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BenjaminGuinet / Viral-domestication-among-Hymenoptera

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This code comes from the paper intituled : "Endoparasitoid lifestyle promotes endogenization and domestication of dsDNA viruses"

DOI: https://doi.org/10.7554/eLife.85993

Genome downloading and processing

Downloading viral databases

NCBI viruses proteins database : https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Protein&VirusLineage_ss=Viruses,%20taxid:10239

Purging of contaminating sequences

Since among the potential candidates, we expected a certain proportion of laboratory or natural contaminants, it was important to exclude as many as possible. Thus, we eliminated the bacterium-specific phage family sequences from the dataset. Finally, historically, the DNA circles within polydnavirus viral particles have been annotated as being of viral origin and named "polydnavirus", when in fact they are virulence factors of eukaryotic origin. The only genes of viral origin known to date are those involved in the production of viral particles. Therefore, we chose to eliminate these proteins from the dataset to avoid false positives.

Search for BUSCO genes for each genome.

This BUSCO step is important in multiple following processes (check assembly quality, build a species phylogeny and build mutliple normal distributions such as the expected coverage or G+C% distrubtion along the genomes.

Important files created :

Search for viral sequence homology for each genome with MMseqs2 search

Image description

Mmseqs2 query = genome ; db : virus protein sequences

Important files created :

*Note : On Lbbe slurm workers, this programm does not run on pbil-27.

Characterization of viral and eukaryotic loci

As a result, we will have a number of viral sequences with homology to certain locus found in Hymenoptera genomes. Among these numerous hits, some candidates may be false positives corresponding to eukaryotic genes present in viruses following a horizontal gene transfer from a eukaryotic to a viral genome, these phenomena being well known in viruses with large genomes (Herniou et al., 2003). In order to eliminate some of these sequences, we use the principle of sequence overlap between viral hits and BUSCO genes (a priori of eukaryotic origin). Thus, the presence of a viral hit at the same location in the genome as that of a BUSCO gene probably indicates a viral capture event of this eukaryotic gene by a viral genome. Using the R code Overlapping_sequences.R, we thus define 3 types of locus, each being defined by overlapping several MMseqs2 hits: (1) viral locus sharing no overlap with a BUSCO gene, (2) Hymenoptera BUSCO loci sharing no overlap with a viral hit and finally (3) a viral/hymenoptera BUSCO locus having an overlap of at least 10% of the size of the viral hit. Thereafter, only the non-overlapping viral loci (category 1) are retained since they are good candidates for endogenization. Finally, during the MMseq2 search, a single viral protein may present homologies at several locations within a scaffold, corresponding to several HSPs (High Scoring Pairs). We concatenate these HSPs in the alignment part of the pipeline (see HSPs analysis).

1) Adding strands and changing the direction of the coordinates in the file result_mmseqs2.m8

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2) For refseq mmseqs2 table

cat /beegfs/data/bguinet/these/Species_genome_names.txt | while read line; do python3 Make_change_strand_mmseqs2.py -b /beegfs/data/bguinet/these/Genomes/${line}/run_mmseqs2_V/result_mmseqs2.m8 -o /beegfs/data/bguinet/these/Genomes/${line}/run_mmseqs2_V -t virus; done

3) For BUSCO tblastn tab (because busco already did a tblastn research, we will take it from the source)

To delete >  cat /beegfs/data/bguinet/these/Species_genome_names.txt | while read line; do python3 Make_change_strand_mmseqs2.py -b /beegfs/data/bguinet/these/Genomes/${line}/run_busco/run_BUSCO_v3/blast_output/tblastn_${line}_BUSCO_v3.tsv -o /beegfs/data/bguinet/these/Genomes/${line}/run_busco/run_BUSCO_v3/blast_output/ -t hymenoptera; done

to keep 
import pandas as pd 

list_of_names1=[]
for names in open("/beegfs/data/bguinet/these/Genomes/Species_genome_names_test.txt","r"):
    list_of_names1.append(names)

list_of_names2=[]

for names in list_of_names1:
    list_of_names2.append(names.replace("\n", ""))

