Readers working on metagenome assembly will enjoy a new paper by Denis Bertrand et al that came out in Nature Biotech. I have not gone through the algorithm yet, but would like to do, when the authors make a pdf copy available.
The “genome assembly era” is finally over. This is clear from the titles of the recent talks such as “Question, is de novo genome assembly a solved problem with long reads, yet?” and “40 years of genome assembly, are we done yet?”. Such titles suggest a perception among the audience that genome assembly is a nearly solved problem, or rather the “big bucks are elsewhere”.
We encourage our readers to take a look at the comparison of long read assemblers by Ryan Wick and Kathryn Holt. The authors benchmarked five different assemblers, namely Canu, Flye, Ra, Unicycler and Wtdbg2.
In this week’s commentary in the membership section, we reviewed the recent advances in the genome assembly field. One paper mentioned there is an excellent PLOS Compbio. review on scaffolding by Jay Ghurye and Mihai Pop. I will skip over the discussion on various long-read technologies and mention a topic with the potential to make substantial improvement in genome assembly.
The genome assembly field continues to be highly active, and the researchers are still coming up with algorithms making significant speed improvements. The following three projects are definitely worth your attention.
While working on RNAse P in microbial genomes, I noticed something very puzzling. An archaeal protein that was never seen before in bacteria was present (and even annotated) in a newly sequenced bacterial genomes. If true, it could completely change the evolutionary understanding of the RNase P protein families.
The de Bruijn graphs are immensely helpful in assembling Illumina sequences, but they often occupy massive amounts of memory, especially for large raw datasets. Our readers interested in representing de Bruijn graphs in compact space should not miss a recent paper by Victoria Crawford, Alan Kuhnle, Christina Boucher, Rayan Chikhi and Travis Gagie. The paper is published in Bioinformatics, but the journal link is not open-source.
This is the third installment of “genome assembly algorithms through jigsaw puzzles”.
We usually post them here every Tuesday, although we are late this week.
You can find all those pieces in one place (and some
more) at this link.
We are developing this tutorial to explain genome assembly algorithms in a simple
manner. In fact, rather than explaining, we expect you to discover the answer
by manually solving a jigsaw puzzle. Later we show you how your solutions
are related to the commonly used algorithms and their variations.
This is the second installment of “genome assembly algorithms through jigsaw puzzles”.
We post them here every Tuesday. You can find all those pieces in one place (and some
more) at this link.
We are developing this tutorial to explain genome assembly algorithms in a simple
manner. In fact, rather than explaining, we expect you to discover the answer
by manually solving a jigsaw puzzle. Later we show you how your solutions
are related to the commonly used algorithms and their variations.
We are developing this tutorial to explain genome assembly algorithms in a simple
manner. In fact, rather than explaining, we expect you to discover the answer
by manually solving a jigsaw puzzle. Later we show you how your solutions
are related to the commonly used algorithms and their variations.
Over the last few days, we have been going through various analysis tools for
analysis of metagenomics data sets. The readers working in the same field may
find the following two papers useful.
