Monday, October 29, 2012

Bioinformatics BI_V0009


title : Bioinformatics with a French accent

author: LD Hurst, L Duret

year: 2005

place of pulbish : Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK. †Pole BioInformatique Lyonnais LaboratoireBBE - UMR CNRS 5558, Université Claude Bernard - Lyon 1, F-69622 Villeurbanne Cedex, France


abstract :

Bioinformatics BI_V0008


title : An international showcase of bioinformatics research

author: Todd Vision

year: 2003

place of pulbish :  Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA


abstract :

Bioinformatics BI_V0007


title : Bioinformatics inspired by a tree

author: A.W Dickerman

year: 2006

place of pulbish : USA

abstract :

Bioinformatics BI_V0006


title : UTILIZATION OF BIOINFORMATICS RESOURCES IN ISOLATION OF POD-SPECIFIC GENES IN THEOBROMA CACAO

author: CL. Tan, JA Verica, A. Young, S. Pishak, SN Maximova

year: 2006

place of pulbish : USA


abstract :

Bioinformatics BI_V0005


title : MINI-BLAST: Computer Systems to Search for the Pattern Sequences in the Bioinformatics Databases

author: Gennadiy Burlak1, Christian Eduardo Martínez Guerrero1, Enrique Merino Pérez2

year: 2001

place of pulbish : IBT, Universidad Nacional Autónoma de México, av. Universidad 2001, Cuernavaca, Mor., CP 62210, México

abstract :

The bioinformatics focus on developing and applying computational-ly intensive techniques to increase the understanding of biological processes. Inthis report we create the compact computer systems mini-blast and methagraphfinding the dna sequences in the bioinformatics databases (dbs) placed in localor web configurations. Our system allows identify the gene sequences relatingto new pattern (metagenome) that is not identified yet in such dbs containingdata on known nucleotides. Such a task is quite expensive and time consumingoperation; therefore for large genomes the parallel algorithms are required. Wedevelop a graphics user-friendly interface (gui) that allows simple input thequery data and representative statistical analysis in the output. Additionally, us-er can select the particular dbs for cases when a specific alignment is required.Although the package is developed in ms .net 3.5/4.0 visual c# system, it workswith no limitations in linux in the mono framework.

Bioinformatics BI_V0004


title : Using Microbial Diversity to Teach Computational Biology and Bioinformatics

author: Sarah M. Boomer1*, Daniel P. Lodge2, Kelly Shipley1, Bryan E. Dutton1

year:

place of pulbish :
1Western Oregon University, Department of Biology, Monmouth, OR 97361
2Oregon State University, Department of Engineering, Corvallis, OR 97331

abstract :

Given that computational skills are central to many sub-disciplines in biology, we developed an undergraduate course called Computational Biology to better prepare students in this widely-applicable field. In this report, we have summarized available resources and original protocol for computational curriculum, all of which have applications beyond microbiology. We have also described specific microbial models that were uniquely selected and employed for class analysis. Using diverse microbial sequences and genomes, students navigated the National Center for Biotechnology Information (NCBI) with an emphasis on database structure, data annotations, effective database searching, understanding genome data archiving and display issues, and using analytical software to identify and rank similar sequences. Next, using original bacterial 16S rRNA sequences from our Red Layer Microbial Observatory project, students assembled and aligned multiple sequence datasets using several tools on the Biology Workbench (BW). Using resulting 16S rRNA alignments, students produced and statistically evaluated phylogenetic trees. Finally, students used a combination of software and data selected from NCBI and BW to analyze model microbial proteins, emphasizing how to view and analyze determined structure data, and how to predict protein structure using sequence information. Repeating all these methods, each student completed an original research project, comparing 20 homologous sequences to address a specific hypothesis of their own design. To complete this report, we summarized and discussed course impact and extensions.

Bioinformatics BI_V0003


title : Bioinformatics and Biomarker Discovery P t 3 E l Part 3: Examples

author: L Wong

year: 2011

place of pulbish : Birkha user Verlag, Basel-Boston-Berlin

abstract :

Bioinformatics BI_V0002


title : Bioinformatik. Methoden zur Vorhersage von RNA- und
Proteinstrukturen (Bioinformatics. Methods for RNA
and protein structure prediction)

author: G. Steger

year: 2003

place of pulbish : Birkha user Verlag, Basel-Boston-Berlin

abstract :

Bioinformatics BI_V0001


title : Bioinformatics as Viewed by a Computer Scientist

author: Raymond Wan

year: 2011

place of pulbish : University of Tokyo

abstract :

Scientists have been interested in biology (including
genetics and molecular biology) for many centuries. Both
to find out more about plants and animals, but of course
to also learn about human health.
Along with physics and chemistry, biology is one of the
natural sciences that many of us (probably) studied in
school.
Over the last decade or two, the amount and type of data
being generated has required computational methods for
data analysis. Simply put, this is the field of bioinformatics
or computational biology.

