Active learning strategies for bioinformatics teaching

The more I read about how active learning techniques improve student learning, the more I am inclined to try out such techniques in my own teaching and training.

I attended the third week of Titus Brown’s “NGS Analysis Workshop”. This third week entailed, as one of the participants put it, ‘the bleeding edge of bioinformatics analysis taught by Software Carpentry instructors’ and was a unique opportunity to both learn different analysis techniques, try out new instruction material, as well as experience different instructors and their way of teaching. On top of that the group was just fantastic to hang out with, and we played a lot of volleyball.

I demonstrated some of my teaching and was asked by one of the students for references for the different active learning approaches I used. Rather then just emailing her, I decided to put these in this blog post.

The motivation of turning to active learning techniques is nicely summarised in a post on the ‘communications of the ACM’ blog entitled “Be It Resolved: Teaching Statements Must Embrace Active Learning and Eschew Lecture”. I highly recommended reading it and checking out the references mentioned. I am by no means an expert in the area, and simply am learning by doing. I have no ways to measure whether the techniques I use are beneficial, but student responses strongly encourage me to keep applying them. My teaching is also very much influenced by my being a Software Carpentry instructor.

The following describes what I do in the de novo genome assembly module of the ‘High Throughput Sequencing technologies and bioinformatics analysis’ course I organise (link to materials). I used part of that module for the NGS Analysis Workshop (link).

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Developments in high throughput sequencing – June 2015 edition

This is the fourth edition of this visualisation, previous editions were in June 2014, October 2013 and December 2012.

As before, full run throughput in gigabases (billion bases) is plotted against single-end read length for the different sequencing platforms, both on a log scale. Yes, I know a certain new instrument seems to be missing, hang on, I’m coming back to that…

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A hybrid model for a High-Performance Computing infrastructure for bioinformatics

I work for the Norwegian High-Throughput Sequencing Centre (NSC), but at the Centre for Ecological and Evolutionary Synthesis (CEES). At CEES, numerous researchers run bioinformatic analyses, or other computation-heavy analyses, for their projects. With this post, I want to describe the infrastructure we use for calculations and storage, and the reason why we chose to set these up the way we did.

In general, when one needs high-performance compute (HPC) infrastructure, a (group of) researcher(s) can purchase these and locate them in or around the office, or use a cloud solution. Many, if not most, universities offer a computer cluster for their researchers’ analysis needs. We chose a hybrid model between the universitys HPC infrastructure and setting up one ourselves. In other words, our infrastructure is a mix of self-owned, and shared resources that we either apply for, or rent.

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On graph-based representations of a (set of) genomes

In 1986, in a letter to the journal Nature, James Bruce Walsh and Jon Marks lamented that the upcoming human genome sequencing project “violates one of the most fundamental principles of modern biology: that species consist of variable populations of organisms”. They further wrote: “As molecular biologists generally ignore any variability within a population, the individual whose haploid [sic] genome will be chosen will provide the genetic benchmark against which deviants are determined”. They conclude that ” ‘the’ genome of ‘the’ human will be sequenced gel by acrylamide gel”.

We have come a long way when it comes to taking population variation into account in molecular/genetic/genomic studies. But these sentiments, expressed already in 1986, echo some of the trends in the human genetics field: the move away from a single, linear representation of ‘the’ human genome. In this post I will provide some background, explain the reasons for moving towards graph-based representations, and indicate some challenges associated with this development.

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Thoughts on a possible Assemblathon3

The Genome10K meeting is ongoing (I am not attending but following through twitter). Today, there will be a talk by Ian Korf about the feasibility of an Assemblathon 3 contest (see this tweet and the schedule). Earlier the @Assemblathon twitter account asked for a wishlist for an Assemblathon 3 through the hashtag #A3wishlist. With this post I want to share my opinion on what a possible Assemblathon 3 could and/or should be about.

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My review of “MinION nanopore sequencing identifies the position and structure of a bacterial antibiotic resistance island”

Earlier this week, the first paper was published describing the use of Oxford Nanopore MinION data to solve a biological question. The paper, entitled “MinION nanopore sequencing identifies the position and structure of a bacterial antibiotic resistance island” came out in Nature Biotechnology (ReadCube link).

I was a reviewer for this manuscript. I have posted my two (signed) review reports on publons. As data and code were made available by the authors (as it should be), I made a (mostly successful) effort to reproduce the computational part of the paper. After I was done with the review report of the second version I could not help myself to have a further look at some of the results. This led to me sending some plots to the authors, and one of these plots ended up becoming figure 1. This was a lot of fun to see in the final version.

Below are some excerpts of the review reports.
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My review of “Long-read, whole-genome shotgun sequence data for five model organisms”

Two days ago, a paper appeared in Nature Scientific Data by Kristi Kim et al, titled “Long-read, whole-genome shotgun sequence data for five model organisms”. This paper describes the release of whole-genome PacBio data by Pacific Biosciences and others, for five model organisms, Escherichia coli, Saccharomyces cerevisiae, Neurospora crassa, Arabidopsis thaliana, and Drosophila melanogaster, using quite recent chemistries.

Beyond the datasets described in the paper, Pacific Biosciences also released whole-genome data for the human genome, and very recently, for Caenorhabditis elegans using the latest P6/C4 chemistry. Check out PacBio devnet, also for data for other applications.

I think it is fantastic that Pacific Biosciences releases these datasets as a service to the community – and obviously to showcase their technology. Company-generated data often represents the best possible data, as it is done by people with very much experience with the technology. It remains to be seen if ‘regular’ owners of PacBio RS II instrument can reach the same level of data quality. Nonetheless, these datasets are very helpful for teaching (see my previous blog post), comparisons with other technologies (I wish a I could make time to throughly compare PacBio data to Moleculo data available from the same species), as well as development of new software applications.

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