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.

In this talk I give an overview of the current background of the projects being performed in our lab as well as some fundamental background on pairwise sequence alignment, one of the underpinnings of much of the work we’re doing.

Special Topics in Data Science:
Algorithms in Computational Biology

We will through the duration of the semester examine common algorithmic solutions to domain specific data science problems, and how to distill computational problems from questions asked in other domains (i.e. computational biology). While the specific applications we will use as examples are in biology, the approaches discussed are applicable to many interdisciplinary fields. That said, this course is self contained and no previous knowledge of biology is needed. The solution techniques include dynamic programming, computational optimization/integer linear programming, and machine learning to name a few.

Some of the specific biological problems to be discussed are:

• pairwise/multiple sequence alignment which has applications not only in biology but in aligning time-series data.
• hashing and sketching which has its roots in web page similarity measurement and plagiarism detection but is now used for genome assembly.
• genome assembly which uses techniques developed for other purposes such as database searching.
• sequence database searching using tools like BLAST which have applications to non-biological databases.
• phylogenomics, that is the study of finding ancestry from sequences, which can be used in exploring unknown history of things like viruses (both biological and digital).

While these are some of the topics we will discuss they are in no way exhaustive of the field; as with previous versions of the course I am open to suggestions of topics of interest to the students enrolled. The only prerequisite is CS 3 (CS 2302), only the minimum amount of biology will be included to understand the underlying question and the extraction of the computational problem, but it will be self contained and none is expected ahead of time.

Special Topics in Data Science:
Algorithms for Computational Biology

This course will cover the algorithms that make modern computational biology and bioinformatics possible. The plan is to cover both foundational algorithms such as sequence alignment, as well as their modern applications in solving problems such a genome assembly. The focus of this course is on how computer scientists apply their knowledge to frame a computational problem inspired by a specific real-world problem and to solve such computational problems. In addition to standard algorithm development, the course will cover the influence of convex optimization (mainly integer linear programming) and machine learning on computational biology. The course assumes no previous knowledge in biology or genetics. The course will build on and enhance students’ basic understanding of the principle of algorithm design and analysis by applying such principles in the context of bioinformatics.

The topics discussed are likely to include:

• Pairwise Sequence Alignment
• Multiple Sequence Alignment
• Genome Assembly
• Metagenomic & Alignment-free Genomic Analaysis
• Phylogenetic Reconstruction
• Integer Linear Programming Applications
• Machine Learning Applications

CS 2302 is a pre-requisite, please contact the instructor with any questions.

Our group studies how to improve science by automating and optimizing the tools used by domain scientists. We do this primarily by making input specific parameter value choices which help to reduce false information introduced by using less than ideal (or default) parameter choice. Using a framework called Parameter Advising we are able to, without an increase in wall clock time in most cases, find parameter vectors that are much better then the defaults. This framework has been applied to both protein multiple sequence alignment and reference-based transcript assembly, but is very general and can be applied to domains both within and outside of computational biology.

Beyond the algorithm configuration problem, Dr. DeBlasio also has interests in hashing and sketching, primarily focused on minimizer schemes (also called winnowing schemes). Minimizer schemes are a method to represent long strings by some representative k-mer (k length substring) in order to improve the resource consumption of sequence analysis applications (such as genomic read mapping, or document similarity).