FUNC

NAME

func − functional analysis of gene data using Gene Ontology

SYNOPSIS

func_hyper -i inputfile.tsv -t termdb-tables_directory -o output-directory [OPTIONS]
func_wilcoxon -i inputfile.tsv -t termdb-tables_directory -o output-directory [OPTIONS]
func_2x2contingency -i inputfile.tsv -t termdb-tables_directory -o output-directory [OPTIONS]
func_binom -i inputfile.tsv -t termdb-tables_directory -o output-directory [OPTIONS]

DESCRIPTION

func is a collection of four programs to analyze the over- and under-representation of data associated with genes or gene products. The test runs seperately for all three taxonomies of Gene Ontology (or the supplied root-nodes).

func_hyper uses a hypergeometric test. Each gene has the binary information whether it is part of the group of interesting genes or not (for example: differently expressed or not). Each group is tested for how strong it differs from the average ratio.

func_wilcoxon ranks all genes using the supplied floating point numbers for each gene. The sum of the ranks in one group is used to calculate the probability that the genes of the group have been choosen from the same distribution as the genes not in the group. An examples for this test can include intensity values from gene expression data or the aminoacid divergence between two species.

func_binom compares whether a group differs in the ratio of 2 features. Each gene has two integers indicating how much of each feature it has. The probability for each group will be calculated using the average rate. This test was used to analyze whether aminoacid-changes accumulated in functional related genes on the human or chimpanzee lineage.

func_2x2contingency tests whether 4 values in an 2x2 table deviate from the expectation given by the tables margin sums. It can be used to test for positively selected groups of genes (McDonald-Kreitman test). The amount of synonymous and nonsynonymous changes beetween two species and the synonymous and nonsynonymous SNPs within one of the two species is needed for each gene as four integer values.

The output of the programs include a list of all groups with p-values for over- and under-representation as well as some general statistics about the significance of the result (the global test statistics). See section FILES for a full description of the output files. Mandatory arguments as well as options are the same for all programs.

MANDATORY ARGUMENTS

-i inputfile.tsv

The inputfile is in a tab separated values format. The first column is an arbitrary string for the name of the gene or gene product and the second column shows one Gene Ontology Identifier. The last column(s) differ for each of the four tests.

func_hyper accepts only 1 or 0 in the third column denoting whether the gene is of interest or not. Example:

func_wilcox accepts one floating point value per gene:

func_binom needs two integer values:

func_2x2contingency needs four values per gene. The order of the values are divergence_synonymous divergence_nonsynonymous diversity_syn diversity_nonsyn. Example:

More then one line with identical data and gene name but different GO-Ids is expected if a gene is annotated to more than one GO group. The data and the GO-Id columns can be omitted, but the information will not be taken into account.

-t termdb-tables_directory

The Gene Ontology DAG will be constructed using three files of the go_termdb_tables distribution. You can download the distribution from http://archive.geneontology.org/full/. Extract it and use the directory as this argument to the func programs.

-o output_directory

An empty directory will be used during the analysis to save temporary files and the resultfiles. Please make shure the directory is read-writeable.

OPTIONS

-c cutoff

The cutoff option can be supplied to ensure that only groups with a certain amount of genes will be taken into account for the analysis. This option defaults to 1.

-g GO_ID1,GO_ID2,...

Use this option to analyse only the groups below certain GO groups. Defaults to GO:0008150,GO:0003674,GO:0005575 for the root nodes of the three taxonomies for Gene Ontology.

-r #randomsets

func_hyper will calculate 1000 randomsets to estimate the background distribution for the global test statistics. Use this option to change that behaviour.

FILES

The following outputfiles will be placed in the output_directory:
statistics.txt

This file contains general information about the supplied data and annotation in the inputfile and the significance of the results for each root node. The latter information includes a test of how significant the result is overall (the global test statistics) and statistics about the amount of groups below certain cutoffs in data and randomsets. It is a good idea to consult this file before you check the results in groups.txt.

groups.txt

All groups with more then cutoff genes are included in this file. For each group the sum of the data of the annotated genes is listed, as well as two p-values -- for the significance in the two directions of the used test -- for each group. Additionally a FWER and an FDR corrected p-value is given for each group and each direction of the test. A value of -1 for the FDR rate means that the value is equal to the FWER.

refin-YEAR-MM-DD.sh

This shell-script can be used to run the refinement. Warning: Do not remove the directory tmp in the output_directory until you finished this test. For a short description of the refinement shellscript see func-refin(1).

SEE ALSO

func-refin(1)
http://func.eva.mpg.de/
/usr/local/doc/func-0.4.7/

AUTHOR

Kay Pruefer <pruefer@eva.mpg.de>, Bjoern Muetzel <muetzel@eva.mpg.de>