About

Steven E. Brenner is a Professor affiliated with the Department of Genetics, Genomics, Evolution, and Development at the University of California, Berkeley. His research description can be found at http://mcb.berkeley.edu/faculty/ggd/brenners.html, indicating his focus on genetics, genomics, evolution, and development. He is based in the 461 Koshland Hall at UC Berkeley, with contact details including a lab phone number (510) 642-9614 and an office address at 461A Koshland Hall. His professional role involves advancing scientific understanding in his field through research and academic contributions.

Research topics

• Medicine
• Genetics
• Biology
• Computer Science
• Computational biology
• Endocrinology
• Psychology
• Pathology
• Evolutionary biology
• Bioinformatics

Selected publications

• Advances in Protein Function Prediction from the Fifth CAFA Challenge

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-30 · 1 citations

  articleOpen access

  The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight wellperforming methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements.

  PublisherDOI

• Improving variant classification through data aggregation and calibration

  Pathology · 2026-02-01

  article
  PublisherDOI

• Calibration of in-frame indel variant effect predictors for clinical variant classification

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-18

  articleOpen access

  Insertions and deletions (indels) represent a substantial source of genetic variation in humans and are associated with a diverse array of functional consequences. Despite their prevalence and clinical importance, indels, particularly short in-frame indels, remain critically understudied compared to single nucleotide variants and are challenging to interpret clinically. While many computational predictors for missense variants have been rigorously evaluated and calibrated for clinical use, the clinical utility of tools for in-frame indels remains uncertain. To address this gap, we have calibrated in-frame indel prediction tools for clinical variant classification. We constructed a high-confidence dataset of in-frame indel variants (≤ 50bp) from clinical and population databases and estimated the prior probability of pathogenicity of a rare in-frame indel observed in a disease-associated gene, and of an insertion and deletion separately. Using a previously developed statistical framework based on local posterior probabilities, we then established score thresholds for eight computational tools, corresponding to distinct evidence levels for pathogenic and benign classification according to ACMG/AMP guidelines. All in-frame indel predictors evaluated here reached multiple evidence levels of pathogenicity and/or benignity, demonstrating measurable clinical value. However, these models consistently exhibited lower performance levels compared to missense predictors, highlighting the need for improved computational approaches for indel classification.

  PublisherDOI

• Nominating Genetically Driven Immune Disease Genes for BEACONS: The First U.S. Multistate Genomic Newborn Screening Initiative

  Journal of Human Immunity · 2026-05-01

  articleOpen access

  Background T cell receptor excision circle (TREC)-based newborn screening (NBS) for severe combined immunodeficiency (SCID) has improved early diagnosis, treatment, and survival of SCID infants and identified additional cases of T lymphopenia requiring intervention. Early detection by whole-genome sequencing (WGS)-based NBS could benefit infants with many additional genetically driven immune diseases (GDIs) that lack traditional NBS screening biomarkers. Building Evidence and Collaboration for GenOmics in Nationwide Newborn Screening (BEACONS), the first research study to integrate WGS into multiple U.S. public health laboratories, will sequence up to 30,000 newborns with parental consent. An NBS-WGS task force established at the 2025 Clinical Immunology Society (CIS) Meeting is collaborating with BEACONS to select early-onset, actionable GDIs to be identified by sequence analysis and reported. Methods Task force experts reviewed GDI disease mechanisms, penetrance, and expressivity to prioritize those requiring targeted surveillance or treatment in the first year of life to prevent morbidity and mortality. Candidate GDI gene sources included the U.S. Recommended Uniform Screening Panel (RUSP), 2024 International Union of Immunological Societies (IUIS) inborn errors of immunity tables, GenIA database, ClinGen Gene Curation Expert Panel (GCEP) curations, OMIM, prior NBS-WGS studies, and GDI clinicians and researchers. Inclusion in the GDI-associated gene list required published evidence for clinical manifestations by age 1 year and alignment with consensus criteria of the International Consortium of Newborn Sequencing (ICoNS). Results The initial BEACONS draft list of 100 immune genes (20 of them RUSP SCID genes) was circulated to NBS-WGS task force members from immunology, genetics, rheumatology, transplant, and related specialties. Thirty-five experts from 20 academic institutions augmented and curated the list, submitting 407 genes associated with early-onset GDI to the BEACONS Gene List Working Group and Steering Committee. The overall final BEACONS list (∼800 genes) will be completed by January 2026, after additional review by participating public health laboratories and the public. Conclusion BEACONS is establishing a consensus-driven, evidence-based gene list as an initial research tool for multiple state public health laboratories, enabling prospective evaluation of the feasibility of population-wide NBS-WGS. The CIS NBS-WGS task force is now providing input regarding variant reporting, educational information sheets to accompany reports, and follow-up diagnostic evaluations and management measures required for each gene-disease relationship.

