Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2007 Apr 3:8:114.
doi: 10.1186/1471-2105-8-114.

From genes to functional classes in the study of biological systems

Fátima Al-Shahrour  1 , Leonardo Arbiza, Hernán Dopazo, Jaime Huerta-Cepas, Pablo Mínguez, David Montaner, Joaquín Dopazo
Affiliations

From genes to functional classes in the study of biological systems

Fátima Al-Shahrour et al. BMC Bioinformatics. .

Abstract

Background: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.

Results: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.

Conclusion: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Threshold-free functional analysis. A list of genes is ranked by their differential expression between two experimental conditions (A and B) using, for example, a t-test which is applied individually to each gene. Columns 1, 2 and 3 represent the position of the genes belonging to three different functional classes (e.g. GO terms, etc.) across the arrangement. The first functional class is completely uncorrelated with the arrangement, while functional classes 2 and 3 are clearly associated to high expression in the experimental conditions B and A, respectively. Dotted lines represent a threshold based on the individual t-tests with some adjustment for multiple testing. The arrow makes reference to the multi-functional character of the genes: a gene can belong to more than one functional class. In this case the gene pointed out by the arrow is in this position not because of its membership to functional class 1 but because is fulfilling the role corresponding to functional class 3, which is related to high expression in experimental condition A.
Figure 2
Figure 2
Interface to the FatiScan program displaying the available model organisms.
Figure 3
Figure 3
A general picture of the results of the FatiScan program. Over- and under-representations of functional classes in both tails of the list of arranged genes can be detected. U-arrow up: functional classes over-represented in the upper part of the list. U-arrow down: functional classes under-represented in the upper part of the list. L-arrow up: functional classes over-represented in the lower part of the list. L-arrow down: functional classes under-represented in the lower part of the list. See text for the different choices of tests that detect the different cases.
Figure 4
Figure 4
Graphical results of FatiScan. Upper part: a summarised view of all the functional classes found. In the case of GO, only the deepest significant terms in the hierarchy are displayed. Lower part, comparison of the distribution of the two GO terms found significantly over-represented in the upper part of the list with respect to the background of GO terms in the rest of genes.
Figure 5
Figure 5
A small segment of the DAG GO hierarchy connecting the top of the biological process category to the term negative regulation of neuron apoptosis.
Figure 6
Figure 6
FatiScan analysis of the comparison between the background distribution of GO terms (black and blue bars) and the distribution of sensory perception of smell GO term (grey and red bars). The last distribution is clearly shifted towards highest values of ω (horizontal axis). The colours black/blue and grey/red make reference to the ω values for which the partitions were found to be significant.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, Timm J, Mintzlaff S, Abraham C, Bock N, Kietzmann S, Goedde A, Toksoz E, Droege A, Krobitsch S, Korn B, Birchmeier W, Lehrach H, Wanker EE. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005;122:957–968. - PubMed
    1. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005;437:1173–1178. - PubMed
    1. Hallikas O, Palin K, Sinjushina N, Rautiainen R, Partanen J, Ukkonen E, Taipale J. Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell. 2006;124:47–59. - PubMed
    1. Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 2004;14:1085–1094. - PMC - PubMed
    1. Stuart JM, Segal E, Koller D, Kim SK. A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003;302:249–255. - PubMed

Publication types

LinkOut - more resources