共查询到20条相似文献,搜索用时 15 毫秒
1.
Philippe Le Flèche Isabelle Jacques Maggy Grayon Sascha Al Dahouk Patrick Bouchon France Denoeud Karsten Nöckler Heinrich Neubauer Laurence A Guilloteau Gilles Vergnaud 《BMC microbiology》2006,6(1):9-14
Background
The classification of Brucella into species and biovars relies on phenotypic characteristics and sometimes raises difficulties in the interpretation of the results due to an absence of standardization of the typing reagents. In addition, the resolution of this biotyping is moderate and requires the manipulation of the living agent. More efficient DNA-based methods are needed, and this work explores the suitability of multiple locus variable number tandem repeats analysis (MLVA) for both typing and species identification. 相似文献2.
Hunter NB Moseley 《BMC bioinformatics》2010,11(1):139
Background
Stable isotope tracing with ultra-high resolution Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) can provide simultaneous determination of hundreds to thousands of metabolite isotopologue species without the need for chromatographic separation. Therefore, this experimental metabolomics methodology may allow the tracing of metabolic pathways starting from stable-isotope-enriched precursors, which can improve our mechanistic understanding of cellular metabolism. However, contributions to the observed intensities arising from the stable isotope's natural abundance must be subtracted (deisotoped) from the raw isotopologue peaks before interpretation. Previously posed deisotoping problems are sidestepped due to the isotopic resolution and identification of individual isotopologue peaks. This peak resolution and identification come from the very high mass resolution and accuracy of FT-ICR-MS and present an analytically solvable deisotoping problem, even in the context of stable-isotope enrichment. 相似文献3.
Paulo C Carvalho Juliana SG Fischer Emily I Chen Gilberto B Domont Maria GC Carvalho Wim M Degrave John R Yates III Valmir C Barbosa 《Proteome science》2009,7(1):6-11
Background
Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge. 相似文献4.
Background
Construction and interpretation of phylogenetic trees has been a major research topic for understanding the evolution of genes. Increases in sequence data and complexity are creating a need for more powerful and insightful tree visualization tools. 相似文献5.
Background
OFFGEL isoelectric focussing (IEF) has become a popular tool in proteomics to fractionate peptides or proteins. As a consequence there is a need for software solutions supporting data mining, interpretation and characterisation of experimental quality. 相似文献6.
Background
Commonly employed clustering methods for analysis of gene expression data do not directly incorporate phenotypic data about the samples. Furthermore, clustering of samples with known phenotypes is typically performed in an informal fashion. The inability of clustering algorithms to incorporate biological data in the grouping process can limit proper interpretation of the data and its underlying biology. 相似文献7.
Pamela A Nieto Paulo C Covarrubias Eugenia Jedlicki David S Holmes Raquel Quatrini 《BMC molecular biology》2009,10(1):63
Background
Normalization is a prerequisite for accurate real time PCR (qPCR) expression analysis and for the validation of microarray profiling data in microbial systems. The choice and use of reference genes that are stably expressed across samples, experimental conditions and designs is a key consideration for the accurate interpretation of gene expression data. 相似文献8.
Background
The volume of data available on genetic variations has increased considerably with the recent development of high-density, single-nucleotide polymorphism (SNP) arrays. Several software programs have been developed to assist researchers in the analysis of this huge amount of data, but few can rely upon a whole genome variability visualisation system that could help data interpretation. 相似文献9.
Aim
There is a wealth of information on species occurrences in biodiversity data banks, albeit presence‐only, biased and scarce at fine resolutions. Moreover, fine‐resolution species maps are required in biodiversity conservation. New techniques for dealing with this kind of data have been reported to perform well. These fine‐resolution maps would be more robust if they could explain data at coarser resolutions at which species distributions are well represented. We present a new methodology for testing this hypothesis and apply it to invasive alien species (IAS).Location
Catalonia, Spain.Methods
We used species presence records from the Biodiversity data bank of Catalonia to model the distribution of ten IAS which, according to some recent studies, achieve their maximum distribution in the study area. To overcome problems inherent with the data, we prepared different correction treatments: three for dealing with bias and five for autocorrelation. We used the MaxEnt algorithm to generate models at 1‐km resolution for each species and treatment. Acceptable models were upscaled to 10 km and validated against independent 10 km occurrence data.Results
Of a total of 150 models, 20 gave acceptable results at 1‐km resolution and 12 passed the cross‐scale validation test. No apparent pattern emerged, which could serve as a guide on modelling. Only four species gave models that also explained the distribution at the coarser scale.Main conclusions
Although some techniques may apparently deliver good distribution maps for species with scarce and biased data, they need to be taken with caution. When good independent data at a coarser scale are available, cross‐scale validation can help to produce more reliable and robust maps. When no independent data are available for validation, however, new data gathering field surveys may be the only option if reliable fine‐scale resolution maps are needed. 相似文献10.
Eric Bareke Michael Pierre Anthoula Gaigneaux Bertrand De Meulder Sophie Depiereux Naji Habra Eric Depiereux 《BMC bioinformatics》2010,11(1):528
Background
Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information. 相似文献11.
Background
Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny. 相似文献12.
13.
14.
Wasinee Rungsarityotin Roland Krause Arno Schödl Alexander Schliep 《BMC bioinformatics》2007,8(1):482
Background
Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes. 相似文献15.
Background
The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways. 相似文献16.
Sotiris Pavlopoulos Trias Thireou George Kontaxakis Andres Santos 《Biomedical engineering online》2007,6(1):36
Background
Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization. 相似文献17.
Background
Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. 相似文献18.
Monica Chagoyen Pedro Carmona-Saez Hagit Shatkay Jose M Carazo Alberto Pascual-Montano 《BMC bioinformatics》2006,7(1):41
Background
Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. 相似文献19.
Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap,a nonlinear algorithm 总被引:2,自引:0,他引:2
Background
Life processes are determined by the organism's genetic profile and multiple environmental variables. However the interaction between these factors is inherently non-linear [1]. Microarray data is one representation of the nonlinear interactions among genes and genes and environmental factors. Still most microarray studies use linear methods for the interpretation of nonlinear data. In this study, we apply Isomap, a nonlinear method of dimensionality reduction, to analyze three independent large Affymetrix high-density oligonucleotide microarray data sets. 相似文献20.