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97 results for “genomic heterogeneity”
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
High heterogeneity in genomic differentiation between phenotypically divergent songbirds: A test of mitonuclear co-introgression
<p>Comparisons of genomic variation among closely related species often show more differentiation in mitochondrial DNA (mtDNA) and sex chromosomes than in autosomes, a pattern expected due to the differing effective population sizes and evolutionary dynamics of these genomic components. Yet, introgression can cause species pairs to deviate dramatically from general differentiation trends. The yellowhammer (<em>Emberiza</em> <em>citrinella</em>) and pine bunting (<em>E</em>. <em>leucocephalos</em>) are hybridizing avian sister species that differ greatly in appearance and moderately in nuclear DNA, but that show no mtDNA differentiation. This discordance is best explained by adaptive mtDNA introgression—a process that can select for co-introgression at nuclear genes with mitochondrial functions (mitonuclear genes). To better understand these discordant differentiation patterns and characterize nuclear differentiation in this system, we investigated genome-wide differentiation between allopatric yellowhammers and pine buntings and compared it to what was seen previously in mtDNA. We found significant nuclear differentiation that was highly heterogeneous across the genome, with a particularly wide differentiation peak on the sex chromosome Z. We further investigated mitonuclear gene co-introgression between yellowhammers and pine buntings and found support for this process in the direction of pine buntings into yellowhammers. Genomic signals indicative of co-introgression were common in mitonuclear genes coding for subunits of the mitoribosome and electron transport chain complexes. Such introgression of mitochondrial DNA and mitonuclear genes provides a possible explanation for the patterns of high genomic heterogeneity in genomic differentiation seen among some species groups.</p>
Inferring and comparing metabolisms across heterogeneous sets of annotated genomes using AuCoMe
<p>CONTENT OF THIS ARCHIVE</p> <p>The Zenodo archive is composed of one file and four main directories:<br> * <strong>analyses</strong> gathers three subdirectories: algae, bacteria, and fungi. It includes all files used to create the figures, supplemental figures, and results of the paper.</p> <p>* <strong>code</strong> contains all AuCoMe and PADMET codes.</p> <p> – <strong>aucome_v0.5.1</strong> this directory gathers the code of AuCoMe used to run the three datasets.</p> <p> – <strong>padmet_v5.0.1</strong> this directory contains the code of PADMET used to run AuCoMe.</p> <p>* <strong>datasets</strong> this directory gathers all datasets on which AuCoMe was run: the bacterial, fungal, and algal datasets, and the 32 synthetic datasets, which contain an <em>E. coli</em> K–12 MG1655 genome to which various degradations were applied, together with 28 other bacterial genomes. It also encompasses the version 23.5 of MetaCyc database.</p> <p>* <strong>scripts_analyses</strong> this directory contains several scripts to generate the figures, supplemental figures and a script to degrade the <em>E. coli</em> K–12 MG1655 genome.</p> <p> </p> <p> </p> <p>1/ Content of the <strong>analyses</strong> repertory<br> It is composed of three subdirectories: <strong>algae</strong>, <strong>bacteria</strong>, and <strong>fungi</strong>.</p> <p>1.1/ Content of the <strong>algae</strong> subdirectory<br> It encompasses 9 files.</p> <p>* <strong>Figure_2_algal_nb_reactions.tsv</strong> for each species of the algal dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2D.</p> <p>* <strong>Figure_S10_Deepec_algal.tsv</strong> for each species of the algal dataset, at each AuCoMe step (robust orthology, non-robust orthology, and annotation or orthology), several measures were computed, i.e.: the number of reactions, the number of ECs, the number of ECs validated by DeepEC, and the ratio number of ECs valided by DeepEC / number of ECs. It was used to design figure S10(b).</p> <p>* <strong>Table_S6_50_random_reactions_found.xlsx</strong> contains manual validation of 50 randomly chosen reactions found in any of the species (is the Supplemental Table S6).</p> <p>* <strong>Table_S7_50_random reactions absent.xlsx</strong> includes manual validation of 50 reactions absent from a species and randomly chosen (is the Supplemental Table S7).