Matt Ralston

Matt Ralston

M.Sc. Bioinfo + 5 years industry exp. - - - Looking for a position in biosciences. I'm gifted at the command line or computer terminal, but I perform best on my feet, standups, cleaning, guitar with samples in the centrifuge, more in the water bath, data being processed on the computer while I sleep, and other assays to plan. Currently writing a DNA sequence graph algorithm and manuscript (for bioRxiv). I play math rock on my guitar, I have some open-source software on GitHub, I pay my bills in the DE-NJ-PA tri-state area near Delco, and I'm launching a DE-based LLC for Univ. of Delaware and local NGS sequencing projects.

kmerdb - k-mer profile distances, clustering, and more

KmerDB tutorial

kmerdb is a PyPI Python package designed to quickly generate k-mer count profiles from sequencing data. Input can be .fa or .fq, and gzipped inputs are supported.

Distance metrics are provided by SciPy, with the exception of the ‘pearson’ distance, which uses the canonical formulation of the correlation coefficient and is written in Cython. Inputs to most downstream commands may be plain .tsv, and Unix piping from standard input is also supported.

When designing this command-line interface (CLI), additional metadata concerns were in mind. How many unique k-mer counts vs total k-mer counts were processed (i.e. was the 5-mer ‘ACAAT’ seen at least once? If seen 0 times, then we define it to be a nullomer.) A YAML-format metadata header is provided alongside the k-mer count vectors to provide summarizations at-a-glance to researchers, conveniently at the top of the compressed file.

The file is gzip or zlib compatible and is otherwise a plain tab-delimited text file under the block-GNU zip file compression, just below the YAML header. view and header functions are provided to quickly access file contents of both .kdb count profile and .kdbg weighted edge list formats.