Applied bioinformatics最新文献

{"title":"Robust Selection of Predictive Genes via a Simple Classifier.","authors":"Veronica Vinciotti,&nbsp;Allan Tucker,&nbsp;Paul Kellam,&nbsp;Xiaohui Liu","doi":"10.2165/00822942-200605010-00001","DOIUrl":"https://doi.org/10.2165/00822942-200605010-00001","url":null,"abstract":"<p><p>Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works. This in turn may lead to better diagnoses and potentially could lead to a cure for that disease. This task becomes extremely challenging when the data are characterised by only a small number of samples and a high number of dimensions, as is often the case with gene expression data. Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for robust identification of the most predictive features in such data. Within this framework, we propose a simple selective naive Bayes classifier discovered using a global search technique, and combine it with data perturbation to increase its robustness for small sample sizes. An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all confounded by small sample sizes and high dimensionality. The method has been shown to be capable of selecting genes known to be associated with prostate cancer and viral infections.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605010-00001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25906539","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":"Contrasting patterns of transcript abundance in tumour tissue and cancer cell lines.","authors":"Austin L Hughes, Nancy L Glenn","doi":"10.2165/00822942-200605040-00002","DOIUrl":"10.2165/00822942-200605040-00002","url":null,"abstract":"<p><p>Comparison of data on transcript abundance in ovarian, prostate and colon tumours with the corresponding cancer cell lines was used to assess the similarities of expression profiles. Although transcript abundances in tumours and cell lines were positively correlated, there were substantial differences with respect to the overall expression pattern. Compared with tumours, cancer cell lines showed more variable patterns of transcript abundance among tissue types. In the ovary and colon, cancer cell lines showed greater overall transcript abundance than normal tissue; this increase was much more marked in the case of the colon. However, in the prostate, cancer cell lines showed overall reduced transcript abundance when compared with normal tissue. Principal component analyses, applied separately to each tissue type, showed that approximately 80% of the variance was explained by overall expression level differences, which were maintained across normal tissue, tumour tissue and cancer cell lines. The remaining variance ( approximately 20%) could be attributed to contrasts in expression pattern among normal tissue, tumour tissue and cancer cell lines. In each dataset and in a combined dataset of transcripts shared among the three datasets, principal components revealed both contrasts in expression pattern between tumour tissue and cancer cell lines, and common features in the expression pattern of cancer cell lines that were distinct from those of tumour tissue and were shared across the different tissue types. These results imply that data on gene expression in cancer cell lines should be used with caution in inferring gene expression of in vivo tumours.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 4","pages":"201-10"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26473721","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":"In-silico pharmacodynamics: correlation of adverse effects of H2-antihistamines with histamine N-methyl transferase binding potential.","authors":"P K Vinod,&nbsp;Badireenath Konkimalla,&nbsp;Nagasuma Chandra","doi":"10.2165/00822942-200605030-00002","DOIUrl":"https://doi.org/10.2165/00822942-200605030-00002","url":null,"abstract":"<p><p>Adverse effects are exhibited by most drugs in current clinical practice, the causes for which are often not known. In this post genomic era, bioinformatics has the potential to address several issues in understanding the mechanism of drug action and in designing improved drugs. This study describes the analysis of the possible pharmacodynamic behaviour of antihistamines blocking the histamine H(2) receptor (H(2)-antihistamines), by adopting the basic tenets of a systems biology approach. The different components that could form an appropriate sub-system are identified, thus providing a system landscape. Docking and analysis of the chosen antihistamines into each of these components resulted in identifying histamine N-methyl transferase (HNMT) as a potential unintended target for H(2)-antihistamines. Correlation with experimental data available from the literature indicates the inhibition of HNMT to be a possible cause for the adverse effects exhibited by these drugs. Implications for design of safer H(2)-antihistamines are discussed. The method reported here has the potential for application as a general strategy in understanding drug effects.