</p> <p>* <strong>Table_S8_reactions_common_only_Cokamuranus_Sjaponica.xlsx</strong> encompasses reactions common to <em>Saccharina japonica</em> and <em>Cladosiphon okamuranus</em> but not found in other brown algae (is the Supplemental Table S8).</p> <p>* <strong>Table_S9_homologues_Esiliculosus_Sjaponica.xlsx</strong> contains additional homologs in <em>E. siliculosus</em> found by BLASTP searches for sequences inferred to be present only in <em>C. okamuranus</em> and <em>S. japonica</em> (is the Supplemental Table S9).</p> <p>* <strong>Table_S10_o-aminophenol_Esiliculosus_holomogues.xlsx</strong> includes additional o-aminophenol oxidases from <em>E. siliculosus</em> and their homologs in other stramenopiles. It is the Supplemental Table S10 with more detail (like the amino acid sequences).</p> <p>* <strong>Table_S11_reactions_cryptophytes_haptophytes_stramenopiles_archeplastida.xlsx</strong> encompasses reactions distinguishing the cryptophyte, haptophyte, stramenopile, and archeplastida groups (is the Supplemental Table S11).</p> <p>* <strong>Table_S12_pathways_cryptophytes_haptophytes_stramenopiles_archeplastida.xlsx</strong> contains shared metabolic pathways as well as the absence of pathways between chryptophytes, haptophytes, stramenopiles, and archaeplastida (is the Supplemental Table S12).</p> <p> </p> <p>1.2/ Content of the <strong>bacteria</strong> subdirectory<br> It gathers 12 files and 9 repertories.</p> <p>* <strong>aucome_final.tsv</strong> output file of the figure S4 comparison bacteria.py script, for each of the 29 bacterial metabolic networks produced with AuCoMe, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>carveme_stat.tsv</strong> output file of the figure S4 comparison bacteria.py script, for each of the 29 bacterial metabolic networks produced with CarveMe, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>ecocyc.padmet</strong> contains the EcoCyc database version 23.5 at the PADMet, is used to generate the Supplemental Fig. S5.</p> <p>* <strong>Figure_2_bacterial_nb_reactions.tsv</strong> for each species of the bacterial dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2B.</p> <p>* <strong>Figure_3_nb_reactions_step.tsv</strong> for each dataset of the 32 synthetic bacterial datasets, this file enumerates the number of reactions at each AuCoMe step. It was used to create figure 3A.</p> <p>* <strong>Figure_3_fmeasure_steps.tsv</strong> for each dataset of the 32 synthetic bacterial datasets, this file indicates the values of the F-measures resulting of the comparison of the GSMNs recovered for each <em>E. coli</em> K–12 MG1655 genome replicate with the gold-standard network EcoCyc. It was used to create figure 3B.</p> <p>* <strong>Figure_S4_output</strong> contains 3 output files of the figure_S4_comparison_bacteria.py script:</p> <p> – <strong>Figure_S4_boxplot_networks.svg</strong> is the Supplemental Figure S4 in high resolution.</p> <p> – <strong>Figure_S4_boxplot_networks.tsv</strong> contains the number of reactions, the type of reactions (All, Reactions with genes, ...), and the used software. Thes data were produced and used in the figure_S4_comparison_bacteria.py script.</p> <p> – <strong>Figure_S4_barplot_time_networks.svg</strong> for each software, shows the required time in seconds used to reconstruct these bacterial metabolic networks.</p> <p>* <strong>Figure_S5_output</strong> encompasses 3 output files of figure S5 reference catalog.py script:</p> <p> – <strong>Figure_S5_ec_union.svg</strong> is the Supplemental Figure S5 in high resolution.</p> <p> – <strong>Figure_S5_ec_union_venn.svg</strong> another visualisation of presenting the results of the Supplemental Fig. S5.</p> <p> – <strong>Figure_S5_refence_ec_catalog_K12MG1655.tsv</strong> contains an EC catalog to <em>E. coli</em> K-12 MG1655 from the BIGG, EcoCyc, KEGG, and ModelSEED databases. This file is used to produce the Supplemental Figure S5.</p> <p>* <strong>Figure_S6_output</strong> includes 2 output files of the figure_S6.py script:</p> <p> – <strong>Figure_S6_comparison_all.svg</strong> is the Supplemental Figure S6 in high resolution.</p> <p> – <strong>Figure_S6_comparison_all.tsv</strong> contains data used to produce the Supplemental Figure S6.</p> <p>* <strong>gapseq_stat.tsv</strong> output file of the figure_S4_comparison_bacteria.py script, for each of the 29 bacterial metabolic networks produced with gapseq, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>jsons_bigg</strong> todate contains the five metabolic networks of <em>E. coli</em> K–12 MG1655 that can find in BIGG at JSON format. These files correspond to the BIGG reference metabolic network on the Supplemental Figure S5.