for sp_name in list_of_names2:
  print(sp_name)
  df1 = pd.read_csv("/beegfs/data/bguinet/these/Genomes/"+sp_name+"/run_busco/run_BUSCO_v3/full_table_"+sp_name+"_BUSCO_v3.tsv",comment='#',sep="\t")
  df1['Strand']='NA'
  row=0
  for Busco_id, Contig in zip (df1['Busco_id'],df1['Contig']):
    if df1.at[row,'Status']=="Missing":
      row+=1
      continue
    else:
      try: 
        df2 = pd.read_csv("/beegfs/data/bguinet/these/Genomes/"+sp_name+"/run_busco/run_BUSCO_v3/augustus_output/predicted_genes/"+Busco_id+".out.1",comment='#',sep="\t",header=None)
      except: 
        try:
            df2 = pd.read_csv("/beegfs/data/bguinet/these/Genomes/"+sp_name+"/run_busco/run_BUSCO_v3/augustus_output/predicted_genes/"+Busco_id+".out.2",comment='#',sep="\t",header=None)
        except:
            df2 = pd.read_csv("/beegfs/data/bguinet/these/Genomes/"+sp_name+"/run_busco/run_BUSCO_v3/augustus_output/predicted_genes/"+Busco_id+".out.3",comment='#',sep="\t",header=None)
      Strand=df2[6][0]
      df1.at[row, 'Strand'] = Strand
      row+=1
      print(row," / ",len(df1['Busco_id']))
  df1.to_csv("/beegfs/data/bguinet/these/Genomes/"+sp_name+"/run_busco/run_BUSCO_v3/full_table_"+sp_name+"_BUSCO_v3_strand.tsv",header=True,sep="\t")

Generate a file : result_mmseqs2_strand_V.m8 de type :

"query", "tlen", "target", "pident", "alnlen", "mismatch", "gapopen","qstart", "qend", "tstart", "tend", "evalue", "bits", "strand"

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4) Switch to R, in order to launch R on the server use */beegfs/data/soft/R-3.5.2/bin/R

/beegfs/data/soft/R-3.5.2/bin/Rscript Overlapping_sequences_BUSCO_Viral_loci.R /beegfs/data/bguinet/these/Species_genome_names.txt /beegfs/data/bguinet/these/Genomes/ #Permet d'executer le fichier . R

Creation of :

Matches_Apis_mellifera_strand_V.m8 : un fichier avec les brains d'affichés ainsi que les coordonnées changées de sens Matches_",i,"_summary_V.txt : a file of type "seqnames" "start" "end" "width" "strand" "type" in which the HSPs are grouped together and have a new coordinate.

Recovery of all loci according to their coordinates in the genomes of each species in a single file. All_fasta_viral_loci.fna

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bash Recover_loci_sequences2.sh /beegfs/data/bguinet/these/Genomes/ /beegfs/home/bguinet/these_scripts_2/ /beegfs/data/bguinet/these/Species_genome_names.txt

Filtering of loci using NR database.

We will remove loci matching with Bacterial of Insects protein sequences fellowing some criterias.

Important file created : /beegfs/data/bguinet/these/Results/Candidate_viral_loci_filtred.m8 (used in Filter candidate)

Gene clustering analysis

We will perform now clustering with the idea of gathering candidate loci and viral proteins in the same clusters by sequence homologies. For this we proceed to 5 steps, the main part of the pipeline code is written in the file Snakefile_clustering_analysis.

Create fasta files with virus and filtred loci to feed the clustering method

cat /beegfs/data/bguinet/these/Viral_sequences_loci/All_fasta_viral_loci_filtred.aa /beegfs/data/bguinet/these/NCBI_protein_viruses/All_viral_protein_sequences_without_contamination_controls.fa >> /beegfs/data/bguinet/these/Results/Candidate_viral_loci_and_viral_protein.aa

Connected component Clustering

Note: We use the following parameters:

--cluster-mode 1 : Connected component clustering that uses the transitive connection to cover more distant homologs.

