Influence of MP 29-02 on ciliary beat frequency in human epithelial cells in vitro.

PURPOSE: MP 29-02, which contains fluticasone propionate and azelastine hydrochloride, is used as a topical nasal application for the treatment of seasonal and perennial allergic rhinitis. Although a multitude of data is available on the clinical symptom reduction and treatment safety of MP 29-02, the effect of MP 29-02 on ciliary beat frequency (CBF) has not been evaluated thus far. METHODS: MP 29-02-containing solution was applied at concentrations of 2.5, 5, 10, and 20% to 14 healthy subjects, and nasal ciliated epithelial cells were then visualized using a phase-contrast microscope. CBF was measured after the application of MP 29-02. For a comparison, fluticasone propionate was used. CBF measurements were then performed for 15 min at 22 °C. Ringer's solution was applied as a negative control. RESULTS: MP 29-02 significantly reduced CBF at all the tested concentrations compared with that of the control group within the observation time. At a 2.5% concentration, MP 29-02 significantly reduced CBF from 6.81 Hz (SD ± 1.35 Hz) at baseline to 4.88 Hz (SD ± 1.52 Hz, p < 0.001) after 15 min. In contrast, for fluticasone propionate, a significant reduction was observed only with the 20% concentration after 5, 10, and 15 min. CONCLUSIONS: MP 29-09 significantly reduced CB, with an almost linear relationship between the MP 29-09 concentration and reduction in CBF. For fluticasone propionate, a significant reduction of CBF was observed only at the highest analyzed concentration. The findings have implications for the long-term use of the MP 29-02. Yet, further clinical studies are needed to confirm these results in vivo, especially in patients with seasonal or perennial allergic rhinits.

Integrating genomes.

As genomic sequencing projects attempt ever more ambitious integration of genetic, molecular, and phenotypic information, a specialization of genomics has emerged, embodied in the subdiscipline of computational genomics. Models inherited from population genetics, phylogenetics, and human disease genetics merge with those from graph theory, statistics, signal processing, and computer science to provide a rich quantitative foundation for genomics that can only be realized with the aid of a computer. Unleashed on a rapidly increasing sample of the planet's 10(30) organisms, these analyses will have an impact on diverse fields of science while providing an extraordinary new window into the story of life.

The UCSC Genome Browser Database: update 2009.

The UCSC Genome Browser Database (GBD, http://genome.ucsc.edu) is a publicly available collection of genome assembly sequence data and integrated annotations for a large number of organisms, including extensive comparative-genomic resources. In the past year, 13 new genome assemblies have been added, including two important primate species, orangutan and marmoset, bringing the total to 46 assemblies for 24 different vertebrates and 39 assemblies for 22 different invertebrate animals. The GBD datasets may be viewed graphically with the UCSC Genome Browser, which uses a coordinate-based display system allowing users to juxtapose a wide variety of data. These data include all mRNAs from GenBank mapped to all organisms, RefSeq alignments, gene predictions, regulatory elements, gene expression data, repeats, SNPs and other variation data, as well as pairwise and multiple-genome alignments. A variety of other bioinformatics tools are also provided, including BLAT, the Table Browser, the Gene Sorter, the Proteome Browser, VisiGene and Genome Graphs.

The UCSC Genome Browser Database: 2008 update.

The University of California, Santa Cruz, Genome Browser Database (GBD) provides integrated sequence and annotation data for a large collection of vertebrate and model organism genomes. Seventeen new assemblies have been added to the database in the past year, for a total coverage of 19 vertebrate and 21 invertebrate species as of September 2007. For each assembly, the GBD contains a collection of annotation data aligned to the genomic sequence. Highlights of this year's additions include a 28-species human-based vertebrate conservation annotation, an enhanced UCSC Genes set, and more human variation, MGC, and ENCODE data. The database is optimized for fast interactive performance with a set of web-based tools that may be used to view, manipulate, filter and download the annotation data. New toolset features include the Genome Graphs tool for displaying genome-wide data sets, session saving and sharing, better custom track management, expanded Genome Browser configuration options and a Genome Browser wiki site. The downloadable GBD data, the companion Genome Browser toolset and links to documentation and related information can be found at: http://genome.ucsc.edu/.

The UCSC genome browser database: update 2007.

The University of California, Santa Cruz Genome Browser Database contains, as of September 2006, sequence and annotation data for the genomes of 13 vertebrate and 19 invertebrate species. The Genome Browser displays a wide variety of annotations at all scales from the single nucleotide level up to a full chromosome and includes assembly data, genes and gene predictions, mRNA and EST alignments, and comparative genomics, regulation, expression and variation data. The database is optimized for fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. In the past year, 22 new assemblies and several new sets of human variation annotation have been released. New features include VisiGene, a fully integrated in situ hybridization image browser; phyloGif, for drawing evolutionary tree diagrams; a redesigned Custom Track feature; an expanded SNP annotation track; and many new display options. The Genome Browser, other tools, downloadable data files and links to documentation and other information can be found at http://genome.ucsc.edu/.

