
Channel.fromPath(params.bed).set {bed_input}
Channel.fromPath(params.genome_fasta).into {fa_overlap; fa_scan; fa_overlap_2}
Channel.fromPath(params.jaspar_db).into {db_for_motivscan; db_for_tomtom}
Channel.fromPath(params.config).set {config}
#!/usr/bin/env nextflow

//setting default values
params.input=""
Expand Down Expand Up @@ -65,36 +59,36 @@ log.info """
Usage: nextflow run pipeline.nf --input [BigWig-file] --bed [BED-file] --genome_fasta [FASTA-file] --jaspar_db [MEME-file]
Required arguments:
--input Path to BigWig-file
--bed Path to BED-file
--genome_fasta Path to genome in FASTA-format
--jaspar_db Path to motif-database in MEME-format
--input Path to BigWig-file
--bed Path to BED-file
--genome_fasta Path to genome in FASTA-format
--jaspar_db Path to motif-database in MEME-format
Optional arguments:
Footprint extraction:
--window_length INT (Default: 200)
--step INT (Default: 100)
--percentage INT(Default: 0)
--window_length INT (Default: 200)
--step INT (Default: 100)
--percentage INT (Default: 0)
Filter unknown motifs:
--min_size_fp INT (Default: 10)
--max_size_fp INT (Default: 100)
--min_size_fp INT (Default: 10)
--max_size_fp INT (Default: 100)
Clustering:
Sequence preparation/ reduction:
--kmer INT Kmer length (Default: 10)
--aprox_motif_len INT Motif length (Default: 10)
--motif_occurence FLOAT Percentage of motifs over all sequences. Use 1 (Default) to assume every sequence contains a motif.
--min_seq_length Interations Remove all sequences below this value. (Default: 10)
Clustering:
Sequence preparation/ reduction:
--kmer INT Kmer length (Default: 10)
--aprox_motif_len INT Motif length (Default: 10)
--motif_occurence FLOAT Percentage of motifs over all sequences. Use 1 (Default) to assume every sequence contains a motif.
--min_seq_length INT Remove all sequences below this value. (Default: 10)
Clustering:
--global INT Global (=1) or local (=0) alignment. (Default: 0)
--identity FLOAT Identity threshold. (Default: 0.8)
--sequence_coverage INT Minimum aligned nucleotides on both sequences. (Default: 8)
--memory INT Memory limit in MB. 0 for unlimited. (Default: 800)
--throw_away_seq INT Remove all sequences equal or below this length before clustering. (Default: 9)
--strand INT Align +/+ & +/- (= 1). Or align only +/+ (= 0). (Default: 0)
--global INT Global (=1) or local (=0) alignment. (Default: 0)
--identity FLOAT Identity threshold. (Default: 0.8)
--sequence_coverage INT Minimum aligned nucleotides on both sequences. (Default: 8)
--memory INT Memory limit in MB. 0 for unlimited. (Default: 800)
--throw_away_seq INT Remove all sequences equal or below this length before clustering. (Default: 9)
--strand INT Align +/+ & +/- (= 1). Or align only +/+ (= 0). (Default: 0)
Motif estimation:
--motif_min_len INT Minimum length of Motif (Default: 8)
Expand All @@ -103,21 +97,27 @@ Optional arguments:
--tomtom_treshold float Threshold for similarity score. (Default: 0.01)
Moitf clustering:
--edge_weight INT Minimum weight of edges in motif-cluster-graph (Default: 5)
--motif_similarity_thresh FLOAT threshold for motif similarity score (Default: 0.00001)
--edge_weight INT Minimum weight of edges in motif-cluster-graph (Default: 5)
--motif_similarity_thresh FLOAT Threshold for motif similarity score (Default: 0.00001)
Creating GTF:
--organism [homo_sapiens | mus_musculus]
--tissues
--organism [homo_sapiens | mus_musculus]
--tissues
All arguments can be set in the configuration files.
"""
} else {
Channel.fromPath(params.input).map {it -> [it.simpleName, it]}.set {bigwig_input}
Channel.fromPath(params.bed).set {bed_input}
Channel.fromPath(params.genome_fasta).into {fa_overlap; fa_scan; fa_overlap_2}
Channel.fromPath(params.jaspar_db).into {db_for_motivscan; db_for_tomtom}
Channel.fromPath(params.config).set {config}
}


bigwig_input.combine(bed_input).set{footprint_in}
/*
this process uses the uncontinuous score from a bigWig file to estimate footpints within peaks of interest
This process uses the uncontinuous score from a bigWig file to estimate footpints within peaks of interest
*/
process footprint_extraction {
conda "${path_env}"
Expand Down Expand Up @@ -225,7 +225,7 @@ process bed_to_clustered_fasta {
tag{name}

input:
set name, file (bed) from clustered_bed
set name, file (bed) from bed_for_motif_esitmation

when:
params.fasta == false
Expand Down Expand Up @@ -349,7 +349,6 @@ process clustered_glam2 {
"""
}

*/

/*
Running Tomtom on meme-files generated by GLAM2.
Expand Down

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