Steven E. Brenner

predNMD improves clinical variant pathogenicity classification by predicting nonsense-mediated mRNA decay (NMD) more accurately than the 50nt rule, using a random forest model trained on diverse datasets.

This work analyzes CAFA5 submissions, showing improved accuracy in protein function prediction and highlights the role of protein language models and 3D structures in performance gains.

This study calibrates in-frame indel variant effect predictors for clinical classification, showing they reach multiple evidence levels but perform worse than missense predictors.

The paper develops a comprehensive list of genetic conditions for genomic newborn screening, including 777 conditions associated with 743 genes, for implementation across multiple U.S. states and territories.

The paper calibrates three new computational tools for clinical variant classification, finding they provide evidence strength comparable to previously recommended tools.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

The paper assesses rare missense variants in genome sequences using PP3/BP4 evidence strength classifications and finds few variants meet strong or moderate criteria.

VIPdb version 2 compiles 403 variant impact predictors, summarizing their features and usage trends over 25 years.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

The paper assesses improvements in genomic variant classification accuracy over time in ClinVar and HGMD by analyzing misclassification rates in inborn errors of metabolism across ancestry groups.

The paper calibrates computational tools for missense variant pathogenicity classification and provides evidence-based revisions to PP3/BP4 ACMG/AMP criteria.

StrVCTVRE predicts the pathogenicity of human genome structural variants using a random forest classifier, achieving 90% sensitivity while filtering half of SVs.

This study evaluates the Full Genome Analysis method for diagnosing rare monogenic disorders, finding a 40% diagnostic yield in clinical cases.

The paper shows that a small number of dysregulated adult-born neurons in the hippocampus disrupt brain-wide functional connectivity and spatial memory.

The paper identifies NMD-targeted transcripts in human cells by analyzing RNA-Seq data from NMD-inhibited HeLa cells, revealing broad regulatory potential of alternative splicing coupled with NMD.

The paper evaluates how parameter choices affect variant interpretation pipelines by systematically perturbing parameters and shows that certain choices significantly impact sensitivity and specificity.

The paper characterizes a regulatory network of splicing factors through alternative splicing coupled with NMD, revealing extensive interactions and NMD targeting of splicing factors.

This study evaluates the quality of exome sequencing from DNA extracted from dried blood spots (DBS) and finds it comparable to whole blood, with similar variant calling accuracy.

Meta-Align improves DNA sequence alignment by incorporating quality scores and handling frameshift errors using an HMM-based approach, outperforming TBLASTX in simulated benchmarks.

The paper uses polysome fractionation data to identify features of NMD-targeted transcripts, finding the EJC model is a strong predictor while long 3 UTRs are not.

Open-source KBase platform unites reference genomes and biochemistry, over 70 apps, Jupyter-based Narratives with provenance, and a Docker SDK for reproducible plant and microbial systems biology.