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 3","pages":"141-50"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605030-00002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26269210","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":"RAMEDIS: the rare metabolic diseases database.","authors":"Thoralf Töpel,&nbsp;Ralf Hofestädt,&nbsp;Dagmar Scheible,&nbsp;Friedrich Trefz","doi":"10.2165/00822942-200605020-00006","DOIUrl":"https://doi.org/10.2165/00822942-200605020-00006","url":null,"abstract":"<p><strong>Unlabelled: </strong>The RAMEDIS system is a platform-independent, web-based information system for rare diseases based on individual case reports. It was developed in close cooperation with clinical partners and collects information on rare metabolic diseases in extensive detail (e.g. symptoms, laboratory findings, therapy and genetic data). This combination of clinical and genetic data enables the analysis of genotype-phenotype correlations. By using largely standardised medical terms and conditions, the contents of the database are easy to compare and analyse. In addition, a convenient graphical user interface is provided by every common web browser. RAMEDIS supports an extendable number of different genetic diseases and enables cooperative studies. Furthermore, use of RAMEDIS should lead to advances in epidemiology, integration of molecular and clinical data, and generation of rules for therapeutic intervention and identification of new diseases.</p><p><strong>Availability: </strong>RAMEDIS is available from http://www.ramedis.de</p><p><strong>Contact: </strong>Thoralf Töpel (thoralf.toepel@uni-bielefeld.de).</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 2","pages":"115-8"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605020-00006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26041961","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":"MPID-T: database for sequence-structure-function information on T-cell receptor/peptide/MHC interactions.","authors":"Joo Chuan Tong,&nbsp;Lesheng Kong,&nbsp;Tin Wee Tan,&nbsp;Shoba Ranganathan","doi":"10.2165/00822942-200605020-00005","DOIUrl":"https://doi.org/10.2165/00822942-200605020-00005","url":null,"abstract":"<p><strong>Unlabelled: </strong>Normal adaptive immune responses operate under major histocompatibility complex (MHC) restriction by binding to specific, short antigenic peptides and presenting them to appropriate T-cell receptors (TcRs). Sequence-structure-function information is critical in understanding the principles governing peptide/MHC (pMHC) and TcR/pMHC recognition and binding. A new database for sequence-structure-function information on TcR/pMHC interactions, MHC-Peptide Interaction Database version T (MPID-T), is now available with the latest available Protein Data Bank (PDB) data and interaction parameters on TcR/pMHC complexes. MPID-T is a manually curated MySQL database containing experimentally determined structures of 187 pMHC complexes and 16 TcR/pMHC complexes available in the PDB. Each structure is manually verified, classified, and analysed for intermolecular interactions (i) between the MHC and its corresponding bound peptide and (ii) between TcR and its bound pMHC complex where TcR structural information is available. The MPID-T database retrieval system has precomputed interaction parameters that include solvent accessibility, hydrogen bonds, gap volume and gap index. Structural visualisation of the TcR/pMHC complex, pMHC complex, MHC or the bound peptide can be performed using freely available graphics applications such as MDL Chime or RasMol, while structural alignment (based on MHC class and peptide length) can be viewed using the Jmol molecular viewer or an MDL Chime-compatible web browser client. MPID-T contains structural descriptors for in-depth characterisation of TcR/pMHC and pMHC interactions. The ultimate purpose of MPID-T is to enhance the understanding of the binding mechanism underlying TcR/pMHC and pMHC interactions by mapping the TcR footprint on the MHC and its bound peptide, as this eventually determines T-cell recognition and binding.</p><p><strong>Availability: </strong>The MPID-T database retrieval system is available at http://surya.bic.nus.edu.sg/mpidt</p><p><strong>Contact: </strong>Joo Chuan Tong (jctong@i2r.a-star.edu.sg).</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 2","pages":"111-4"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605020-00005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26041960","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":"Machine learning for detecting gene-gene interactions: a review.","authors":"Brett A McKinney, David M Reif, Marylyn D Ritchie, Jason H Moore","doi":"10.2165/00822942-200605020-00002","DOIUrl":"10.2165/00822942-200605020-00002","url":null,"abstract":"<p><p>Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 2","pages":"77-88"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244050/pdf/nihms343668.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26041957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MetaBasis: a web-based database containing metadata on software tools and databases in the field of bioinformatics.","authors":"Vassilis Atlamazoglou,&nbsp;Trias Thireou,&nbsp;Yannis Hamodrakas,&nbsp;George Spyrou","doi":"10.2165/00822942-200605030-00007","DOIUrl":"https://doi.org/10.2165/00822942-200605030-00007","url":null,"abstract":"<p><strong>Unlabelled: </strong>We have developed an integrated web-based relational database information system, which offers an extensive search functionality of validated entries containing available bioinformatics computing resources. This system, called MetaBasis, aims to provide the bioinformatics community, and especially newcomers to the field, with easy access to reliable bioinformatics databases and tools. MetaBasis is focused on non-commercial and open-source software tools.