========== NOTE ==========
0. Contact [email protected]
1. ACF = Arthurian Circle Finder
2. ACF takes fasta and fastq, though the quality information is NOT used.
3. Fasta/q header (such as ">Default_header") MUST be converted to ACF style so that multiple samples can be processes in one run.
   Change ">Default_header" into ">Truseq_sample_Default_header", where "sample" are the names of your samples.
   Make sure there is No underline within the sample name.
   e.g. ">Truseq_ctrl.1_Default_header" and ">Truseq_AGO2KO.1_Default_header" are OK; ">Truseq_ctrl_1_Default_header" is BAD because ACF will record "ctrl" instead of "ctrl_1".
4. Assume reads are obtained by Directional Sequencing, and the reads are assumed to be reverse-complementary to mRNAs.
   So  if the reads are actually sense (the same 5'->3' direction as mRNA), please reverse-complement all reads.
   And if the reads are actually stransless or pair-ended, please run in parallel original reads and reverse-complemented reads.
   No pair-end information is used, as the exact junction-site must be supported by a single read. Seeing is believing.
5. Pipeline through ACF_MAKE.pl is highly recommended. To see the arguments needed in each perl script, simply run the script without any arguments.
6. Splicing Strength module is adpoted from MaxEnt module from here : http://genes.mit.edu/burgelab/maxent/download/. Many thanks to Christoph Burge lab.
7. For non-commercial purposes the code is released under the General Public License (version 3). See the file LICENSE for more detail. If you are interested in commercial use of this software contact the author.
by arthur 2013-11-01
========== NOTE ==========


========= pipeline ========
1. Map all Tophat2-unmapped-reads to genome using BWA, seperate :
    1-part
    2-part-same-chromosome-same-strand  => true positive
    2-part-same-chromosome-diff-strand
    >2-part-same-chromosome             => some might be true positive, if they are originated from short exons
    >=2-part-diff-chromosome            => true negative
2. Estimate splicing strength using Christoph Berge's method for "2-part-same-chromosome-same-strand". report:
    forward-splice
    back-splice and canonical splice-motif {[GT-AT],[GC-AG],[AT-AC]}, calculate strength using CB
    back-splice and non-canonical splice-motif, using Christoph Berge's method, slide to find the maximal score and border
   Try to rescue circles using ">2-part-same-chromosome"
3. Check if both splice sites of the cirRNAs are on known exon-borders. report:
    both known exon-border (termed : MEA. [Match with Existing Annotation. somehow more reliable])
    at least one splice site sits on unknown exon-border (termed : CBR. [Chris Berge Rescued])
4. Build gtf and pseudo-transcript for results from step3
5. Map all Tophat2-unmapped-reads to pseudo-transcipts and estimate the abundance of circRNAs
6. Generate bed track for visulization
========= pipeline ========


========== 0. pre-process ==========
#1# copy folders "CB_splice" and "ACF_code" to local server

#2# Change fasta/fastq header format
#MUST-DO#
perl change_fastq_header.pl SRR650317_1.fasta SRR650317_1.fa Truseq_SRR650317left
perl change_fastq_header.pl SRR650317_2.fasta SRR650317_2.fa Truseq_SRR650317right
#MUST-DO#

#3# merge sequences from multiple fasta/fastq files into one fasta file
perl Truseq_merge_unique_fa.pl UNMAP newid SRR650317_1.fa SRR650317_2.fa
#alternatively, if there are many files to merge, generate a file (named filelist for example) contains the full-path of each file
perl Truseq_merge_unique_filelist.pl UNMAP newid filelist

#4# build BWA index, using verion 0.73a (support for higher versions will be added soon)
#   please use the included package OR download at http://sourceforge.net/projects/bio-bwa/files/bwa-0.7.3a.tar.bz2/download
/bin/bwa073a/bwa index /data//iGenome/human/Ensembl/GRCh37/Sequence/BWAIndex/genome.fa

#5# prepare for annotation
# use the gtf file from iGenome package    or    download ensembl gtf here : ftp://ftp.ensembl.org/pub/current_gtf/
perl get_split_exon_border_biotype_genename.pl /data/iGenome/human/Ensembl/GRCh37/Annotation/Genes/genes.gtf /data/iGenome/human/Ensembl/GRCh37/Annotation/Genes/Homo_sapiens.GRCh37.71_split_exon.gtf
========== 0. pre-process ==========



========== 1. How_to_Find_Your_Circles ==========
# make tab-delimited SPEC file
# These 9 parameters are mandatory :