</p> <p>* <strong>jsons_modelseed</strong> todate includes the metabolic network of <em>E. coli</em> K–12 MG1655 that can find in ModelSEED at JSON format. It is the ModelSEED reference metabolic network on the Supplemental Figure S5.</p> <p>* <strong>kegg_ecs.txt</strong> input file of the figure_S5_reference_catalog.py script, it contains matches between EC numbers and all the entries of <em>E. coli</em> K–12 MG1655 in the KEGG database.</p> <p>* <strong>mapping_modelseed_ec.tsv</strong> input file of the figure_S4_comparison_bacteria.py script, it encompasses matches between ModelSEED reactions and EC numbers.</p> <p>* <strong>modelseed_stat.tsv</strong> output file of the figure_S4_comparison_bacteria.py script, for each of the 29 bacterial metabolic networks produced with ModelSEED, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>networks_aucome</strong> for each of the 29 bacteria, contains a metabolic networks at the PADMet format obtained with AuCoMe.</p> <p>* <strong>networks_carveme</strong> for each of the 29 bacteria, contains a metabolic networks at the SBML format got to CarveMe.</p> <p>* <strong>networks_gapseq</strong> composes of 29 subdirectories (one for each bacterium). All these subdirectories contain 10 files about the metabolic networks a obtained with gapseq:</p> <p> – <strong>species-all-Pathways.tbl</strong> encompasses data on pathways at TBL format.</p> <p> – <strong>species-all-Reactions.tbl</strong> includes data on reactions at TBL format.</p> <p> – <strong>species-draft.RDS</strong> is a draft metabolic network at RDS (R Data Format).</p> <p> – <strong>species-draft.xml</strong> is a draft metabolic network at SBML format.</p> <p> – <strong>species-medium.csv</strong> encompasses all the metabolites allow the default medium.</p> <p> – <strong>species.RDS</strong> is the final metabolic network at RDS (R Data Format).</p> <p> – <strong>species-rxnWeights.RDS</strong> is a temporary file nedeed to gapseq fill at RDS (R Data Format).</p> <p> – <strong>species-rxnXgenes.RDS</strong> is a temporary file nedeed to gapseq fill at RDS (R Data Format).</p> <p> – <strong>species-Transporter.tbl</strong> includes data on transporters at TBL format.</p> <p> – <strong>species.xml</strong> is the final metabolic network at SBML format.</p> <p>* <strong>networks_modelseed</strong> includes two subdirectories:</p> <p> – <strong>sbml</strong> for each of the 29 bacteria, encompasses a metabolic networks at the SBML format got to ModelSEED.</p> <p> – <strong>tsv</strong> for each of the 29 bacteria, contains two TSV files:</p> <p> – <strong>genomeset__species.gbk_genome.fbamodel-compounds.tsv</strong> includes data on compounds at TSV format.</p> <p> – <strong>genomeset__species.gbk_genome.fbamodel-reactions.tsv</strong> encompasses data on reactions at TSV format.</p> <p>* <strong>time_carveme.txt</strong> input file of the figure S4 comparison bacteria.py script, for each of the 29 bacteria it stores the running time of CarveMe (in seconds) to reconstruct a metabolic network.</p> <p>* <strong>time_gapseq.txt</strong> input file of the figure S4 comparison bacteria.py script, for each of the 29 bacteria it stores the running time of gapseq (in seconds) to reconstruct a metabolic network.</p> <p> </p> <p>1.3/ Content of the <strong>fungi</strong> repertory<br> It contains three files and five directories.</p> <p>* <strong>All-pathways-of-S.-cerevisiae-S288c.txt</strong> encompasses all the YeastCyc pathways.</p> <p>* <strong>Figure_2_fungal_nb_reactions.tsv</strong> for each species of the fungal dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2C.</p> <p>* <strong>Figure_S7_output</strong> contains 11 output files of the figure S7 comparison pathway fungi.py script:</p> <p> – <strong>completion_pathway_species.svg</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains a subfigure of the Supplemental Fig. S7.</p> <p> – <strong>fungi_stats.tsv</strong> is the Supplemental Table S5.</p> <p> – <strong>pathway_venn_species.png</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), includes a Venn diagram about all the pathways found with the 3 software (AuCoMe, gapseq, and ModelSEED).</p> <p>* <strong>Figures_S8_S9_output</strong> contains 11 files, in all these files, a comparison of all pathways of metabolic networks of <em>S. cerevisiae</em> S288C obtained with AuCoMe and gapseq to those of YeastCyc was released.