The UCSC Genome Browser Database: update 2006.

The University of California Santa Cruz Genome Browser Database (GBD) contains sequence and annotation data for the genomes of about a dozen vertebrate species and several major model organisms. Genome annotations typically include assembly data, sequence composition, genes and gene predictions, mRNA and expressed sequence tag evidence, comparative genomics, regulation, expression and variation data. The database is optimized to support fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. The Genome Browser displays a wide variety of annotations at all scales from single nucleotide level up to a full chromosome. The Table Browser provides direct access to the database tables and sequence data, enabling complex queries on genome-wide datasets. The Proteome Browser graphically displays protein properties. The Gene Sorter allows filtering and comparison of genes by several metrics including expression data and several gene properties. BLAT and In Silico PCR search for sequences in entire genomes in seconds. These tools are highly integrated and provide many hyperlinks to other databases and websites. The GBD, browsing tools, downloadable data files and links to documentation and other information can be found at http://genome.ucsc.edu/.

Transcriptome and genome conservation of alternative splicing events in humans and mice.

Combining mRNA and EST data in splicing graphs with whole genome alignments, we discover alternative splicing events that are conserved in both human and mouse transcriptomes. 1,964 of 19,156 (10%) loci examined contain one or more such alternative splicing events, with 2,698 total events. These events represent a lower bound on the amount of alternative splicing in the human genome. Also, as these alternative splicing events are conserved between the human and mouse transcriptomes they should be enriched for functionally significant alternative splicing events, free from much of the noise found in the EST libraries. Further classification of these alternative splicing events reveals that 1,037 (38.4%) are due to exon skipping, 497 (18.4%) are due to alternative 3' splice sites, 214 (7.9%) are due to alternative 5' splice sites, 75 (2.8%) are due to intron retention and the other 875 (32.4%) are due to other, more complicated, alternative splicing events. In addition, genomic sequences nearby these alternative splicing events display increased sequence conservation. Both the alternatively spliced exons and the proximal intron show increased levels of genomic conservation relative to constitutively spliced exons. For exon skipping events both intron regions flanking the exon are conserved while for alternative 5' and 3' splicing events the conservation is greater near the alternative splice site.

The UCSC Genome Browser Database.

The University of California Santa Cruz (UCSC) Genome Browser Database is an up to date source for genome sequence data integrated with a large collection of related annotations. The database is optimized to support fast interactive performance with the web-based UCSC Genome Browser, a tool built on top of the database for rapid visualization and querying of the data at many levels. The annotations for a given genome are displayed in the browser as a series of tracks aligned with the genomic sequence. Sequence data and annotations may also be viewed in a text-based tabular format or downloaded as tab-delimited flat files. The Genome Browser Database, browsing tools and downloadable data files can all be found on the UCSC Genome Bioinformatics website (http://genome.ucsc.edu), which also contains links to documentation and related technical information.

Assembly of the working draft of the human genome with GigAssembler.

The data for the public working draft of the human genome contains roughly 400,000 initial sequence contigs in approximately 30,000 large insert clones. Many of these initial sequence contigs overlap. A program, GigAssembler, was built to merge them and to order and orient the resulting larger sequence contigs based on mRNA, paired plasmid ends, EST, BAC end pairs, and other information. This program produced the first publicly available assembly of the human genome, a working draft containing roughly 2.7 billion base pairs and covering an estimated 88% of the genome that has been used for several recent studies of the genome. Here we describe the algorithm used by GigAssembler.

Promoter region-based classification of genes.

In this paper we consider the problem of extracting information from the upstream untranslated regions of genes to make predictions about their transcriptional regulation. We present a method for classifying genes based on motif-based hidden Markov models (HMMs) of their promoter regions. Sequence motifs discovered in yeast promoters are used to construct HMMs that include parameters describing the number and relative locations of motifs within each sequence. Each model provides a Fisher kernel for a support vector machine, which can be used to predict the classifications of unannotated promoters. We demonstrate this method on two classes of genes from the budding yeast, S. cerevisiae. Our results suggest that the additional sequence features captured by the HMM assist in correctly classifying promoters.

Rapid discrimination among individual DNA hairpin molecules at single-nucleotide resolution using an ion channel.

