Last year we published Mash (Ondov et al. 2016) for the rapid comparison of genomes and metagenomes. Mash builds on Andrei Broder’s foundational paper “On the resemblance and containment of documents” (Broder 1997), and uses the MinHash technique to rapidly estimate resemblance, e.g. the similarity of two whole genomes. Mash is great for this purpose, but is not well suited for estimating containment, e.g. detecting genomes contained within a metagenome. Implementing containment was the next logical step for Mash — it is in the title of the Broder paper, after all! Here we introduce a new Mash screen operation that implements containment in the context of genomics.

Chirag Jain recently presented a paper at RECOMB’17 titled “A fast approximate algorithm for mapping long reads to large reference databases” (preprint | proceedings). This paper describes the algorithms behind MashMap, which is our new tool designed for approximate read mapping. Chirag joined the lab last year as a summer fellow, and I asked him to write a new read mapper. (How else does one learn bioinformatics?) He clearly lived up to the challenge, and I think the paper contains some useful ideas for the looming “long-read” era. I wanted to summarize those ideas here for anyone who missed RECOMB.

We recently participated in a collaborative effort to sequence, assemble, and analyze a human genome (GM12878) using the Oxford Nanopore MinION (Jain et al. 2017). As part of that project, Josh Quick and Nick Loman developed a nanopore sequencing protocol capable of generating “ultra-long” reads of length 100 kb and greater. In the paper we predict that reads of such length could enable the most continuous human assemblies to date, with NG50 contig sizes exceeding 30 Mbp. Thus far, we have only collected 5x coverage using the ultra-read protocol and cannot fully test this prediction. However, another human dataset, the “Cliveome”, lets us compare the effect of read length and coverage on nanopore assembly. Here we present a brief analysis of that assembly, which achieved a remarkable contig NG50 of 24.5 Mbp.

HLA*PRG:LA approximates the graph alignment process by starting with linear sequence alignments. It brings down the resource requirements per sample for the HLA typing process to 30GB RAM/30 CPU hours, and produces highly accurate calls.

An international consortium recently released ~30x coverage of a human immortalized cell line (NA12878) sequenced using Oxford Nanopore MinION instruments. Release 3 of this dataset included 39 flowcells, which generated 14,183,584 reads and 91,240,120,433 bases, mostly using the 1D ligation prep, but with a few rapid kit runs as well. Our friends Nick Loman and Jared Simpson asked if we could assemble this data with Canu. Of course we said yes.