An Exploration Of The Data Engineering Requirements For Bioinformatics
Data Engineering Podcast
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When i first started on pc, it was, the scientists would come to me with their analyses. And maybe that would take like, a week to run, and i would hand it back to them. But now many companies have kind of risen to that challenge. We have some really interesting libraries out there. You know, we have r shiny and dashd to babel that have really catered themselves to the data science eco system. I think my favorite has been using the apache parquet file type and using a high performance compute cluster.
Summary
Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research, namely bioinformatics. This brings with it a unique set of challenges for data collection, data management, and analytical capabilities. In this episode Jillian Rowe shares her experience of working in the field and supporting teams of scientists and analysts with the data infrastructure that they need to get their work done. This is a fascinating exploration of the collaboration between data professionals and scientists.
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- Your host is Tobias Macey and today I’m interviewing Jillian Rowe about data engineering practices for bioinformatics projects
Interview
- Introduction
- How did you get involved in the area of data management?
- How did you get into the field of bioinformatics?
- Can you describe what is unique about data needs in bioinformatics?
- What are some of the problems that you have found yourself regularly solving for your clients?
- When building data engineering stacks for bioinformatics, what are the attributes that you are optimizing for? (e.g. speed, UX, scale, correctness, etc.)
- Can you describe a typical set of technologies that you implement when working on a new project?
- What kinds of systems do you need to integrate with?
- What are the data formats that are widely used for bioinformatics?
- What are some details that a data engineer would need to know to work effectively with those formats while preparing data for analysis?
- What amount of domain expertise is necessary for a data engineer to work in life sciences?
- What are the most interesting, innovative, or unexpected solutions that you have seen for manipulating bioinformatics data?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on bioinformatics projects?
- What are some of the industry/academic trends or upcoming technologies that you are tracking for bioinformatics?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Bioinformatics
- How Perl Saved The Human Genome Project
- Neo4J
- AWS Parallel Cluster
- Datashader
- R Shiny
- Plotly Dash
- Apache Parquet
- Dask
- HDF5
- Spark
- Superset
- FastQ file format
- BAM (Binary Alignment Map) File
- Variant Call Format (VCF)
- HIPAA
- DVC
- LakeFS
- BioThings API
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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