Applied bioinformatics最新文献

{"title":"Natively Disordered Proteins","authors":"P. Romero, Z. Obradovic, A. Dunker","doi":"10.1002/9783527619498.CH41","DOIUrl":"https://doi.org/10.1002/9783527619498.CH41","url":null,"abstract":"Proteins can exist in at least three forms: the ordered form (solid-like), the partially folded form (collapsed, molten globule-like or liquid-like) and the extended form (extended, random coil-like or gas-like). The protein trinity hypothesis has two components: (i) a given native protein can be in any one of the three forms, depending on the sequence and the environment; and (ii) function can arise from any one of the three forms or from transitions between them. In this study, bioinformatics and data mining were used to investigate intrinsic disorder in proteins and develop neural network-based predictors of natural disordered regions (PONDR) that can discriminate between ordered and disordered residues with up to 84% accuracy. Predictions of intrinsic disorder indicate that the three kingdoms follow the disorder ranking eubacteria < archaebacteria ≪ eukaryotes, with approximately half of eukaryotic proteins predicted to contain substantial regions of intrinsic disorder. Many of the known disordered regions are involved in signalling, regulation or control. Involvement of highly flexible or disordered regions in signalling is logical: a flexible sensor more readily undergoes conformational change in response to environmental perturbations than does a rigid one. Thus, the increased disorder in the eukaryotes is likely the direct result of an increased need for signalling and regulation in nucleated organisms. PONDR can also be used to detect molecular recognition elements that are disordered in the unbound state and become structured when bound to a biologically meaningful partner. Application of disorder predictions to cell-signalling, cancer-associated and control protein databases supports the widespread occurrence of protein disorder in these processes.","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"130 12","pages":"105-113"},"PeriodicalIF":0.0,"publicationDate":"2008-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/9783527619498.CH41","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50794674","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}

{"title":"Vibrio cholerae CTXPhi phage repressor gene is borne on an insertion sequence-like element and encodes a putative transposase.","authors":"G Uma,&nbsp;N Chandramohanakumar","doi":"10.2165/00822942-200605010-00003","DOIUrl":"https://doi.org/10.2165/00822942-200605010-00003","url":null,"abstract":"<p><p>Bioinformatics analyses show the presence of a novel insertion sequence-like element in the CTXPhi phage of Vibrio cholerae. The solitary open reading frame encompassed by the element is known to encode a repressor (RstR) of phage DNA replication and is responsible for phage heteroimmunity. Analysis of the nucleotide and protein sequence of the repressor and its flanking non-coding regions indicates that it resembles distinctly simple bacterial insertion elements in numerous aspects. Based on the results of extensive sequence analysis, we propose that the rstR gene is borne on an insertion sequence-like element and that the RstR protein encodes a putative transposase. We put forward the possibility that a bi-directional promoter could regulate the divergent transcription of the neighbouring genes of rstR and rstA. The possibility of overlapping reading frames yielding distinct repressor and transposase proteins is also expanded based on the analyses.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 1","pages":"21-8"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605010-00003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25906541","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}

{"title":"BioParser: a tool for processing of sequence similarity analysis reports.","authors":"Marcos Catanho,&nbsp;Daniel Mascarenhas,&nbsp;Wim Degrave,&nbsp;Antonio Basílio de Miranda","doi":"10.2165/00822942-200605010-00007","DOIUrl":"https://doi.org/10.2165/00822942-200605010-00007","url":null,"abstract":"<p><strong>Unlabelled: </strong>The widely used programs BLAST (in this article, 'BLAST' includes both the National Center for Biotechnology Information [NCBI] BLAST and the Washington University version WU BLAST) and FASTA for similarity searches in nucleotide and protein databases usually result in copious output. However, when large query sets are used, human inspection rapidly becomes impractical. BioParser is a Perl program for parsing BLAST and FASTA reports. Making extensive use of the BioPerl toolkit, the program filters, stores and returns components of these reports in either ASCII or HTML format. BioParser is also capable of automatically feeding a local MySQL database with the parsed information, allowing subsequent filtering of hits and/or alignments with specific attributes. For this reason, BioParser is a valuable tool for large-scale similarity analyses by improving the access to the information present in BLAST or FASTA reports, facilitating extraction of useful information of large sets of sequence alignments, and allowing for easy handling and processing of the data.</p><p><strong>Availability: </strong>BioParser is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 2.0 license terms (http://creativecommons.org/licenses/by-nc-nd/2.0/) and is available upon request. Additional information can be found at the BioParser website (http://www.dbbm.fiocruz.br/BioParser.html).</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 1","pages":"49-53"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605010-00007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25908651","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}

