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Lecture 11, part 1 of 1
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November 30,  2017

Genome Annotation


Yandell Y,Ence D (2012) A beginner's guide to eukaryotic genome annotation. Nature Reviews Genetics 13:329-342  doi:10.1038/nrg3174


B. Impact of the quality of the assembly on annotation

C. Repeat identification and masking

D. Similarity based alignment of protein and RNA sequences to coding regions

E. Prediction of gene structure

F. GFF3 Genome Annotation format

A. Overview

The goal of genome annotation is to identify features found in a genome, including coding sequences for proteins and RNAs, repetitive elements and a spectrum of other types of information found in genomes. The end product is an annotated genome, as represented by an entry in the GenBank database.

An example of an annotated genome is shown below. A region of the C. elegans chromosome III is shown in the artemis viewer. Note that two genes are shown on the forward strand, and a single gene on the reverse strand. The lower panel gives the text annotation corresponding to features shown in the viewer.

Genome annotation is best described as a pipeline of steps.

The example at right shows the NCBI eukaryotic genome annotation pipeline, which combines information for several sources, when available. These include the RefSeq genes and mRNA data, known protein sequences, as well as RNA seq data, if available.

Coding regions are found through simple BLAST searches. The coding regions are more precisely annotated using ab initio gene prediction software to produce gene models. Gene models include precise locations of exon/intron boundaries, as inferred from mRNA or EST data.


B. Impact of the quality of the assembly on annotation

As with everything else, the quality of the starting material is the single most important factor determining the results. A low quality genome will give low quality gene calls. Two parameters that can indicate whether a genome is worth annotating are N50 and percent coverage.

N50 - N50 is the median contig size, such that 50% of the contigs are larger than N50, and 50% are smaller. It makes sense that if N50 contig sizes are smaller than the size of genes, then most genes would be broken between two or more contigs. Therefore, one guideline for a quality assembly is to have contigs larger than the median size of genes.

Figure 1 shows a direct relationship between genome size and the median size of a gene. In terms of successful genome annotation, we need larger contigs for large eukaryotic genomes than we do for prokaryotic genomes.

Fig. 1 from Yandell and Ence

percent coverage - The percentage of the genome covered by an assembly. If genome size, as estimated from the assembly is close to the actual genome size, then most genes will probably be found, especially where the number of scaffolds is not large. Not getting the entire genome may still not be a problem in eukaryotic genomes with lots of repetitive sequences. The repetitive fraction of a genome tends to be underestimated in incomplete genome assemblies, because repetitve sequences often prevent the assembly of larger contigs.

C. Repeat identification and masking

Because much of the annotation process relies upon similarity comparisons, it is critical to mask repeats within your genomic sequences. Two types of repeats need to be masked:

Software such as RepeatMasker ( will search for these types of repeats in genomic sequences, and mask them in one of two ways:
For repetitive elements, repeat masking programs require a database of known repeats to be masked. These are easily available for model genomes. However, for most genomes, you would need to create your own database of repetitive elements to be masked.


Repeat masking is actually part of genome annotation, since the output from repeat masking programs can be used to annotate the location of known repetitive elements in genome.

D. Similarity based alignment of protein and RNA sequences to coding regions

This step amounts to the discovery of protein coding regions, often annotated as CDS regions.

Similarity search programs such as BLASTX can compare a genomic nucleotide sequence with a protein database to find potential coding regions.

In the example at right, output for a 9.5 kb contig from a fungal genome shows three potential coding regions. The right most coding region appears to include an intron between two CDS regions.

E. Prediction of gene models

Similarity alignment results only give the approximate locations of CDS features. The precise exon/intron structure of a gene, along with other features such as ribosome binding sites, 5'UTRs, 3'UTRs or promoter regions, require more sophisticated gene prediction tools. The goal is to create a model of a gene describing these features with precise begin and end points.

For example, splice junctions are predicted using tools such as splign. Splign predicts exon/intron structure based on cDNA/EST sequences found in genome databases. Splign works in three steps:

  1. local alignment - performs a standard Smith-Waterman alignment between a set of cDNA sequences and the genomic sequence
  2.  alignments are used to identify the location of each cDNA in the genomic data
  3. an optimal alignment is performed on the cDNAs and the genomic data, using a splice site prediction model.
In the example, the sequence of an intron boxed in green is shown. Note the AG and GT donor/acceptor sites flanking the intron, are highlighted in green.


Annotation pipelines such as MAKER combine information from many sources, including gene predictors, BLAST, and repeat maskers, to create integrated alignments. At right is an gene model, viewed in the Apollo genome viewer.


It is critical to emphasize the importance of having RNA transcript data to inform geneome annotation pipelines. Without these data, predictions will be poor. As well, RNAseq data is essential for annotation of alternative splicing. Consequently, any genomic sequencing project should include RNA sequencing as well.

F. GFF3 Genome Annotation format

The results of genome annotation are usually saved in GFF3 files. GFF3 is an extensive format that can represent locations strands and identifiers for just about any feature that can be annotated. It is a generally universal feature format, but there are issues, with specific software applications, which may not be fully compliant with GFF3. As well, there are some conflicts between NCBI annotation and GFF3*.

The specifications for GFF3 can be found at
 0  ##gff-version   3
 1  ##sequence-region   ctg123 1 1497228       
 2  ctg123 . gene            1000  9000  .  +  .  ID=gene00001;Name=EDEN
 3  ctg123 . TF_binding_site 1000  1012  .  +  .  ID=tfbs00001;Parent=gene00001

 4  ctg123 . mRNA            1050  9000  .  +  .  ID=mRNA00001;Parent=gene00001;Name=EDEN.1
 5  ctg123 . five_prime_UTR  1050  1200  .  +  .  Parent=mRNA0001
 6  ctg123 . CDS             1201  1500  .  +  0  Parent=mRNA0001
 7  ctg123 . CDS             3000  3902  .  +  0  Parent=mRNA0001
 8  ctg123 . CDS             5000  5500  .  +  0  Parent=mRNA0001
 9  ctg123 . CDS             7000  7600  .  +  0  Parent=mRNA0001
10  ctg123 . three_prime_UTR 7601  9000  .  +  .  Parent=mRNA0001

11  ctg123 . mRNA            1050  9000  .  +  .  ID=mRNA00002;Parent=gene00001;Name=EDEN.2
12  ctg123 . five_prime_UTR  1050  1200  .  +  .  Parent=mRNA0002
13  ctg123 . CDS             1201  1500  .  +  0  Parent=mRNA0002
14  ctg123 . CDS             5000  5500  .  +  0  Parent=mRNA0002
15  ctg123 . CDS	     7000  7600	 .  +  0  Parent=mRNA0002
16  ctg123 . three_prime_UTR 7601  9000	 .  +  .  Parent=mRNA0002

17  ctg123 . mRNA            1300  9000  .  +  .  ID=mRNA00003;Parent=gene00001;Name=EDEN.3
18  ctg123 . five_prime_UTR  1300  1500	 .  +  .  Parent=mRNA0003
19  ctg123 . five_prime_UTR  3000  3300	 .  +  .  Parent=mRNA0003
20  ctg123 . CDS             3301  3902  .  +  0  Parent=mRNA0003
21  ctg123 . CDS	     5000  5500	 .  +  2  Parent=mRNA0003
22  ctg123 . CDS	     7000  7600	 .  +  2  Parent=mRNA0003
23  ctg123 . three_prime_UTR 7601  9000	 .  +  .  Parent=mRNA0003


Why are NCBI GFF3 files still broken?

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Lecture 11, part 1 of 1
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