Cyclebase.org a comprehensive multi-organism online database of cell-cycle experiments

Gauthier, Nicholas Paul; Larsen, Malene Erup; Wernersson, Rasmus; Lichtenberg, Ulrik de; Jensen, Lars Juhl; Brunak, Søren; Jensen, Thomas Skøt

doi:10.1093/nar/gkm729

Cited by 66 publications

(93 citation statements)

References 34 publications

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“…Many biological processes are controlled by characteristic temporal changes in the concentrations of specific biomolecules. For instance, the cell cycle is accompanied by periodic changes in mRNA and protein expression [42][43][44] . Likewise, the circadian cycle 45 involves concerted changes in abundances of proteins, protein modifications, mRNAs and metabolites 46 .…”

Section: Box 3 Data Integrationmentioning

confidence: 99%

The Perseus computational platform for comprehensive analysis of (prote)omics data

et al. 2016

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A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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Section: Box 3 Data Integrationmentioning

confidence: 99%

The Perseus computational platform for comprehensive analysis of (prote)omics data

et al. 2016

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“…Periodic genes with their estimated peak-time and score for S. cerevisiae, S. pombe, and H. sapiens were obtained from CycleBase (www. cyclebase.org) (Gauthier et al, 2008).…”

Section: Gene Orthology Group Determinationsmentioning

confidence: 99%

“…We have compared our C. albicans periodic gene list to those of S. cerevisiae (Cho et al, 1998;Spellman et al, 1998;Pramila et al, 2006), S. pombe (Rustici et al, 2004;Oliva et al, 2005;Peng et al, 2005), and H. sapiens (Whitfield et al, 2002;Bar-Joseph et al, 2008), as compiled in the public database CycleBase (Gauthier et al, 2008). In total, 2168 orthologous gene groups, with at least one member in each of the four species, were constructed from published lists of interspecies orthologues (Figure 3; for details, see Materials and Methods).…”

Section: Conservation Of Cell Cycle-regulated Genesmentioning

confidence: 99%

Transcriptional Analysis of the Candida albicans Cell Cycle

Côté

Hogues

Whiteway

2009

MBoC

100

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We have examined the periodic expression of genes through the cell cycle in cultures of the human pathogenic fungus Candida albicans synchronized by mating pheromone treatment. Close to 500 genes show increased expression during the G1, S, G2, or M transitions of the C. albicans cell cycle. Comparisons of these C. albicans periodic genes with those already found in the budding and fission yeasts and in human cells reveal that of 2200 groups of homologous genes, close to 600 show periodicity in at least one organism, but only 11 are periodic in all four species. Overall, the C. albicans regulatory circuit most closely resembles that of Saccharomyces cerevisiae but contains a simplified structure. Although the majority of the C. albicans periodically regulated genes have homologues in the budding yeast, 20% (100 genes), most of which peak during the G1/S or M/G1 transitions, are unique to the pathogenic yeast.

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“…For examples, please refer to the large circadian clock database CircaDB (40) (http://circadb.org) or the famous cell-cycle data base Cyclebase (43) (http://www.cyclebase.org) among others. Each of these websites contains numerous data sets.…”

Section: Discussionmentioning

confidence: 99%

Order restricted inference for oscillatory systems for detecting rhythmic signals

Larriba

Rueda

Fernández

et al. 2016

Nucleic Acids Research

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Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic. Availability and Implementation: A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm. Contact: peddada@niehs.nih.gov

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Cyclebase.org a comprehensive multi-organism online database of cell-cycle experiments

Cited by 66 publications

References 34 publications

The Perseus computational platform for comprehensive analysis of (prote)omics data

The Perseus computational platform for comprehensive analysis of (prote)omics data

Transcriptional Analysis of the Candida albicans Cell Cycle

Order restricted inference for oscillatory systems for detecting rhythmic signals

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