  PublisherDOI

• Disruption of the moonlighting function of CTF18 in a patient with T-lymphopenia

  Frontiers in Immunology · 2025-02-14

  articleOpen access

  Introduction Newborn screening for immunodeficiency has led to the identification of numerous cases for which the causal etiology is unknown. Methods Here we report the diagnosis of T lymphopenia of unknown etiology in a male proband. Whole exome sequencing (WES) was employed to nominate candidate variants, which were then analyzed functionally in zebrafish and in mice bearing orthologous mutations. Results WES revealed missense mutations in CHTF18 that were inherited in an autosomal recessive manner. CTF18, encoded by the CHTF18 gene, is a component of a secondary clamp loader, which is primarily thought to function by promoting DNA replication. We determined that the patient’s variants in CHTF18 (CTF18 R751W and E851Q) were damaging to function and severely attenuated the capacity of CTF18 to support hematopoiesis and lymphoid development, strongly suggesting that they were responsible for his T lymphopenia; however, the function of CTF18 appeared to be unrelated to its role as a clamp loader. DNA-damage, expected when replication is impaired, was not evident by expression profiling in murine Chtf18 mutant hematopoietic stem and progenitor cells (HSPC), nor was development of Ctf18-deficient progenitors rescued by p53 loss. Instead, we observed an expression signature suggesting disruption of HSPC positioning and migration. Indeed, the positioning of HSPC in ctf18 morphant zebrafish embryos was perturbed, suggesting that HSPC function was impaired through disrupted positioning in hematopoietic organs. Discussion Accordingly, we propose that T lymphopenia in our patient resulted from disturbed cell-cell contacts and migration of HSPC, caused by a non-canonical function of CHTF18 in regulating gene expression.

  PublisherOA PDFDOI

• Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

  UNC Libraries · 2025-03-20

  articleOpen access
  PublisherDOI

• Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology

  2025-12-01

  articleOpen accessSenior author

  When evaluations aren't trustworthy, entire research programs can chase mirages. Objective benchmarks and independent assessment have repeatedly catalyzed progress across computational biology, from protein structure prediction to variant interpretation and single‑cell analysis. This workshop gathers leaders of community challenges and benchmarking infrastructures together with domain experts to provide a contemporary view of how to design trustworthy evaluations, why blind prediction matters, and where standards, infrastructure, and policy must evolve to meet the demands of AI‑driven biology. We summarize the motivation and scope of the workshop; provide background on methodological and infrastructural advances that enable rigorous benchmarking; highlight invited speakers' contributions; and outline anticipated outcomes and community calls‑to‑action.

  PublisherDOI

• Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers

  Human Genetics · 2025-02-11 · 1 citations

  articleOpen access

  Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

  PublisherOA PDFDOI

• Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

  Human Genetics · 2025-03-01 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Session Introduction: Precision Medicine: Integrating Large-Scale Data and Intermediate Phenotypes for Understanding Health and Treating Disease

  2025-12-01

  articleOpen access1st authorCorresponding

  The field of precision medicine has undergone rapid development over the past three decades, driven by advances in high-throughput molecular profiling, large-scale electronic health data, and computational modeling. The central objective is to refine disease risk prediction, diagnosis, and treatment strategies by incorporating genetic, molecular, environmental, and clinical information into individualized care. However, the effective integration of these heterogeneous data sources presents substantial analytical challenges. The 2026 Precision Medicine session of the Pacific Symposium on Biocomputing (PSB) highlights computational methods that bridge large-scale biological data and intermediate phenotypes, emphasizing approaches that advance mechanistic understanding, risk prediction, and clinical utility. The contributions span multi-modal risk modeling, biomarker discovery, and causal inference frameworks, demonstrating the breadth and depth of research in computational precision medicine.

  PublisherDOI

Recent grants

• NIH Grant R01AI105776

  NIH · $2.4M · 2018

• NIH Grant R01GM071655

  NIH · $1.3M · 2011

• NIH Grant R01GM073109

  NIH · $2.2M · 2016

• NIH Grant P50GM062412

  NIH · $41.3M · 2008

• NIH Grant R01GM07174903S2

  NIH · $266k · 2012

Frequent coauthors

• John‐Marc Chandonia

  Lawrence Berkeley National Laboratory

  74 shared

• John Moult

  University of Maryland, College Park

  36 shared

• Predrag Radivojac

  Northeastern University

  33 shared

• Maxim Shatsky

  Lawrence Berkeley National Laboratory

  29 shared

• Anne O’Donnell‐Luria

  Broad Institute

  29 shared

• Naomi K. Fox

  Lawrence Berkeley National Laboratory

  28 shared

• Vikas Pejaver

  Icahn School of Medicine at Mount Sinai

  28 shared

• Aashish N. Adhikari

  Illumina (United States)

  27 shared