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Hello all! I'm giving two metagenomics talks - a tech talk and a bio talk - at the Joint Genome Institute on May 7, 2020. The abstracts are below.

The JGI just moved to a new building at LBNL, so these talks are much more accessible to the UC Berkeley and LBNL communities than they would have been a year ago. I hope interested people can make it!

The talks will be in the afternoon on May 7th at the Integrative Genomics Building, LBNL Bldg 91-310. I've put the tentative times down. I'll update this post with final times and contact information for security + parking passes closer to the day.

Bio talk: Novel approaches to metagenome analysis reveal microbial signatures of IBD

(This will be the Science and Technology seminar, 3-4pm on May 7.)

Inflammatory bowel disease (IBD) is a spectrum of diseases characterized by chronic inflammation of the intestines; it is likely caused by host-mediated inflammatory responses at least in part elicited by microorganisms. As of 2015, 1.3% of US adults have been diagnosed with IBD. To date, although significant microbial associations have been uncovered, no causative or consistent microbial signature has been associated with IBD.

In a metaanalysis of six IBD cohorts comprising 2290 gut microbiome shotgun metagenomes, we sought to uncover microbial signatures of IBD. We developed a k-mer-based analysis approach based on sourmash scaled signatures that comprehensively characterizes each metagenome sample. We demonstrate that this approach explains substantial PCoA variation across samples, and that patient, study, and diagnosis account for the majority of variation. We then built an accurate random forest classifier to predict IBD subtype. This classifier is built on approximately 14,000 predictive k-mers and outperforms all previously published work for characterization of IBD subtype. We next sought to uncover the biological signal of the predictive k-mers. To determine the origin of the predictive k-mers, we used sourmash gather to search 400,000 microbial genomes from GenBank as well as recent human metagenome reanalysis efforts.

We found that 69% of predictive k-mers were contained in 129 genomes, many of which match known IBD correlates. We reasoned that many additional predictive k-mers were likely in the pangenomes of these 129 predictive genomes, so we next used spacegraphcats to query neighborhoods in compact de Bruijn graphs and extract sequences that were near our predictive genomes in graph space. This increased the annotated fraction of predictive k-mers to 85%.

This suggests that ~16% of predictive k-mers originate from strain-variable or accessory components of pangenomes, and that this variation is hidden from referenced-based approaches but is important for determining IBD subtypes. Interestingly, the fraction of predictive k-mers associated with the 129 genomes changed substantially after spacegraphcats queries. For example, a genome from the genus Bacteroides increased from owning 2.1% to 10.7% of predictive k-mers, surpassing the genome that was most predictive prior to spacegraphcats queries (Clostridiales bacterium, 2.9% to 7.4%). We are now working to bioinformatically characterize the genes associated with the pangenomes.

Our pipeline is lightweight and open source, extensible to similar comparative metagenomic studies, and has the potential to improve diagnostic criteria for IBD subtype.

Tech talk: No k-mer left behind.

(This is part of the Compute Next Generation talk series at JGI, 2-3pm on May 7.)

Here at the DIB Lab @ UC Davis, we've developed and implemented a few techniques that might be of interest to microbiology and metagenomics computational researchers. In this tech talk, will dig into the theory and implementation of our approaches, and discuss some of our current and future use cases. While there may be some extreme speculation involved, I will be sure to highlight it as such :).

The first technique is DensityHash, an extension and simplification of the modulo hash technique proposed as an alternative to MinHash by Broder (1997). Briefly, we massively downsample k-mers by intersecting with a subset of hash space. This permits efficient and accurate estimation of Jaccard similarity and containment on large sequencing data sets. We have implemented this technique in sourmash (github.com/dib-lab/sourmash), which offers a pleasant user experience for comparing samples, searching large databases (e.g. all of GenBank), estimating the composition of metagenomes, and discovering contaminated MAGs, among others. We also have a taxonomic module that slices and dices arbitrary taxonomies, and associates them with hashes for fun and profit.

The second technique is neighborhood query into large compact De Bruijn graphs, using dominating sets. Briefly, we implement a practically efficient linear-time neighborhood clustering on metagenome compact De Bruijn graphs, and then use this to query and characterize neighborhoods. This is implemented in spacegraphcats (github.com/spacegraphcats/spacegraphcats/). Spacegraphcats permits recovery of accessory elements and strain variation from metagenomes, for additional fun and profit.

All of our software is open source under the BSD license, developed openly on GitHub, and implemented in a combination of Python and Rust. We use automated tests, continuous integration, code coverage analysis, and pull request review in our development processes.

References:

sourmash: Pierce at al., 2019

spacegraphcats: Brown et al., 2020

Hope to see you there!

--titus