  #--cov-mode 0 : Bidirectional coverage, where only sequences with overlapping sequence lengths greater than 30% of the longer of the two sequences are clustered, 
  #(in this case always the viral sequence since the loci are defined by viral hits). 
  #-evalue 0.0001 : To eliminate false positives. 
  # --cluster-reassign : During the cascade clustering of Mmseqs2, as the representative of a cluster can change at each iteration, 
  #it can happen that some members that were already close to a cluster do not fulfill the clustering criteria anymore. We therefore correct this by reassigning 
  #these sequences

Important file created :

We can now run several analysis on loci :

Candidate Gene informations

All the Rules and codes are written in the Snakefile : Snakefile_add_informations

TAX ID information

Retrieving the taxonomic and ontological informations provided by each of the viral genes candidates to a viral domestication allows us to know more about its origin. We can thus retrieve very valuable information such as the origin of the donor virus, its family, for which product it codes, in which cell compartment it is expressed, etc. For this we query SQlite databases for taxonomy and the UniProt package present in python for gene ontology.

Prerequisites

1) Create sqlite database using taxadb in order to recover the taxa informations
pip install taxadb
2) Get TAxid of all target protein seqs and add it on the column in the merged silix_blast dataframe
/beegfs/data/bguinet/myconda/bin/taxadb download -o taxadb
/beegfs/data/bguinet/myconda/bin/taxadb create -i taxadb --dbname taxadb.sqlite --fast

Important file created : /beegfs/data/bguinet/these/Results/Candidate_viral_loci_filtred_clu_filtred_aligned_taxid.m8

Column added :

The process took 13 minutes for 26318 taxids.

The number of cluster is : xxx The number of viral protein is : xxx

Gene Ontology information

The function of the candidats genes in wasps can be explored via the presence or absence of key viral genes and their expression. Whereas functional characterization of virus genes is mostly lacking, a wealth of information is available about the functions of homologous genes within the other closly related virus genes. By extension, the functions of the knwon virus genes can be used to predict the functions of the candidat genes.

Important file created : /beegfs/data/bguinet/these/Results/Candidate_viral_loci_filtred_clu_filtred_aligned_taxid_ontology.m8

Column added :

The process took 54 minutes for 26319 sequences.

The number of cluster is : xxx The number of viral protein is : xxx

Interproscan analysis on candidat loci -

Each candidate locus should, if of viral origin, return particular domains known to be present in free viruses when tested via HMMER. This analysis will thus allow us to (i) confirm the viral character of a candidate locus and (ii) study the presence or absence of known domains within the loci, telling us more about the putative function of this locus beyond a simple ontological assignment by taking over the one assigned to a homologous viral protein.

Important file created : /beegfs/data/bguinet/these/Results/Candidate_viral_loci_filtred_clu_filtred_aligned_taxid_ontology_interproscan.m8

Merge HMMER and 2LCA analysis #Depracted

Important file created : /Viralprot_vs_Viral_loci_result_all_match_and_cluster_taxid_ontology_2LCA_HMMER.m8

Column added :

Evaluation of viral integration through the study of the Genomic environment

Image description

Horizontal gene transferst (HGT) is defined as the accidental acquisition of DNA from another species independently of reproduction and evolutionary distance. To date, the majority of cases of horizontal gene transferst have been found in bacteria and unicellular eukaryotes (Koonin, 2016), as these organisms feed on the phagocytosis of bacteria and other microorganisms, resulting in frequent contact with prokaryotic DNA, which may predispose them to incorporate genetic material into their genomes. Conversely, relatively few cases of functional HGT in eukaryotes have been detected or studied in detail. Indeed, HGT events appear to be rarer in eukaryotes since, (i) DNA is protected by a nucleus, which makes contact between exogenous genetic elements and the genome more difficult, and (ii) in metazoan species at least, the germ line is usually separated from the rest of the cells, so a gene acquired in a metazoan has no chance of being fixed in a population if it does not reach the germ line (Danchin, 2016). Despite these constraints, genes conferring a function have been acquired by HGTs in both vertebrates and invertebrates (e.g. in insects (Moran & Jarvik, 2010), nematodes (Scholl et al., 2003), fish (Sun et al., 2015), terrestrial plants (Quispe-Huamanquispe, Gheysen & Kreuze, 2017) or humans (Crisp et al., 2015)). However, several initial reports that drew much attention to HGTs in humans could not be confirmed by further examination (Crisp et al., 2015), which casts doubt on the true extent of HGTs. Indeed, contamination of DNA samples or low quality genome assembly can easily lead to apparent HGA.

