This is a recipe that provides a time- and memory- efficient way to loosely estimate the likely size of your assembled genome or metagenome from the raw reads alone. It does so by using digital normalization to assess the size of the coverage-saturated de Bruijn assembly graph given the reads …
read moreThere are comments.
This recipe provides a time-efficient way to determine whether you've saturated your sequencing depth, i.e. how much new information is likely to arrive with your next set of sequencing reads. It does so by using digital normalization to generate a "collector's curve" of information collection.
Uses for this recipe …
read moreThere are comments.
Inspired by Sarah Bisbing's excellent post on her first year as a faculty member, here are the questions I remember asking myself during my first six years:
Year 0: What science do I want to do?
Year 1: What the hell am I doing all day and why am I …
read moreThere are comments.
The below is a recipe for subsetting a high-coverage data set to a given average coverage. This differs from digital normalization because the relative abundances of reads should be maintained -- what changes is the average coverage across all the reads.
Uses for this recipe include subsampling reads from a super-high …
read moreThere are comments.
In recent days, we've gotten several requests, including two or three on the khmer mailing list, for ways to extract shotgun reads based on their coverage with respect to the reference. This is fairly easy if you have an assembled genome, but what if you want to avoid doing an …
read moreThere are comments.
I just finished reading Svante Paabo's autobiography, Neanderthal Man: In Search of Lost Genomes. The book is perfect -- if you're a biologist of any kind, you'll understand most of it without any trouble, and even physicists can probably get a lot out of the story (heh).
The book describes Svante …
read moreThere are comments.