| 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 |
It is estimated that between 46% and 65.5% of human genes could be considered as duplicate genes\footnote{The estimate vary strongly depending on the criteria in use} [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.
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\footenote{An hexaploid cell have three pairs of homologous chromosomes} due to several 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 /Triticum aestivum/ hybridisation, which consisted in the union of the chromosome set of a /Triticum/ species with those 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 results in 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 proceed 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. Gene 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 has been found at duplicate segments extremities, in /Drosophila/ [cite:@lallemandOverviewDuplicatedGene2020].
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 may evolve after duplication: they may be inactivated, becoming glspl:pseudogene; they may be deleted or conserved and so, they may acquire new functions.
Duplicate genes may be inactivated and become pseudogenes. These pseudogenes keep a gene-like structure, which degrades as and when further genome modifications occur. However, they are no longer expressed.
For instance, in the set of olfactory receptor genes result from several duplication and deletion events (in /Drosophila/: [cite:@nozawaEvolutionaryDynamicsOlfactory2007]), after which the duplicate may specialize in the detection of a particular chemical compound.
Two duplicate genes with the same original function may encounter a gls:subfunctionalization by which each gene conserves only one part of the function.
[cite:@lallemandOverviewDuplicatedGene2020] review the different methods used to detect duplicate genes. These methods depend on the type of duplicate genes they target and vary on computation burden as well as ease of use [cite:@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 a 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].
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.
* Objectives for the internship
** Scientific questions
The underlying question of FTAG Finder is the study of the evolutionary fate of duplicate genes in Eukaryotes.
** 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 [[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, and their use have been on the rise since they came out in 2012 and 2013 respectively [cite:@djaffardjyDevelopingReusingBioinformatics2023].
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
where $m$ is number of rows in the matrix, and $M_{pq}$ is the value in the $p, q$ cell of the matrix $M$.
This operator strengthens the edges with higher weights and tend to anihilate edges with lower flow.
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).