Thus, among potential candidates for horizontal transfer followed by domestication, there may be false positives. These false positives can then correspond to two things. (i) It may be a natural contamination, i.e. we find a viral sequence not integrated in the eukaryotic genome but present in the eukaryotic cell. It will then probably be a free-living virus present in the cell for which its genome has been sequenced and then assembled during sequencing. This type of contaminant is nevertheless of interest since it potentially allows us to know that a virus infects the eukaryote under study, then it is also possible to reconstruct its viral genome from the viral scaffolds. (ii) It may be a laboratory contaminant, i.e. during the laboratory handling steps, external viral particles may have infected the samples.

One way to make the hypothesis of viral endogenization of these candidate genes more likely is to study the genomic environment around these genes.

The sequencing data available allows us to obtain coverage along the genomes. For this we perform a mapping using the Hisat2 software. The idea being to discriminate the genomic environments of scaffolds, starting from the principle that each genomic entity has a tendency to have a cover and a composition in its own base, corresponding for the first to the abundance of this compartment in the extract of DNA, and for the second the average number of G + C bases. Thus, it is possible to sort the scaffolds according to their coverage and their base composition. Here we distinguish the scaffolds comprising a depth and a base composition of the “nuclear gene” type as being those which show no significant difference compared to the coverage or a base composition observed by the busco genes.

A non-parametric pvalue is calculated, for this we sampled 100 loci of the size of the scaffold of interest within the set of typical BUSCO eukaryotic scaffolds (Chimera). These 100 samples represent a distribution of the typical coverage expected under the hypothesis H0 that it is a eukaryotic scaffold. The pvalue then corresponds to the proportion of BUSCO coverage values ​​more extreme than that observed within the candidate scaffold (bilateral test). We reject the H0 hypothesis if it is a eukaryotic scaffold with a 5% probability of error.

Another way to asses wether one viral candidat gene is likely endogenised or not into the eucaryote genome is to look among the known Hymenoptera genes such as the BUSCO genes, which are found near candidate genes, that is to say on the same scaffold. Such an observation allows us to think that it is indeed a locus integrated in the genome of the Hymenoptera.

Image_description

Eucaryotic genes in scaffolds

If a scaffold containing genes of viral origin that are candidates for domestication while also containing genes that are typically eukaryotic, then this gives an additional clue to the endogenous aspect of the gene in question since it will not support the hypothesis that the scaffold is indeed of eukaryotic origin.

For this analysis we will therefore first need to predict the genes in the genomes of our species.

For this we will use an ab initio approach of the Augustus type which will work using the training parameters of the previous BUSCO analyses for these genomes.

1- Gene prediction allong genomes**

1-1- Extract the scaffolds containing candidates**

Important file created : /beegfs/data/bguinet/these/Genomes/{species_names}/All_scaffoldcontaining{species_names}_candidates.fna (All scaffolds fasta sequences containing)

1-2- Run augustus on each fasta files**

Important file created : /beegfs/data/bguinet/these/Genomes/{species_names}/run_augustus/run_augustus_new.out (All augustus predictions)

2- Taxonomic assignation for predicted augustus genes**

Important file created : /beegfs/data/bguinet/these/Results/Candidate_viral_loci_filtred_clu_filtred_aligned_taxid_ontology_interproscan_augustus.m8

Column added :

Repeat elements in scaffolds

Since few sequenced viral genomes have ETs, or rarely more than one, then the probability that a viral gene flanked by an ET (which would have jumped into the viral genome beforehand) will integrate into the host genome is low. (Gilbert et al 2017). It is thought that this is because the majority of ET integrations in viral genomes are deleterious and are therefore eliminated by negative selection. This is even lower if 2 ETs are found in the flanks of the EVEs and/or if it can be shown that the ET(s) present in the flanks are present elsewhere in the host genome.

Important file created :

Column added :

Scaffolds coverage and GC content (Before, you need to run hisat2 on all genomes to get coveregae depth)

First we will have to generate for each genome a chimeric sequence wich simply corresponds to one chimeric sequence composed of all the scaffold where have been found at least once a Busco sequence.