</p> <p> – <strong>comparison_yeastcyc.png</strong> is a picture about number of pathways true positive, false positive, and false negative are found, according the used method (AuCoMe and gapseq).</p> <p> – <strong>completion_pathway_gapseq.svg</strong> includes the number of pathways common or specific to YeastCyc and gapseq with their completeness ratio predicted by gapseq.</p> <p> – <strong>Figure_S8_completion_pathway_aucome.svg</strong> contanis the number of pathways common or specific to YeastCyc and AuCoMe with their completeness ratio predicted by AuCoMe, is the Supplemental Figure S8.</p> <p> – <strong>Figure_S9_venn_diagram_70_100.svg</strong> is the Supplemental Figure S9. All pathways of AuCoMe, gapseq and YeastCyc with a completion rate between 50% and 70% are compared.</p> <p> – <strong>venn_diagram.svg</strong> in this picture, all pathways are compared.</p> <p> – <strong>venn_diagram 50.svg</strong> all pathways of AuCoMe, gapseq and YeastCyc with a completion rate less than 50% are compared.</p> <p> – <strong>venn_diagram_50_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate less than 50%.</p> <p> – <strong>venn_diagram_50_70.svg</strong> all pathways of AuCoMe, gapseq, and YeastCyc with a completion rate between 50% and 70% are compared.</p> <p> – <strong>venn_diagram_50_70_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate between 50% and 70%.</p> <p> – <strong>venn diagram_70_100_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate between 70% and 100%.</p> <p> – <strong>yeast_cyc_comparison.tsv</strong> contains the number of pathways true positive, false positive, and false negative are found, according the used method (AuCoMe and gapseq).</p> <p>* <strong>Figure_S10_Deepec_fungal.tsv</strong> for each species of the fungal dataset, at each AuCoMe step (robust orthology, non-robust orthology, and annotation or orthology), several measures were computed, i.e.: the number of reactions, the number of ECs, the number of ECs valided by DeepEC, and ratio number of ECs validated by DeepEC / number of ECs. It was used to design figure S10(a).</p> <p>* <strong>networks_aucome</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains a metabolic networks at the PADMet format obtained with AuCoMe.</p> <p>* <strong>networks_gapseq</strong> is composed of 5 subdirectories (one for each fungus). All these subdirectories contain two files about the metabolic networks a obtained with gapseq:</p> <p> – <strong>species-all-Pathways.tbl</strong> encompasses data on pathways at TBL format.</p> <p> – <strong>species-all-Reactions.tbl</strong> includes data on reactions at TBL format.</p> <p>* <strong>networks_modelseed</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains two TSV files:</p> <p> – <strong>species.gbk_genome.draftModel-compounds.tsv</strong> includes data on compounds at TSV format.</p> <p> – <strong>species.gbk_genome.draftModel-reactions.tsv</strong> encompasses data on reactions at TSV format.</p> <p> </p> <p> </p> <p>2/ Content of the <strong>code</strong> repertory<br> It gathers two directories <strong>aucome v0.5.1</strong> and <strong>padmet_v5.0.1</strong>.</p> <p>2.1/ Content of the <strong>aucome v0.5.1</strong> subdirectory<br> This directory contains a copy of the AuCoMe project on the GitHub site: <a href="https://github.com/AuReMe/aucome">https://github.com/AuReMe/aucome</a> (downloaded the 15/11/2022). It is composed of two subdirectories and five files:<br> * <strong>LICENCE</strong> licence of the AuCoMe software.</p> <p>* <strong>README.rst</strong> README of the AuCoMe software.</p> <p>* <strong>requirements.txt</strong> contains the list of requires Python packages.</p> <p>* <strong>setup.cfg</strong> contains metadata about AuCoMe package and is used with setup.py to distribute AuCoMe.</p> <p>* <strong>setup.py</strong> contains various information relevant to the AuCoMe package including options and metadata. Then, it is used to distribute AuCoMe with PyPI. It is also used to create an entrypoint when installing it with pip.</p> <p>* <strong>recipes</strong> this subdirectory contains two files:<br> – <strong>Dockerfile</strong> contains instructions to run AuCoMe in a Docker environment.<br> <br> – <strong>Singularity</strong> contains instructions to run AuCoMe in a Singularity container.