RNA Characterization of Secondary Structure Motifs Database (RNA CoSSMos) Computing large-scale distance matrices on GPU [2012] | Ahmed Shamsul Arefin, Ph.D. Computing Large-scale Distance Matrices on GPU Ahmed Shamsul Arefin, Carlos Riveros, Regina Berretta, Pablo Moscato Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine (CIBM) School of Electrical Engineering and Computer Science The University of Newcastle, Australia Callaghan, NSW 2308, Australia Email: Ahmed.Arefin, Carlos.Riveros, Regina.Berretta, Pablo.Moscato @newcastle.edu.au Abstract —A distance matrix is simply an n two-dimensional arra y tha t con tain s pai rwi se dis tan ces of a set of poi nts in a metric space. increases, th e co mp ut at io n of di st an ce ma tr ix be co mes ve ry sl ow or incomputable on traditional general purpose computers. The cal cul ati on of dis tan ce mat ric es is a com ple te dat a intensive operation that acts as a preliminary step to many com put ati ona l met hod s, suc h as fea tur e set ana lys is, gen e expr essi on data sets analy sis, dif fere nt time series data sets analy sis etc. row s and m of points in d (1) for any x,y,z x y
mirDIP: microRNA Data Integration Portal mirDIP integrates twelve microRNA prediction datasets from six microRNA prediction databases, allowing users to customize their microRNA target searches. Combining microRNA predictions allows users to obtain more robust target predictions, giving you more confidence in your microRNA targets. See the instruction page for more information. All contents copyright , Jurisica Lab, Ontario Cancer Institute, Princess Margaret Hospital/UHN. Last modified January, 2012. All downloads and use of this database are subject to the following terms. Permission to use, copy, and modify this database hereby granted to all academic and not-for-profit institutions without fee, provided that name of organization and author appear in all copies of the database. Reference: Shirdel EA, Xie W, Mak TW, Jurisica I, 2011 NAViGaTing the Micronome .
Providers by Location There are hundreds of companies and institutes devoted to the study of the genetic code (genome), which are making tremendous strides in understanding the mechanisms of life (including individual people) at the most fundamental of levels. I’ve listed the ones that I can find at – hopefully helping future biologists and computer scientists see what they might do as a genome scientist, young researchers find jobs, start-up companies find customers, collaborators and investors, and the rest of us learn what’s going on in this fascinating field, or at least find hope in our rapidly advancing understanding of cancer and other complex diseases. Now I’m beginning profile them as I read what’s written on their websites and categorize them as to what they hope to do or provide. I’m starting with Ambry Genetics. Ambry Genetics – : Hello and thanks for stopping by! Three Bioinformatics Tools that Any Scientist Can Learn Today Summary About the author:
SAVoR: Sequencing Annotation and Visualization of RNA structures. Gallery An example showing the double-stranded RNA (dsRNA) read coverage along a tRNA from D. melanogaster (FlyBase ID: FBtr0071626). The orange-red color scheme is used here. An example showing the structure score (log-ratio of dsRNA-seq to ssRNA-seq read coverage) along the protein-coding F54E12.3 transcript in C. elegans. The blue-red color scheme is used here with default thresholds. An example showing the dsRNA endpoint abundance along a U2 snRNA from D. melanogaster (FlyBase ID: FBtr0074208). An example showing 3 SNPs from a 300bp region of intron 1 of the FTO gene (chr16:53820377-53820676). An example showing the conservation (phyloP46wayAll) score along mir-105. Help topics SAVoR generates high-quality secondary structure models with various sequencing-oriented annotation options such as read coverage, endpoint locations, and SNP calls. Four input types are currently supported: Rfam ID (e.g. RefSeq/SGD/TAIR ID (e.g. Rfam: Directly uses the specified Rfam structure. 1. 2. 3. 4. F.
TaxMan: Inspect your rRNA amplicons and taxa assignments Brandt, B.W., Bonder, M.J., Huse, S.M. and Zaura, E. (2012) TaxMan: a server to trim rRNA reference databases and inspect taxonomic coverage. . CORE: Griffen, A.L., Beall, C.J., Firestone, N.D., Gross E.L., DiFranco, J.M., Hardman, J.H., Vriesendorp, B., Faust, R.A., Janies, D.A. and Leys, E.J. (2011) CORE: A phylogenetically-curated 16S rDNA database of the core oral microbiome. PLoS ONE 6(4): e19051. HOMD: Chen, T., Yu, W-Han, Izard, J., Baranova, O.V., Lakshmanan, A., Dewhirst, F.E. (2010) The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information. Greengenes: DeSantis, T. SILVA: Pruesse, E., Quast, C., Knittel, K., Fuchs, B., Ludwig, W., Peplies, J. and Glöckner, F.O. (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Vaginal 16S reference package, by Hoffman, N., Srinivasan, S. and Matsen, M.
Rtips KOSMOS-Korea SKKU Morph Server Introduction KOSMOS is the first web server to provide the structural biology community with both harmonic and anharmonic analyses of macromolecular structures including DNA, RNA, and Proteins. Online users can request thermal fluctuation study or transient pathway generation by simply submitting PDB ID or uploading personal data files through the query page. All the simulation outputs have been deposited into NMA and ENI database where most of data are disclosed to the public unless users request to limit accessibility to their data. Users can also enjoy versatility of KOSMOS through advanced query by utilizing several unique applications of elastic network models for their own purpose. Harmonic - Normal Mode Analysis (NMA) Normal mode analysis (NMA) is a useful tool to understand the harmonic behaviors (thermal fluctuations) of a macromolecule around its equilibrium. Anharmonic (Pathway Generation) - Elastic Network Interpolation (ENI) Getting Start!
psRobot: Plant Small RNA Analysis Toolbox - Powered by omicslab PsRobot is designed to analyze batch of plant small RNA data. The online version of psRobot has two modules: The stem-loop small RNA prediction (try it out) module can identify stem-loop shaped smRNAs, including their expression in major plant smRNA biogenesis gene mutants and smRNA associated protein complexes to give clues to the smRNA generation and functional processes, their genome locations and precursor sequences. The second module, small RNA target prediction (try it out) module, can return target prediction results of smRNAs, including smRNA target list, target multiplicity, target site conservation and biological data support, such as degradome data and target expression data in small RNA biogenesis mutants. PsRobot also features a local version. For more detailed information and user guide, please check our manual page. Wu HJ, Ma YK, Chen T, Wang M, Wang XJ. (2012) PsRobot: a web-based plant small RNA meta-analysis toolbox.
start?utm_source=dlvr The goal of QGRS-H Predictor is to map and analyze phylogenetically conserved putative Quadruplex forming 'G'-Rich Sequences (QGRS) in the mRNAs, ncRNAs and other nucleotide sequences -e.g. Promoter and Telomeric and gene flank regions. The putative G-quadruplexes are identified using the following motif: Where x = # guanine tetrads in the G-quadruplex and y1, y2, y3 = length of gaps QGRS-H Predictor web tool generates information on composition and distribution of putative homologous G-quadruplexes in semi-globally aligned nucleotide sequences based on published algorithms. Enter 2 sequences in the input area to the left to run an analysis. The program is limited to mRNA sequences less than 10,000 bases in length. Quick-start tutorialProject background Please cite: Menendez, C., Frees, S., and Bagga, P. (2012) QGRS-H Predictor: A Web Server for Predicting Homologous Quadruplex forming G-Rich Sequence Motifs in Nucleotide Sequences.