The idea is to create a chimeric huge fasta file for each genome species. Each chimeric sequence will be the sum of all scaffolds that had at least one BUSCO sequence into it (which means that this scaffold is likely from eucaryot origin)

To do so we will get the BUSCO summary table generated by the tblasn analysis, this will give us the scaffold that have at least on BUSCO.

Because we can aldo find virus loci into the same scaffold as BUSCO, will also have to remove from these scaffolds the putative viral loci.

To do so we will get the viral summary table generated by the mmseqs2 analysis, this will five us the coordinates of the viral loci into each scaffold.

We will use these coordinates in order to remove them from the scaffolds of interest.

Important file created : /beegfs/data/bguinet/these/Results/Candidate_viral_loci_filtred_clu_filtred_aligned_taxid_ontology_interproscan_augustus_repeat_env.m8

Column added :

The process took 54 minutes for 26319 sequences.

The number of cluster is : 3972

Evolutionary history of clusters

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Summary

Studying the domestication of genes of viral origin by eukaryotic genomes implies an interest in the evolutionary history of their acquisition. At this stage we have clusters of homologous genes sharing a common evolutionary history. These clusters are composed of both genes of viral origin and loci present in eukaryotic genomes.

When a domestication event of a viral gene takes place within a Eukaryotic genome, we expect that the phylogenetic reconstruction of this gene will show us a phylogenetic tree well characterized by horizontal gene transfer events from the viral genome(s) to the Eukaryotic genome(s). Such a tree should then be incongruous and show a viral phylogeny in which we observe the insertion of branches of eukaryotic origin. If this is the case, it suggests that these loci of viral origin have been integrated into the eukaryotic genomes (in this respect, we propose an approach to test the hypothesis that the gene is well integrated into the genome, and that it is not a natural or laboratory contaminant - see Integration-assessment.

In order to reconstruct the phylogenetic history of our clusters and since they are sequences with a relatively long divergence time, we will use amino acid alignments (amino acids being more conserved over long periods of time). Amino acid alignment is performed with the ClustalOmega program which is a new multi-sequence alignment program that uses seeded guide trees and HMM profiling techniques. These alignments also allow us to merge together several HSPs (a single viral sequence having a hit blast with two regions close to the genome). We then use these protein alignments to build a phylogeny with the IQTree program.

slowly decaying under neutral molecular evolution because they are no longer active. However in some cases EVEs provide a new function to the host and their genes can evolve under positive or negative selection.

If we are now able to observe these horizontal transfer events, it is possible that (i) these genes may have subsequently pseudogenized as they slowly decaying under neutral molecular evolution because they are no longer active in eukaryotic genomes, or (ii) they may have provided an evolutionary advantage to their recipients, in which case such genes should have a complete reading frame without stop codon. In addition, if this type of gene provides a fitness advantage for its recipient, we expect it to be strongly constrained by selection.

A particularly useful statistic for answering this question is operable for protein coding genes. It involves calculating the ratio of non-synonymous to synonymous substitutions ω = dN/ dS (non-synonymous substitutions are nucleotide changes that alter the protein sequence, synonymous substitutions do not). This ratio then measures the strength and mode of natural selection acting on protein genes, with ω > 1 indicating a positive selection (adaptive or diversifying), ω = 1 indicating a neutral evolution, and ω < 1 indicating a negative selection (purifying). The ω ratio summarizes the rates of gene evolution, and can be an informative feature, as it identifies the most (or least) conserved genes and also identifies genes that may have undergone periods of adaptive evolution.

In our study we know that the majority of viral genes known to have been domesticated are involved in the formation of vehicles to address virulence factors that disable the immune system of their hosts. These genes are thus very important for the wasp's reproductive success and therefore its fitness depends on them. Most non-synonymous changes in the coding regions of this gene should then negatively alter the structure and function of the protein and should therefore be deleterious, whereas most synonymous changes should be almost neutral. As a result, we should see a ω < 1 for most domesticated genes.

The program we use CODEML implemented in the python package ETE3 (and all other programs) to compute dN/ dS requires alignments based on the codons of the DNA sequences of all the genes in each orthologic group, so the gaps must be positioned so as not to alter the reading frame. For this we will use the program MACSE and its sub-program MACSE alignSequences. This sub-program will allow to align the coding sequences at the protein level, then to retro-translate these alignments to the nucleotide format. It thus favours nucleotide spacing stretches which are multiples of three but also considers those inducing frame shifts, when they allow to recover the structure of the underlying codon.