#1# where you put BWA aligner
perl -e 'print join("\t","BWA_folder","/home/bin/bwa037a/"),"\n";' > SPEC_example.txt
#2# where you put BWA genome index
perl -e 'print join("\t","BWA_genome_Index","/data/iGenome/mouse/Ensembl/NCBIM37/Sequence/BWAIndex/genome.fa"),"\n";' >> SPEC_example.txt
#3# where you put genome sequences. Note that there MUST be one fasta sequence per chromosome. A concatenated fasta file does NOT work. 
perl -e 'print join("\t","BWA_genome_folder","/data/iGenome/mouse/Ensembl/NCBIM37/Sequence/Chromosomes/"),"\n";' >> SPEC_example.txt
#4# where you put ACF scripts
perl -e 'print join("\t","ACF_folder","/home/ACF/"),"\n";' >> SPEC_example.txt
#5# where you put Splicing Strength calculation module
perl -e 'print join("\t","CBR_folder","/home/CB_splice/"),"\n";' >> SPEC_example.txt
#6# where you put processed gtf. This is different from the gtf file from Ensembl or UCSC website. See Point-5 in Session "0. pre-process"
perl -e 'print join("\t","Agtf","/data/iGenome/mouse/Ensembl/NCBIM37/Annotation/Genes/Mus_musculus.NCBIM37.67_split_exon.gtf"),"\n";' >> SPEC_example.txt
#7# where you put reads that cannot be mapped to either genome or transcriptome directly
perl -e 'print join("\t","UNMAP","UNMAP"),"\n";' >> SPEC_example.txt
#8# where you put expression file of the collasped reads
perl -e 'print join("\t","UNMAP_expr","UNMAP_expr"),"\n";' >> SPEC_example.txt
#9# the length of your sequencing reads
perl -e 'print join("\t","Seq_len","150"),"\n";' >> SPEC_example.txt

# These 13 parameters are optional:
#10# the minimum distance of a back-splice (default = 100). The smaller this value, the more likely you can find circles from short exons
perl -e 'print join("\t","minJump","100"),"\n";' >> SPEC_example.txt
#11# the maximum distance of a back-splice (default = 1000000). The larger this value, the more likely you can find circles from long genes
perl -e 'print join("\t","maxJump","1000000"),"\n";' >> SPEC_example.txt
#12# the minimum score for the sum of splicing strength at both splice site (default = 10). A value small than 6 might suggest the circle is not generated from splicing, providing it is true
perl -e 'print join("\t","minSplicingScore","10"),"\n";' >> SPEC_example.txt
#13# the minimum number of samples that detect any given circle (default = 1).
perl -e 'print join("\t","minSampleCnt","1"),"\n";' >> SPEC_example.txt
#14# the minimum number of reads (from all samples) that detect any given circle (default = 2).
perl -e 'print join("\t","minReadCnt","2"),"\n";' >> SPEC_example.txt
#15# the minimum mapping quality of any given sequence (default = 30). A value larger than 20 is recommended.
perl -e 'print join("\t","minMappingQuality","30"),"\n";' >> SPEC_example.txt
#16# the minimum percentage of any given read is aligned (default = 0.9). The larger the better.
perl -e 'print join("\t","Coverage","0.9"),"\n";' >> SPEC_example.txt
#17# the minimum number of bases reach beyond the back-splice-site (default = 6). The larger the less likely of false-positive.
perl -e 'print join("\t","minSpanJunc","6"),"\n";' >> SPEC_example.txt
#18# the maximum error rate for re-alignment (default = 0.05). The smaller the more better.
perl -e 'print join("\t","ErrorRate","0.05"),"\n";' >> SPEC_example.txt
#19# the strand information of sequencing (default = "no"). "+" if the reads are sense to transcripts; "-" if the reads are anti-sense to transcripts; "no" if the sequencing is un-stranded.
perl -e 'print join("\t","Strandness","no"),"\n";' >> SPEC_example.txt
#20# the number of threads used for BWA alignment (default = 30).
perl -e 'print join("\t","Thread","30"),"\n";' >> SPEC_example.txt
#21# pre-defined circle annotation in bed6 or bed12 format (default = "no", replace the file name to include annotated circRNAs)
perl -e 'print join("\t","pre_defined_circle_bed","no"),"\n";' >> SPEC_example.txt


# make Pipeline Bash file
perl ~/ACF/ACF_MAKE.pl SPEC_example.txt BASH_example.sh


# find circles
bash BASH_example.sh
========== 1. How_to_Find_Your_Circles ==========




========== 2. How_to_Visulize_Your_Circles ==========
# circles are stored in three files:
circle_candidates_MEA.bed12
circle_candidates_CBR.bed12
circle_candidates_MuS.bed12
# the higher the value in 5th column, the more "likely" that circle is true
# the name in 4th column can be seperated by "|" into four segments: circle-ID, sum-of-Splicing-Strength, number-of-supporting-samples, number-of-supporting-reads 
# the sequence for the "middle exons" in almost every circle is hypothetically filled in using the annotations provided, true sequences could be determined by integrating mRNA-Seq data and validated by inward and outward PCRs.

