Tag: methods

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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: 

mchiment@MNE762:~/Desktop/docker-playground/my-greeting$cat Dockerfile 
#specify the base image
FROM alpine
#specify what to build
RUN /bin/echo "greeting!" > /root/my_message
#give default command
CMD ["/bin/cat", "/root/my_message"]

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.

refine.bio

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.

Refine.bio 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, refine.bio 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.

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My favorite talks from GLBIO2017 in Chicago

GLBIO2017

I just got back from Great Lakes Bio 2017 (GLBIO2017) at the University of Illinois-Chicago (UIC) campus.  It was a great meeting and I really enjoyed the quality of the research presented as well as the atmosphere of the campus and neighborhood.

I was very surprised by just how nice the Chicago “West Loop” neighborhood near Randolph Street and down towards Greektown really is.  I had some great meals, including a memorable Italian dinner at Formentos.

But the purpose of this post is to briefly describe a few of my favorite talks from the meeting.  So here goes, in no particular order:

Kevin White, Tempus Labs:

I was really impressed with Kevin White’s GLBIO2017 talk and demo of his company’s technology (despite the ongoing technical A/V issues!)  Tempus labs is a clinical sequencing company but also an informatics company focused on cancer treatment that seeks to pull together all of the disparate pieces of patient data that float around in EHR databases and are oftentimes not connected in meaningful ways.

The company sequences patient samples (whole exome and whole genome) and then also hoovers up reams of patient EHR data using Optical Character Recognition (OCR), Natural Language Processing (NLP), and human expert curation to turn the free-form flat text of medical records from different clinics and systems into a form of “tidy data” that can be accessed from an internal database.

Then, clinical and genomic data are combined for each patient in a deep-learning system that looks at treatments and outcomes for other similar patients and presents the clinician with charts that show how patients in similar circumstances fared with varying treatments, given certain facts of genotype and tumor progression, etc…  The system is pitched as “decision support” rather than artificial “decision making.”  That is, a human doctor is still the primary decider of treatment for each patient, but the Tempus deep learning system will provide expert support and suggest probabilities for success at each critical care decision point.

The system also learns and identifies ongoing clinical trials, and will present relevant trials to the clinician so that patients can be informed of possibly beneficial trials that they can join.

Murat Eren,  merenlab.org

Murat Eren’s talk on tracking microbial colonization in fecal microbiome transplantation (i.e., “poop pills”) was excellent and very exciting.  Although the “n” was small (just 4 donors and 2 recipients) he showed some very interesting results from transferring fecal microbiota (FM) from healthy individuals to those with an inflammatory bowel disease.

Among the interesting results are the fact that he was able to assemble 97 metagenomes in the 4 donor samples.  Following the recipients at 4 and 8-weeks post FM transplant showed that the microbial genomes could be classed into those that transfer and colonize permissively (both recipients), those that colonize one or the other recipient, and those that fail to colonize both.  Taxa alone did not explain why some microbes colonized easily, while other failed to colonize.

He also showed that 8 weeks post FM transplant, the unhealthly recipients had improved symptoms but also showed that in a PCA analysis of the composition of the recipient gut and the healthy human gut from 151 human microbiome project (HMP) samples, the recipients moved into the “healthy” HMP cluster from being extreme outliers on day 0.

He also investigated differential gene function enrichment between the permissive colonizers and the microbes that never colonized recipient’s guts and found that sporulation genes may be a negative factor driving the failure (or success) of transplantation.   He proposed that the recent and notable failure of the Seres microbiome drug in clinical trials may be owing to the fact that the company killed the live cultures in favor of more stable spore-forming strains when formulating the drug.  His work would suggest that these strains are less successful at colonizing new hosts.

Bo Zhang, 3D genome browser

With the ever-increasing volume of genomic and regulatory data and the complexity of that data, there is a need for accessible interfaces to it.   Bo Zhang’s group at Penn State has worked to make a new type of genome browser available that focuses on the 3D structure of the genome, pulling together disparate datatypes including chromatin interaction data, ChIP-Seq, RNA-Seq, etc…  You can also browse a complete view of the regulatory landscape and 3D architecture of any region of the genome.  You can also check the expression of any queried gene across hundreds of tissue/cell types measured by the ENCODE consortium.  On the virtual 4C page, they provide multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET and cross-cell-type correlation of proximal and distal DHSs.

The 3D Genome Browser flow chart.

 

All in all, GLBIO2017 was a very enjoyable and informative meeting where I met a lot of great colleagues and learned much.  I am looking forward to next year!