Summary

  • Expert in Proteomics and Cell Signalling with 7 years of experience leading statistical investigation of Mass Spectrometry data in oncology and model organisms.
  • Led analyses into biomarkers, mechanism of action, and toxicology for more than 10 oncology drug development programs over the last 2 years.
  • Strong data engineering background with 7 years of experience building scalable automated pipelines and analytical software for genomics and proteomics data.

Experience

Senior Biological Data Scientist

Bristol Myers Squibb, Boston, MA

August 2024 - August 2026 (Contingent)

Achievements

  • Delivered key analyses allowing an early stage drug program to reach an expedited development milestone in Q1 2026. Contribute analyses to an FDA IND submission.
  • Refined statistical analysis SOP with batch correction, pathway enrichment, and network analysis, allowing expansion of experiments into 5 to 10 cell lines split across multiple plates.
  • Re-engineered the core mass spectrometry Nextflow pipeline to utilize parallel AWS Batch computation, speeding up processing time per plate by up to 10x.
  • Engineered an AWS Athena backed MCP server for Olink and SomaScan experimental results, allowing AI enabled meta-analysis of probe quality, co-expression, and differential expression.

Activities

  • Provided statistical leadership for 5 to 10 early stage drug development projects a month, including investigation of mechanism of action, toxicology, and drug resistance.
  • Provided software development for a suite of RShiny GUIs and R packages to power the analysis of Mass Spectrometry proteomics and phosphoproteomics data.
  • Engineered automated Nextflow pipelines on AWS Batch and bespoke databases in AWS Athena to support the analysis of large scale proteomics experiments in the cloud.

Software Engineer

Ginkgo Bioworks, Boston, MA

July 2024 - August 2024

Achievements

  • Engineered an end to end system for Capillary Gel Electrophoresis data, including binary data processing, automated ETL, and interactive data analysis in React.
  • Developed Copy Counter, an AWS based pipeline popular among Ginkgo project teams which determined copy number of integrated DNA using NGS data.

Activities

  • Engineered production grade relational databases, Python RESTful APIs, and React applications for cell engineering and instrument data analysis.
  • Implemented and maintained Apache Airflow pipelines for the automated analysis of 1000s of NGS, analytical chemistry, and molecular biology instrument samples a day.

Graduate Research Assistant

University of Washington, Laboratory of Judit Villén, Seattle, WA

Sept 2016 - June 2022

Achievements

  • Engineered PyAscore, a versatile, C++ accelerated Python package for post translational modification localization, an essential step in analyte identification.
  • Led investigation of a large scale yeast cell signalling dataset, providing a detailed map of protein co-regulation. Published analyses in Nature Structural and Molecular Biology.

Activities

  • Automated the analysis of 1000s of phosphoproteomics samples using Snakemake in order to enhance Phosphopedia, a database for the design of targeted mass spectrometry assays.
  • Performed statistical analyses of phosphoproteomics perturbation panels with 100s of samples, with data cleaning and regulatory network analysis in R.

Education

PhD in Genome Sciences

University of Washington, Sept 2016 - June 2022

  • Big Data in Genomics and Neuroscience Training Grant (2018; NIH T32 LM012419)
  • Advanced Data Science Certificate

BS in Molecular and Cellular Biology

Western Washington University, Sept 2011 - Dec 2015

  • Outstanding Graduate WWU Biology, Magna Cum Laude
  • Mathematics Minor

Selected Publications

* indicates co-first authorship

  • AS Barente*, Z Zhao*, Z Zheng*, C Wu, K Singh, Z Wang, A Hall, L Menard, I Neuhaus, M Kurki, A Palotie, M Daly, S Finer, D van Heel, M Pietzner, C Langenberg, J Maranville, T Wang, B Sun. Systematic comparison of affinity proteomic technologies across multiple cohorts provides insights into generalizability of proteomic machine learning models. 2026, Nature Communications. ACCEPTED FOR PUBLICATION

  • F Chu, SC Jenson, AS Barente, NC Heller, ED Merkley, KH Jarman. MARLOWE: An Untargeted Proteomics, Statistical Approach to Taxonomic Classification for Forensics. 2025, Journal of Proteome Research.

  • M Leutert, AS Barente, NK Fukuda, RA Rodriguez-Mias, J Villén. The regulatory landscape of the yeast phosphoproteome. 2023, Nature Structural & Molecular Biology.

  • AS Barente, J Villén. A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data. 2023, J. Proteome Res. Special Issue on Software Tools and Resources.

  • IR Smith, JK Eng, AS Barente, A Hogrebe, A Llovet, RA Rodriguez-Mias, J Villén. Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification. 2022, Analytical Chemistry.

  • A Hogrebe, KN Hess, A Llovet, YJ Ramos, AS Barente, D Hernandez‐Portugues, IR Smith, RA Rodríguez‐Mias, J Villén. IsobaricQuant enables cross‐platform quantification, visualization, and filtering of isobarically‐labeled peptides. 2022 PROTEOMICS.

  • IR Smith, KN Hess, AA Bakhtina, AS Valente, RA Rodríguez-Mias, and J Villén. Identification of phosphosites that alter protein thermal stability. 2021, Nature Methods.

  • M Martin-Perez, T Ito, A Grillo, AS Valente, J Han, S Entwisle, H Huang, D Kim, M Yajima, M Kaeberlein, and J Villén. PKC downregulation upon rapamycin treatment attenuates mitochondrial disease. 2020, Nature Metabolism.

  • SW Entwisle, CM Calejman, AS Valente, RT Lawrence, C Hung, DA Guertin, and J Villén. Proteome and phosphoproteome analysis of brown adipocytes reveals that RICTOR loss dampens global insulin/AKT signaling. 2020, Molecular & Cellular Proteomics.