Conference report: GLBIO2019

I just returned from another great experience at Great Lakes Bio 2019 (#GLBIO2019), a regional meeting of the International Society of Computational Biologists (ISCB). Below I’ll summarize briefly a few of the talks that I found most interesting to me personally (there were several parallel tracks, so I did not attend all talks).

Docker workshop taught by Sara Stevens

On Sunday of the conference, I attended a 3-hour workshop introducing Docker technology held in the beautiful and very modern Wisconsin Institutes of Discovery building. The course was taught by Sara Stevens, an expert in data science and bioinformatics with the data science hub at UWisconsin-Madison.

We worked through an initial “hello world” application of Docker on our laptops, writing a Dockerfile that became an image and finally a container instance of that image:

Then we progressed into more complex Dockerfile builds, including one that would install a mini-python distro and run a program. This included installing some libraries with pip within the image, and running a script.

Overall, I learned a lot and got a good grasp of the Docker basics to build upon for future work.

Integrative analysis for fine mapping of genetic variants, Sunduz Keles

In this talk, the issue of how to make sense of GWAS data was addressed. If you have a collection of SNPs, how to you follow up with which genes to study, which mechanisms to propose, etc… This talk introduced a tool, atSNP Search, which uses transcription factor position-weight matrices (PWMs) and assesses the impact of a SNP on TF DNA-binding activity within the local area of the SNP using the PWMs.

From the website:

atSNP identifies and quantifies best DNA sequence matches to the transcription factor PWMs with both the reference and the SNP alleles in a small window around the SNP location (up to +/- 30 base pairs and considering subsequences spanning the SNP position). It evaluates statistical significance of the match scores with each allele and calculates statistical significance of the score difference between the best matches with the reference and SNP alleles.

The talk also introduced a method, “FM-HighLD”, which asks whether you can substitute functional annotations of SNPs for “massive parallel reporter arrays” (MPRAs) which are considered “gold standard” for SNP/eQTL function. The idea is to use MPRA results and their correlation to functional annotations to calibrate the model and then apply that to eQTLs or GWAS SNPs with no MPRA results, but functional annotations from public databases.

There is over $4 Billion worth of publicly-funded RNAseq and microarray data in the public repositories. Studies have shown that analysts can spend up to 30% of a project’s time just searching, accessing, downloading, and preprocessing these data. is an attempt to “harmonize” thousands of gene expression datasets by downloading and pre-processing them using a common pipeline and common reference. This is only possible owing to the innovation of pseudo-alignment in methods like kallisto and salmon.

In the background, runs on Amazon Web Services, which gives the project unlimited compute and storage to scale according to their needs. In addition to standardized gene expression processing, sample metadata are also harmonized, where keywords are mapped to standard ontologies for ease of comparison.

Monitoring crude oil spills with 16S and machine-learning, Stephen Techtmann

In this work, Dr. Techtmann’s group was interested in looking at the response of fresh water microbiomes drawn from Lake Superior to the introduction of different types of oil (a complex chemical substance that acts as a carbon food source). Their team drew lake water samples and incubated them with different oils (heavy crude, refined crude, etc…) and then assessed taxonomic abundance using 16S amplicon gene sequencing.

The taxa abundances were used to train a Random Forest model to predict oil contamination status. RF methods produced a model with extremely high accuracy, AUC > 0.9. They found that two taxa predominantly distinguish the oil samples from the lake water samples.