added conda; bed input

pipeline.nf

@@ -1,30 +1,33 @@
 //!/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}
 
-params.min_seq = 10
+bigwig_input.combine(bed_input).set{footprint_in}
 
 process footprint_extraction {
+	conda "${path_env}"
 
 	tag{name}
 
 	input:
-	set name, file (bigWig) from bigwig_input
+	set name, file (bigWig), file (bed) from footprint_in
 
 	output:
 	set name, file ('*.bed') into bed_for_overlap_with_TFBS
 
 	script:
 	"""
-	python ${path_bin}/call_peaks.py --bigwig ${bigWig} --bed ${bed} --output_file ${name}_called_peaks.bed --window_length ${params.window_length} --step ${params.step} --threshold ${params.threshold_footprint_extraction}
+	python ${path_bin}/call_peaks.py --bigwig ${bigWig} --bed ${bed} --output_file ${name}_called_peaks.bed --window_length ${params.window_length} --step ${params.step} --percentage ${params.percentage}
 	"""
 }
 
 //Abfrage ob ausgeführt werden muss.
 process extract_known_TFBS {
+	conda "${path_env}"
 
 	input:
 	file (fasta) from fa_overlap
@@ -43,6 +46,7 @@ bed_for_overlap_with_TFBS.combine(known_TFBS_for_overlap).combine(fa_overlap_2).
 
 
 process overlap_with_known_TFBS {
+	conda "${path_env}"
 
 	input:
 	set file (bed_footprints), file (bed_motifs), file (fasta) from for_overlap
@@ -59,6 +63,7 @@ process overlap_with_known_TFBS {
 
 
 process clustering {
+	conda "${path_env}"
 
 	input:
 	file (fasta) from FASTA_for_clustering
@@ -74,6 +79,7 @@ process clustering {
 
 // Converting BED-File to one FASTA-File per cluster
 process bed_to_clustered_fasta {
+		conda "${path_env}"
 
     tag{name}
     publishDir '/mnt/agnerds/Rene.Wiegandt/10_Master/tmp/', mode: 'copy'
@@ -98,6 +104,7 @@ fasta_for_glam2 = fasta_for_glam2.flatten().map {it -> [it.simpleName, it]}
 //Running GLAM2 on FASTA-files.
 //Generating Motifs through alignment and scoring best local matches.
 process glam2 {
+		conda "${path_env}"
 
     tag{name}
 
@@ -117,6 +124,7 @@ process glam2 {
 //Running Tomtom on meme-files generated by GLAM2.
 //Tomtom searches motifs in databases.
 process tomtom {
+		conda "${path_env}"
 
     tag{name}
 
@@ -165,6 +173,7 @@ process check_for_unknown_motifs {
 
 //Get the best(first) Motif from each MEME-file
 process get_best_motif {
+	conda "${path_env}"
 
 	input:
 	set name, file(meme), file(tsv) from meme_for_scan
@@ -183,6 +192,7 @@ best_motif.combine(fa_scan).set {files_for_genome_scan}
 
 
 process genome_scan {
+	conda "${path_env}"
 
 	input:
 	set name, file(meme), file(fasta) from files_for_genome_scan
@@ -211,6 +221,7 @@ process cluster_quality {
 
 
 process create_GTF {
+	conda "${path_env}"
 
 	output:
 	file ('*.gtf') into gtf_for_uropa