Applied bioinformatics最新文献_第8页

PIMWalker: visualising protein interaction networks using the HUPO PSI molecular interaction format. 可视化蛋白相互作用网络使用HUPO PSI分子相互作用格式。

Applied bioinformatics Pub Date : 2005-01-01 DOI: 10.2165/00822942-200504020-00007

Alain Meil, Patrick Durand, Jérôme Wojcik

引用次数: 15

Editorial foreword. 编辑前言。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403040-00001

Allen Rodrigo

引用次数: 0

PathAligner: metabolic pathway retrieval and alignment. PathAligner:代谢途径检索和对齐。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403040-00006

Ming Chen, Ralf Hofestädt

{"title":"PathAligner: metabolic pathway retrieval and alignment.","authors":"Ming Chen, Ralf Hofestädt","doi":"10.2165/00822942-200403040-00006","DOIUrl":"https://doi.org/10.2165/00822942-200403040-00006","url":null,"abstract":"Motivation: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways.Results: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways.Availability: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"3 4","pages":"241-52"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200403040-00006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25118641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

A study of statistical methods for function prediction of protein motifs. 蛋白质基序功能预测的统计方法研究。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403020-00006

Tao Tao, Cheng Xiang Zhai, Xinghua Lu, Hui Fang

{"title":"A study of statistical methods for function prediction of protein motifs.","authors":"Tao Tao, Cheng Xiang Zhai, Xinghua Lu, Hui Fang","doi":"10.2165/00822942-200403020-00006","DOIUrl":"https://doi.org/10.2165/00822942-200403020-00006","url":null,"abstract":"Automatic discovery of new protein motifs (i.e. amino acid patterns) is one of the major challenges in bioinformatics. Several algorithms have been proposed that can extract statistically significant motif patterns from any set of protein sequences. With these methods, one can generate a large set of candidate motifs that may be biologically meaningful. This article examines methods to predict the functions of these candidate motifs. We use several statistical methods: a popularity method, a mutual information method and probabilistic translation models. These methods capture, from different perspectives, the correlations between the matched motifs of a protein and its assigned Gene Ontology terms that characterise the function of the protein. We evaluate these different methods using the known motifs in the InterPro database. Each method is used to rank candidate terms for each motif. We then use the expected mean reciprocal rank to evaluate the performance. The results show that, in general, all these methods perform well, suggesting that they can all be useful for predicting the function of an unknown motif. Among the methods tested, a probabilistic translation model with a popularity prior performs the best.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"3 2-3","pages":"115-24"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200403020-00006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"24941799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

BLMT: statistical sequence analysis using N-grams. BLMT:使用n图进行统计序列分析。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403020-00013

Madhavi Ganapathiraju, Vijayalaxmi Manoharan, Judith Klein-Seetharaman

{"title":"BLMT: statistical sequence analysis using N-grams.","authors":"Madhavi Ganapathiraju, Vijayalaxmi Manoharan, Judith Klein-Seetharaman","doi":"10.2165/00822942-200403020-00013","DOIUrl":"https://doi.org/10.2165/00822942-200403020-00013","url":null,"abstract":"Unlabelled: Statistical analysis of amino acid and nucleotide sequences, especially sequence alignment, is one of the most commonly performed tasks in modern molecular biology. However, for many tasks in bioinformatics, the requirement for the features in an alignment to be consecutive is restrictive and \"n-grams\" (aka k-tuples) have been used as features instead. N-grams are usually short nucleotide or amino acid sequences of length n, but the unit for a gram may be chosen arbitrarily. The n-gram concept is borrowed from language technologies where n-grams of words form the fundamental units in statistical language models. Despite the demonstrated utility of n-gram statistics for the biology domain, there is currently no publicly accessible generic tool for the efficient calculation of such statistics. Most sequence analysis tools will disregard matches because of the lack of statistical significance in finding short sequences. This article presents the integrated Biological Language Modeling Toolkit (BLMT) that allows efficient calculation of n-gram statistics for arbitrary sequence datasets.Availability: BLMT can be downloaded from http://www.cs.cmu.edu/~blmt/source and installed for standalone use on any Unix platform or Unix shell emulation such as Cygwin on the Windows platform. Specific tools and usage details are described in a \"readme\" file. The n-gram computations carried out by the BLMT are part of a broader set of tools borrowed from language technologies and modified for statistical analysis of biological sequences; these are available at http://flan.blm.cs.cmu.edu/.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"3 2-3","pages":"193-200"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200403020-00013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"24942894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

HCVDB: hepatitis C virus sequences database. 丙型肝炎病毒序列数据库。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403040-00005

