Related to the work we presented at ISMB last year, our new work on making a new hashing scheme that improves on Jaccard and Hamming distances for searching large sequences was recently accepted to ISMB/ECCB 2019 in Basel, Switzerland. This is work with Guillaume Marçais, Carl Kingsford, and Prashant Pandey. A preprint of the manuscript is on bioRxiv (see Publications).
Author: Dan DeBlasio
ISCB-SC 2018 Symposia Article Published
Our editorial describing the symposia that were hosted by the ISCB Student Council over 2018 calendar year was recently published in F1000 Research. I am proud to say this is the first year I attended 2 out of the 3. While I was not a primary organizer for any of the events, all three were well received and the symposia chairs, the committee members, and all of the attendees are to thank.
https://f1000research.com/articles/8-34/v1
With the publication of this article I am also ending my tenure on the ISCB-SC Executive Team, which has been a great experience.
Toward building an automated bioinformatician: parameter advising for improved scientific discovery
Modern scientific software has a large number of tunable parameters that need to be adjusted to ensure computational performance and accuracy of the results. When these parameter choices are made incorrectly we may overlook significant results or falsely report insignificant ones. Optimizing the parameter choices for one input may not provide an assignment that’s good for another, so this parameter optimization process typically needs to be repeated for each new piece of data. Standard machine learning methods for solving this problem need to repeatedly run the software which may not be suitable in practice. Because of the time consumption required to optimize parameters and the possible loss of accuracy that can result when chosen incorrectly, the default parameter vector that are provided by the tool developer is often used. These defaults are designed to work well on average, but most interesting cases are rarely “average”.
In this talk, I describe my first steps in automatically learning the correct program configuration for biological applications using a framework we call “Parameter Advising”. To apply this framework to the problem of multiple sequence alignment we developed an accuracy estimator, called Facet, to help choose alignments since no ground truth is available in practice. When we use Facet for advising on the Opal aligner we boost accuracy by 14.6% on the hardest-to-align benchmarks. For the reference-based transcript assembly problem, when applying parameter advising to the Scallop assembler we see an increase in accuracy of 28.9%. The framework is general and can be extended to other problems in computational biology and beyond. I will discuss possible areas where parameter advising could be used to automatically learn to run complex analysis software.
Parametric sequence alignment
This lecture describes parametric sequence alignment as presented in Section 13.1 of Dan Gusfield’s book Algorithms on Strings, Trees, and Sequences.
Given two sequences, can you determine how many choices of values of the standard sequence alignment objective function’s parameters give distinct optimal alignments. In other words, how many alignments are optimal for some setting of the objective function’s parameters. This lecture discusses how to approach and answer this question.
Biological Data Science at Cold Spring Harbor Laboratory
I have been invited to give a talk at the Cold Spring Harbor Laboratory Biological Data Science meeting (#biodata18) November 7-10. My talk is preliminarily titled “Building an automated bioinformatician—More accurate, large-scale genomic discovery using parameter advising”.
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