PI: Steven E. Brenner
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 currently involved in an effort to explore whether genome sequencing at birth can be used as an effective means for newborn screening. 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) project4, 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 privacy5.
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 NMD6. 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 species7, 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 worm8-12. 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 degradation13.
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 relationships14. 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 Annotation15. We are also experimentally validating the function predictions, with a focus on the Nudix family16. 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 efforts19.
Recent selected publications
1. Brenner SE. 2007. Common sense for our genomes. Nature 449:783-784. doi:10.1038/449783a
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. 2015. Combined immunodeficiency due to MALT1 mutations, treated by hematopoietic cell transplantation. Journal of Clinical Immunology 35:135-146. PMCID:4352191. doi:10.1007/s10875-014-0125-1
4. Callaway E. 2010. Mutation-prediction software rewarded. Nature. published online. doi:10.1038/news.2010.679
5. 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]
6. 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]
7. 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]
8. 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]
9. Boyle A, et al. 2014. Comparative analysis of regulatory information and circuits across distant species. Nature 512:453-456. PMCID:4336544. doi:10.1038/nature13668 [PDF 9.5M]
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. Graveley BR, Brooks AN, Carlson JW, Duff MO, Landolin J, Yang L, et al. 2011. The developmental transcriptome of Drosophila melanogaster. Nature 471:473-479. PMCID:3075879. doi:10.1038/nature09715 [PDF 1.9M]
12. 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]
13. 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
14. 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]
15. Radivojac P, et al. 2013. A large-scale evaluation of computational protein function prediction. Nature Methods 10:221-227. PMCID:3584181. doi:10.1038/nmeth.2340 [PDF 720K]
16. Xu A, Desai AM, Brenner SE, Kirsch JF. 2013. A continuous fluorescence assay for the characterization of Nudix hydrolases. Analytical Biochemistry 437:178-184. PMCID:3744803. doi:10.1016/j.ab.2013.02.023 [PDF .6M]
17. Fox NK, Brenner SE, Chandonia JM. 2013. SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Research 42:D304-9. PMCID:3965108 doi:10.1093/nar/gkt1240 [PDF 2.4M]
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]
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