UAS Integration in the NAS Project UAS Commericalization Industry Conference

Description and Update of NASA UAS in the NAS Projec

DNA Methylation Profiling from High-Throughput Sequencing Data

In this chapter we will review the common steps in the analysis of whole genome singlebase-pair resolution methylation data including the pre-processing of the reads, the alignment and the read out of the methylation information of individual cytosines. We will specially focus on the possible error sources which need to be taken into account in order to generate high quality methylation maps. Several tools have been already developed to convert the sequencing data into knowledge about the methylation levels. We will review the most used tools discussing both technical aspects like user-friendliness and speed, but also biologically relevant questions as the quality control. For one of these tools, NGSmethPipe, we will give a step by step tutorial including installation and methylation profiling for different data types and species. We will conclude the chapter with a brief discussion of NGSmethDB, a database for the storage of single-base resolution methylation maps that can be used to further analyze the obtained methylation maps.This work was supported by the Ministry of Innovation and Science of the Spanish Government [BIO2010-20219 (M.H.), BIO2008-01353 (J.L.O.)]; ‘Juan de la Cierva’ grant (to M.H.) and Basque Country ‘Programa de formación de investigadores’ grant (to G.B.)

Phylogenetic distribution of large-scale genome patchiness

[Background] The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness) has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. [Results] The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris), birds (Gallus gallus), fishes (Danio rerio), invertebrates (Drosophila melanogaster and Caenorhabditis elegans), plants (Arabidopsis thaliana) and yeasts (Saccharomyces cerevisiae). We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. [Conclusion] Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level.This work was supported by the Spanish Government (BIO2005-09116-C03-01) and Plan Andaluz de Investigación (CVI-162, P06-FQM-01858, P07-FQM-03163 and TIC-640)

UAM Coordination and Assessment Team (UCAT) NASA UAM Update for ARTR

No abstract availabl

Emerging Commercial UAS Operating Environments (OE)

These presentations discuss the UAS in the NAS Project and how it is designed to eliminate technical and regulatory barriers currently affecting civil UAS flying

WordCluster: detecting clusters of DNA words and genomic elements

<p>Abstract</p> <p>Background</p> <p>Many <it>k-</it>mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds.</p> <p>Results</p> <p>We introduce here an algorithm to detect clusters of DNA words (<it>k-</it>mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used <it>WordCluster </it>to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome.</p> <p>Conclusions</p> <p><it>WordCluster </it>seems to predict biological meaningful clusters of DNA words (<it>k-</it>mers) and genomic entities. The implementation of the method into a web server is available at <url>http://bioinfo2.ugr.es/wordCluster/wordCluster.php</url> including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes.</p