{"title":"Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.","authors":"Anders Bresell,&nbsp;Bo Servenius,&nbsp;Bengt Persson","doi":"10.2165/00822942-200605040-00005","DOIUrl":"https://doi.org/10.2165/00822942-200605040-00005","url":null,"abstract":"<p><p>Gene expression and proteomics analysis allow the investigation of thousands of biomolecules in parallel. This results in a long list of interesting genes or proteins and a list of annotation terms in the order of thousands. It is not a trivial task to understand such a gene list and it would require extensive efforts to bring together the overwhelming amounts of associated information from the literature and databases. Thus, it is evident that we need ways of condensing and filtering this information. An excellent way to represent knowledge is to use ontologies, where it is possible to group genes or terms with overlapping context, rather than studying one-dimensional lists of keywords. Therefore, we have built the ontology annotation treebrowser (OAT) to represent, condense, filter and summarise the knowledge associated with a list of genes or proteins. The OAT system consists of two disjointed parts; a MySQL database named OATdb, and a treebrowser engine that is implemented as a web interface. The OAT system is implemented using Perl scripts on an Apache web server and the gene, ontology and annotation data is stored in a relational MySQL database. In OAT, we have harmonized the two ontologies of medical subject headings (MeSH) and gene ontology (GO), to enable us to use knowledge both from the literature and the annotation projects in the same tool. OAT includes multiple gene identifier sets, which are merged internally in the OAT database. We have also generated novel MeSH annotations by mapping accession numbers to MEDLINE entries. The ontology browser OAT was created to facilitate the analysis of gene lists. It can be browsed dynamically, so that a scientist can interact with the data and govern the outcome. Test statistics show which branches are enriched. We also show that the two ontologies complement each other, with surprisingly low overlap, by mapping annotations to the Unified Medical Language System. We have developed a novel interactive annotation browser that is the first to incorporate both MeSH and GO for improved interpretation of gene lists. With OAT, we illustrate the benefits of combining MeSH and GO for understanding gene lists. OAT is available as a public web service at: http://www.ifm.liu.se/bioinfo/oat.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 4","pages":"225-36"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605040-00005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26473724","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}

{"title":"MultiPrimer: software for multiplex primer design.","authors":"Chungnan Lee,&nbsp;Jain-Shing Wu,&nbsp;Yow-Ling Shiue,&nbsp;Hong-Long Liang","doi":"10.2165/00822942-200605020-00004","DOIUrl":"https://doi.org/10.2165/00822942-200605020-00004","url":null,"abstract":"<p><p>This study presents a novel application, MultiPrimer, to assist users to design multiplex PCR primers. MultiPrimer adopts an algorithm based on a genetic algorithm and can efficiently solve the multi-objective optimisation problem. A match pattern model is proposed to speed up examination of the specificity constraint. MultiPrimer is able to find a set of primer pairs simultaneously for a set of gene family sequences and multiple targets for a single cDNA sequence. A public version of the MultiPrimer software is available from http://edith.cse.nsysu.edu.tw/new/software/MultiPrimer.htm.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 2","pages":"99-109"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605020-00004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26041959","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}

{"title":"EasyExonPrimer: automated primer design for exon sequences.","authors":"Xiaolin Wu,&nbsp;David J Munroe","doi":"10.2165/00822942-200605020-00007","DOIUrl":"https://doi.org/10.2165/00822942-200605020-00007","url":null,"abstract":"<p><strong>Unlabelled: </strong>EasyExonPrimer is a web-based software that automates the design of PCR primers to amplify exon sequences from genomic DNA. EasyExonPrimer is written in Perl and uses Primer3 to design PCR primers based on the genome builds and annotation databases available at the University of California, Santa Cruz (UCSC) Genome Browser database (http://genome.ucsc.edu/). It masks repeats and known single nucleotide polymorphism (SNP) sites in the genome and designs standardised primers using optimised conditions. Users can input genes by RefSeq mRNA ID, gene name or keyword. The primer design is optimised for large-scale resequencing of exons. For exons larger than 1 kb, the user has the option of breaking the exon sequence down into overlapping smaller fragments. All primer pairs are then verified using the In-Silico PCR software to test for uniqueness in the genome. We have designed >1000 pairs of primers for 90 genes; 95% of the primer pairs successfully amplified exon sequences under standard PCR conditions without requiring further optimisation.</p><p><strong>Availability: </strong>EasyExonPrimer is available from http://129.43.22.27/~primer/. The source code is also available upon request.</p><p><strong>Contact: </strong>Xiaolin Wu (forestwu@mail.nih.gov).</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 2","pages":"119-20"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605020-00007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26041962","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}

{"title":"A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.","authors":"Sankar Bokka,&nbsp;Sunil K Mathur","doi":"10.2165/00822942-200605040-00009","DOIUrl":"https://doi.org/10.2165/00822942-200605040-00009","url":null,"abstract":"<p><p>Microarray experiments contribute significantly to the progress in disease treatment by enabling a precise and early diagnosis. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The statistical methods currently used to analyse microarray data are inadequate, mainly due to the lack of understanding of the distribution of microarray data. We present a nonparametric likelihood ratio (NPLR) test to identify differentially expressed genes using microarray data. The NPLR test is highly robust against extreme values and does not assume the distribution of the parent population. Simulation studies show that the NPLR test is more powerful than some of the commonly used methods, such as the two-sample t-test, the Mann-Whitney U-test and significance analysis of microarrays (SAM). When applied to microarray data, we found that the NPLR test identifies more differentially expressed genes than its competitors. The asymptotic distribution of the NPLR test statistic and the p-value function is presented. The application of the NPLR method is shown, using both synthetic and real-life data. The biological significance of some of the genes detected only by the NPLR method is discussed.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 4","pages":"267-76"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605040-00009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26473710","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}

