Category: c

Finishing touches are in place for my convert2bed tool (GitHub site).

This utility converts common genomics data formats (BAM, GFF, GTF, PSL, SAM, VCF, WIG) to lexicographically-sorted UCSC BED format. It offers two benefits over alternatives:

  • It runs about 3-10x as fast as bedtools *ToBed equivalents
  • It converts all input fields in as non-lossy a way as possible, to allow recovery of data to the original format

As an example, here we use convert2bed on a 14M-read, indexed BAM file to a sorted BED file (data are piped to /dev/null) on a 4 GB, dual-Core 2 (2.4 GHz) workstation running RHEL 6:

$ samtools view -c ../DS27127A_GTTTCG_L001.uniques.sorted.bam
14090028

Conversion is performed with default options (sorted BED as output, using BEDOPS sort-bed):

$ time ./convert2bed -i bam < ../DS27127A_GTTTCG_L001.uniques.sorted.bam > /dev/null
[bam_header_read] EOF marker is absent. The input is probably truncated.

real 3m5.508s
user 0m25.702s
sys 0m8.602s

Here is the same conversion, performed with bedtools v2.22 bamToBed and sortBed:

$ time ../bedtools2/bin/bamToBed -i ../DS27127A_GTTTCG_L001.uniques.sorted.bam | ../bedtools2/bin/sortBed -i stdin > /dev/null

real    28m22.057s
user    2m58.579s
sys     0m41.605s

The use of convert2bed for this file offers a 9.1x speed improvement. Other large BAM files show similar conversion speedups.

Further time reductions are conferred with use of bam2bedcluster and bam2starchcluster scripts (TBA) which make use of GNU Parallel or a Sun Grid Engine job scheduler, reducing conversion time even further by breaking conversion tasks down by chromosome.

When testing is complete, code will be wrapped into the upcoming BEDOPS v2.4.3 release. Source is now available via GitHub.

Read More

For scientific work, I have used matrix2png to make a nice PNG image from a text-formatted matrix of data values. PNG looks great on the web, but it doesn’t translate well to making publication-quality figures.

My thought was to take matrix2png and — with the help of Haru (libharu) — turn it into matrix2pdf. Maybe I can get this going on Github.

Read More

Here are some useful resources for open source C and C++ -based OCR libraries that could run under iOS (need to check licensing):

The end goal is to be able to use an iPhone to read LED displays, as commonly found on meters, etc. and then do something useful with that data (upload it somewhere, tagged with geodata). An aggregate of hundreds or thousands of users could conceivably collect data useful for themselves and also for the group as a whole.

Read More

I wrote a data extraction utility which uses PolarSSL to export a Base64-encoded SHA-1 digest of some internal metadata (a string of JSON-formatted data), to help validate archive integrity:

$ unstarch --sha1-signature .foo
7HkOxDUBJd2rU/CQ/zigR84MPTc=

So far, so good.

But now I want to validate that the metadata are being digested correctly through some independent means, preferably via the command-line, so that I can perform regression testing. I can use the openssl, xxd and base64 tools together to test that I get the same answer:

$ unstarch --list-json-no-trailing-newline .foo \
| openssl sha1 \
| xxd -r -p \
| base64
7HkOxDUBJd2rU/CQ/zigR84MPTc=

As a note to myself: I end up stripping the trailing newline from the JSON output of unstarch because this is what the PolarSSL library ends up digesting. This very nearly had me doubting whether PolarSSL was working correctly, or whether my command-line test was correct!

Read More