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zhixingfengzhixingfeng
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update version and usage of iGDA
1 parent de714a8 commit ee9b934

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Lines changed: 19 additions & 33 deletions

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‎iGDA.xcodeproj/project.xcworkspace/xcuserdata/zhixingfeng.xcuserdatad/UserInterfaceState.xcuserstate‎

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‎src/main.cpp‎

Lines changed: 19 additions & 33 deletions
Original file line numberDiff line numberDiff line change
@@ -38,45 +38,31 @@ using namespace TCLAP;
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void print_usage()
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{
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cout << "Version 0.9.3" << endl << endl;
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cout << "To detect low-frequency SNVs, use:" << endl;
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cout << "(PacBio data) igda_pipe_detect_pb infile(bam or sam file) reffile contextmodel outdir" << endl;
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cout << "(Nanopore data) igda_pipe_detect_ont infile(bam or sam file) reffile contextmodel outdir" << endl << endl;
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cout << "Version 1.0.1" << endl << endl;
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cout << "To detect minor SNVs, use:" << endl;
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cout << "(PacBio data) igda_pipe_detect -m pb bamfile reffile contextmodel outdir" << endl;
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cout << "(Nanopore data) igda_pipe_detect -m ont bamfile reffile contextmodel outdir" << endl << endl;
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cout << "to detect combination of low-frequency SNVs, use:" << endl;
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cout << "(PacBio data) igda_pipe_phase_pb indir(output of igda_pipe_detect_pb) reffile outdir" << endl;
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cout << "(Nanopore data) igda_pipe_phase_ont indir(output of igda_pipe_detect_ont) reffile outdir" << endl << endl;
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cout << "To phase minor SNVs, use:" << endl;
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cout << "(PacBio data) igda_pipe_phase -m pb indir(outdir of igda_pipe_detect) reffile outdir" << endl;
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cout << "(Nanopore data) igda_pipe_phase -m ont indir(outdir of igda_pipe_detect) reffile outdir" << endl << endl;
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cout << "Please note that reffile is the reference fasta file. Current version assumes there is only one contig in samfile and reffile." << endl << endl;
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cout << "contextmodel is the context effect model trained on independent data. They can be download in https://github.com/zhixingfeng/igda_contextmodel" << endl;
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cout << "bamfile is the aligned bam file (indexed)." << endl;
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cout << "reffile is the reference fasta file." << endl;
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cout << "contextmodel is the context effect model trained on independent data. They can be download in https://github.com/zhixingfeng/igda_contextmodel" << endl << endl;;
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cout << "Output format:" << endl;
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cout << "For detecting low-frequency SNVs, realign.var in outdir is the final result." << endl;
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cout << "Column 1 is the locus of detected SNVs." << endl;
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cout << "Column 2 is the alternative base of detected SNVs." << endl;
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cout << "The other columns are reserved for internal use" << endl << endl;
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cout << "Output:" << endl << endl;
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cout << "For detecting minor SNVs, realign.vcf in outdir is the final result." << endl << endl;
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cout << "For detecting combinations of low-frequency SNVs, realign.ann.tested.ft.count.ft.assembled.count.nc.ft in outdir is the final result." << endl;
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cout << "Each row is a contig" << endl;
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cout << "Column 1 is the SNVs of the contigs. It is encoded, for each integer x, floor(x/4) = 0-based locus, and x modulo 4 = base" << endl;
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cout << "Column 2 is start locus (0-based)" << endl;
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cout << "Column 3 is end locus (0-based)" << endl;
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cout << "Column 4 is number of reads aligned to the contig" << endl;
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cout << "Column 5 is coverage of the contig" << endl;
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cout << "For phasing minor SNVs, contigs.sam, contigs.fa, and contigs.ann are the final results." << endl;
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cout << "In the contigs.ann file, each row is a contig." << endl;
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cout << "Column 1 is chromosome name." << endl;
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cout << "Column 2 is the SNVs of the contig. It is encoded, for each integer x, floor(x/4) = 0-based locus, and x modulo 4 = base (0=A, 1=C, 2=G, 3=T)" << endl;
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cout << "Column 3 is start locus (0-based)" << endl;
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cout << "Column 4 is end locus (0-based)" << endl;
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cout << "The other columns are reserved for internal use" << endl << endl;
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cout << "For any questions, inquiry of source code or example data, contact zhixing.feng@mssm.edu" << endl;
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/*cout << "igda [command]" << endl;
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cout << "command = encode : binary coding SNVs of each aligned read." << endl;
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cout << " cmpreads : pairwise comparison of encoded reads."<< endl;
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cout << " dforest : detecting SNVs by dforest algorithm (unable to detect SNVs if no reads cover multiple real SNVs)." << endl;
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cout << " contexteffect : pileup reads and get context effect." << endl;
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cout << " detectsingle : detecting SNVs locus by locus (lower sensitivity compared to dforest but able to detect SNVs if no reads cover multiple real SNVs)." << endl;
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cout << " rdim : removing undetected SNVs from encoded reads." << endl;
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cout << " ann : clustering reads by adaptive nearest neighbor clustering." << endl;*/
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//cout << "command = samtofa, bamtofa, m5tofa, encode, cmpreads, bin2txt, txt2bin, dforest, sort, filter, contexteffect, merge, mergeall, dist, pileup_var, pileup_reads, samtom5" << endl;
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cout << "For any questions, contact zhixing.feng@mssm.edu or zxfeng.thu@gmail.com" << endl;
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}
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int main(int argc, const char * argv[])

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