</p> <p>* <strong>aucome</strong> this directory contains 11 Python files:<br> – <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>__main__.py</strong> contains the functions implementing the command-line interface of AuCoMe.</p> <p> – <strong>analysis.py</strong> contains the functions to analyse the AuCoMe results.</p> <p> – <strong>check.py</strong> contains the functions to check the input files.</p> <p> – <strong>compare.py</strong> contains the functions to compare the AuCoMe results between two distinct subgroups.</p> <p> – <strong>orthology.py</strong> contains the functions to propagate reaction through orthology.</p> <p> – <strong>reconstruction.py</strong> contains the functions to perform the reconstruction of draft GSMNs by using Pathway Tools in a parallel implementation.<br> <br> – <strong>spontaneous.py</strong> contains the functions to add spontaneous reactions to some GSMNs if it completes MetaCyc metabolic pathway.</p> <p> – <strong>structural.py</strong> contains the functions to check that no reactions are missing due to missing gene structures. A genomic search is performed for all reactions present in one organism but not in another.<br> <br> – <strong>utils.py</strong> contains a function to analyse the configuration file.</p> <p> – <strong>workflow.py</strong> contains functions to run all the steps of AuCoMe.</p> <p> </p> <p>2.2/ Content of the <strong>padmet_v5.0.1</strong> subdirectory<br> This directory contains a copy of the PADMET project on the GitHub site: <a href="https://github.com/AuReMe/padmet/">https://github.com/AuReMe/padmet/</a> (downloaded the 15/11/2022). It is composed of two subdirectories and six files:<br> * <strong>CHANGELOG.md </strong>records of all notable changes made in the PADMET project.</p> <p>* <strong>docs</strong> this repertory contains all the documentation files of PADMET package in the RST format.</p> <p>* <strong>LICENCE</strong> licence of the PADMET package.</p> <p>* <strong>README.md</strong> manual of the PADMET package.</p> <p>* <strong>requirements.txt</strong> contains the list of requires Python packages.</p> <p>* <strong>setup.cfg</strong> contains metadata about PADMET package and is used with setup.py to distribute PADMET.</p> <p>* <strong>setup.py</strong> contains various information relevant to the PADMET package including options and metadata. Then, it is used to distribute PADMET with PyPI. It is also used to create an entrypoint when installing it with pip.<br> <br> * <strong>padmet</strong> this repertory grathers two files and two subdirectories:<br> – <strong>__init__.py</strong> indicates the version of PADMET.</p> <p> – <strong>__main__.py</strong> contains the functions implementing the command-line interface of PADMET.</p> <p> – <strong>classes</strong> contains 7 files.</p> <p> – <strong>utils</strong> contains 4 files and 3 subdirectories.</p> <p><br> 2.2.1/ Content of the <strong>class</strong> subdirectory<br> The class repertory contains 7 files.<br> * <strong>__init__.py </strong>indicates the directory as a python module.</p> <p>* <strong>instantiation.py</strong> contains a function to instantiate padmet object.</p> <p>* <strong>node.py</strong> contains a class defining a Node object which is representing an element in a metabolic network (e.g: compound, reaction).</p> <p>* <strong>padmetRef.py</strong> contains a class defining a PadmetRef object which is representing a database of metabolic network.</p> <p>* <strong>padmetSpec.py</strong> creates a PadmetSpec object which is representing the metabolic network of a species/organism based on a reference database PadmetRef.</p> <p>* <strong>policy.py</strong> contains a class defining a Policy object that is defining the types of Relations and Nodes of a network.</p> <p>* <strong>relation.py</strong> contains a class defining a Relation object which is representing a link between two elements (Node) in a metabolic network.</p> <p><br> 2.2.2/ Content of the <strong>utils</strong> subdirectory<br> The utils directory contains 4 files and 3 subdirectories.<br> * <strong>__init__.py</strong> indicates the directory as a python module.</p> <p>* <strong>gbr.py</strong> implements a lexical analysis to handle genes relationship associated with a reaction, either a complex (with and relation between genes) or isozyme (with or relation between genes).</p> <p>* <strong>sbmlPlugin.py</strong> contains functions to handle SBML element (ex: species or reaction), then it returns all the sections named notes in a dictionary.<br> <br> * <strong>utils.py</strong> contains a function that checks paths of file.