RNA and DNA strands produce ionic current signatures when driven through an alpha-hemolysin channel by an applied voltage. Here we combine this nanopore detector with a support vector machine (SVM) to analyze DNA hairpin molecules on the millisecond time scale. Measurable properties include duplex stem length, base pair mismatches, and loop length. This nanopore instrument can discriminate between individual DNA hairpins that differ by one base pair or by one nucleotide.

Support vector machine classification and validation of cancer tissue samples using microarray expression data.

MOTIVATION: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. RESULTS: We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97,802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets. AVAILABILITY: The SVM software is available at http://www.cs. columbia.edu/ approximately bgrundy/svm.

A discriminative framework for detecting remote protein homologies.

A new method for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a generative statistical model for a protein family, in this case a hidden Markov model. This general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.

Genie--gene finding in Drosophila melanogaster.

A hidden Markov model-based gene-finding system called Genie was applied to the genomic Adh region in Drosophila melanogaster as a part of the Genome Annotation Assessment Project (GASP). Predictions from three versions of the Genie gene-finding system were submitted, one based on statistical properties of coding genes, a second included EST alignment information, and a third that integrated protein sequence homology information. All three programs were trained on the provided Drosophila training data. In addition, promoter assignments from an integrated neural network were submitted. The gene assignments overlapped >90% of the 222 annotated genes and 26 possibly novel genes were predicted, of which some might be overpredictions. The system correctly identified the exon boundaries of 70% of the exons in cDNA-confirmed genes and 77% of the exons with the addition of EST sequence alignments. The best of the three Genie submissions predicted 19 of the annotated 43 gene structures entirely correct (44%). In the promoter category, only 30% of the transcription start sites could be detected, but by integrating this program as a sensor into Genie the false-positive rate could be dropped to 1/16,786 (0.006%). The results of the experiment on the long contiguous genomic sequence revealed some problems concerning gene assembly in Genie. The results were used to improve the system. We show that Genie is a robust hidden Markov model system that allows for a generalized integration of information from different sources such as signal sensors (splice sites, start codon, etc.), content sensors (exons, introns, intergenic) and alignments of mRNA, EST, and peptide sequences. The assessment showed that Genie could effectively be used for the annotation of complete genomes from higher organisms.

Knowledge-based analysis of microarray gene expression data by using support vector machines.

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

A probabilistic approach to consensus multiple alignment.

We consider the problem of obtaining the maximum a posteriori probability (MAP) estimate of a consensus ancestral sequence for a set of DNA sequences. Our maximization method, called ASA (dnA Sequence Alignment), can be applied to the refinement of noisy regions of a DNA assembly, to the alignment of genomic functional sites, or to the alignment of any set of DNA sequences related by a star-like phylogeny. Along with the optimal consensus, ASA finds suboptimal solutions together with their relative probabilities. The probabilistic approach makes it possible to establish the limits to which an ancestor can in principle be recovered from diverged sequences. In simulations on rather short synthetic sequences (of length up to 80) with different coverage and error rates ranging from 5% to 30%, ASA restored the consensus from noisy observations essentially as best as is theoretically possible for the given error rates. We also illustrate the performance of ASA on the alignment of E.Coli promoters and the Alu-Sb subfamily of human repeat sequences. Since our model is a special case of a profile HMM, we give a comparison between these two approaches, as well as with other DNA alignment methods.

Genome-wide bioinformatic and molecular analysis of introns in Saccharomyces cerevisiae.

Introns have typically been discovered in an ad hoc fashion: introns are found as a gene is characterized for other reasons. As complete eukaryotic genome sequences become available, better methods for predicting RNA processing signals in raw sequence will be necessary in order to discover genes and predict their expression. Here we present a catalog of 228 yeast introns, arrived at through a combination of bioinformatic and molecular analysis. Introns annotated in the Saccharomyces Genome Database (SGD) were evaluated, questionable introns were removed after failing a test for splicing in vivo, and known introns absent from the SGD annotation were added. A novel branchpoint sequence, AAUUAAC, was identified within an annotated intron that lacks a six-of-seven match to the highly conserved branchpoint consensus UACUAAC. Analysis of the database corroborates many conclusions about pre-mRNA substrate requirements for splicing derived from experimental studies, but indicates that splicing in yeast may not be as rigidly determined by splice-site conservation as had previously been thought. Using this database and a molecular technique that directly displays the lariat intron products of spliced transcripts (intron display), we suggest that the current set of 228 introns is still not complete, and that additional intron-containing genes remain to be discovered in yeast. The database can be accessed at http://www.cse.ucsc.edu/research/compbi o/yeast_introns.html.

Using the Fisher kernel method to detect remote protein homologies.

A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a hidden Markov model. The general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.