2,443 research outputs found
<i>De Novo</i> Discovery of Structured ncRNA Motifs in Genomic Sequences
No full textDe novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.</p
Faster Genome Annotation of Non-coding RNA Families without Loss of Accuracy
No full textRNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are slow. This paper shows how to make CMs faster while provably sacrificing none of their accuracy. Specifically, based on the CM, our software builds a profile hidden Markov model (HMM), which filters the genome database. This HMM is a rigorous filter, i.e., its filtering eliminates only sequences that provably could not be annotated as homologs. The CM is run only on what remains. Optimizing the HMM for filtering involves minimizing an exponential objective # Dept. of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WA, USA, 98195, [email protected] + Depts. of Computer Science & Engineering and Genome Sciences, University of Washington, Box 352350, Seattle, WA, USA, 98195, [email protected] c ACM, 2004. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in Proc. Eighth Annual Inter. Conf. on Computational Molecular Biology (RECOMB) , 2004. See http://recomb04.sdsc.edu/
Parallel RAMs with Owned Global Memory and Deterministic Context-Free Language Recognition
No full textWe identify and study a natural and frequently occurring subclass of Concurrent Read, Exclusive Write Parallel Random Access Machines (CREW-PRAMs). Called Concurrent Read, Owner Write, or CROW-PRAMs, these are machines in which each global memory location is assigned a unique "owner" processor, which is the only processor allowed to write into it. Considering the difficulties that would be involved in physically realizing a full CREW-PRAM model, it is interesting to observe that in fact, most known CREW-PRAM algorithms satisfy the CROW restriction or can be easily modified to do so. This paper makes three main contributions. First, we formally define the CROW-PRAM model and demonstrate its stability Department of Computer Science, York University, Toronto, Canada M3J 1P3; [email protected]. y Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350; [email protected]. z Parts of this work were done at the Department of ..
Letter from Walter Frank, Acting Chairman, American Civil Liberties Union, to Rt. Rev. Edward L. Parsons, November 9, 1942
No full textLetter from Walter Frank to Bishop Edward L. Parsons regarding Parson's involvement in the Korematsu case. Frank discusses "a controversy concerning the handling of the Korematsu case," and urges that "the Northern California branch, in handling the appeal in the Korematsu case, conform to the policy laid down by the overwhelming joint referendum vote of the National Committee and board." Frank states: "It should be emphasized that the Union does not attack the underlying presidential power."The ACLU-Northern California case file records contain legal documents and correspondence pertaining to the case argued before the Supreme Court in Korematsu v. United States (1944), challenging the constitutionality of Executive Order 9066
Parallel RAMs with Owned Global Memory and Deterministic Context-Free Language Recognition
No full textWe identify and study a natural and frequently occurring subclass of Concurrent-Read, Exclusive-Write Parallel Random Access Machines (CREW-PRAMs). Called Concurrent-Read, Owner-Write, or CROW-PRAMs, these are machines in which each global memory location is assigned a unique "owner" processor, which is the only processor allowed to write into it. Considering the difficulties that would be involved in physically realizing a full CREW-PRAM model, it is interesting to observe that in fact, most known CREW-PRAM algorithms satisfy the CROW restriction or can be easily modified to do so. This paper makes three main contributions. First, we formally define the CROW-PRAM model and demonstrate its stability Department of Computer Science, CCB126, York University, Toronto, Canada M3J 1P3; [email protected]. y Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350; [email protected]. z Parts of this work were done at the Depart..
Fieldnotes on vist to the Columbus Museum of Art to view collection Walter O. and Walter L. Mayo
No full textThe author in her visit discusses the carvings on exhibition of Walter L. Mayo and Walter O. Mayo and how they were influencedhttps://digital.kenyon.edu/mac_interviews/1002/thumbnail.jp
Vitalistic information systems in the South African public health system : a transactional analysis perspective
No full textIncludes bibliographical references
Letter to Walter Frank, Acting Chairman, American Civil Liberties Union, November 11, 1942
No full textAuthor's name omitted from bottom of page, is likely Ernest Besig. Letter responds to Walter Frank's letter to Bishop Edward L. Parson on November 9, 1942. Author asserts that at the time of taking on the Korematsu case, ACLU of Northern California was in accordance with national policy. Author writes that the case will go forward, and hopes that the Board of ACLU national will reconsider its position.The ACLU-Northern California case file records contain legal documents and correspondence pertaining to the case argued before the Supreme Court in Korematsu v. United States (1944), challenging the constitutionality of Executive Order 9066
The analysis of RNA-Seq experiments using approximate likelihood
No full textThesis (Ph.D.)--University of Washington, 2020The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Polya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression
Discovery and Applications of Bacterial Noncoding RNAs
No full textThesis (Ph.D.)--University of Washington, 2012Noncoding RNAs (ncRNAs) are functional transcripts that do not code for proteins. Many of them play indispensible roles in the cell. For example, the ribosomal RNAs make up the ribosome that is the factory for making proteins and riboswitches bind to small metabolites in the cell and regulate gene expression. Computational discovery of ncRNAs is challenging, however, because ncRNAs evolve rapidly on the nucleotide level while preserving secondary structure. In the first part of this thesis, we develop two clustering algorithms that are robust to weak sequence homology signals and are applicable on the genomic scale. We show that both algorithms can recover most known ncRNA families and as few as 5 homologous sequences are needed to predict a strong motif. In the second part of the thesis, we investigate whether secondary structure in- formation improves maximum likelihood tree inference for ncRNAs. An accurate phylogenetic tree has important biological and clinical applications: it can be used to infer the function of novel organisms and understand the evolutionary history of species. We show that using structure information, a more realistic gap model, and a maximum likelihood approach improves phylogenetic tree inference. In the third part of the thesis, we develop a method for profiling human gut microbial communities using high-throughput sequencing. Our method works on Illumina short reads and does not require assembly or taxonomic identification. We show that it can differentiate between the gut microbiota of healthy individuals at low sequencing depth, making it a cost-effective screening tool for large population studies. In the final part of the thesis, we use a standard additions experiment to examine sequencing bias and errors in Illumina HiSeq. We identify features associated with systematic errors and develop an error correction pipeline. We show that our method reduces base errors and produces better species diversity estimates
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