#QC pipeline for simulated data

/opt/gaoyan/script/realdata

##classification  
		
	sh classification.sh SAMPLE_NAME reads.fa

return species number

##concoct
	
	sh concoct.sh SAMPLE_NAME contigs.fa PEreads1 PEreads2 species_number
	
generate concoct_SAMPLE_NAME/contigs/megahit_c10K.fa, concoct_SAMPLE_NAME/output/clustering_gt1000.csv 
	
output directory: concoct_SAMPLE_NAME

##reads mapping contigs

	sh reads_mapping.sh SAMPLE_NAME contigs_cutting.fa PEreads1 PEreads2

generate reads_mapping_contigs_SAMPLE_NAME/out.F

output directory: reads_mapping_contigs_SAMPLE_NAME

##find target_cluster

	sh target_find.sh contigs_cutting.fa marker.fa clustering_gt1000.csv SAMPLE_NAME
	
generate marker_blast_SAMPLE_NAME/target

output directory: marker_blast_SAMPLE_NAME

##analysis clusters

	sh blast_megahit.sh contigs_cutting.fa blast_result_file clustering_gt1000.csv out.F DIR_NAME
	
generate DIR_NAME/contig_list, DIR_NAME/contig_species, DIR_NAME/cluster_s* DIR_NAME/count, DIR_NAME/count_read

output directory: DIR_NAME

##evaluation

	sh target_evaluation.sh clusterN(target cluster) contig_species cluster_sN(target cluster) out.F DIR_NAME

generate DIR_NAME/result

output directory: DIR_NAME

##dataloss

	sh dataloss.sh contigs_list clustering_gt1000.csv out.F DIR_NAME
	
generate DIR_NAME/result_dataloss

output directory: DIR_NAME

##extract target reads
	sh findclusterreads.sh cluster_sN(target cluster) out.F SAMPLE_NAME

generate target_reads_SAMPLE_NAME/clusterreads

output directory: target_reads_SAMPLE_NAME

	python extract.py

generate target_reads.fa

##coverage

	python coverage_percent.py
	
return percent of positions with positive coverage 


**scripts could be found in /home/xw/QC

**tools utilized by us
Parallel_Meta http://computationalbioenergy.org/parallel-meta.html
MEGAHIT https://github.com/voutcn/megahit
CONCOCT https://github.com/BinPro/CONCOCT
BLAST https://blast.ncbi.nlm.nih.gov/Blast.cgi