24 KiB
Further development on FTAG Finder, a pipeline to identify Gene Families and Tandemly Arrayed Genes
| key | abbreviation | full form |
|---|---|---|
| TAG | TAG | Tandemly Arrayed Genes |
| FTAGFinder | FTAG Finder | Families and Tandemly Arrayed Genes Finder |
| WGD | WGD | Whole Genome Duplication |
| MCL | MCL | Markov Clustering |
| BLAST | BLAST | Basic Local Alignment Search Tool |
| GO | GO | Gene Ontology |
| label | name | description |
|---|---|---|
| singleton | singleton | A gene with a single copy |
| polyploidisation | polyploidisation | Mechanism leading to the acquisition of at least three versions of the same original genome in a species |
| pseudogene | pseudogene | A gene-like sequence that lost its capacity to transcribe |
| segment_duplcation | segment duplication | Long stretches of DNA sequences with high identity score |
| retroduplication | retroduplication | Duplication of a gene through retro-transcription of its RNA transcript |
| autopolyploidisation | autopolyploidisation | Polyploidisation within the same species |
| allopolyploidisation | allopolyploidisation | Polyploidisation with genetic material coming from a diverged species |
| polyspermy | polyspermy | Fertilization of an egg by more than one sperm |
| segment_duplication | segment duplication | DNA sequences present in multiple locations within a genome that share high level of sequence identity |
| subfunctionalization | subfunctionalization | Fate of a duplicate gene which gets a part of the original gene function, the function being shared among multiple duplicates |
| orthologues | orthologues | Homologous genes whose divergence started at a speciation event |
| neofunctionalization | neofunctionalization | Acquisition of a new function by the duplicate gene |
keywords: duplicate genes, tandemly arrayed genes, pipeline
Scientific context
≤ttrine{D}uplicate genes represent an important fraction of Eukaryotic genes: It is estimated that between 46% and 65.5% of human genes could be considered as duplicate1 [cite:@correaTransposableElementEnvironment2021]. Duplicate genes offers a pool of genetic material available for further experimentation during species evolution.
Gene duplication mechanisms
Multiple mechanisms may lead to a gene duplication. Their effect ranges from the duplication of the whole genome to the duplication of a fragment of a gene.
Whole genome duplication and polyploidisation
During an event of gls:WGD, the entire set of genes present on the chromosomes is duplicated (cref:fig:gene-duplication-mechanisms (A)). gls:WGD can occur thanks to gls:polyspermy or in case of a non-reduced gamete. Gls:polyploidisation is a mechanism leading to a species with at least three copies of an initial genome. A striking example is probably Triticum aestivum (wheat) which is hexaploid due to hybridisation events [cite:@golovninaMolecularPhylogenyGenus2007a].
We distinguish two kinds of glspl:polyploidisation, based on the origin of the duplicate genome: (i) Gls:allopolyploidisation occurs when the supplementary chromosomes come from a divergent species. This is the case for the Triticum aestivum hybridisation, which consisted in the union of the chromosome set of a Triticum species with that of an Aegilops species. (ii) Gls:autopolyploidisation consists in the hybridisation or duplication of the whole genome within the same species.
Unequal crossing-over
Another source of gene duplication relies on unequal crossing-over. During cell division, a crossing-over occurs when two chromatids exchange fragments of chromosome. If the cleavage of the two chromatids occurs at different positions, the shared fragments may have different lengths. Homologous recombination of such uneven crossing-over leads to the incorporation of a duplicate region, as depicted in cref:fig:gene-duplication-mechanisms (B, C). This mechanism leads to the duplication of the whole set of genes present in the fragment. These duplicate genes locate one set after the other: we call them gls:TAG. Gls:TAG are the kind of gene duplication we will be particularly interested in during this internship.
Retroduplication
Transposable elements play a major role in genome plasticity, and enable gene duplication too. Retrotransposons, or RNA transposons are one type of transposable elements. They share similar structure and replication mechanisms with retroviruses. Retrotransposons replicate in the genome through a mechanism known as "copy-and-paste". These transposons typically contain a reverse transcriptase gene. This enzyme proceeds in the reverse transcription of an mRNA transcript into its reverse complementary DNA sequence which can then insert elsewhere in the genome. More generally, gls:retroduplication refers to the duplication of a sequence through reverse transcription of a RNA transcript. Genes duplicated through retroduplication lose their intronic sequences and bring a polyA tail with them in their new locus (cref:fig:gene-duplication-mechanisms (D)).
Transduplication
DNA transposons are another kind of transposable elements whose transposition mechanism can also lead to gene duplication. This type of transposable element moves in the genome through a mechanism known as "cut-and-paste". A typical DNA transposon contains a transposase gene. This enzyme recognizes two sites surrounding the donnor transposon sequence in the chromosome resulting in a DNA cleavage and an excision of the transposon. The transposase can then insert the transposon at a new genome locus. A transposon may bring a fragment of a gene during its transposition in the new locus (cref:fig:gene-duplication-mechanisms (E)), leading to the duplication of this fragment.
