Introduction

Genome annotation is the process of attaching biological information to sequences. It consists of three main steps:

• identifying portions of the genome that do not code for proteins
• identifying elements on the genome, a process called gene prediction, and
• attaching biological information to these elements.

Agenda

In this tutorial, we will deal with:

1. Introduction
2. Introduction into File Formats
3. Structural Annotation
   1. Sequence Features
   2. Gene Prediction
4. Functional Annotation
   1. Similarity Searches (BLAST)
   2. More Similarity Search Tools in Galaxy
   3. Identification of Gene Clusters

Introduction into File Formats

FASTA

DNA and protein sequences are written in FASTA format where you have in the first line a “>” followed by the description. In the second line the sequence starts.

GFF3

The general feature format (gene-finding format, generic feature format, GFF) is a file format used for describing genes and other features of DNA, RNA and protein sequences.

GENBANK

The genbank sequence format is a rich format for storing sequences and associated annotations.

Structural Annotation

For the genome annotation we use a piece of the Aspergillus fumigatus genome sequence as input file.

Sequence Features

First we want to get some general information about our sequence.

Hands-on: Sequence composition

1. Count the number of bases in your sequence (compute sequence length)
2. Check for sequence composition and GC content (geecee).
3. Plot the sequence composition as bar chart.

Gene Prediction

At first you need to identify those structures of the genome which code for proteins. This step of annotation is called “structural annotation”. It contains the identification and location of open reading frames (ORFs), identification of gene structures and coding regions, and the location of regulatory motifs. Galaxy contains several tools for the structural annotation. Tools for gene prediction are Augustus (for eukaryotes and prokaryotes) and glimmer3 (only for prokaryotes).

Hands-on: Gene prediction

We use Augustus for gene prediction.

1. Use the genome sequence (FASTA file) as input.
2. Choose the right model organism, gff format output.
3. Select all possible output options.

Augustus will provide three output files: gff3, coding sequences (CDS) and protein sequences.

Question

How many genes are predicted?

Solution

Check the output: augustus_output

Hands-on: tRNA and tmRNA Prediction

Use Aragorn for tRNA and tmRNA prediction.

1. As input file use the Aspergillus genome sequence. You can choose the genetic code (e.g. bacteria).

2. Select the topology of your genome (circular or linear).

   Question

   Are there tRNAs or tmRNAs in the sequence?

Details: Aragorn in depth

read more about Aragorn here.

Functional Annotation

Similarity Searches (BLAST)

Functional gene annotation means the description of the biochemical and biological function of proteins. Possible analyses to annotate genes can be for example:

• similarity searches
• gene cluster prediction for secondary metabolites
• identification of transmembrane domains in protein sequences
• finding gene ontology terms
• pathway information

For similarity searches we use NCBI BLAST+ blastp to find similar proteins in a protein database.

Hands-on: Similarity search

1. tool As input file, select the protein sequences from Augustus.
2. Choose the protein BLAST database SwissProt and the output format xml.

1. Parsing the xml output (Parse blast XML output) results in changing the format style into tabular.

   Question

   What information do you see in the BLAST output?

From BLAST search results we want to get only the best hit for each protein.

1. tool Therefore apply the tool BLAST top hit descriptions with number of descriptions =1 on the xml output file.

   Question

   For how many proteins we do not get a BLAST hit?

2. tool Choose the tool Select lines that match an expression and enter the following information: Select lines from [select the BLAST top hit descriptions result file]; that [not matching]; the pattern [gi].

   

   Comment: Results file

   The result file will contain all proteins which do not have an entry in the second column and therefore have no similar protein in the SwissProt database.

   Comment: Obtaining unannotated proteins for analysis

   For functional description of those proteins we want to search for motifs or domains which may classify them more. To get a protein sequence FASTA file with only the not annotated proteins, use the tool Filter sequences by ID from a tabular file and select for Sequence file to filter on the identifiers [Augustus protein sequences] and for Tabular file containing sequence identifiers the protein file with not annotated sequences. The output file is a FASTA file with only those sequences without description.

This file will be the input for more detailed analysis:

• Interproscan is a functional prediction tool. Select all applications and run it on your protein file.

• WolfPSort predicts eukaryote protein subcellular localization. Filter the result file for the best ranked localization hit. Use Filter data on any column using simple expressions with c4==1. The parameter c4==1 means: filter and keep all results where in column 4 is a “1”.

• TMHMM finds transmembrane domains in protein sequences. The number of amino acids in transmembrane helices should be >18. This information can be found in column 3. Filter the result file c3>17.99.

• BLAST2GO maps BLAST results to GO annotation terms.

BLAST Programs

Details: Organism not available in a BLAST database

If you have an organism which is not available in a BLAST database, you can use its genome sequence in FASTA file for BLAST searches “sequence file against sequence file”. If you need to search in these sequences on a regularly basis, you can create a own BLAST database from the sequences of the organism. The advantage of having a own database for your organism is the duration of the BLAST search which speeds up a lot.

NCBI BLAST+ makeblastdb creates a BLAST database from your own FASTA sequence file. Molecule type of input is protein or nucleotide.

Details: Further Reading about BLAST Tools in Galaxy

Cock et al. (2015): NCBI BLAST+ integrated into Galaxy

Cock et al. (2013): Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology

More Similarity Search Tools in Galaxy

• VSEARCH: For processing metagenomic sequences, including searching, clustering, chimera detection, dereplication, sorting, masking and shuffling. VSEARCH stands for vectorized search, as the tool takes advantage of parallelism in the form of SIMD vectorization as well as multiple threads to perform accurate alignments at high speed. VSEARCH uses an optimal global aligner (full dynamic programming Needleman-Wunsch), in contrast to USEARCH which by default uses a heuristic seed and extend aligner. This results in more accurate alignments and overall improved sensitivity (recall) with VSEARCH, especially for alignments with gaps.

Details: `vsearch` in depth

Documentation for vsearch see here.

• Diamond: Diamond is a high-throughput program for aligning a file of short reads against a protein reference database such as NR, at 20,000 times the speed of Blastx, with high sensitivity.

Details: Diamond in depth

Buchfink et al. (2015): Fast and sensitive protein alignment using Diamond.

• Kraken: Kraken BLAST is a highly scalable, extremely fast, commercial, parallelized implementation of the NCBI BLAST application.

Identification of Gene Clusters

For identification of gene clusters, antiSMASH is used. The tool uses genbank file as input files and predicts gene clusters. Output files are a html visualization and the gene cluster proteins.

Hands-on: antiSMASH analysis

tool Import this dataset into your Galaxy history and run antiSMASH to detect gene clusters. The genbank file contains a part of the Streptomyces coelicolor genome sequence.

Question

Which gene clusters are identified?

When you have a whole genome antiSMASH analysis, your result may look like this:

At the end, you can extract a reproducible workflow out of your history. The workflow should look like this:

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

Feedback

Did you use this material as an instructor? Feel free to give us feedback on how it went.
Did you use this material as a learner or student? Click the form below to leave feedback.

Citing this Tutorial

1. Anika Erxleben, Björn Grüning, 2022 Genome Annotation (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/genome-annotation/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-genome-annotation,
author = "Anika Erxleben and Björn Grüning",
title = "Genome Annotation (Galaxy Training Materials)",
year = "2022",
month = "10",
day = "18"
url = "\url{https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/genome-annotation/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!