FND-Homology

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Abstract

Homology is the most important concept for bioinformatics, since shared ancestry allows many inferences about the structure and function of proteins. This unit introduces the concept and explores MBP1_<YFO> relationships.


 


This unit ...

Prerequisites

You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:

  • Biomolecules: The molecules of life; nucleic acids and amino acids; the genetic code; protein folding; post-translational modifications and protein biochemistry; membrane proteins; biological function.
  • The Central Dogma: Regulation of transcription and translation; protein biosynthesis and degradation; quality control.
  • Evolution: Theory of evolution; variation, neutral drift and selection.

You need to complete the following units before beginning this one:


 


Objectives

This unit will ...

  • ... introduce the concept of homology, define orthologues and paralogues and discuss reasons for and consequences of gene conservation;
  • ... explore public database resources to find orthologues by BLAST and in pre-annotated databases.


 


Outcomes

After working through this unit you ...

  • ... define "homology", "orthologue" and "paralogue", and use the terms correctly, and with a precise understanding of their meaning and implications;
  • ... are familar with issues around the definition of homologous genes and domains;
  • ... know about sequence similarity and other measures that can identify related proteins and be able to use this to define your own exploratory strategies;
  • ... have identified the RBM for the saccharomyces cerevisiae Mbp1 gene in YFO and explored other databses that make pre-annotated relatedness information available.


 


Deliverables

  • Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
  • Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
  • Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.


 


Evaluation

Evaluation: NA

This unit is not evaluated for course marks.


 


Contents

Task:


Considerations for the YFO "Mbp1"

 
Consider!

In the BIN-Storing_data unit you have found the protein of YFO that is most similar to yeast Mbp1, in YFO. Consider if this protein is homologous to the yeast protein. For most of these questions, you will probably not know the answer right now, but we will find out more in later units.

  • Are the sequences similar?
Obviously you have found the YFO sequence as a result of a BLAST search and you probably known that BLAST finds similar sequences in large databases. But it will usually always find something, and that could be a chance similarity. Significant similarity would be very high, would extend over the whole length of the protein, could be restricted to individual domains. When would you say: similar enough?
  • Do the proteins have similar structures?
If your protein happens to have had a part of its structure analyzed by X-ray crystallography, you could compare the structures. However, this is unlikely for the Mbp1 relatives - except for the ankyrin domains. These are ubiquitous protein-protein interaction motifs and won't tell us much more than that. It's unlikely that other (parts of) the YFO protein structure are known.
  • What about patterns of conserved residues?
We need more proteins to consider that - and we need to align them.
  • Are the proteins known to perform similar functions?
That might require function prediction. There might be an annotation in the FASTA header of the YFO protein - but it's likely to be made based on homology to the yeast protein. Could be experimental evidence though - check carefully, just in case.

All of these considerations lead to bioinformatics queries that we will pursue in later units.


 


Defining orthologs

For functional inference between organisms, the key is to find orthologs.

To be reasonably certain about orthology relationships, one needs to construct and analyze detailed evolutionary trees. This is computationally expensive and the results are not always unambiguous. But a number of different strategies are available that use approximations, or precomputed results to define orthologs. These are especially useful for large, cross genome surveys. They are less useful for detailed analysis of individual genes.

Orthologs by RBM (Reciprocal Best Match)
The RBM criterion is only an approximation to orthology, but computationally very tractable and usually correct. To find an RBM, first search for the best match of a gene in the target genome, then check whether that best match retrieves the original query when it used to serach in the source genome. You have already done the first step when you identified the best match of yeast Mbp1 in YFO. Now do the second step.

Get the ID for the gene which you have identified and annotated as the best BLAST match for Mbp1 in YFO and confirm that this gene has Mbp1 as the most significant hit in the yeast proteome. The results are unambiguous, but there may be residual doubt whether these two best-matching sequences are actually the most similar orthologs.

Task:

  1. Navigate to the BLAST homepage and access the protein BLAST page.
  2. Copy the RefSeq identifier for MBP1_<YFO> from your journal into the search field (You can search directly with an NCBI identifier IF you want to search with the full-length sequence.)
  3. Set the database to refseq;
  4. restrict the species to Saccharomyces cerevisiae.
  5. Run BLAST.
  6. Keep the window open for the next task.

The top hit should be yeast Mbp1 (NP_010227). Discuss on the list if it is not.

If the top hit is NP_010227, you have confirmed the RBM or BBM criterion (Reciprocal Best Match or Bidirectional Best Hit, respectively).

Task:
Explain to someone you know why RBM is expected to find orthologous pairs of genes. Don't paraphrase the fact that they do, or merely describe how an RBM analysis works, but explain why we can expect it to be successful in identifying an evolutionary relationship when all we have are measures of pairwise similarity.