# their expression per sample is stored here:
circle_candidates_MEA.expr
circle_candidates_CBR.expr
circle_candidates_MuS.expr
# the second column denote the name of the gene from which this circRNA is derived 
========== 2. How_to_Visulize_Your_Circles ==========







========== Tutorial ==========
#1# pre-processing
wget ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByExp/sra/SRX%2FSRX218%2FSRX218203/SRR650317/SRR650317.sra
fastq-dump.2 --fasta 0 --split-files SRR650317.sra

perl Truseq_merge_unique_fa.pl human_unmap SRR650317_1.fasta SRR650317_2.fasta
ln -s human_unmap UNMAP1
ln -s human_unmap_expr UNMAP_expr1
# As acfs require sequencing reads to be stranded, reverse complement all reads to fake strandness
perl -ne 'chomp; if(m/^>/){s/^>//; print ">rc".$_,"\n";}else{my $t=$_; $t=~tr/[ATCG]/[TAGC]/; my $r=scalar reverse $t; print $r,"\n";}' UNMAP1 > UNMAP1.rc
cat UNMAP1 UNMAP1.rc > UNMAP
perl -e 'open IN,"UNMAP_expr1"; my $line=<IN>; print $line; while(<IN>){chomp; my @a=split("\t",$_); print join("\t",@a),"\n"; $a[0]="rc".$a[0]; print join("\t",@a),"\n";}' > UNMAP_expr

# use the gtf provided in iGenome package    or   get Homo_sapiens.GRCh37.71.gtf from  ftp://ftp.ensembl.org/pub/current_gtf/
perl get_split_exon_border_biotype_genename.pl /data/iGenome/human/Ensembl/GRCh37/Annotation/Genes/genes.gtf /data/iGenome/human/Ensembl/GRCh37/Annotation/Genes/Homo_sapiens.GRCh37.71_split_exon.gtf


#2# generate parameter file
perl -e 'print join("\t","BWA_folder","/home/bin/bwa073a/"),"\n";' > SPEC_example.txt
perl -e 'print join("\t","BWA_genome_Index","/data/iGenome/human/Ensembl/GRCh37/Sequence/BWAIndex/genome.fa"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","BWA_genome_folder","/data/iGenome/human/Ensembl/GRCh37/Sequence/Chromosomes/"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","ACF_folder","/home/ACF/"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","CBR_folder","/home/CB_splice/"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","Agtf","/data/iGenome/human/Ensembl/GRCh37/Annotation/Genes/Homo_sapiens.GRCh37.71_split_exon.gtf"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","UNMAP","UNMAP"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","UNMAP_expr","UNMAP_expr"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","Seq_len","76"),"\n";' >> SPEC_example.txt

# if you don't want to change the default parameters, you don't need to run the belowing command-lines.
perl -e 'print join("\t","Thread","30"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","minJump","100"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","maxJump","1000000"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","minSplicingScore","0"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","minSampleCnt","1"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","minReadCnt","1"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","minMappingQuality","20"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","Coverage","0.9"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","minSpanJunc","6"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","ErrorRate","0.05"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","Strandness","-"),"\n";' >> SPEC_example.txt
perl -e 'print join("\t","pre_defined_circle_bed","no"),"\n";' >> SPEC_example.txt


#3# generate pipeline
perl ACF_MAKE.pl SPEC_example.txt BASH_example.sh


#4# get your circRNAs
bash BASH_example.sh


#5# take a look at these :
circle_candidates_MEA.bed12      # high-confident circles that cross annotated boundarys of exon(s)
circle_candidates_CBR.bed12      # low-confident circles (could still be true)
circle_candidates_MuS.bed12      # circles originated from very short exons [therefore might be a bit more difficult to validate]
circle_candidates_MEA.expr
circle_candidates_CBR.expr
circle_candidates_MuS.expr
========== Tutorial ==========



========== Change Log ==========
Update on 2015-03-09 :
   Now ACF can include pre-defined circRNA annotations from a bed6 or bed12 file (and their authenticity will be checked, so please adjust (minJump, maxJump, minSplicingScore) accordingly ).
   This way, you can both predict novel circRNAs in your data and estimate the abundance of annotated circRNAs.
========== Change Log ==========