Manage episode 258019327 series 1297742
Bioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!
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- Your host as usual is Tobias Macey and today I’m interviewing Ariya Shajii about Seq, a programming language built for bioinformatics and inspired by Python
- How did you get introduced to Python?
- Can you start by describing what Seq is and your motivation for creating it?
- What was lacking in other languages or libraries for your use case that is made easier by creating a custom language?
- If someone is already working in Python, possibly using BioPython, what might motivate them to consider migrating their work to Seq?
- Can you give an impression of the scope and nature of the tasks or projects that a biologist or geneticist might build with Seq?
- What was your process for identifying and prioritizing features and algorithms that would be beneficial to the target audience?
- For someone using Seq can you describe their workflow and how it might differ from performing the same task in Python?
- How is Seq implemented?
- What are some of the features that are included to simplify the work of bioinformatics?
- What was your process of designing the language and runtime?
- How has the scope or direction of the project evolved since it was first conceived?
- What impact do you anticipate Seq having on the domain of bioinformatics and genomics?
- What have you found to be the most interesting, unexpected, and/or challenging aspects of building a language for this problem domain?
- What is in store for the future of Seq?
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- MIT CSAIL
- Intermediate Representation
- Moore’s Law
- Smith Waterman Algorithm
- Hamming Distance
- Pattern Matching in Functional Programming
- SIMD == Single Instruction Multiple Data
- Computational Genomics
- Sequence Read Archive public data set
- Google Cloud Life Sciences