| 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 |
\lettrine{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 duplicate[fn:: The estimate vary strongly depending on the criteria in use, because ancient duplication event may be hard to detect.] [cite:@correaTransposableElementEnvironment2021].
#+begin_src emacs-lisp :exports results :results value raw
(setq fig:gene-duplication-mechanisms "
#+label: fig:gene-duplication-mechanisms
#+caption[Different types of duplication] Different types of duplication. (A) Whole genome duplication. (B) An unequal crossing-over leads to a duplication of a fragment of a chromosome. (C) In tandem duplication, two (set of) genes are duplicated one after the other. (D) Retrotransposon enables retroduplication: a RNA transcript is reverse transcribed and inserted back without introns and with a polyA tail in the genome. (E) A DNA transposon can acquire a fragment of a gene. (F) Segmental duplication corresponds to long stretches of duplicated sequences with high identity. Adapted from [cite:@lallemandOverviewDuplicatedGene2020] (fig. 1)
\caption[Different types of duplication]{\label{fig:gene-duplication-mechanisms}Different types of duplication. (A) Whole genome duplication. (B) An unequal crossing-over leads to a duplication of a fragment of a chromosome. (C) In tandem duplication, two (set of) genes are duplicated one after the other. (D) Retrotransposon enables retroduplication: a RNA transcript is reverse transcribed and inserted back without introns and with a polyA tail in the genome. (E) A DNA transposon can acquire a fragment of a gene. (F) Segmental duplication corresponds to long stretches of duplicated sequences with high identity. Adapted from \autocite{lallemandOverviewDuplicatedGene2020} (fig. 1).}
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.
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.
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.
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)).
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.
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].
\caption[Fate of duplicate genes]{\label{fig:fate-duplicate-genes} Fate of duplicate genes. An original gene with four functions is duplicated. Its two copies may both keep the original functions (functional redoundancy). The original functions may split between the different copies (subfunctionalization). One of the copy may acquire a new function (neofunctionalization). It may also degenerate and lose its original functions (pseudogenization).
Adapted from \href{https://commons.wikimedia.org/wiki/File:Evolution_fate_duplicate_genes_-_vector.svg}{Smedlib}, \href{https://creativecommons.org/licenses/by-sa/4.0}{CC BY-SA 4.0}, via Wikimedia Commons}
In his book /Evolution by Gene Duplication/, Susumu [[latex:textsc][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.
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.
# *** Neofunctionalization
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.
# *** Subfunctionalization
Two duplicate genes with the same original function may encounter a gls:subfunctionalization: each gene conserves only one part of the function.
# *** Functional redundancy
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.
\caption[Schematic representation of TAG definitions]{\label{fig:tag-definitions} Schematic representation of TAG definitions. Several genes are represented on a linear chromosome. The red box represent a singleton gene. Orange boxes represent a TAG with two duplicate genes seperated by 7 other genes ($\mathrm{TAG}_7$). Four green boxes constitute a TAG, the gene at the extremities are seperated by three genes ($\mathrm{TAG}_3$). The two blue boxes represents a TAG with two genes next to each other ($\mathrm{TAG}_0$). The bended edges represents the homology links between each pair of genes within a TAG.}}
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]).
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=.
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.
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].
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.
FTAG Finder proposes three clustering algorithm alternatives: single linkage, Markov Clustering [cite:@vandongenNewClusterAlgorithm1998] or Walktrap [cite:@ponsComputingCommunitiesLarge2005].
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.
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 [[latex:textsc][Charles]] worked on the Galaxy version of the FTAG Finder pipeline during her M1 internship [cite:@charlesFinalisationPipelineFTAG2023]. I will continue this work.
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].
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 [[latex:textsc][Benjamini]] and [[latex:textsc][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].
# To test this, we can use a $\Chi^2$ test of goodness of fit or a Student $t$-test.
*** 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.
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.
MCL uses two operations on a stochastic matrix representation $M$ of the graph first derived from the adjacency matrix, namely /expansion/ and /inflation/. Expansion consists in elevating the matrix to a power $r$, and subsequently scaling its columns so that they sum to 1 again. The image of the inflation operator $\Gamma_r$ is defined as
The application of both operator iteratively eventually ends up in a partition of the initial graph's edges into clusters of closely connected nodes (corresponding, in our case to gene families).
