Brenner Laboratory: Computational Genomics Research Group

Computational Genomics Research Group
University of California, Berkeley

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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. doi: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. PMCID:4352190. doi: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. PMCID:5215776. doi:10.1056/NEJMoa1509164. [PDF 0.6M] [Appendix 5.9M]
4. Adhikari AN, Gallagher RC,Wang Y, Currier RJ, Amatuni G, Bassaganyas L, Chen F, Kundu K, Kvale M, Mooney SD, Nussbaum RL, Randi SS, Sanford J, Shieh JT, Srinivasan R, Sunderam U, Tang H, Vaka D, Zou YY, Koenig BA, Kwok PY, Risch N, Puck JM, Brenner SE. 2020. The role of exome sequencing in newborn screening for inborn errors of metabolism. Nature Medicine. doi:10.1038/s41591-020-0966-5 [PDF]
5. Hoskins RA, Repo S, Barsky D, Andreoletti G, Moult J, Brenner SE. 2017. Reports from CAGI: The Critical Assessment of Genome Interpretation. Human Mutation 38:1039-1041. PMID:28544272 doi:10.1002/humu.23290
6. Brenner SE. 2013. Be prepared for the big genome leak. Nature 498:139. doi: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. doi:10.1038/nature05676 [PDF 1.3M]
8. Hansen KD, Lareau LF, Blanchette M, Green RE, Meng Q, Rehwinkel J, Gallusser FL, Izaurralde E, Rio DC, Dudoit S, Brenner SE. 2009. Genome-wide identification of alternative splice forms down-regulated by nonsense-mediated mRNA decay in Drosophila. PLoS Genetics 5:e1000525. PMCID:2689934. doi: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. PMCID:4155737 doi: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. PMCID:4079965. doi:10.1101/gr.170100.113 [PDF 3.6M] [supplementary material 18M]
11. Brooks AN, Duff MO, May G, Yang L, Bolisetty M, Landolin J, Wan K, Sandler J, Celniker SE, Graveley BR, Brenner SE. 2015. Regulation of alternative splicing in Drosophila by 56 RNA binding proteins. Genome Research. doi: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. PMCID:4379411. doi: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. PMCID:3205580. doi: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. PMCID:5015320. doi: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. PMCID:5389931. doi:10.1002/prot.25223 [Advance access PDF 2.1M] [Supp 2.4M]
16. Nguyen V, Park A, Xu A, Srouji J, 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. doi:10.1002/prot.25163 [PDF 0.4M] [Supp 0.07M]
17. Chandonia JM, Fox NK, Brenner SE. 2017. SCOPe: Manual curation and artifact removal in the structural classification of proteins;extended database. Journal of Molecular Biology 429:348-55. PMID: 27914894. PMCID:PMC5272801. doi:10.1016/j.jmb.2016.11.023 [PDF 0.4M]
18. Fox NK, Brenner SE, Chandonia JM. 2015. The value of protein structure classification information surveying the scientific literature. Proteins: Structure, Function, and Bioinformatics. doi:10.1002/prot.24915 [Advance access PDF 1.3M]
19. Chandonia JM, Brenner SE. 2006. The impact of structural genomics: expectations and outcomes. Science 311:347-351. doi: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. doi:10.1093/nar/gky1134