I am very new with the GO analysis and I am a bit confuse how to do it my list of genes.
I have a list of genes (n=10):
and I simply want to find their function and I've been suggested to use GO analysis tools. I am not sure if it's a correct way to do so. here is my solution:
So, I've got a list with EntrezID that are assigned to several GO terms for each genes. for example:
My question is. how can I find the function for each of these genes in a simpler way and I also wondered if I am doing it right or? because I want to add the function to the gene_list as a function/GO column.
Thanks in advance,
I hope I get what you are aiming here.
BTW, for bioinformatics related topics, you can also have a look at biostar which have the same purpose as SO but for bioinformatics
If you just want to have a list of each function related to the gene, you can query database such ENSEMBl through the biomaRt bioconductor package which is an API for querying biomart database. You will need internet though to do the query.
Bioconductor proposes packages for bioinformatics studies and these packages come generally along with good vignettes which get you through the different steps of the analysis (and even highlight how you should design your data or which would be then some of the pitfalls).
In your case, directly from biomaRt vignette - task 2 in particular:
Note: there are slightly quicker way that the one I reported below:
You need to create your query (your list of ENTREZ ids). To see which filters you can query:
And then you want to retrieve attributes. your GO number and description. To see the list of available attributes
For you, the query would look like something as:
The query itself can take a while.
Then you can always collapse the information in two columns (but I won't recommend it for anything else that reporting purposes).
If you want to query a past version of the ensembl database:
and then the query would be:
However, if you had in mind to do a GO enrichment analysis, your list of genes is too short.
Notice: Public access to GOFFA will be disabled on Friday, March 17, 2017. If you have questions, please contact NCTRBioinformaticsSupport@fda.hhs.gov .
GOFFA is a tool developed for ArrayTrack™ that takes a list of genes and identifies terms in Gene Ontology (GO) associated with those genes. GOFFA provides tools to view/access the following:
GOFFA also provides two novel methods to visualize and interpret results—GOPath and GO TreePrune.
Running GOFFA requires an Internet connection and Java Runtime Environment (JRE) 1.6 or greater installed. To install or upgrade Java please visit the Oracle Java website .
Citation - Please cite the following for publications that incorporate analysis using GOFFA:
Sun, H. Fang, H. Chen, T. Perkings, R. and Tong, W. "GOFFA: Gene Ontology For Functional Analysis - Software for gene ontology-based functional analysis of genomic and proteomic data." BMC Bioinformatics. 7(Suppl 2):S23, 2006.
Please e-mail NCTRBioinformaticsSupport@fda.hhs.gov with any questions or problems running GOFFA.
One of the main uses of the GO is to perform enrichment analysis on gene sets. For example, given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene set.Enrichment analysis tool
Users can perform enrichment analyses directly from the home page of the GOC website. This service connects to the analysis tool from the PANTHER Classification System. which is maintained up to date with GO annotations. The PANTHER classification system is explained in great detail in Mi H et al, PMID: 23868073. The list of supported gene IDs is available from the PANTHER website.Using the GO enrichment analysis tools
First, paste or type the names of the genes to be analyzed, one per row or separated by a comma. The tool can handle both MOD specific gene names and UniProt IDs (e.g. Rad54 or P38086). Second, select the name of the species (e.g. S. cerevisiae or H. sapiens ) from the Species pull down and the ontology where you want to calculate the enrichment (biological process, molecular function, or cellular component). To minimize search time, the tool searches only one ontology at a time.Interpreting the Results Table
The results page displays a table that lists significant shared GO terms (or parents of GO terms) used to describe the set of genes that users entered on the previous page, the background frequency. the sample frequency. Expected p-value. an indication of over/underrepresentation for each term, and p-value. In addition, the results page displays all the criteria used in the analysis. Any unresolved gene names will be listed on top of the table.Background Frequency and Sample Frequency
Background frequency is the number of genes annotated to a GO term in the entire background set, while sample frequency is the number of genes annotated to that GO term in the input list. For example, if the input list contains 10 genes and the enrichment is done for biological process in S. cerevisiae whose background set contains 6442 genes, then if 5 out of the 10 input genes are annotated to the GO term: DNA repair, then the sample frequency for DNA repair will be 5/10. Whereas if there are 100 genes annotated to DNA repair in all of the S. cerevisiae genome, then the background frequency will be 100/6442.Overrepresented or Underrepresented
The symbols + and - indicate over or underrepresentation of a term.
P-value is the probability or chance of seeing at least x number of genes out of the total n genes in the list annotated to a particular GO term, given the proportion of genes in the whole genome that are annotated to that GO Term. That is, the GO terms shared by the genes in the user's list are compared to the background distribution of annotation. The closer the p-value is to zero, the more significant the particular GO term associated with the group of genes is (i.e. the less likely the observed annotation of the particular GO term to a group of genes occurs by chance).
In other words, when searching the process ontology, if all of the genes in a group were associated with "DNA repair", this term would be significant. However, since all genes in the genome (with GO annotations) are indirectly associated with the top level term "biological_process", this would not be significant if all the genes in a group were associated with this very high level term.External tools
There are a number of different tools that provide enrichment capabilities. Some of these are web-based, others may require the user download an application or install a local environment. Tools differ in the algorithms they use, and the statistical tests they perform.
Some other examples of enrichment tools are:
I need to make a recommendation to people working in a wet-lab looking for an easy to use tool that does GO term enrichment determination. For those unfamiliar with the concept it means that given a list of gene names they want to find out which gene ontology terms are present in numbers that are above random chance.
There is a huge list here yet a random sampling of the tools mentioned there has lead me to many non-working sites. Other tools seem out of date or just not reliable.
What tool do you use to solve this problem?
ADD COMMENT • link •
modified 3 months ago by jin • 60 • written 7.0 years ago by Biostar User • 990
I think most of the enrichment analysis tools deals with same class of statistics methods (p-value, FDR, Boneferroni etc). Defining background is a very important in such enrichment methods. To get real meaning of enrichment with respect to your experiments, you should be able to upload the background. For example, if you are looking at a set of a genes from a particular tissue, a background of that tissue give more meaningful results than a background of whole genome.
ADD COMMENT • link written 6.9 years ago by Khader Shameer ♦ 17k
yeah i read a paper in which they did the Gene Ontology Term Enrichment analysis exactly like u said here. and i wanna analyse my data in that way ( significantly differentially regulated transcripts Enrichment analysis against unchanged background transcripts ). my data is RNQ-seq, so how could i make regulation level unchanged transcripts set as a background analyse Ontology Term Enrichment? what kind of approach or tool should i use there? would u give me some suggestions?
ADD REPLY • link modified 22 months ago • written 22 months ago by Kurban • 140
Like several of the others I also recommend DAVID to wet lab biologists. It is well maintained, but you should check the version on the particular species annotation(s) they are currently using as it it sometimes not the latest.
They use a variant of the Fisher exact statistic for their p-value calculations called the EASE score which they wrote up in a paper a few years back http://www.ncbi.nlm.nih.gov/pubmed/14519205 which is more conservative that the standard.
ADD COMMENT • link written 6.9 years ago by Ian Simpson • 900Similar posts • Search »
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