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SUMMARY:Accelerating Genomic Data Science and AI/ML with Composability - T
 revor Manz\, Nezar Abdennur
DTSTART;TZID=US/Pacific:20250709T104500
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DTSTAMP:20260904T232250Z
UID:pretalx-scipy2025-PRSN9R@cfp.scipy.org
DESCRIPTION:The practice of data science in genomics and computational bio
 logy is fraught with friction. This is largely due to a tight coupling of 
 bioinformatic tools to file input/output. While omic data is specialized a
 nd the storage formats for high-throughput sequencing and related data are
  often standardized\, the adoption of emerging open standards not tied to 
 bioinformatics can help better integrate bioinformatic workflows into the 
 wider data science\, visualization\, and AI/ML ecosystems. Here\, we prese
 nt two bridge libraries as short vignettes for composable bioinformatics. 
 First\, we present Anywidget\, an architecture and toolkit based on modern
  web standards for sharing interactive widgets across all Jupyter-compatib
 le runtimes\, including JupyterLab\, Google Colab\, VSCode\, and more. Sec
 ond\, we present Oxbow\, a Rust and Python-based adapter library that unif
 ies access to common genomic data formats by efficiently transforming quer
 ies into Apache Arrow\, a standard in-memory columnar representation for t
 abular data analytics. Together\, we demonstrate the composition of these 
 libraries to build a custom connected genomic analysis and visualization e
 nvironments. We propose that components such as these\, which leverage sci
 entific domain-agnostic standards to unbundle specialized file manipulatio
 n\, analytics\, and web interactivity\, can serve as reusable building blo
 cks for composing flexible genomic data analysis and machine learning work
 flows as well as systems for exploratory data analysis and visualization.
LOCATION:Room 317
URL:https://cfp.scipy.org/scipy2025/talk/PRSN9R/
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