Making an evidence based annotation with MAKER

Overview

The first run of Maker will be done without ab-initio predictions. What are your expectations for the resulting gene build? In essence, we are attempting a purely evidence-based annotation, where the best protein- and EST-alignments are chosen to build the most likely gene models. The purpose of an evidence-based annotation is simple. Basically, you may try to annotate an organism where no usable ab-initio model is available. The evidence-based annotation can then be used to create a set of genes on which a new model could be trained on (using e.g. Snap or Augustus). Selection of genes for training can be based on the annotation edit distance (AED score), which says something about how great the distance between a gene model and the evidence alignments is. A score of 0.0 would essentially say that the final model is in perfect agreement with the evidence.

Let's do this step-by-step:

Prepare the folder and input data

Create the folder where we will launch this maker run.

cd ~/annotation_course/practical2/maker/

Link the raw computes you want to use into your folder. The files you will need are:

ln -s ~/annotation_course/data/raw_computes/repeatmasker.genome.gff
ln -s ~/annotation_course/data/raw_computes/repeatrunner.genome.gff

In addition, you will also need the genome sequence.

ln -s ~/annotation_course/data/genome/genome.fa

Then you will also need EST and protein fasta file: 

ln -s ~/annotation_course/data/evidence/est.genome.fa 
ln -s ~/annotation_course/data/evidence/proteins.genome.fa

To finish you will could use a transcriptome assembly (This one has been made using Stringtie):

ln -s ~/annotation_course/data/RNAseq/stringtie/stringtie2genome.genome.gff

/!\ Always check that the gff files you provides as protein or EST contains match/match_part (gff alignment type ) feature types rather than genes/transcripts (gff annotation type) otherwise MAKER will not use the contained data properly. Here we have to fix the stringtie gff file.

gff3_sp_alignment_output_style.pl --gff stringtie2genome.genome.gff -o stringtie2genome.genome.ok.gff

You should now have 2 repeat files, 1 EST file, 1 protein file, 1 transcript file, and the genome sequence in the working directory.

For Maker to use this information, we need create the three config files, typing this command:

maker -CTL

You can leave the two files controlling external software behaviors untouched but you need to provide the proper parameters in the file called maker_opts.ctl. To edit the maker_opts.ctl file you can use the nano text editor: 

nano maker_opts.ctl

In the maker_opts.ctl you will set:

You can list multiple files in one field by separating their names by a comma ','.

This time, we do not specify a reference species to be used by augustus, which will disable ab-initio gene finding. Instead we set:

protein2genome=1
est2genome=1

This will enable gene building directly from the evidence alignments.

Before running MAKER you can check you have modified the maker_opts.ctl file properly here.
/!\ Be sure to have deactivated the parameters model_org= # and repeat_protein= # to avoid the heavy work of repeatmasker.

Run Maker

If your maker_opts.ctl is configured correctly, you should be able to run maker:

maker -c 8

This will start Maker on 8 cores, if everything is configured correctly. This will take a little while and process a lot of output to the screen. Luckily, much of the heavy work - such as repeat masking - are already done, so the total running time is quite manageable, even on a small number of cores.

Once the run is finished, check that everything went properly. If problems are detected, launch MAKER again.

maker_check_progress.sh

Inspect the output (optional)

Here you can find details about the MAKER output.

Compile the output

Once Maker is finished, compile the annotation:

maker_merge_outputs_from_datastore.pl --output maker_evidence

We have specified a name for the output directory since we will be creating more than one annotation and need to be able to tell them apart.

This should create a maker_evidence folder containing all computed data including maker.gff which is the maker annotation file and genome.all.maker.proteins.fasta which is the protein fasta file of this annotation. Those two files are the most important outputs from this analysis.

=> You could sym-link the maker.gff and genome.all.maker.proteins.fasta files to another folder called e.g. dmel_results, so everything is in the same place in the end. Just make sure to call the links with specific names, since any maker output will be called similarly.

Inspect the gene models

To get some statistics of your annotation you could read the maker_stat.txt file from the maker_evidence folder or launch this script that work on any gff file :

gff3_sp_statistics.pl --gff maker_evidence/annotationByType/maker.gff

We could now also visualise the annotation in the Webapollo genome browser.