Guildeline Required For Gene Ontology, after Differntial expression analysis

Question

[hirahameed222]

Hello!

I have done differential expression analysis for my RNA-seq data set by following the HISAT, Stringtie and Ballgown protocol. The next thing i want to do is some basic gene ontology. Can anyone please suggest me some Tools, protocol to follow for desired work.

Have you any guidelines ?

Thank you in advance

Answer 1

[lakhujanivijay]

check out topGO

[hirahameed222]

Can you please share any tutorial or guide me how to do this for basic Ontology process. I am new to deal with RNA-seq data and i am doing this work for the first time and it will be easy to learn if i follow some protocol.

[Kevin Blighe]

A simple one, here: A: GO analysis using topGO

Also, please read the topGO vignette: http://bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/topGO.pdf

Answer 2

[Luiz]

You have more complex solutions in R by topGO package as recommended by bioExplorer. I would suggest DOSE and others by Guangchuang Yu available on bioconductor repository. A simple solution can be the use of WebGestalt http://webgestalt.org or network analyst with gene ontology enrichment in network menu networkanalyst.ca/faces/home.xhtml

[hirahameed222]

can you please explain for wheat RNA-seq data Can i use this ?

[Luiz]

Basically any data that you have an gene ID (entrez, RefSeq, Ensembl, Symbol..) and a value of expression.

For example, WebGestalt requires only your gene list. While network analyst can be used with your gene list only and your gene list + values of expression (positive or negative, such as log2 fold change). The packages of Guangchuang Yu can be used in R based on entrez ID to construct a table of GO:ID and build the plot. I would try to made this enrichment with online tools (webgestalt, network analyst, string) before reproduce in R.

Answer 3

[Gio12]

Although you may need to may an account (which is free), omictools can be used to search for numerous bioinformatic programs that are designed for whatever task you have in mind.

Answer 4

[i.sudbery]

If you are analysing RNA-seq data then you need to take account of the bias introduced by gene length and expression level. See my answer to this question A: What's your preferred pathway enrichment analysis tool after DEG analysis and wh for suggestions.

Answer 5

[jared.andrews07]

enrichR is extremely easy to use. clusterProfiler is a more advanced solution that takes a little bit more time to get acquainted with.