Segment duplication
Finally, glspl:segment_duplication, also called low copy repeats are long stretches of DNA with high identity score (cref:fig:gene-duplication-mechanisms (F)). Their exact duplication mechanism remains unclear [cite:@lallemandOverviewDuplicatedGene2020]. They may come from an accidental replication, distinct from an uneven cross-over or a double stranded breakage. Transposable elements may well be involved in the mechanism, as a high enrichment of transposable elements is found next to duplicate segment extremities, in Drosophila [cite:@lallemandOverviewDuplicatedGene2020].
Fate of duplicate genes in genome evolution
In his book Evolution by Gene Duplication, Susumu Ohno proposed that gene duplication plays a major role in species evolution [cite:@ohnoEvolutionGeneDuplication1970], because it provides new genetic materials to build on new phenotypes while keeping a backup gene for the previous function. Indeed, duplicate genes evolve after duplication: they may be inactivated, and become glspl:pseudogene; they may be deleted or conserved, and if conserved, the may or may not acquire a new function. cref:fig:fate-duplicate-genes depicts the different possible fates of a duplicate gene.
As genome evolves, duplicate genes may be inactivated and become pseudogenes. These pseudogenes keep a gene-like structure which degrades as and when further genome modifications occur but they are no longer expressed.
After duplication, the new gene copy may gain a new function. We call this possible outcome gls:neofunctionalization. For instance, the current set of olfactory receptor genes result from several duplication and deletion events (for Drosophila, see: [cite/t:@nozawaEvolutionaryDynamicsOlfactory2007]), after which each duplicate olfactory gene specialized in the detection of a particular chemical compound.
Two duplicate genes with the same original function may encounter a gls:subfunctionalization: each gene conserves only one part of the function.
Another possibility is that the two gene copies keep the ancestral function, resulting in a functional redoundancy. In this case the quantity of gene product may increase.
Methods to identify duplicate genes
Different methods exists to detect duplicate genes. These methods depend on the type of duplicate genes they target and vary on computation burden as well as in the ease of use (for a review, see [cite/t:@lallemandOverviewDuplicatedGene2020]).
Paralog detection
Paralogs are homologous genes derived from a duplication event. We can identify them as homologous genes coming from the same genome, or as homologous genes between different species once we filtered out gls:orthologues (homologous genes derived from a speciation event).
We can use two gene characteristics to assess the homology between two genes: gene structure or sequence similarity.
The sequence similarity can be tested with a sequence alignment tool, such as BLAST [cite:@altschulBasicLocalAlignment1990], Psi-BLAST, and HMMER3 [cite:@johnsonHiddenMarkovModel2010], or diamond [cite:@buchfinkSensitiveProteinAlignments2021], which are heuristic algorithms, which means they may not provide the best results, but do so way faster than exact algorithms, such as the classical Smith and Waterman algorithm [cite:@smithIdentificationCommonMolecular1981] or its optimized versions PARALIGN [cite:@rognesParAlignParallelSequence2001] or SWIMM.
This is the case for Triticum aestivum hybridisation, which consisted in the union of the
chromosome set of a Triticum species with those of an Aegilops species
FTAG Finder
Developed in the LaMME laboratory, the FTAG Finder (Families and Tandemly Arrayed Genes Finder) pipeline is a simple pipeline targeting the detection of gls:TAG from the proteome of single species [cite:@bouillonFTAGFinderOutil2016].
The pipeline proceeds in three steps. First, it estimates the homology links between each pair of genes. Then, it deduces the gene families. Finally, it searches for gls:TAG.
Estimation of homology links between genes
This step consists in establishing a homology relationship between each genes in the proteome.
In this step, the typical tool involved is BLAST (Basic Local Alignment Search Tool) [cite:@altschulBasicLocalAlignment1990] run "all against all" on the proteome.
Several BLAST metrics can be used as an homology measure, such as bitscore, identity percentage, E-value or variations of these. The choice of metrics can affect the results of graph clustering in the following step, and we should therefore chose them carefully [cite:@gibbonsEvaluationBLASTbasedEdgeweighting2015].
Identification of gene families
Based on the homology links between each pair of genes, we construct an undirected weighted graph whose vertices correspond to genes and edges to homology links between them. We apply a graph clustering algorithm on the graph in order to infer the gene families corresponding to densely connected communities of vertices. FTAG Finder proposes three clustering algorithm alternatives: single linkage, Markov Clustering [cite:@vandongenNewClusterAlgorithm1998] or Walktrap [cite:@ponsComputingCommunitiesLarge2005].