{"title":"Mprobe 2.0: computer-aided probe design for oligonucleotide microarray.","authors":"Wuju Li,&nbsp;Xiaomin Ying","doi":"10.2165/00822942-200605030-00006","DOIUrl":"https://doi.org/10.2165/00822942-200605030-00006","url":null,"abstract":"<p><strong>Unlabelled: </strong>DNA chips have proven to be effective tools in detecting gene expression levels. Compared with DNA chips using complementary DNA as probes, oligonucleotide microarrays using oligonucleotides as probes have attracted great attention because of their well known advantages. The design of gene-specific probes for each target is essential to the development of oligonucleotide microarrays. We have previously reported the development of a probe design software termed Mprobe 1.0. Here, we present a new version of this software, termed Mprobe 2.0. Several new features are included in Mprobe 2.0. Firstly, a paradox-based sequence database management system has been developed and integrated into the software, which consequently allows interoperability with sequences in GenBank, EMBL, and FASTA formats. Secondly, in contrast to setting a fixed threshold for the secondary structure of probes in Mprobe 1.0 and other related software, Mprobe 2.0 employs a different method. After parameters such as GC type, probe melting temperature and GC contents have been evaluated, candidate probes are sorted by the free energy from high to low value, followed by specificity analysis. Thirdly, Mprobe 2.0 provides users with substantial parameter options in the visual mode. Mprobe 2.0 possesses an easier interface for users to manage sequences annotated in different formats and design the optimal probes for oligonucleotide microarrays and other applications.</p><p><strong>Availability: </strong>The program is free for non-commercial users and can be downloaded from the web page http://www.biosun.org.cn/mprobe/</p><p><strong>Contact: </strong>Wuju Li (wujuli@yahoo.com or liwj@nic.bmi.ac.cn).</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 3","pages":"181-6"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605030-00006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26211734","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}

{"title":"The use of background signal in the transformation of cDNA-microarray measurements.","authors":"Suzy Van Sanden,&nbsp;Tomasz Burzykowski","doi":"10.2165/00822942-200605030-00004","DOIUrl":"https://doi.org/10.2165/00822942-200605030-00004","url":null,"abstract":"<p><p>As the application field of microarrays grows, so does the need for appropriate statistical tools to analyse the signal intensity measurements. Normalisation procedures are required to make the signals from different channels and arrays comparable. One objective, which is also the focus of this report, is to remove the curvature seen on plots of the log ratio versus the mean log intensity values of two channels.A number of methods already exist that are based on the assumption of a shift between the measurements of the two channels. In this article, we explore the use of background measurements to estimate and correct for the shift. We compare our proposal with some well known methods by applying them to microarrays from two studies. These two studies investigate the effect of vegetable diets on the gene expression in colon and lung tissue of mice. Graphical illustrations and a robust summary statistic show that all transformations are an improvement to the raw data. Overall, the best results are obtained with our proposed transformation that takes the background measurements into account.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 3","pages":"161-72"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605030-00004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26269212","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}

{"title":"Comparisons of annotation predictions for affymetrix GeneChips.","authors":"Maria Stalteri,&nbsp;Andrew Harrison","doi":"10.2165/00822942-200605040-00006","DOIUrl":"https://doi.org/10.2165/00822942-200605040-00006","url":null,"abstract":"<p><p>We have compared Affymetrix and Bioconductor annotations for the MOE430A (mouse) GeneChip array. The mappings of probe sets to LocusLink identifiers (LocusIDs) were found to be dynamic, with many changes between successive releases of annotation for both Affymetrix and Bioconductor. There are 49 probe sets that are assigned to one LocusID by Affymetrix and to a different LocusID by Bioconductor from mid-2004 onwards. For virtually all of these examples, the Affymetrix annotation was found to be the one that is in agreement with the current gene prediction. Reference sequence (RefSeq) identifiers are considered to be the gold standard of annotations. However, we could not use these identifiers to discriminate between the accuracy of Bioconductor and Affymetrix because not all of the probes map to the RefSeq transcript to which the probe set is assigned. Moreover, in some cases, probes align to regions downstream of the 3' end of a RefSeq transcript. Adjacent genes were found to be a major cause of discrepancies between the Bioconductor and Affymetrix assignments. Case studies of several probe sets indicated that incorrect assignments are caused by the UniGene cluster assignments of expressed sequence tags representing the probe sets, and by errors in GenBank sequences. Our results indicate that there are a number of errors remaining in the annotation sources used by the microarray community.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 4","pages":"237-48"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605040-00006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26473707","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}