Warning: Only works on UseGalaxy.eu

Currently this tutorial requires an Apollo server to be deployed by the administrator. This will currently only work on UseGalaxy.eu, hopefully this list will expand in the future.

Introduction

After automatically annotating your genome using Funannotate or Maker for example, it is important to visualize your results so you can understand what your organism looks like, and then to manually refine these annotations along with any additional data you might have. This process is most often done as part of a group, smaller organisms may be annotated individually though.

Apollo Dunn et al. 2019 provides a platform to do this. It is a web-based, collaborative genome annotation editor. Think of it as “Google Docs” for genome annotation, multiple users can work together simultaneously to curate evidences and annotate a genome.

This demo is inspired by the Apollo User’s Guide, which provides additional guidance.

Comment: Ask your instructor!

This tutorial shows you how to use Galaxy to set up a genome curation project by loading a new organism in Apollo. If you only want to focus on the usage of Apollo, your instructor might already have created for you an organism in Apollo. In this case select the Use a pre-created organism option below (you will skip a few steps in Galaxy), and select the organism prepared for you by your instructor.

If you’re doing this tutorial on your own, or if the instructor has not set up a specific Apollo organism for you, select Create your own organism.

hands_on Hands-on: Choose Your Own Tutorial

This is a "Choose Your Own Tutorial" section, where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial

Create your own organism Use a pre-created organism

Agenda

In this tutorial, we will cover:

1. Introduction
2. Preparing for Apollo
   1. Data upload
   2. Using Apollo for Annotation
3. Apollo
   1. Evidence tracks
   2. Adding new genes
   3. Editing a gene structure
   4. Working with isoforms
   5. Viewing and reverting changes
   6. Adding more functional annotation
4. Exporting and collaborating
5. Conclusion
6. What’s next?

Preparing for Apollo

Apollo

From the Apollo user manual:

The major steps of manual annotation using Apollo can be summarized as follows:

1. Locate a chromosomal region of interest.
2. Determine whether a feature in an existing evidence track provides a reasonable gene model to start annotating.
3. Drag the selected feature to the ‘User Annotation’ area, creating an initial gene model.
4. Use editing functions to edit the gene model if necessary.
5. Check your edited gene model for consistency with existing homologs by exporting the FASTA formatted sequence and searching a protein sequence database, such as UniProt or the NCBI Non Redundant (NR) database, and by conducting preliminary functional assignments using the Gene Ontology (GO) database.

The first four steps are generally the process of structural annotation (the process of identifying the correct gene model), and the last includes functional annotation (the process of assigning a putative function to a gene in your annotations).

Evidence tracks

Let’s start by looking at the tracks available to us, and then turning on the annotation track so we can start exploring our data.

Hands-on: Visualize the annotation

1. In the right hand panel at the top click on Tracks to open the track listing

   

2. In the Annotation group, select the annotation.gff3 track.

   You can either activate tracks in bulk, by clicking on the checkbox to the right of the group name (“Annotation”), or by clicking on the group name to expand the section, and then selecting individual tracks.

3. Zoom to a region of the genome.

   1. In the left hand Annotation Window, at the top navigation bar you will find a textbox which shows the current location on the genome.
   2. Edit this and enter scaffold_1:3000..12000
   3. Press Go or use Enter on your keyboard.

   

Comment: Track names

Apollo uses the dataset names from your Galaxy history to name the tracks in the track list.

We can now see an evidence track: annotation.gff3 is the output of the Funannotate tutorial. In a de novo annotation project, we probably will only have the outputs of various gene callers, and potentially some expression evidence like RNA-Seq.

For this organism, we only have one track in the Annotation group, but an organism can have multiple annotations coming from different sources.

Adding new genes

With the selected track, if you look along the genome, you will see many genes that were predicted by Funannotate. Each of them as a name assigned by Funannotate based on automatic functional annotation. If you right click on gene, an select View details, you can get access to detailed information on the predicted gene, including its unique name (e.g. FUN_000002), functional annotation (e.g. InterPro:IPR006594, GO:0005515) or the coding sequence (CDS).

