Multiple mechanisms may lead to gene duplication. We review them in this section.
*** Segment duplication
*** Retroduplication
Retrotransposons, or RNA transposons are one type of transposable elements. Retrotransposons share similar structure and mechanism with retroviruses.
They may replicate in the genome through a mechanism known as "copy-and-paste".
These transposons are typically composed of a reverse transcriptase gene. This enzyme gene may proceed in the reverse transcription of an mRNA transcript into DNA sequence which can then be inserted elsewhere in the genome.
More generally, retroduplication refers to the duplication of a region in a chromosome through reverse transcription or a transcript.
*** Transduplication
DNA transposons are another type of transposable element whose transposition mechanism can also lead to gene duplication.
A typical DNA transposon contains a transposase gene. This enzyme recognize two sites surrounding the donnor transposon sequence in the chromosome resulting in a DNA cleavage and excision of the transposon. The transposase can then insert the transposon in a new place of the genome.
A crossing-over may occur during cell division. A fragment of chromosome is exchanged between two chromatids of a pair of chromosome. If the cleavage of the two chromatids occured at different positions on both chromosomes, the shared fragments may have different lengths. When the repair of missing fragment is performed, the resulting chromosome will incorporate a duplicate region of the chromosome, leading to a potential duplication for genes present in this region, as represented in figure [[fig:gene-duplication-mechanisms]].
This mechanism leads to the duplication of the whole set of genes present in the inserted fragment. These genes are duplicated one after the other in second array of genes placed after the original one and are thus called Tandemly Arrayed Genes.
** Role of duplicate genes in genome evolution
In his book /Evolution by Gene Duplication/, Susumu \textsc{Ohno} proposed that gene duplication plays a major role in species evolution [cite:@ohnoEvolutionGeneDuplication1970].
** Methods to identify duplicate genes
\textsc{Lallemand} et al. review the different methods used to detect duplicate genes. These methods are dependant on the type of duplicate genes they target [cite:@lallemandOverviewDuplicatedGene2020].
*** FTAG Finder
Developped in the LaMME laboratory, this pipeline targets the detection of gene family and tandemly arrayed genes from a given species' proteome [cite:@bouillonFTAGFinderOutil].
**** Estimation of homology links between genes
This steps consists in establishing a relation between each genes in a genome.
In this step, the typical tool involved is =BLAST= (Basic Local Alignment Search Tool) [cite:@altschulBasicLocalAlignment1990] run on the whole proteome.
Several =BLAST= metrics can be used as an homology measure, such as bitscore, identity percentage, E-value or modifications of thoses. The choice of the metrics used may have an impact on the results of graph clustering step [cite:@gibbonsEvaluationBLASTbasedEdgeweighting2015].
**** Identification of gene families
Based on the homology links between each pair of genes, we construct a weighted undirected graph whose vertices corresponds to genes and edges to homology links.
Then, a graph clustering algorithm is applied on this graph in order to infer the gene families.
The team choosed to propose three clustering algorithms: Single linkage, Markov Clustering or Walktrap.
Galaxy is a web-based platform for performing accessible data analysis pipeline, mostly used for genomic data analysis [cite:@goecksGalaxyComprehensiveApproach2010].
Last year, Séanna \textsc{Charles}, worked on the Galaxy's version of the gls:FTAG Finder pipeline [cite:@charlesFinalisationPipelineFTAG2023] during her M1 intenship. I will continue this work.
** 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 are 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 data flows [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 2016 respectively [cite:@djaffardjyDevelopingReusingBioinformatics2023].
These tools ease the deployment of large scale data analysis workflow with reproducible output.
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).