Bioinformatics BI_E0010


title : SHARE: A Semantic Web Query Engine for Bioinformatics

author: Ben P. Vandervalk, E. Luke McCarthy, Mark D. Wilkinson

year: 2009

place of pulbish : Springer Berlin Heidelberg

abstract :

Driven by the goal of automating data analyses in the field of bioinformatics, SHARE (Semantic Health and Research Environment) is a specialized SPARQL engine that resolves queries against Web Services and SPARQL endpoints. Developed in conjunction with SHARE, SADI (Semantic Automated Discovery and Integration) is a standard for native-RDF services that facilitates the automated assembly of services into workflows, thereby eliminating the need for ad hoc scripting in the construction of a bioinformatics analysis pipeline.

Bioinformatics BI_E0009


title : European Molecular Biology Organization Practical Course on COMPUTATIONAL MOLECULAR EVOLUTION

author: Giorgos Kotoulas, Antonis Magoulas, Stelios Kastrinakis, Eftichia Mironaki, Pelagia Petraki

year: 2006

place of publish : Germany


abstract :

Bioinformatics BI_E0008


title : A grid-oriented genetic algorithm framework for bioinformatics

author: Hiroaki Imade, Ryohei Morishita, Isao Ono, Norihiko Ono, Masahiro Okamoto

year: 2004

place of pulbish : Springer-Verlag

abstract :

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks, which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory.

Bioinformatics BI_E0007


title : Bioinformatics Visualization and Integration with Open Standards: The Bluejay Genomic Browser

author: Andrei L. Turinsky1, Andrew C. Ah-Seng1, Paul M.K. Gordon1, Julie N. Stromer1, Morgan L. Taschuk1, Emily W. Xu1, Christoph W. Sensen1

year: 2005

place of pulbish : canada

abstract :

We have created a new Java™-based integrated computational environment for the exploration of genomic data, called Bluejay. The system is capable of using almost any XML file related to genomic data. Non-XML data sources can be accessed via a proxy server. Bluejay has several features, which are new to Bioinformatics, including an unlimited semantic zoom capability, coupled with Scalable Vector Graphics (SVG) outputs; an implementation of the XLink standard, which features access to MAGPIE Genecards as well as any BioMOBY service accessible over the Internet; and the integration of gene chip analysis tools with the functional assignments. The system can be used as a signed web applet, Web Start, and a local stand-alone application, with or without connection to the Internet. It is available free of charge and as open source via http://bluejay.ucalgary.ca.

Bioinformatics BI_E0006


title : Bioinformatics approaches for the classification of G-protein-coupled receptors

author: Anna Gaulton and Teresa K Attwood

year: 2003

place of pulbish : School of Biological Sciences and Department of Computer Science,
University of Manchester, Oxford Road, Manchester M13 9PT, UK

abstract :

G-protein-coupled receptors are found abundantly in the human
genome, and are the targets of numerous prescribed drugs.
However, many receptors remain orphaned (i.e. with unknown
ligand specificity), and others remain poorly characterised, with
little structural information available. Consequently, there is often
a gulf between sequence data and structural and functional
knowledge of a receptor. Bioinformatics approaches may offer
one approach to bridging this gap. In particular, protein family
databases, which distil information from multiple sequence
alignments into characteristic signatures, could be used to
identify the families to which orphan receptors belong, and might
facilitate discovery of novel motifs associated with ligand binding
and G-protein-coupling.

Bioinformatics BI_E0005


title : Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics

author: Marylyn D. Ritchie, Christopher S. Coffey, Jason H. Moore

year: 2004

place of pulbish : Springer Berlin Heidelberg

abstract :

The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.

Bioinformatics BI_E0004


title : An Optimal Algorithm for Maximum-Sum Segment and Its Application in Bioinformatics

author: Tsai-Hung Fan, Shufen Lee, Hsueh-I Lu, Tsung-Shan Tsou, Tsai-Cheng Wang, Adam Yao

year: 2003

place of pulbish : Springer Berlin Heidelberg

abstract :

We study a fundamental sequence algorithm arising from bioinformatics. Given two integers L and U and a sequence A of n numbers, the maximum-sum segment problem is to find a segment A[i,j] of A with L = j+i+1 = U that maximizes A[i]+A[i+1]+···+A[j]. The problem finds applications in finding repeats, designing low complexity filter, and locating segments with rich C+G content for biomolecular sequences. The best known algorithm, due to Lin, Jiang, and Chao, runs in O(n) time, based upon a clever technique called left-negative decomposition for A. In the present paper, we present a new O(n)-time algorithm that bypasses the left-negative decomposition. As a result, our algorithm has the capability to handle the input sequence in an online manner, which is clearly an important feature to cope with genome-scale sequences. We also show how to exploit the sparsity in the input sequence: If A is representable in O(k) space in some format, then our algorithm runs in O(k) time. Moreover, practical implementation of our algorithm running on the rice genome helps us to identify a very long repeat structure in rice chromosome 1 that is previously unknown.