Details: Gene colors

Each gene color corresponds to an open reading frame on the genome. This allows to quickly see if two genes that are very close are on the same open reading frame, which could mean that they can be merged into a single gene, if other evidences support this.

What we want now is first to check that the structure of the genes predicted by Funannotate are in good shape. To do this, we will display an additional evidence tracks.

Hands-on: Display RNA-Seq track

1. In the right hand panel at the top click on Tracks to open the track listing

2. In the RNA-Seq group, select the rnaseq.bam - SNPs/Coverage track and then the rnaseq.bam track.

These new tracks represent the result of aligning RNA-Seq reads along the genome, with two different representation. It should look like that:

The first track (rnaseq.bam - SNPs/Coverage) is a coverage xy-plot, representing the number of reads aligned on the genome at each position. The little coloured teardrops represent positions where mismatches where found between some reads and the reference sequence (SNPs or sequenceing errors). Light grey regions corresponding to portions of the genomes where RNASeq were mapped, and darker grey regions corresponding to introns (= regions were some reads were found to match both the end of an exon, and the start of the next one).

The second track (rnaseq.bam) represent the alignment of each individual RNA-Seq read. Red reads were aligned in the forward strand, and blue ones in the reverse strand.

Let’s suppose you want to improve the annotation of one of the gene. To do it, you can either click on one of its intron and drag and drop it to the User-created Annotations track (yellow background, at the top), or if you prefer, right click on one of its intron and select Create new annotation > gene. It should appear shortly in the User-created Annotation track.

You can also see that it appears now in the list of genes in the right panel, at the gene and the mRNA level (as a gene can have multiple isoforms).

The User-created Annotation track is where you can make modifications to genes, like changing their coordinates, or their name and functional annotation. If you right click on a gene in this track, you will see all the possibilities offered by apollo.

Currently, the gene we added has a meaningless name. Let’s improve that: right click on the gene, and click on Open Annotation (alt-click).

One way to give a better name is to find a similarity with genes of another species: if you right click on the gene, you can get the protein sequence by clicking on Get Sequence, and then compare it with a sequence database with Blast for example (using Galaxy, or the NCBI blast form for example).

In this case, the blast result suggests that this name is similar to alpha/beta-hydrolase genes in other fungi species. This is not a very strong evidence, but for this exercise, let’s use this as a name: write alpha/beta-hydrolase in the Name field (type it manually and select it from the drop down list).

Details: Naming genes

Giving a proper name to a gene is not always easy. Should it include “Putative” or not? What if multiple names can apply? Should it be lowercase or uppercase? The important thing is to always use the same naming rules when working on a full annotation, and to agree on these rules with other collaborators. Usually, big annotation consortiums have naming guidelines that you are supposed to follow.

We have just edited the gene name, but Apollo allows to edit information at the mRNA level too. Click on the Sync name with transcript button to copy the gene name to the mRNA name. It should now display in the User-created Annotation track. To check what you can edit at the mRNA level, just click on the corresponding mRNA in the list above:

You should see alpha/beta-hydrolase in the Name field.

Comment: Saving your work

You do not need to do anything specific to Save your work in Apollo. Just like Google Docs, each modification is immediately saved, and any other user working on the same genome will instantly see the changes you make.

Editing a gene structure

Apollo allows to edit the whole structure of a gene. If you zoom to the 5’ end of the gene, you will notice two things:

• a few RNA-Seq reads were mapped a little before the 5’ limit of our gene
• there is another potential start codon a few bases after the one that was predicted by Funannotate

Let’s modify the 5’ of the gene to add a 5’ untranslated region. Although we don’t have enough evidences to be sure that the start codon should be changed, for this exercise will we do it too. To do it, all you need to do is click on the 5’ limit of the gene and drag it to the desired position. You will notice that the structure of the gene will be shortly changed. Changing the start codon can be done by right clicking on the new position and clicking on “Set Translation Start”

The new white region corresponds to the newly create 5’ untranslated region.

This kind of modifications is very common when using Apollo, and you can perform it at the gene level, and at the exon/intron level (an exclaim mark appears when Apollo detects that an exon/intron junction doesn’t match with the cannonical acceptor/donor sequences).

