diff --git a/topics/proteomics/tutorials/metaproteomics/tutorial.md b/topics/proteomics/tutorials/metaproteomics/tutorial.md index 8a7819ff327be..944aa300f1e33 100644 --- a/topics/proteomics/tutorials/metaproteomics/tutorial.md +++ b/topics/proteomics/tutorials/metaproteomics/tutorial.md @@ -25,9 +25,6 @@ subtopic: multi-omics tags: [microbiome] --- -# Introduction - - In this metaproteomics tutorial we will identify expressed proteins from a complex bacterial community sample. For this MS/MS data will be matched to peptide sequences provided through a FASTA file. @@ -117,7 +114,7 @@ will be used to match MS/MS to peptide sequences via a sequence database search. For this, the sequence database-searching program called [SearchGUI](https://compomics.github.io/projects/searchgui.html) will be used. The created dataset collection of the three *MGF files* in the history is used as the MS/MS input. -#### SearchGUI +### SearchGUI > SearchGUI > @@ -183,7 +180,7 @@ SearchGUI archive file) that will serve as an input for the next section, Peptid > {: .comment} -#### PeptideShaker +### PeptideShaker [PeptideShaker](https://compomics.github.io/projects/peptide-shaker.html) is a post-processing software tool that processes data from the SearchGUI software tool. It serves to organize the Peptide-Spectral @@ -262,7 +259,7 @@ proteins and provides a fast matching algorithm for peptides. > or operated on within Galaxy. {: .comment} -#### Recieving the list of peptides: Query Tabular +### Recieving the list of peptides: Query Tabular In order to use *Unipept*, a list containing the peptide sequences has to be generated. The tool **Query Tabular** can load tabular data (the PSM report in this case) into a SQLite data base. @@ -364,7 +361,7 @@ Therefore we can search the database for the peptides and count the occurrence w {: .hands_on} -#### Retrieve taxonomy for peptides: Unipept +### Retrieve taxonomy for peptides: Unipept The generated list of peptides can now be used to search via *Unipept*. We do a taxonomy analysis using the UniPept pept2lca function to return the taxonomic lowest common ancestor for each peptide: @@ -496,7 +493,7 @@ This allows to get an insight of the **biological process**, the **molecular fun > {: .comment} -#### Data upload +### Data upload For this tutorial, a tabular file containing the relevant GO terms has been created. It contains the GO aspect, the ID and the name. It is available at Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.839701.svg)](https://doi.org/10.5281/zenodo.839701). @@ -533,7 +530,7 @@ It is available at Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8 > {: .details} -#### Retrieve GO IDs for peptides: Unipept +### Retrieve GO IDs for peptides: Unipept The **UniPept** application `pept2prot` can be used to return the list of proteins containing each peptide. The option `retrieve extra information` option is set to `yes` so that we retrieve Gene Ontology assignments (`go_references`) @@ -559,7 +556,7 @@ for each protein. {: .hands_on} -#### Combine all information to quantify the GO results +### Combine all information to quantify the GO results As a final step we will use **Query Tabular** in a more sophisticated way to combine all information to quantify the GO analysis. The three used file and the extracted information are: