Felipe da Veiga Leprevost

Biomedical Data Science & Visualization

About Me

I am a molecular biologist, and systems analyst by training with extensive hypothesis-driven research experience and strong skills in bioinformatics data analysis, data wrangling, statistical analysis, and data visualization.

I have almost twenty years of experience in wet-lab, cell biology, molecular biology, molecular parasitology, genomics, and proteomics.

My Research

My research concentrates on the area of bioinformatics, proteomics, and data integration. I am particularly interested in mass spectrometry-based proteomics, software development for proteomics, cancer proteogenomics, and transcriptomics. The computational methods and tools previously developed by my colleagues and me, such as PepExplorer, MSFragger, Philosopher, and PatternLab for Proteomics, are among the most referred proteome informatics tools and are used by hundreds of laboratories worldwide.

I am also a Proteogenomics Data Analysis Center (UM-PGDAC) member as part of the NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative for processing and analyzing hundreds of cancer proteomics samples. UM-PGDAC develops advanced computational infrastructure for comprehensive and global characterization of genomics, transcriptomics, and proteomics data collected from several human tumor cohorts using NCI-provided biospecimens. Since 2019 I have been working as a bioinformatics data analyst with the University of Michigan Proteomics Resource Facility, which provides state of the art capabilities in proteomics to the University of Michigan investigators, including Rogel Cancer Center investigators as Proteomics Shared Resource.

Contact

Dr. Felipe da Veiga Leprevost
Research faculty at University of Michigan Medical School
Associate faculty at the Michigan Institute for Data Science - MIDAS
Member of the Rogel Cancer Center
Department of Pathology
Ann Arbror, MI, USA
felipevl@umich.edu

Curriculum Vitae

Education & Professional Experience

Research Faculty (since 2019)
University of Michigan, pathology department, Ann Arbor, USA
Services: Software development for computational proteomics, bioinformatics support

Research Fellow (2015-2019)
University of Michigan, pathology department, Ann Arbor, USA Services: Software development for computational proteomics, bioinformatics support analysis

Research Fellow (2014-2015)
Fiocruz - Laboratory for Computational and Strucutural Proteomics, Curitiba, Brazil
Topics: Proteomics, mass spectrometry

Ph.D. in Bioinformatics (2014-2015)
Fiocruz - Laboratory for Computational and Strucutural Proteomics, Curitiba, Brazil
Topics: Proteomics, mass spectrometry
Thesis: PepExplorer: a similarity-driven tool for analyzing de novo sequencing results

B.Tech in Systems Analysis & Development (2010-2014)
Positivo University, Curitiba, Brazil
Thesis: Development of a distributed system for hospital-based digital signage

MS in Molecular & Cellular Biology (2006-2009)
Fiocruz, Rio de Janeiro, RJ
Thesis: Characterization of genes with unknown function with expression associated to infective forms of Trypanosoma cruzi

Intership in Molecular Biology (2006)
Parana Federal University, Diagnostic Center Marcos Enrietti
Tasks: Cultivation and handling of Apicomplexa parasites

Intership in Parasitology (2005)
Positivo University, Curitiba, Brazil
Tasks: Microscopical identification of micro-parasites

Bsc in Biology (2003-2006)
Positivo University, Curitiba, Brazil
Thesis: Recombinant antibody production against Trypanosoma cruzi proteins by Phage Display

Teaching

Perl for Bioinformatics (Masters program in Molecular & Cellular Biology, Fiocruz , 2012–2014)

Selected Publications

  • Gillette MA, Satpathy S, Cao S, et al. Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma. Cell. 2020;182(1):200-225.e35.

  • Clark DJ, Dhanasekaran SM, Petralia F, et al. Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma Cell. 2019;179(4):964-983.e31.

  • da Veiga Leprevost F, Haynes SE, Avtonomov DM, et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat Methods. 2020;17(9):869-870.

  • Kong AT, da Veiga Leprevost F, Avtonomov DM, Mellacheruvu D, Nesvizhskii AI. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods. 2017;14(5):513-520.

Full List of Publications

Skills

  • Languages: Brazilian Portuguese (native), English (fluent)

  • Programming: R, Python, Perl, Go, C#, Java, CSS, HTML, SQL/SQLite

  • Bioinformatics: Transcriptome, and Proteome analysis, functional analysis

  • Statistics: R (limma, edgeR, ComBat, DreamAI, mice, broom, tidyuniverse)

  • Visualization: R (ggplot2, Shiny, plotly, echarts4r, leaflet, tmap, gganimate)

  • Markup: Markdown, R (RMarkdown, Quarto, blogdown, bookdown, xaringan), LaTeX

Memberships & Services