Detection of TAGs
The final step of FTAG Finder consists in the identification of gls:TAG from the gene families and the positions of genes. For a given chromosome, the tool seeks genes belonging to the same family and located close to each other. The tool allows a maximal number of genes between the homologous genes, with a parameter set by the user. Cref:fig:tag-definitions is a schematic representation of some possible gls:TAG positioning on a genome associated with their definition in FTAG Finder Find Tags step.
Objectives for the internship
Scientific questions
The underlying question of FTAG Finder is the study of the evolutionary fate of duplicate genes in Eukaryotes. Duplicate genes are
Extend the existing FTAG Finder Galaxy pipeline
Galaxy is a web-based platform for running accessible data analysis pipelines, first designed for use in genomics data analysis [cite:@goecksGalaxyComprehensiveApproach2010]. Last year, Séanna Charles worked on the Galaxy version of the FTAG Finder pipeline during her M1 internship [cite:@charlesFinalisationPipelineFTAG2023]. I will continue this work. FTAG Finder is currently deployed on the server of the Laboratoire d'Analyse et Modélisation d'Évry[fn: http://stat.genopole.cnrs.fr/galaxy ].
Port FTAG Finder pipeline on a workflow manager
Another objective of my internship will be to port FTAG Finder on a workflow manager better suited to larger and more reproducible analysis.
We will have to make a choice for the tool we will use. The two main options being Snakemake and Nextflow. Snakemake is a python powered workflow manager based on rules à la GNU Make [cite:@kosterSnakemakeScalableBioinformatics2012]. Nextflow is a groovy powered workflow manager, which rely on the data flows paradigm [cite:@ditommasoNextflowEnablesReproducible2017]. Both are widely used in the bioinformatics community. Their use have been on the rise since they came out in 2012 and 2013 respectively [cite:@djaffardjyDevelopingReusingBioinformatics2023].
Methodological approaches
Based on the output of the FTAG Finder pipeline, which consist in lists of genes, researchers could perform bespoke subsequent analyses on TAGs.
Analysis of over-represented gene functions among TAGs
The gls:GO describes biological concepts across three main classes: Cellular Component, Molecular Function and Biological Process. It describe a controlled vocabulary of concepts and the relationship between them. The genes with known functions can be associated with a particular GO term. We can perform an GO enrichment analysis to assess whether a particular GO term is over-represented in a particular gene list, compared to an other. We can use a Fisher exact test, using the FDR (False Discovery Rate) control procedure of Benjamini and Hocheberg to do so.
Let $go$ be a GO term. We construct a contingency matrix based on the count of genes associated with this GO term (or associated with one of its brother GO term) for the reference gene list and the list of interest (here, the list of genes in a TAG) (see cref:tab:fisher-test-contigency-table).
Are TAG located preferentially on specific chromosome region?
Are there chromosomes enriched or depleted in TAG?
Do genes located next to each other in a TAG share the same orientation?
The concordance of two genes of a TAG falls in three possible cases: either both genes are on the same strand (\(\rightarrow \rightarrow\)), either they have a divergent orientation (\(\leftarrow \rightarrow\)), or a convergent one (\(\rightarrow \leftarrow\)). Graham conjectured that genes of a TAG that are close to each other would be more likely to share the same orientation, and it seems to be effectively the case [cite:@shojaRoadmapTandemlyArrayed2006].
TODO write down the hypotheses
What is the robustness and accuracy of the detection method?
[cite/t:@le-hoangEtudeTranscriptomiqueGenes2017] started analyses of the impact of parameter choice on FTAG Finder output lists. A more detailed benchmark of FTAG Finder in comparison with other methods on some known test dataset might be of particular interest.
References
Summary
Duplicate genes is an important feature of Eukaryotic genomes. They contribute to the plasticity of genome, hence to the capacity of species to evolve.
Several mechanisms may lead to gene duplication. Among them, an unequal crossing-over leads to the formation of Tandemly Arrayed Genes (TAG) corresponding to homologous genes located one set after the other on the same chromosome.
There are multiple methods for detecting duplicate genes from sequences. These methods vary in terms of the particular gene duplication mechanism they target, computational efficiency and ease of use.
FTAG Finder is a simple Galaxy pipeline aiming at the detection of families of duplicate genes and the identification of TAG based on the proteome of a single species. FTAG Finder is developed in the Laboratoire de Mathématiques et Modélisation d'Évry, where I will do my internship.
On the one hand, the aim of my internship is to extend the current Galaxy implementation of FTAG Finder with new export lists best suited to the analysis requirements of the laboratory. On the other hand, the objective of my internship will be to port the Galaxy pipeline on another scientific workflow manager better suited to reproducible analyses such as Snakemake and Nextflow.
Then, the updated version of the FTAG Finder pipeline will be used to perform an analysis on the TAG of a model species, to assess its proper behavior. A benchmark of the pipeline will probably be run to compare the FTAG Finder with alternative published methods targetting duplicate genes and TAG in particular.
The estimate vary strongly depending on the criteria in use, because ancient duplication event may be hard to detect.