Invited talks (video)

Bioinformatics Learning 2.0: proposing an open source consortium for bioinformatics teaching materials

Christopher Lee
(University of California, Los Angeles)

I propose an open source consortium for bioinformatics teaching materials, including textbook chapters, slides, concept tests, homework and exam questions and answers, programming problems and data analysis projects, and software tools for using these materials in class and out.

To seed this effort I am contributing materials from two courses: a bioinformatics theory course (for Computer Science students) emphasizing probabilistic models and methods; and a genomics and computational biology course (for Life Science students).

This effort is based on several principles. First, bioinformatics is highly interdisciplinary, yet bioinformatics textbooks tend to each reflect only one disciplinary part of that. Furthermore, both available textbooks and the traditional lecture method fall far short of giving students adequate exercises to truly learn the concepts and skills. In effect, the job of writing all these teaching materials is too big for any one person. Instead, every teacher should be enabled to focus on writing materials in areas where they are expert, while drawing whatever materials they want from everyone else, via an open source consortium for sharing teaching materials.

Second, bioinformatics teaching should draw lessons from other fields such as physics teaching, where it has been shown that traditional lecturing (passive learning) is far less effective than active learning, where students answer and discuss problems in class. Specifically, I have developed teaching materials and software tools for in-class concept tests, defined as a question that challenges the students' understanding of a specific concept.

Whereas ROSALIND computational problems may be viewed as empirical (implicit) tests of mastery of a concept or skill, in-class concept testing explicitly teaches such mastery by challenging students to think about how to use a concept, and rapidly exposing the most common errors for all to see and understand. I illustrate with examples from the approximately 300 bioinformatics concept tests I have written for this effort.

I also present software tools for in-class concept testing, and for selecting and "re-compiling" content in flexible ways. Finally, I will discuss critical issues for such a consortium, such as automatic authorship tracking, sharing, and security.

For more details, see http://thinking.bioinformatics.ucla.edu/teaching.

Estimating the statistical significance of sequence motifs

Uri Keich
(University of Sydney)

The identification of transcription factor binding sites is an important step in understanding the regulation of gene expression. To address this need, many motif-finding tools have been described that can find short sequence motifs given only an input set of sequences.

Somewhat surprisingly, development of the significance analysis of the motifs reported by those motif finders has lagged considerably behind the extensive development of the finders themselves. Nevertheless, this analysis is often crucial in helping scientists decide whether or not to carry the predicted motifs to the next stage of their analysis. We will discuss the problem of evaluating the statistical significance of sequence motifs in the general context of evaluating the statistical significance of an observed result.

Alignment Beyond Sequences: Forwards and Backwards in Colors and DAGs

Michael Brudno
(University of Toronto)

In this lecture I will present the algorithmic challenges presented by two novel types of sequencing technologies: the SOLiD system, which generates color-space reads, and Single-Molecule Sequencing systems, which have an extremely high indel error rate, but can read each piece of DNA two or more times. I will then explain how classical string alignment algorithms must be adopted to deal with this type of data, in particular explaining the generalization of sequence alignment to the Weighted Sequence Graph abstraction, and showing how this can be further adopted to work with color-space data.