A tool for splicing junction calling from RNA-Seq data
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ResultAnalyze
algorithms
.gitattributes
.gitignore
Alignment.cpp
Alignment.h
CandidateSitesCaller.cpp
CandidateSitesCaller.h
Coverage.cpp
Coverage.h
HardClipReads.cpp
HardClipReads.h
JunctionCaller.cpp
JunctionCaller.h
Makefile
Makefile.bak
ReferenceSeq.cpp
ReferenceSeq.h
SplicingJumper_1
TrainingSet.cpp
TrainingSet.h
bam_alignment_record.h
bam_header_record.h
bam_parse.cpp
bam_parse.h
fai_parser.cpp
fai_parser.h
fasta_parser.cpp
fasta_parser.h
khash.h
kseq.h
main.cpp
public_func.cpp
public_func.h
public_parameters.h
readme.txt

readme.txt

Usage: ./SpliceJumper [options] 

Required options:
		  -P/G        indicates collecting features / indicates output results 
                  -r FILE   reference file(indexed)
                  -b/i FILE   input bam/prediction file(index and sorted)
                  -o FILE   output file name
                  -l INT    read length
Optimal options
                  -c INT    slack value for split position with default 3
                  -m DOUBLE mean insert size
                  -v DOUBLE standard variation of insert size
                  
1.Feature collection:
./SpliceJumper -P -r ./human_g1k_v37.fasta -b ./simulated_data_input.bam -o test_sim.txt -l 100 -c 5

2. Training and prediction
2.1 Training
python easy.py training_data

2.2 Predicting
..\windows\svm-predict data.scale Trained.model predictResult

3. Output generated splicing junctions
./SpliceJumper -G -i prediction.txt -o result.txt