Christophe Combet, François Penin, Christophe Geourjon, Gilbert Deléage

{"title":"HCVDB: hepatitis C virus sequences database.","authors":"Christophe Combet, François Penin, Christophe Geourjon, Gilbert Deléage","doi":"10.2165/00822942-200403040-00005","DOIUrl":"https://doi.org/10.2165/00822942-200403040-00005","url":null,"abstract":"Unlabelled: To date, more than 30 000 hepatitis C virus (HCV) sequences have been deposited in the generalist databases DNA Data Bank of Japan (DDBJ), EMBL Nucleotide Sequence Database (EMBL) and GenBank. The main difficulties with HCV sequences in these databases are their retrieval, annotation and analyses. To help HCV researchers face the increasing needs of HCV sequence analyses, we developed a specialised database of computer-annotated HCV sequences, called HCVDB. HCVDB is re-built every month from an up-to-date EMBL database by an automated process. HCVDB provides key data about the HCV sequences (e.g. genotype, genomic region, protein names and functions, known 3-dimensional structures) and ensures consistency of the annotations, which enables reliable keyword queries. The database is highly integrated with sequence and structure analysis tools and the SRS (LION bioscience) keywords query system. Thus, any user can extract subsets of sequences matching particular criteria or enter their own sequences and analyse them with various bioinformatics programs available on the same server.Availability: HCVDB is available from http://hepatitis.ibcp.fr.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"3 4","pages":"237-40"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200403040-00005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25118640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

Constructing sequence alignments from a Markov decision model with estimated parameter values. 用估计参数值的马尔可夫决策模型构造序列比对。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403020-00010

Fern Y Hunt, Anthony J Kearsley, Agnes O'Gallagher

引用次数: 0

Microarray Data Analysis 微阵列数据分析

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403040-00004

R. X. Menezes, J. Boer, H. V. Houwelingen

引用次数: 43

Haplotype parsing: methods for extracting information from human genetic variations. 单倍型分析:从人类遗传变异中提取信息的方法。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403020-00012

Russell Schwartz

{"title":"Haplotype parsing: methods for extracting information from human genetic variations.","authors":"Russell Schwartz","doi":"10.2165/00822942-200403020-00012","DOIUrl":"https://doi.org/10.2165/00822942-200403020-00012","url":null,"abstract":"While the shared consensus genetic sequence of our species contains a great deal of information about our common biology, there is also much to be learned from the subtle genetic variations across our species. These variations are believed to be generally of little or no direct functional significance and predominantly reflect the chance accumulation of small genetic changes since our emergence as a species. Therefore, they carry little useful information when observed in a single individual. When tallied across a whole population though, these chance mutations can teach us a great deal about our evolutionary history and the patterns of inheritance in particular individuals. In particular, frequently observed patterns of single nucleotide polymorphisms (SNPs) in a population can identify segments of chromosome that have been passed down largely intact through long stretches of our evolution. Finding these frequently conserved chromosomal segments, or haplotypes, and developing methods to identify haplotype patterns in particular individuals, will in turn help us to identify those particular segments that carry genetic factors influencing risk for many common human diseases. To make the best use of this data, we will need to develop new models for the encoding of information in genome variations--the \"language of genetic variation\"--and new algorithms for fitting datasets to those models. This article surveys past work by the author and colleagues on this problem, utilising computational methods for locating frequent patterns in haploid sequence data, and \"parsing\" sequences so as to optimally explain them given the knowledge of the general population structure. The author's recent work in this area has been compiled into a set of computational tools available at http://www-2.cs.cmu.edu/~russells/software/hapmotif.html.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"3 2-3","pages":"181-91"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200403020-00012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"24942893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space. GoSurfer:一个用于在基因本体空间中比较分析大型基因集的图形交互工具。

Applied bioinformatics Pub Date : 2004-01-01 DOI: 10.2165/00822942-200403040-00009

Sheng Zhong, Kai-Florian Storch, Ovidiu Lipan, Ming-Chih J Kao, Charles J Weitz, Wing H Wong

{"title":"GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space.","authors":"Sheng Zhong, Kai-Florian Storch, Ovidiu Lipan, Ming-Chih J Kao, Charles J Weitz, Wing H Wong","doi":"10.2165/00822942-200403040-00009","DOIUrl":"https://doi.org/10.2165/00822942-200403040-00009","url":null,"abstract":"Unlabelled: The analysis of complex patterns of gene regulation is central to understanding the biology of cells, tissues and organisms. Patterns of gene regulation pertaining to specific biological processes can be revealed by a variety of experimental strategies, particularly microarrays and other highly parallel methods, which generate large datasets linking many genes. Although methods for detecting gene expression have improved substantially in recent years, understanding the physiological implications of complex patterns in gene expression data is a major challenge. This article presents GoSurfer, an easy-to-use graphical exploration tool with built-in statistical features that allow a rapid assessment of the biological functions represented in large gene sets. GoSurfer takes one or two list(s) of gene identifiers (Affymetrix probe set ID) as input and retrieves all the Gene Ontology (GO) terms associated with the input genes. GoSurfer visualises these GO terms in a hierarchical tree format. With GoSurfer, users can perform statistical tests to search for the GO terms that are enriched in the annotations of the input genes. These GO terms can be highlighted on the GO tree. Users can manipulate the GO tree in various ways and interactively query the genes associated with any GO term. The user-generated graphics can be saved as graphics files, and all the GO information related to the input genes can be exported as text files.Availability: GoSurfer is a Windows-based program freely available for noncommercial use and can be downloaded at http://www.gosurfer.org. Datasets used to construct the trees shown in the figures in this article are available at http://www.gosurfer.org/download/GoSurfer.zip.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"3 4","pages":"261-4"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200403040-00009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25118538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 122