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Computer Science

Research Presentation Video

Watch Eleanor Cawthon '15 discuss her research project.

Parallelization of Gene Sequence Alignment Tools Using SeqDB

Eleanor Swent Cawthon (2015); Additional Collaborator(s): Kirsten Fagnan (Lawrence Berkeley National Laboratory); Brian (Bushnell Joint Genome Institute); Mentor(s): Katerina Antypas (Lawrence Berkeley National Lab)

Abstract: Genome researchers around the world use the FASTQ file format to represent genome sequence data. Because the data in a single FASTQ file must be accessed sequentially, this standard has created a bottleneck in the performance of sequence alignment tools. This study examines the extent to which using SeqDB, an alternative to FASTQ based on the Hierarchical Data Format, can improve the performance of the BBMap sequence alignment tool. BBMap, was modified to support input in SeqDB format natively. Throughput of BBMap's format conversion tool was measured when the same read data were given in uncompressed FASTQ, Gzip-compressed FASTQ, and SeqDB formats. The modified version of BBMap processed reads at a rate that increased at a rate of approximately 22.3 reads per millisecond per doubling of the number of threads, up to a maximum of 126 reads per millisecond with eight threads. Average throughput was 300 reads per millisecond for uncompressed FASTQ and 227 reads per millisecond for Gzip-compressed FASTQ. These rates did not vary substantially with the number of threads used. Input file size was not found to be related to SeqDB’s throughput. The results of this investigation suggest that SeqDB has the potential to be a scalable solution to one significant input and output bottleneck, but that additional changes in BBMap will be required in order for SeqDB support to match or exceed the performance of older formats.
Funding Provided by: U.S. Department of Energy Office of Science

Integrating the Grace Programming Language into DrRacket

Richard Yannow (2014); Student Collaborator(s): Nicholas Cho (2015); Mentor(s): Kim Bruce

Abstract: The Grace programming language project was started with the intention of developing an object-oriented programming language that would make it easy to teach programming to novices. To this end, we have to provide not only a simple and flexible language amenable to different teaching styles and programming paradigms, but also a robust environment in which novices can learn to program. We decided to take DrRacket, an integrated development environment (IDE) for the Racket programming language, and extend it to support Grace, allowing us to take advantage of its numerous built-in beginner-friendly features through Racket’s language-binding capabilities. In order for DrRacket to understand Grace code, we wrote a parser that takes Grace code, or the surface representation, and interprets it to build an Abstract Syntax Tree (AST), or the underlying syntax; a typechecker that builds a type environment and supports the typechecking of any combination of statically-typed and dynamically-typed code; and an interpreter that translates the semantics of the AST into Racket code, so that DrRacket can evaluate it as it would any other program.
Funding Provided by: Pomona College SURP

Impro-Visor: Audio Input and Style Recognition

Anna Turner (2015); Student Collaborator(s): Hayden Blauzvern (2016 HMC); Nate Tarrh (2014 Tufts University); Kelly Lee (2016 HMC); Mentor(s): Robert Keller (HMC)

Abstract: We present research on Impro-Visor, intelligent music software dedicated to helping both beginner and expert jazz musicians improve their playing. We first introduce the creation of audio input capabilities. We accomplish this through SuperCollider, a programming language used for audio synthesis. We use pitch and onset detection to detect notes and rests, which we then send as MIDI input to Impro-Visor. We integrate existing external software to make this feature as portable as possible. We also describe an automated method for style recognition of jazz melodies through the use of supervised training. We train a neural network to recognize defining stylistic elements of specific musicians. We then present melodies to a critic for judgment on a grading scale, and for prediction of the musicians to whom the melodies sound most similar.
Funding Provided by: National Science Foundation (HMC)

Walking in place using the Microsoft Kinect to explore a large VE

Kevin Nguyen (2016); Student Collaborator(s): Preston Tunnell Wilson (2016 Rhodes College); Additional Collaborator(s): Kyle Dempsey (Mississippi University for Women); Mentor(s): Betsy Williams-Sanders (Rhodes College)

Abstract: One way to permit free exploration of any sized virtual environment (VE) and provide some of the inertial cues of walking is to have users “walk in place” (WIP) [Williams et al. 2011]. With WIP, each step is treated as a virtual translation even though the participant remains in the same location. In our prior work [Williams et al. 2011], we had success in implementing a WIP method using an inexpensive Nintendo Wii Balance Board. We showed that participants’ spatial orientation was the same as normal walking and superior to joystick navigation. There were two major drawbacks to this WIP algorithm. First, our step detection algorithm had a half–step lag. Second, participants found it slightly annoying to walk in place on the small board. Thus, the current work seeks to overcome these limitations by implementing an algorithm to WIP using two Microsoft Kinect sensors (150 USD each). Specifically, we are interested in seeing how well users can explore a large VE by WIP with the Kinect (WIP–K). Due to the large size of the VE, comparing these results to normal physical walking is not possible. Therefore, we directly compare WIP–K to joystick navigation. Also, we examine scaling the translational gain of WIP–K so that one “step” carries the user forward two steps (referred to as WIP–K x 2). Thus, this within–subject experiment compares subjects’ spatial orientation as they navigate a VE in three conditions: Joystick, WIP–K, WIP–K x 2.
Funding Provided by: Pomona College SURP