#+caption[Different types of duplications]: Different types of duplications. (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. *Source* Adapted from [cite:@lallemandOverviewDuplicatedGene2020].
These transposons are typically composed of a reverse transcriptase gene. This enzyme 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 of a chromosome through reverse transcription or a RNA transcript.
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.
In an event of whole genome duplication, the entire set of genes present on the chromosomes is duplicated.
Whole genome duplication is more frequent in plants.
A striking example is probably the /Triticum/ genus (wheat) in which some species (such as /T. aestivum/) are hexaploid, due to hybridisation events [cite:@golovninaMolecularPhylogenyGenus2007].
We distinguish two kinds of polyploidisation, based on the origin of the duplicate genome:
- Allopolyploidisation occurs when the supplementary chromosomes comes from an other species. This is the case for /Triticum aestivum/ hybridisation.
- Autopolyploidisation consist in the hybridisation of the genome within the same species.
Whole genome duplication can occur thanks to polyspermy or in case of a non-reduced gamete, for instance.
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. An array of genes is duplicated after the original array and are thus called Tandemly Arrayed Genes.
In his book /Evolution by Gene Duplication/, Susumu [[latex:textsc][Ohno]] proposed that gene duplication plays a major role in species evolution [cite:@ohnoEvolutionGeneDuplication1970].
[[latex: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].
Developped in the LaMME laboratory, this pipeline targets the detection of gene families and tandemly arrayed genes from a given species' proteome [cite:@bouillonFTAGFinderOutil].
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 variations on those. The metrics choice may have an impact on the results of graph clustering in the following step [cite:@gibbonsEvaluationBLASTbasedEdgeweighting2015].
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.
Galaxy is a web-based platform for performing accessible data analysis pipeline, first designed for use in genomic data analysis [cite:@goecksGalaxyComprehensiveApproach2010].
Last year, Séanna [[latex:textsc][Charles]], worked on the Galaxy's version of the gls:FTAG Finder pipeline during her M1 internship [cite:@charlesFinalisationPipelineFTAG2023]. 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).
