Brenner Laboratory: Computational Genomics Research Group

Computational Genomics Research Group
University of California, Berkeley

Recent news

Preprint posted: Bergquist T, Stenton SL, Nadeau EAW, Byrne AB, Greenblatt MS, Harrison SM, Tavtigian SV, O'Donnell-Luria A, Biesecker LG, Radivojac P, Brenner SE, Pejaver V, ClinGen Sequence Variant Interpretation Working Group. 2024. Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria. bioRxiv. https://doi.org/10.1101/2024.09.17.611902
Paper fully published: Stenton SL, Pejaver V, Bergquist T, Biesecker LG, Byrne AB, Nadeau EA, Greenblatt MS, Harrison SM, Tavtigian SV, Radivojac P, Brenner SE, O'Donnell-Luria A, ClinGen Sequence Variant Interpretation Working Group. 2024. Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations. Genetics in Medicine 26:101213. PMID:39030733 https://doi.org/10.1016/j.gim.2024.101213
VIPdb v2 paper fully published: Lin Y, Menon AS, Hu Z, Brenner SE. 2024. Variant Impact Predictor database (VIPdb), version 2: Trends from three decades of genetic variant impact predictors. Human Genomics 18:90. PMID:39198917 PMC11360829 https://doi.org/10.1186/s40246-024-00663-z
CAGI Flagship Paper published: The Critical Assessment of Genome Interpretation Consortium. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Read it here.
Graduate student alumna Jingyi (Jessica) Li wins the ISCB Overton Prize. She is the second lab alum to win the prize.
Post-doctoral alumna Jingqi Chen earned tenure at Fudan University.
Steven Brenner's comment on the FDA Notice of Proposed Rulemaking for Laboratory Developed Tests FDA NPRM on LDTs has been posted as Comment ID: FDA-2023-N-2177-6839, Tracking Number: lpr-usbk-9xba. Read it here.

Full Publications List [Google Scholar] [myNCBI]

Introduction

Individual genome interpretation. We have a longstanding interest in personal genome interpretation¹. In particular, we have been analyzing the genomes of newborns with undiagnosed disease, and using the sequencing information to correctly diagnose them—sometimes a decade before they would have received correct diagnoses otherwise (if ever)^2,3. We are exploring how genome sequencing at birth can be used as an effective means for newborn screening⁴. This would supplement or replace the mass spectrometry methods currently to identify diseases that are not clinically evident but if untreated have led to severe consequences including death. We also organize the Critical Assessment of Genome Interpretation (CAGI) project⁵, which aims to establish and advance the state-of-the-art in genome interpretation. Our group also has an interest in genetic data sharing and privacy⁶.

Gene regulation by alternative splicing and nonsense-mediated mRNA decay. Nonsense-mediated mRNA decay (NMD) is a cellular RNA surveillance system that recognizes transcripts with premature termination codons and degrades them. We discovered large numbers of natural alternative splice forms that appear to be targets for NMD, and we have seen that this is a mode of gene regulation. All conserved members of the SR family of splice regulators have an unproductive alternative mRNA isoform targeted for NMD⁷. Strikingly, the splice pattern for each is conserved in mouse and always associated with an ultraconserved or highly-conserved region of perfect identity between human and mouse. Remarkably, this seems to have evolved independently in every one of the genes, suggesting that this is a natural mode of regulation. We are using RNA-Seq to explore the pervasiveness of NMD in numerous species⁸, and to understand its behavior, finding that 20% of expressed human genes make isoforms targeted for degradation. As part of a modENCODE consortium, we discovered the repertoire of targets for alternative splicing in the fly, as well as unexpected relationships between the development of fly and worm^9,10,11. We are detailing the evolution of this gene-expression regulation mechanism, having initially discovered that the oldest known alternative splicing is for regulation, targetting transcripts for degradation¹².

Prediction of protein function using Bayesian phylogenomics. We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Unfortunately, these predictions have littered the databases with erroneous information, for a variety of reasons including the propagation of errors and the systematic flaws in BLAST and related methods. In collaboration with Michael Jordan's group, we have developed a statistical approach to predicting protein function that uses a protein family's phylogenetic tree, as the natural structure for representing protein relationships¹³. We overlay on this all known protein functions in the family. We use a model of function evolution to then infer the functions of all other protein functions. Even our initial implementations of this method, called SIFTER (Statistical Inference of Function Through Evolutionary Relationships) have performed better than other methods in widespread use. SIFTER was recently honored as the best-performing sequence-based method in the Critical Assessment of Function Annotation¹⁴. We are also experimentally validating the function predictions, with a focus on the Nudix family^15,16. We are also involved in maintaining the SCOPe: Structural Classification of Proteins—extended database¹⁷, a key resource for understanding protein structure data¹⁸. We analyze structural characterization efforts^19,20.

