Journal of bioinformatics最新文献

{"title":"The International Society of Computational Biology presents: the Great Lakes Bioinformatics Conference, May 16-18, 2014, Cincinnati, Ohio","authors":"Jim Cavalcoli, L. Welch, B. Aronow, S. Drăghici, D. Kihara","doi":"10.1093/bioinformatics/btt673","DOIUrl":"https://doi.org/10.1093/bioinformatics/btt673","url":null,"abstract":"The International Society of Computational Biology presents: the Great Lakes Bioinformatics Conference, May 16–18, 2014, Cincinnati, Ohio Jim Cavalcoli, Lonnie Welch, Bruce Aronow, Sorin Draghici and Daisuke Kihara University of Michigan (Steering Committee Chair), Ohio University (Steering Committee Chair), Childrens Hospital Medical Center, University of Cincinnati (Conference Chair), Wayne State University (Conference Chair) and Purdue University (Conference Chair)","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76064929","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":"ISCB/SPRINGER series in computational biology","authors":"A. Dress, M. Linial, O. Troyanskaya, M. Vingron","doi":"10.1093/bioinformatics/btt630","DOIUrl":"https://doi.org/10.1093/bioinformatics/btt630","url":null,"abstract":"In late 2012, the International Society for Computational Biology (ISCB) and Springer partnered together to enhance the Springer book series in computational biology. The two worked closely together to come up with a strategy to bring to ISCB members and the community at large educational materials that would not only educate the community but also help advance the science. Sponsored by ISCB, the computational biology series publish the latest high-quality research devoted to specific issues in computer-assisted analysis of biological data. The main emphasis is on current scientific developments and innovative techniques in computational biology (bioinformatics), bringing to light methods from mathematics, statistics and computer science that directly address biological problems currently under investigation. The series offer publications that present the state-of-the-art regarding the problems in question, show computational biology/bioinformatics methods at work and discuss anticipated demands regarding developments in future methodology. Titles can range from focused monographs, to undergraduate and graduate textbooks and professional text/reference works. Additionally, ISCB members will receive a 25% discount on book purchases within the series. Springer is seeking to publish quality books in the areas including, but not limited to, databases, data analysis and ontologies; functional and comparative genomics; gene regulation and transcriptomics; protein interactions and networks; data, literature and text mining; molecular sequence analysis; biological networks; sequencing and genotyping technologies; population genetics; systems biology; imaging and visualization; computational proteomics; molecular structural biology; evolution and phylogenetics; metagenomics; biomedical applications; high performance biocomputing; and synthetic biological systems. Book proposal submission details can be found at the book series Web site (http:// www.springer.com/series/5769).","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89238007","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":"Response to 'Comments on \"MMFPh: A Maximal Motif Finder for Phosphoproteomics Datasets\"'","authors":"Tuobin Wang, A. Kettenbach, S. Gerber, C. Bailey-Kellogg","doi":"10.1093/bioinformatics/bts347","DOIUrl":"https://doi.org/10.1093/bioinformatics/bts347","url":null,"abstract":"Recently, two new approaches to find overrepresented motifs in phosphoproteomics datasets have been introduced: our MMFPh (Wang et al., 2012) and He et al.’s Motif-All (BMC Bioinformatics 2011). Both methods espouse the importance of completeness— finding all motifs supported by the data—in contrast to previous approaches that may miss some motifs due to algorithmic choices. As we discuss in the Introduction of our article, however, while both methods seek to identify all significant motifs, they employ different significance assessments. In some cases, the difference does not matter much if at all. However, as we show in the Results, in some cases the difference leads to Motif-All finding many more motifs than MMFPh, and many more than those that are biologically supported, including known false positives planted in synthetic datasets. They also lead to Motif-All occasionally missing a motif found by MMFPh, though not for the datasets and parameter settings employed in the presented examples. Since MMFPh and Motif-All employ different notions of significance, it is not surprising that empirically they do not find exactly the same sets of motifs. He et al. (submitted for publication) elaborate on this finding by providing a theoretical characterization with respect to their notion of significance, which is a global assessment of an entire peptide. In contrast, MMFPh employs a local assessment of individual amino acid/position pairs during construction of a motif [our Equation (1)], as introduced by the popular Motif-X approach to phosphorylation motif discovery","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74466340","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":"Response to Letter to the Editor by Philip Good on To Permute or Not to