Bioinformatics BI_E0003


title : Ontology-based integration for bioinformatics

author: Vaida Jakonien_e and Patrick Lambrix

year: 2005

place of pulbish : Department of Computer and Information Science Linkopings universitet, Linkoping, Sweden

abstract :

Information integration systems support re-
searchers in bioinformatics to retrieve data
from multiple biological data sources. In this
paper we argue that the current approaches
should be enhanced by ontological knowledge.
We identify the di erent types of ontologi-
cal knowledge that are available on the Web
and propose an approach to use this knowl-
edge to support integrated access to multi-
ple biological data sources. We also show
that current ontology-based integration ap-
proaches only cover parts of our approach

Bioinformatics BI_E0002


title : Current bioinformatics tools in genomic biomedical research (Review)

author: ANDREAS TEUFEL, MARKUS KRUPP, ARNDT WEINMANN and PETER R. GALLE

year: 2006

place of pulbish : Department of Medicine I, Johannes Gutenberg University, Langenbeckstr. 1, D-55101 Mainz, Germany


abstract :

On the advent of a completely assembled human
genome, modern biology and molecular medicine stepped into
an era of increasingly rich sequence database information and
high-throughput genomic analysis. However, as sequence
entries in the major genomic databases currently rise exponentially,
the gap between available, deposited sequence data
and analysis by means of conventional molecular biology is
rapidly widening, making new approaches of high-throughput
genomic analysis necessary. At present, the only effective
way to keep abreast of the dramatic increase in sequence and
related information is to apply biocomputational approaches.
Thus, over recent years, the field of bioinformatics has rapidly
developed into an essential aid for genomic data analysis and
powerful bioinformatics tools have been developed, many of
them publicly available through the World Wide Web. In this
review, we summarize and describe the basic bioinformatics
tools for genomic research such as: genomic databases, genome
browsers, tools for sequence alignment, single nucleotide
polymorphism (SNP) databases, tools for ab initio gene
prediction, expression databases, and algorithms for promoter
prediction.

Bioinformatics BI_E0001


title : Bioinformatics-Guided Identification and Experimental Characterization of Novel RNA Methyltransferases
author: J.M. Bujnicki, L.Droogmans, H.Grosjean, S.K. Purshothaman, B.Lapeyre
year: 2008
place of pulbish : Springer Berlin Heidelberg
abstract :

Naturally occurring RNAs contain numerous chemically altered nucleosides. They are formed by enzymatic modification of the primary transcripts during the complex RNA maturation process. To date, a total of 96 structurally distinguishable modified nucleosides originating from different types of RNAs from many diverse organisms of the three major phylogenetic domains of life have been reported (Rozenski et al. 1999); http://medstat.med.utah.edu/RNAmods; and references therein). The pattern of modifications (type and location) depends on the RNA molecule considered, as well as, on the organism or the organelle they originate from.However, the largest number of modified nucleosides with the greatest structural diversity (a total of 81) is found in transfer RNAs, especially in tRNAs from higher organisms (Sprinzl et al. 1998; http://www.uni-bayreuth.de/departments/biochemie/trna). Other types of RNA (snRNA, snoRNA, rRNA,mRNA) also contain modified nucleosides (see http://rna.wustl.edu/snoRNAdb), however, their occurrence and particularly their diversity are lower than in tRNAs (see, for example,Limbach et al. 1995;Motorin and Grosjean 1998).

Sunday, October 28, 2012

Phylogenetics PG_Q0007


Title : Advances in the phylogenesis of Agaricales and its higher ranks and strategies for establishing phylogenetic hypotheses
Author : Rui-lin Zhao, Dennis E. Desjardin, Kasem Soytong and Kevin D. Hyde
Year Publish : 2008
Place of Publish : Springer
Abstract :
We present an overview of previous research results on the molecular phylogenetic analyses in Agaricales and its higher ranks (Agaricomycetes/Agaricomycotina/Basidiomycota) along with the most recent treatments of taxonomic systems in these taxa. Establishing phylogenetic hypotheses using DNA sequences, from which an understanding of the natural evolutionary relationships amongst clades may be derived, requires a robust dataset. It has been recognized that single-gene phylogenies may not truly represent organismal phylogenies, but the concordant phylogenetic genealogies from multiple-gene datasets can resolve this problem. The genes commonly used in mushroom phylogenetic research are summarized.