With any level of divergence that provides sufficient power to detect adaptive evolution, alignments will contain errors that cause false positives and false negatives. In particular, insertions and deletions are a major source of false positives in the detection of adaptive evolution. To eliminate potentially unreliable alignment columns or codons we use TrimAl . Trimming filtering of codon alignments via Trimal and its automate1 option will produce a more conservative analysis, reducing both false positives and true positives.

There are many calculation methods to evaluate the mode and strength of natural selection in protein coding sequences. The most popular is the Goldman-Yang model, in which there are independent DNA processes at each codon position, such that a different matrix is estimated for the 1st, 2nd and 3rd codon position (Goldman and Yang, 1994). In the Muse-Gaut model(Muse and Gaut, 1994), there is a DNA substitution process that applies equally to all three positions, resulting in a common substitution matrix between them. We chose to use the less frequently used type model Muse-Gaut because it has much less bias compared to the Goldman-Yang model, which produces significantly biased dN/dS estimates on realistic sequence data (Stephanie J. Spielman and Claus O. Wilke, 2015). For this we used the parameters in codeml: estFreq=1 (the frequency/fitness parameters are estimated by ML from the data) & CodonFreq=4 (use the MG94 codon model) see file control.py to add these changes to the ete3 evoltree package.

At this level we have both a phylogeny reconstructing the evolutionary history of the studied cluster, as well as a codon alignment cleaned of ambiguous sites.

Studying the profiles of oppositional selections on loci of viral origin in the Hymenoptera genomes means facing three problems:

1- Given that the sampling of viral sequences and the sampling of genomes of Hymenoptera species is today largely underestimated, we only have a remnant of the evolutionary history of the acquisition of these genes. We must therefore carefully analyze the results chosen.

2- Since this is a viral gene that has been integrated into a eukaryotic genome, if this eukaryotic locus is present only in a eukaryotic species, then estimating a dN/dS value means averaging the evolutionary forces that operated on this gene when it was present in the viral genome and those that have operated since its integration into the eukaryotic genome. Thus, the only way to calculate a dN/dS having a biological meaning is (i) when at least two phylogenetically close species share a homology for this gene (we thus measure the selection force that has operated on this gene since its acquisition by the common entree of the two species), or (ii) if this gene of viral origin has integrated into a viral genome and then duplicated (we thus measure the selection force that has operated on this gene since the duplication of this gene).

3- Finally, adaptive or purifying evolution rarely occurs in all species of a phylogeny, the most likely scenario is that positive or negative selection has occurred in certain branches of the phylogeny. It is therefore appropriate to focus on phylogenies of clusters with certain very specific branches that probably translate a horizontal transfer event from a viral gene to one or more Hymenoptera species.

How to identify these horizontal transfer events? One of the options is to compare the phylogenetic tree of the species and that of the gene (cluster). We then expect a likely HGT event to correspond to an event where several phylogenetically close species all share the same gene of viral origin and are close within the gene tree. For this we use a letic analysis Monophyletic.py. Develop ///

This analysis then allows us to target the monophyletic groups and thus to run an estimation of the dN/dS for each of the monophyletic groups along their branches dNdS_analyser.py. We can then statistically compare a null model with free branches and an alternative model in which we force the branches of interest to evolve under a neutral regime (dN/dS = 1). If this likelihood ratio test returns a pvalue < 0.05, it means that the branches studied evolve significantly under a selection regime different from neutral, therefore different from zero.

Alignment of clusters

**Working Directory : /beegfs/data/bguinet/these/Gene_phylogeny

For each cluster the idea is to build a phylogeny in order to assess whether the candidate loci are under purifying, neutral or positif selection.

Assembly of the clustered sequences in a common file

By taking the blast informations of loci present within each cluster and the corresponding viral hits, we will create the cluster file (AA and NT)

Image description

Important file created :

Alignment of each clusters

We will first do a first alignment using CLUSTAO in Amino acide sequences in order to target HSP sequences (when within a cluster, 2 sequences from the same species and same scaffold are overllaping within the alignement).

