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Pindel can detect breakpoints of large deletions, medium sized insertions, inversions, tandem duplications and other structural variants at single-based resolution from next-gen sequence data. It uses a pattern growth approach to identify the breakpoints of these variants from paired-end short reads.

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Pindel

Compiling

To compile Pindel you need three things: GNU Make and GCC (which usually are already installed on Linux) and htslib. The last is not installed on Linux by default, but it can be retrieved with:

git clone https://github.com/samtools/htslib

htslib needs to be built before you can start installing Pindel. (Go to htslib's directory, and follow the directions in its 'readme'. At the time of writing this (February 2016) it simply works if you give the commands "make" and "sudo make install".)

To compile Pindel on OS X, you may need to do more work - 'regular' installation under OS X may work, but in some cases OS X gives problems with the OpenMP library Pindel uses for speedup. In those cases, please follow the instruction on the following page to update your gcc

http://www.ficksworkshop.com/blog/14-coding/65-installing-gcc-on-mac

If htslib has been cloned and installed, go to the pindel directory ([my-path]/pindel) and use the INSTALL script there in the following way:

./INSTALL [path-to-htslib]

for example

./INSTALL ../htslib

After this, you can run pindel by using

./pindel [options]

Plain "./pindel" without command line options will list all available command line options, the FAQ in the Pindel root directory includes a usage example.

If there are any problems with installing or running Pindel, you may be able to find the solution in the FAQ (the FAQ file stored in the same directory as the INSTALL script), otherwise, feel free to open an issue on github (https://github.com/genome/pindel/issues) or to contact the main author, Kai Ye, at kaiye@xjtu.edu.cn

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Pindel can detect breakpoints of large deletions, medium sized insertions, inversions, tandem duplications and other structural variants at single-based resolution from next-gen sequence data. It uses a pattern growth approach to identify the breakpoints of these variants from paired-end short reads.

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