Selected publications

1. Brenner SE. 2007. Common sense for our genomes. Nature 449:783-784. PMID:17943102 https://doi.org/10.1038/449783a [PDF .2M]
2. Patel JP, et al. 2015. Nijmegen breakage syndrome detected by newborn screening for T cell receptor excision circles (TRECs). Journal of Clinical Immunology 35:227-233. PMID:25677497 PMC4352190 https://doi.org/10.1007/s10875-015-0136-6
3. Punwani D, et al. 2016. Multisystem anomalies in severe combined immunodeficiency with mutant Bcl11b. New England Journal of Medicine 375:2165-2176. PMID:27959755 PMC5215776 https://doi.org/10.1056/NEJMoa1509164 [PDF 0.6M] [Appendix 5.9M]
4. Adhikari AN, et al. 2020. The role of exome sequencing in newborn screening for inborn errors of metabolism. Nature Medicine 26:1392-1397. PMID:32778825 PMC8800147 https://doi.org/10.1038/s41591-020-0966-5 [PDF]
5. The Critical Assessment of Genome Interpretation Consortium. 2024. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Genome Biology 25:53. PMID:38389099 PMC10882881 https://doi.org/10.1186/s13059-023-03113-6
6. Brenner SE. 2013. Be prepared for the big genome leak. Nature 498:139. PMID:23765454 https://doi.org/10.1038/498139a [original PDF .2M] [annotated PDF 1M] [archival commentary page]
7. Lareau LF, Inada M, Green RE, Wengrod JC, Brenner SE. 2007. Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature 446:926-929. PMID:17361132 https://doi.org/10.1038/nature05676 [PDF 1.3M]
8. Hansen KD, et al. 2009. Genome-wide identification of alternative splice forms down-regulated by nonsense-mediated mRNA decay in Drosophila. PLoS Genetics 5:e1000525. PMID:19543372 PMC2689934 https://doi.org/10.1371/journal.pgen.1000525 [PDF .5M]
9. Gerstein M, et al. 2014. Comparative analysis of the transcriptome across distant species. Nature 512:445-448. PMID:25164755 PMC4155737 https://doi.org/10.1038/nature13424 [PDF 4M]
10. Li JJ, Huang H, Bickel PJ, Brenner SE. 2014. Comparison of D. melanogaster and C. elegans developmental stages, tissues, and cells by modENCODE RNA-seq data. Genome Research 24:1086-1101. PMID:24985912 PMC4079965 https://doi.org/10.1101/gr.170100.113 [PDF 3.6M] [supplementary material 18M]
11. Brooks AN, et al. 2015. Regulation of alternative splicing in Drosophila by 56 RNA binding proteins. Genome Research 25:1771-80. PMID:26294686 PMC4617972 https://doi.org/10.1101/gr.192518.115 [PDF 5MB]
12. Lareau LF, Brenner SE. 2015. Regulation of splicing factors by alternative splicing and NMD is conserved between kingdoms yet evolutionarily flexible. Molecular Biology and Evolution 32:1072-1079. PMID:25576366 PMC4379411 https://doi.org/10.1093/molbev/msv002
13. Engelhardt BE, Jordan MI, Srouji JR, Brenner SE. 2011. Genome-scale phylogenetic function annotation of large and diverse protein families. Genome Research 21:1969-1980. PMID:21784873 PMC3205580 https://doi.org/10.1101/gr.104687.109 [PDF 1.2M]
14. Jiang Y et al. 2016. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biology 17:184. PMID:27604469 PMC5015320 https://doi.org/10.1186/s13059-016-1037-6 [PDF 12.9M]
15. Srouji JR, Xu A, Park A, Kirsch JK, Brenner SE. 2017. The evolution of function within the Nudix homology clan. Proteins: Structure, Function, and Bioinformatics 85:775-811. PMID:27936487 PMC27936487 https://doi.org/10.1002/prot.25223 [Advance access PDF 2.1M] [Supp 2.4M]
16. Nguyen VN, Park A, Xu A, Srouji JR, Brenner SE, Kirsch JF. 2016. Substrate specificity characterization for eight putative Nudix hydrolases. Evaluation of criteria for substrate identification within the Nudix family. Proteins: Structure, Function, and Bioinformatics 84:1810-1822. PMID:27618147. PMC5158307. https://doi.org/10.1002/prot.25163 [PDF 0.4M] [Supp 0.07M]
17. Chandonia J-M, Guan L, Lin S, Yu C, Fox NK, Brenner SE. 2022. SCOPe: improvements to the Structural Classification of Proteins—extended database to facilitate variant interpretation and machine learning. Nucleic Acids Res 50:D553-559. PMID:34850923 PMC8728185 https://doi.org/10.1093/nar/gkab1054
18. Fox NK, Brenner SE, Chandonia JM. 2015. The value of protein structure classification information—surveying the scientific literature. Proteins: Structure, Function, and Bioinformatics 83:2025-2038. PMID:26313554 PMC4609302 https://doi.org/10.1002/prot.24915
19. Chandonia JM, Brenner SE. 2006. The impact of structural genomics: expectations and outcomes. Science 311:347-351. PMID:16424331 https://doi.org/10.1126/science.1121018 [PDF .2M]
20. Chandonia JM, Fox NK, Brenner SE. 2019. SCOPe: Classification of large macromolecular structures in the Structural Classification of Proteins—extended database. Nucleic Acids Research 47:475-481. PMID:30500919 PMC6323910 https://doi.org/10.1093/nar/gky1134