Important file created : /beegfs/data/bguinet/these/Cluster_alignment3/Cluster_alignment/{cluster_number}.aa.aln (All amino acide alignement clusters)

HSPs analysis

Now that we aligned each cluster, we will target all HSP and merge them within the previous files.

Image description

*Candidates HSPs are candidats that are in the same scaffold and same species, then they could be duplicates or HSPs. A HSP is defined when the ratio between number of AA matching with another AA / nb AA matching with a gap is < 0.20.

Exemple : if scaffold1.1:1-900(-):Linepithema_humile & scaffold1:1200-2000(-):Linepithema_humile are overlapping > 20 aa, the sequences are merged and called scaffold1.1:2-HSPs(-):Linepithema_humile

Important file created : /beegfs/data/bguinet/these/Monophyletic_assessment3/Monophyletic_tab.tab (A table used within the Monophyletic_analysis rule from Snakefile_add_informations which stores all the Loci corresponding HSP names)

Alignment of DNA sequences with MACSE

The idea here is to use MACSE v2 software so first we will align the nucleotides sequences using the MACSE subprogram alignSequences which aligns protein-coding sequences at the nucleotide level while scoring the considered nucleotide alignments based on their amino acid translation. It thus favours nucleotide gap stretches that are multiple of three but also considers those inducing frameshifts, when they allow to recover the underlying codon structure. MACSE therefore produces alignments which benefit from the higher similarity of amino acid sequences while accounting for frameshifts and stop codons that could occur in pseudogenes or in poor quality sequences;

Then, because our analyse is sensitive to alignment errors (dN/dS estimation), we will use a post filtering of the alignment at the amino acid level (using trimAl) and we will report this AA masking/filtering at the nucleotide level using reportMaskAA2NT

Important file created : /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/{cluster_number}_NT.dna (The NT codon alignment file) /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/{cluster_number}_AA.dna (The AA codon alignment file)

Trimming on the codon alignment

First Codon file should be unaligned. We can remove gaps from the input MSA using readal. Because macse symbols add "!" for frame shifts and in the process dnds this symbol is not tolerated, we will replace them in all alignments with a space Then we can trim the non-homoogous sites by giving as an input a protein-based MSA and a multiFASTA codon file (option -backtrans). Then, trimAl will trim the protein MSA and return as output the codon-based alignment.

Then we can use trimAl as intended - bear in mind I'm using the "ignorestopcodon" flag to remove the stopcodon. Alternatively, you can use "splitbystopcodon"

Important file created : /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/{cluster_number}_NT.dna (The NT codon alignment file) /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/{cluster_number}_AA.dna (The AA codon alignment file) /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/Cluster_NT.dna_without_shift (The NT codon alignment file without "!") /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/Cluster_AA.dna_without_shift (The AA codon alignment file without "!") /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/Cluster_NT.dna_without_shift_cleaned (The NT codon alignment file without "!" and trimmed) /beegfs/data/bguinet/these/Cluster_alignment/Codon_alignment_clusters/Cluster_AA.dna_without_shift_cleaned (The AA codon alignment file without "!" and trimmed)

Phylogeny of clusters

Cluster phylogeny will be performed on the AA alignments of MACSE because, as we have already performed blast analyses on viral protein coding sequences, hymenoptera loci should also be coding sequences. Thus, the loci should be aligned with a method that includes coding information to align the sequences. In addition, we have not included a trimming step on amino acid alignment for phylogeny since this may result in loss of information.

The topological alignments have not been trimmed because publications have shown that there was no difference when using Trimal (Trili strategy) the pvalue shows all non-significant except for HMMercleaner ot Ommacse, so filtering is useless (does not change topology when using a statistic based on the distance of quads between topologies), there is however an effect on the branch length (Ranwez and Chantret (2020) analysis and see also Ge Tan et al 2015 for older), so it is necessary to trim when we want to do a dN/dS analysis. There is often a tendency to infer from the selection because of alignment errors

Image description

Important file created : /beegfs/data/bguinet/these/Cluster_phylogeny/{CLuster_number}_AA.dna.treefile (The phylogenetic newick file of the cluster alignment)