If you can't figure it out, ask on the mailing list.


Orthology by annotation
The NCBI precomputes gropus of related genes and makes them available via the HomoloGene dtatabase from the RefSeq database entry for your protein.

Task:

  1. Navigate to the RefSeq protein page for MBP1_<YFO>. (There should be a link from the query identifier in your BLAST result page).
  2. Follow the Homologene link in the right-hand menu under Related information.

You should see a number of genes that are considered homologous other fungi, but there is no way to tell whether these are orthologues, and the links to proteins with shared domains shows you that there are several that share (non-specific) ankyrin domains, and only a few that also have the (highly specific) Kila-N (or APSES) domain.


Orthologs by eggNOG
The eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database contains orthologous groups of genes at the EMBL. It seems to be continuously updated, and the search functionality is reasonable. Try the search with the MBP1_<YFO> refseq identifier. What I see are orthologs annotated in non-fungi but to the ankyrin domain, which is a meaningless relationship. Alignments and trees are also available, as are database downloads for algorithmic analysis.

 

Powell et al. (2014) eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res 42:D231-9. (pmid: 24297252)

[ PubMed ] [ DOI ] With the increasing availability of various 'omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.


Orthologs at OrthoDB
OrthoDB includes a large number of species, among them all of our protein-sequenced fungi. However the search function (by keyword - try "Mbp1") retrieves many paralogs together with the orthologs, for example, the yeast Soc2 and Phd1 proteins are found in the same orthologous group these two are clearly paralogs and again results focus on ankyrin-domain containing proteins.

 

Waterhouse et al. (2013) OrthoDB: a hierarchical catalog of animal, fungal and bacterial orthologs. Nucleic Acids Res 41:D358-65. (pmid: 23180791)

[ PubMed ] [ DOI ] The concept of orthology provides a foundation for formulating hypotheses on gene and genome evolution, and thus forms the cornerstone of comparative genomics, phylogenomics and metagenomics. We present the update of OrthoDB-the hierarchical catalog of orthologs (http://www.orthodb.org). From its conception, OrthoDB promoted delineation of orthologs at varying resolution by explicitly referring to the hierarchy of species radiations, now also adopted by other resources. The current release provides comprehensive coverage of animals and fungi representing 252 eukaryotic species, and is now extended to prokaryotes with the inclusion of 1115 bacteria. Functional annotations of orthologous groups are provided through mapping to InterPro, GO, OMIM and model organism phenotypes, with cross-references to major resources including UniProt, NCBI and FlyBase. Uniquely, OrthoDB provides computed evolutionary traits of orthologs, such as gene duplicability and loss profiles, divergence rates, sibling groups, and now extended with exon-intron architectures, syntenic orthologs and parent-child trees. The interactive web interface allows navigation along the species phylogenies, complex queries with various identifiers, annotation keywords and phrases, as well as with gene copy-number profiles and sequence homology searches. With the explosive growth of available data, OrthoDB also provides mapping of newly sequenced genomes and transcriptomes to the current orthologous groups.


Orthologs at OMA

OMA (the Orthologous Matrix) maintained at the Swiss Federal Institute of Technology contains a large number of orthologs from sequenced genomes. Searching with the refseq identifier of MBP1_<YFO> will probably retrieve hits that you can access via the "Orthologs" tab. As a whole this database is well constructed, the output is useful, and data is available for download and API access; this would be the resource of my first choice for pre-computed orthology queries.

 

Altenhoff et al. (2011) OMA 2011: orthology inference among 1000 complete genomes. Nucleic Acids Res 39:D289-94. (pmid: 21113020)

[ PubMed ] [ DOI ] OMA (Orthologous MAtrix) is a database that identifies orthologs among publicly available, complete genomes. Initiated in 2004, the project is at its 11th release. It now includes 1000 genomes, making it one of the largest resources of its kind. Here, we describe recent developments in terms of species covered; the algorithmic pipeline--in particular regarding the treatment of alternative splicing, and new features of the web (OMA Browser) and programming interface (SOAP API). In the second part, we review the various representations provided by OMA and their typical applications. The database is publicly accessible at http://omabrowser.org.

... see also the related articles, much innovative and carefully done work on automated orthologue definition by the Dessimoz group.


Orthologs by syntenic gene order conservation
OMA also provides synteny information, one hallmark of an orthologous relationship (Why?).





 


Further reading, links and resources

 


Notes


 


Self-evaluation

 



 



 

If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.


 

About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-09-30

Version:

1.0

Version history:

  • 1.0 First live version
  • 0.1 First stub

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