Permute","authors":"V. Calian, J. Hsu","doi":"10.1093/bioinformatics/btq313","DOIUrl":"https://doi.org/10.1093/bioinformatics/btq313","url":null,"abstract":"In current practice, such as GWAS (genome-wide association studies), permutation is often applied to multiple testing for association between large number of features [e.g. single nucleotide polymorphisms (SNPs)] and phenotypes (Hahn et al., 2008). Inferring that there is a difference between the phenotypic groups X and Y in some of the features is not very useful. One has to know for which features there is a difference. Exchangeability, a necessary condition for the validity of permutation tests, might be applicable if subjects are assigned randomly to treatments and the treatment is totally innocuous. However, instead of randomized, controlled clinical trials, bioinformatics discovery studies are mostly retrospective. Huang et al. (2006) gives examples of how permutation testing may fail to control Type I error when exchangeability does not hold. Equally important, Theorem 2.2 of this paper gives a succinct condition on when permutation testing is valid, even when exchangeability fails. This condition is as follows. In testing the null hypothesis that there is no difference in an entire set of features between groups X and Y , when the sample sizes are equal, even if the data distributions FX and FY have unequal even order cumulants, so long as they have equal odd higher order (third order and higher) cumulants, permutation testing controls Type I error rate. This precise condition is the basis for the subsequent papers Xu and Hsu (2007) and Calian et al. (2008) to uncover the Marginal-Determines-the Joint (MDJ) distribution condition needed for permutation multiple testing to control multiple testing error rates. Regardless of sample sizes, permutation multiple tests may not control false discoveries of which features are predictive of phenotype, unless it is assumed that the joint distributions of nonpredictive features are identical between the X and Y groups. Checking this assumption on the joint distribution using the data","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88301609","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":"Papers on normalization, variable selection, classification or clustering of microarray data","authors":"David M. Rocke, T. Ideker, O. Troyanskaya, John Quackenbush, J. Dopazo","doi":"10.1093/bioinformatics/btp038","DOIUrl":"https://doi.org/10.1093/bioinformatics/btp038","url":null,"abstract":"Over the last decade or so, there have been large numbers of methods published on approaches for normalization, variable (gene) selection, classification, and clustering of microarray data. As indicated in the scope document for Bioinformatics, this requires papers describing new methods for these problems to meet a very high standard, showing important improvement in results for real biological data, as well as novelty. In this editorial, we describe some standards that need to be met for papers in these areas to be seriously considered. We ask that prospective authors consider these points carefully before submission of their papers to Bioinformatics. The Role of Simulation. Simulation can be useful in investigating the properties of various methods of data analysis. Yet there are important barriers to credible use of simulation in microarray studies, largely due to what we don’t know about the statistical distribution of measured gene expression levels. First, the distribution across transcripts of true expression values is dependent on the biological state of the tissue or cell, and for a given state this is unknown, even in distributional form, and may further exhibit genespecific and platform-specific effects. Second, the correlation within biological replicates of true expression is unknown, and is likely unknowable in detail given that it is","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75260159","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 response to comment on 'A congruence index for testing topological similarity between trees'","authors":"D. M. Vienne, T. Giraud, O. Martin","doi":"10.1093/BIOINFORMATICS/BTN535","DOIUrl":"https://doi.org/10.1093/BIOINFORMATICS/BTN535","url":null,"abstract":"In response to comment on ‘A congruence index for testing topological similarity between trees’ Damien M. de Vienne1,∗, Tatiana Giraud1 and Olivier C. Martin2,3 1Univ Paris-Sud, Laboratoire de Recherche en Informatique, UMR8623, Orsay F-91405; CNRS, Orsay F-91405, 2Univ Paris-Sud, UMR8626, LPTMS, Orsay F-91405; CNRS, Orsay F-91405 and 3Univ Paris-Sud, UMR8120, Laboratoire de Genetique Vegetale du Moulon, Gif-sur-Yvette F-91190, France","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74658373","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 response to \"On E-value for tandem MS scoring schemes\"","authors":"Jainab Khatun, Morgan C. Giddings","doi":"10.1093/bioinformatics/btn252","DOIUrl":"https://doi.org/10.1093/bioinformatics/btn252","url":null,"abstract":"We thank Mark Segal for raising the issue of interpreting MS/MS scores. As he noted, we used a method proposed by Fenyo and Beavis (FB) (2003) to asses the significance of identification using HMM_Score. In his letter, Segal makes two basic assertions about this use: (1) that the extreme value distribution does not apply for the MS/MS database scoring systems used by FB and our HMM and (2) the linear tail