Frontiers of Bioinformatics Unsolved Problems and Challenges (2005) / Chapter Skim
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Presentation Abstracts
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FRONTIERS OF BIOINFORMATICS: UNSOLVED PROBLEMS AND CHALLENGES October 15-17, 2004 PRESENTATION ABSTRACTS 3
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Finally, data from Saccharomyces, where genome-wide knockouts and phenotypic data have been compared to expression data, have shown that no simple relationship exists between genes selected on the basis of their expression level changes and the biology of the perturbed system (Birrell et al., 2002, PNAS, 99, 8778)
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Hence, at the methylation coalface, we face a far more interesting and complex clinical situation than the current emphasis on hardware changes such as mutations, SNPs, and gross genomic imbalances, since methylation signatures are dynamic and context dependent. They provide snapshots of the current network status and hence of our current cellular operating systems.
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One interesting finding is the large number of ribosomal pseudogenes in the human genome, with 80 functional ribosomal proteins giving rise to ~2,000 ribosomal protein pseudogenes. At end I will talk broadly about pseudogenes, in terms of their composition and mutation rates and I will compare pseudogenes in the human with those in a number of other model organisms, including worm, fly, yeast, and various prokaryotes.
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that about 5% of the human genome shows signs of being under purifying selection. Purifying selection occurs in the most important functional segments of the genome, where random mutations are mostly deleterious and hence are rejected by natural selection, leaving the orthologous segments in different species more similar than would be expected under a "neutral" mutation model.
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I will briefly describe some genome rearrangements algorithms and show how these algorithms shed light on previously unknown features of mammalian evolution. In particular, they provide evidence for extensive reuse of breakpoints from the same relatively short regions and reveal a great variability in the rate of micro-rearrangements along the genome.
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Here, I start off by describing an emerging, so far undescribed, new gene family in human that appears to drive the shaping of up to 10% of human chromosome II. Then I illustrate more generally the dynamics of gene content in metazoan genomes and how it correlates with various other measurements of genome evolution such as intron content, protein architecture or synteny.
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This mutational asymmetry has acted over long evolutionary periods to produce a compositional asymmetry within transcribed regions of mammalian genomes. In more recent work, Dick Hwang in my lab has developed a powerful Bayesian Markov Chain Monte Carlo approach that allows systematic exploration of variation in context-dependent rates and mutational asymmetry with respect to position within an evolutionary tree and within a sequence.
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The information can be applied in structural genomics to find protein partners which can be co-expressed and co-crystallized to give structures of complexes. These inferred interactions can be compared to directly measured protein interactions, collected in the Database of Interacting Proteins: http://dip.doe-mbi.ucla.edu/.
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I will describe our algorithm for predicting target genes of novel transcription factors, based on their amino acid sequence and on knowledge of the binding pattern of other proteins in their family. It is possible that in the future such approaches may enable the determination of the regulatory networks in the cell based on genomic sequence data alone.
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Computational methods for inferring protein interactions are likewise attracting much interest. Particularly remarkable has been the setup of CAPRI (Critical Assessment of PRedicted Interactions)
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The diversity of noncoding RNAs in nature is largely unknown, because RNA genes have been difficult to detect systematically, and most current genefinding approaches focus exclusively on protein coding genes. Genome sequence analysis, functional genomics, and new computational algorithms have enabled several recent experiments that have begun to show that RNA genes and RNA-based regulatory circuits are more prevalent that we suspected.
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Indeed, it is well known that human transcripts contain a vast excess of sequences that match the consensus splice site motifs as well as authentic sites yet are virtually never used in splicing socalled `decoy' splice sites and pairs of decoy splice sites known as `pseudoexons'. The ability of the splicing machinery to reliably distinguish authentic exons and splice sites from a large excess of these imposters implies that sequence features outside of the canonical splice site/branch site elements must play important roles in splicing of most or all transcripts.
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A second key ingredient is the development of multiple metrics of rates for different evolutionary processes, and of different types of selection pressure. We have used metrics for a wide variety of evolutionary processes -- exon creation and loss; splice site movement; protein reading frame preservation; point substitution rates and selection pressures; premature termination codons, and conditional selection pressures -- to examine the role of alternative splicing in the evolution of mammalian genomes.
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Berman Protein Data Bank; Research Collaboratory for Structural Bioinformatics, Rutgers, the a State University of New Jersey The RCSB Protein Data Bank (PDB; www.pdb.org) is a publicly accessible information portal for researchers and students interested in structural biology.
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Specifically, we evaluate six publicly available structure alignment programs: SSAP, STRUCTAL, DALI, LSQMAN, CE and SSM by aligning all 8,581,970 protein structure pairs in a test set of 2,930 protein domains specially selected from CATH v.2.4 to ensure sequence diversity. Our own method STRUCTAL has also been run on SCOP v.
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How large is the error rate in computational gene structure predictions? In view of inevitable transitive gene structure annotation when comparing genomes, assessments of accuracy are of paramount importance.
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This talk presents observations about control of gene and protein expression in Arabidopsis thaliana based on the following data sources: tissue specific MPSS data and Affymetrix gene expression data on stress response, genome-wide prediction of binding site clusters for known transcription factors, evaluation of alternative splicing evident in cDNA and EST sequences, and microRNA identification and mRNA target prediction (Hoth et al 2003)
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be standardized to allow for integration of data across multiple conditions. The best progress has been made in standardizing exchange of genotype and some types of phenotype data, most notably microarray expression data.
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serve as representative of highly expressed genes, and our method specifies genes with rather similar codon usages as PHX genes. These assignments are reasonable under fast growth conditions, where there is a need for many ribosomes, for proficient transcription and translation, and for many CH proteins to ensure properly folded, modified, and translocated protein products.
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coli in PHX genes plus the addition of actin, cofilin and related genes. The most PHX genes of Drosophila encode the cytoskeletal proteins.
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