To guide you doing these changes, you should look at all the tracks available for the genome you study. RNA-Seq track are very helpful to determine the limits of coding sequences on the genome. Other tracks can be helpful, like alignements of transcripts or proteins from closely related species (or even big databanks like Swissprot or NR).

Working with isoforms

In the eukaryote world, with the process of alternative splicing, multiple mRNA sequences (isoforms) can be generated for a single gene, based on the use of various splice site combinations. This process has a complex regulation mechanism allowing to express or not each isoform depending on multiple conditions (tissue, developmental stage, environmental conditions, …).

Often automatic annotation softwares produce gene models composed of many exons, but does not give the detail of the sequence of each potential isoforms. However, in some cases, the RNA-Seq data and the scientific knowledge on specific gene families can be used to annotate each isoform of a gene.

Apollo allows to do that: just duplicate an existing gene model at the same position. Each copy will be considered as an isoform of the same gene. You can then make all the modifications you want to each one independently.

The isoforms will appear in the gene list, under their common gene.

As isoforms regulation depends heavily on multiple conditions (tissue, developmental stage, environmental conditions, …), it is important when annotating a new genome to have access to a lot of RNA-Seq data sequenced in multiple conditions and from different tissues.

Viewing and reverting changes

Everything you do in Apollo is tracked in a database. If you right click on the gene, and select Show History, you have access to the full list of all the actions that were performed on it.

When you click on one of the steps, you can see below the list a preview of how the gene looked at the time. And you can revert to a specific version of the gene by clicking on the arrow button on the right.

Adding more functional annotation

Sometimes you’ll want to modify a gene that was predicted by FunAnnotate, just to add functional annotation to it, like Gene Ontology terms, of references to external databases (UniProt, InterPro, …). This can be done using Apollo, have a look at the Apollo tutorial for prokaryotes for more details.

Exporting and collaborating

Conclusion

Congratulations, you finished this tutorial! By using Apollo and JBrowse, you learned how to manually refine predicted annotations and export them to Galaxy for future analyses. You also learn how to give access to your project at any other researcher, making it a real collaborative solution. Although you use a pre-created organism, remember that you can use Galaxy to add your own organism to Apollo, and give access to your project at any other researcher, making it a real collaborative solution.

A similar tutorial for prokaryote genomes exists, using different types of evidence tracks, feel free to have a look at it to learn more.

When refinement is sufficient an updated or new version of the annotation may be exported as GFF3 as well as published as a new JBrowse directory for inspection.

What’s next?

After generating your refined annotation, you’ll want to merge it back into the official gene sets. A future tutorial will show you how to do it within Galaxy.

If a de novo set, you can export it as GFF3 and load it into a tool like Tripal to provide visualization.

Key points

• Apollo is the Google Docs of the genome annotation world, real-time collaborative genome annotation.

• Apollo allows a group to view and manually refine predicted genome annotations

• Use Apollo to edit annotations within your group.

• Export manual annotations as GFF3.

Frequently Asked Questions

Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Genome Annotation topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum

References

1. Dunn, N. A., D. R. Unni, C. Diesh, M. Munoz-Torres, N. L. Harris et al., 2019 Apollo: Democratizing genome annotation (A. E. Darling, Ed.). PLOS Computational Biology 15: e1006790. 10.1371/journal.pcbi.1006790

Glossary

OGS

Official Gene Set

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Citing this Tutorial

1. Anthony Bretaudeau, Erasmus+ Programme, 2022 Refining Genome Annotations with Apollo (eukaryotes) (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/apollo-euk/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{genome-annotation-apollo-euk,
author = "Anthony Bretaudeau and Erasmus+ Programme",
title = "Refining Genome Annotations with Apollo (eukaryotes) (Galaxy Training Materials)",
year = "2022",
month = "10",
day = "18"
url = "\url{https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/apollo-euk/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
    doi = {10.1016/j.cels.2018.05.012},
    url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}
}
                   

Congratulations on successfully completing this tutorial!