fitting of the log survival function is not robust. He proposes a method that he authored as an alternative for estimating evd parameters that he says may be more robust, and also points to a method by Shen et al. that is specific to assessing significance of proteins/peptides identifications using MS/MS data. While it is valuable to examine whether there exist better ways of statistically interpreting the results of MS/MS search, in his letter, Segal did not provide any clear supporting evidence for his claim that the MS/MS scorers cannot use E-values. In our case, we calculate a score distribution for all random matches on-the-fly, then deriving the survival function, s, (the cumulative probability distribution) and finally, fitting a line to log of this function for the high-scoring portion of s. We verified the methodology for a series of randomly chosen HMM_Score search results, observing that in all cases, the fit had very high correlation values (R2 > 0.9). All subsequent validation of HMM_Score was performed using the E-values produced, and as reported the system performs well.","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83920058","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":"Reply to \"Comment on causality and pathway search in microarray time series experiment\"","authors":"N. Mukhopadhyay, Snigdhansu Chatterjee","doi":"10.1093/bioinformatics/btn019","DOIUrl":"https://doi.org/10.1093/bioinformatics/btn019","url":null,"abstract":"We thank Professors Nagarajan and Upreti for their interest in our paper, Mukhopadhyay and Chatterjee (2007). There, we propose using Granger causality-based pathway detection in an acyclic, homoscedastic framework for microarray time-series expressions; which are generally short-duration time series involving very large number of genes. Professors Nagarajan and Upreti point out that in the presence of heteroscedasticity, and a cycle like ‘gene x regulates the expression of gene y and simultaneously gene y regulates the expression of gene x’, Granger causality tests may not be informative. Here, we adopt the term ‘heteroscedasticity’ (‘homoscedasticity’) to mean the unconditional variance of the white noise, represented as a bivariate vector in the Euclidean co-ordinate system, is different (same) in different co-ordinate directions. Thus, in essence, if the assumptions about the acyclic and homoscedastic nature of the time series are violated, tests for causality detection may fail. This is an important point, since when a contemporaneous cyclic relationship is present, the notion of causality makes little sense. In the context of economics, Eichler (2007) present a treatment of contemporaneous correlation as well as Granger causality. Extreme heteroscedasticity may be indicative of improper normalization of gene expressions. At the end of their letter, Dr Nagarajan and Dr Upreti mention the normalization step. Proper normalization should remove wide discrepancy in noise variance, hence nowadays microarray datasets are typically available in de facto normalized version. The data used in Mukhopadhyay and Chatterjee (2007) is also normalized. However, difference in technical variance, as indicated by Professors Nagarajan and Upreti, may still be present. And that will violate the assumption of our method (as well as many other statistical comparison methods relying on common unknown variance). Professor Nagarajan, in review, kindly suggested references for two-gene systems whose time-profile may not fit into to a homoscedastic, cause-effect framework. Thus, a full vector autoregression structure may be needed to capture their mutual dependence at various lags (including lag zero). It can be guessed that multi-gene systems exist whose temporal codependency nature is extremely complex. Although current knowledge about gene regulatory networks is limited, some biology experts we consulted believe that cyclical patterns may be found in large multi-gene networks as a part of a feedback procedure, if they are studied over long enough time spans. A proper approach to elicit such patterns would be to conduct multivariate, possibly non-stationary, time-series analysis with all the genes over a long time horizon. This is not feasible currently, since present state-of-the-art microarray time series experiments are of short duration and typically involve very large number of genes. Hence, restricting the network to acyclic ones is, in our opinion, a small pri","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80394750","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":"Metabolic systems cost-benefit analysis for interpreting network structure and regulation - Erratum","authors":"R. Carlson","doi":"10.1093/bioinformatics/btm318","DOIUrl":"https://doi.org/10.1093/bioinformatics/btm318","url":null,"abstract":"","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75053687","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":"Response to comments on \"Bayesian Hierarchical Error Model for Analysis of Gene Expression Data\"","authors":"HyungJun Cho, Jae K. Lee","doi":"10.1093/bioinformatics/btl333","DOIUrl":"https://doi.org/10.1093/bioinformatics/btl333","url":null,"abstract":"","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/bioinformatics/btl333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72397829","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}