</p><p><strong>Availability: </strong>http://metabasis.bioacademy.gr/</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 3","pages":"187-92"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605030-00007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26211735","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":"REGANOR: a gene prediction server for prokaryotic genomes and a database of high quality gene predictions for prokaryotes.","authors":"Burkhard Linke,&nbsp;Alice C McHardy,&nbsp;Heiko Neuweger,&nbsp;Lutz Krause,&nbsp;Folker Meyer","doi":"10.2165/00822942-200605030-00008","DOIUrl":"https://doi.org/10.2165/00822942-200605030-00008","url":null,"abstract":"<p><strong>Unlabelled: </strong>With >1,000 prokaryotic genome sequencing projects ongoing or already finished, comprehensive comparative analysis of the gene content of these genomes has become viable. To allow for a meaningful comparative analysis, gene prediction of the various genomes should be as accurate as possible. It is clear that improving the state of genome annotation requires automated gene identification methods to cope with the influence of artifacts, such as genomic GC content. There is currently still room for improvement in the state of annotations. We present a web server and a database of high-quality gene predictions. The web server is a resource for gene identification in prokaryote genome sequences. It implements our previously described, accurate gene finding method REGANOR. We also provide novel gene predictions for 241 complete, or almost complete, prokaryotic genomes. We demonstrate how this resource can easily be utilised to identify promising candidates for currently missing genes from genome annotations with several examples. All data sets are available online.</p><p><strong>Availability: </strong>The gene finding server is accessible via https://www.cebitec.uni-bielefeld.de/groups/brf/software/reganor/cgi-bin/reganor_upload.cgi. The server software is available with the GenDB genome annotation system (version 2.2.1 onwards) under the GNU general public license. The software can be downloaded from https://sourceforge.net/projects/gendb/. More information on installing GenDB and REGANOR and the system requirements can be found on the GenDB project page http://www.cebitec.uni-bielefeld.de/groups/brf/software/wiki/GenDBWiki/AdministratorDocumentation/GenDBInstallation</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 3","pages":"193-8"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605030-00008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26211736","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 flexible integration and visualisation system for biomarker discovery.","authors":"Mary Gaylord,&nbsp;John Calley,&nbsp;Huahong Qiang,&nbsp;Eric W Su,&nbsp;Birong Liao","doi":"10.2165/00822942-200605040-00004","DOIUrl":"https://doi.org/10.2165/00822942-200605040-00004","url":null,"abstract":"<p><p>Biological data have accumulated at an unprecedented pace as a result of improvements in molecular technologies. However, the translation of data into information, and subsequently into knowledge, requires the intricate interplay of data access, visualisation and interpretation. Biological data are complex and are organised either hierarchically or non-hierarchically. For non-hierarchically organised data, it is difficult to view relationships among biological facts. In addition, it is difficult to make changes in underlying data storage without affecting the visualisation interface. Here, we demonstrate a platform where non-hierarchically organised data can be visualised through the application of a customised hierarchy incorporating medical subject headings (MeSH) classifications. This platform gives users flexibility in updating and manipulation. It can also facilitate fresh scientific insight by highlighting biological impacts across different hierarchical branches. An example of the integration of biomarker information from the curated Proteome database using MeSH and the StarTree visualisation tool is presented.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 4","pages":"219-23"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605040-00004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26473723","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":"Assessing the reliability of amplified RNA used in microarrays: a DUMB table approach.","authors":"Edward D Bearden,&nbsp;Pippa M Simpson,&nbsp;Charlotte A Peterson,&nbsp;Marjorie L Beggs","doi":"10.2165/00822942-200605020-00001","DOIUrl":"https://doi.org/10.2165/00822942-200605020-00001","url":null,"abstract":"<p><p>A certain minimal amount of RNA from biological samples is necessary to perform a microarray experiment with suitable replication. In some cases, the amount of RNA available is insufficient, necessitating RNA amplification prior to target synthesis. However, there is some uncertainty about the reliability of targets that have been generated from amplified RNA, because of nonlinearity and preferential amplification. This current work develops a straightforward strategy to assess the reliability of microarray data obtained from amplified RNA. The tabular method we developed, which utilises a Down-Up-Missing-Below (DUMB) classification scheme, shows that microarrays generated with amplified RNA targets are reliable within constraints. There was an increase in false negatives because of the need for increased filtering. Furthermore, this analysis method is generic and can be broadly applied to evaluate all microarray data. A copy of the Microsoft Excel spreadsheet is available upon request from Edward Bearden.</p>","PeriodicalId":87049,"journal":{"name":"Applied bioinformatics","volume":"5 2","pages":"67-76"},"PeriodicalIF":0.0,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2165/00822942-200605020-00001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26041956","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}