</p> <p>* <strong>connection</strong> this subdirectory contains 22 files:<br> - <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>biggAPI_to_padmet.py</strong> allows to extract the BIGG database from the API to create a padmet. An Internet access is required.</p> <p> – <strong>check_orthology_input.py</strong> is written to check if the metabolic network and the proteome of the model organism use the same identifiers for genes (or at least more than a given cutoff), before running orthology based reconstruction.</p> <p> – <strong>enhanced_meneco_output.py</strong> extracts the results from Meneco gap-filling to add more information to the gap-filled reactions. Then it returns a PADMET file with more information for each reaction.</p> <p> – <strong>extract_orthofinder.py</strong> after running Orthofinder on n FASTA files, it reads the output file ’Orthogroups.tsv’ to identify the orthologous genes. It is used by AuCoMe to extract the orthologous genes.<br> <br> – <strong>extract_rxn_with_gene_assoc.py</strong> from a given SBML file, it creates a SBML with only the reactions associated to a gene.<br> <br> – <strong>gbk_to_faa.py</strong> extracts protein sequence from a GenBank into a FASTA file with Biopython package.</p> <p> – <strong>gene_to_targets.py</strong> from a list of genes, it gets the products associated with the reactions linked to the genes. For example: R1 is linked to G1, R1 produces M1 and M2, this script outputs: M1, M2.</p> <p> – <strong>get_metacyc_ontology.py</strong> from the PadmetRef of MetaCyc, it creates the MetaCyc ontology.</p> <p> – <strong>metexploreviz_export.py</strong> converts a PADMET object representing a metabolic network into a JSON compatible with MetExplore.<br> <br> – <strong>modelSeed_to_padmet.py</strong> from ModelSEED reactions and pathways files, it creates a PADMET.<br> <br> – <strong>network_to_gnn.py</strong> creates input for GNN (Graph Neural Networks) from PADMET or SBML.</p> <p> – <strong>padmet_to_asp.py</strong> converts PADMET to Answer Set Programming.</p> <p> – <strong>padmet_to_matrix.py</strong> creates a stoichiometry matrix from a PADMET file, in which the columns represent the reactions and rows represent metabolites.</p> <p> – <strong>padmet_to_padmet.py</strong> allows to merge 1-n PADMET.<br> <br> – <strong>padmet_to_tsv.py</strong> converts a PADMET representing a database (PadmetRef) and/or a PADMET representing a model (PadmetSpec) to TSV files.</p> <p> – <strong>pgdb_to_padmet.py</strong> reads a PGDB folder (from BIOCYC/Pathway Tools) and creates a PADMET. It is used by AuCoMe to create PADMET files from PGDB in the annotation-based step.</p> <p> – <strong>sbmlGenerator.py</strong> contains functions to generate SBML files from PADMET and TXT files usign the libsbml package. It is used by AuCoMe to create SBML files at the annotation-based, orthology and final steps.</p> <p> – <strong>sbml_to_curation_form.py</strong> extracts one or several reactions from a SBML file to the form used in AuReMe for curation.</p> <p> – <strong>sbml_to_padmet.py</strong> converts a SBML file into a PADMET file (with or without a reference database).</p> <p> – <strong>sbml_to_sbml.py</strong> creates a SBML file from another one. Use it to change the SBML level.</p> <p> – <strong>wikiGenerator.py</strong> contains all necessary functions to generate wiki pages from a PADMET file and update a wiki online. It requires WikiManager module (with wikiMate, Vendor).</p> <p>* <strong>exploration</strong> this subdirectory contains 15 files:<br> - <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>compare_padmet.py</strong> compares 1-n PADMET files, and creates a folder with 4 output files (compounds.tsv, genes.tsv, pathways.tsv and reactions.tsv). It is used by AuCoMe to create these files to analyse the metabolic networks.</p> <p> – <strong>compare_sbml.py</strong> compares 2 or 1-n SBML, then it creates two output files reactions.tsv and metabolites.tsv with the reactions/metabolites in each SBML files.</p> <p> – <strong>compare_sbml_padmet.py</strong> compares reaction identifiers in SBML versus PADMET, then returns the number of reactions in both, and reaction identifiers not in SBML or not in PADMET.</p> <p> – <strong>convert_sbml_db.py</strong> uses the MetaNetX database to check or convert a SBML. Flat files from MetaNetx are required to run this script. They can be found in the AuReMe workflow or from the MetaNetx website.</p> <p> – <strong>dendrogram_reactions_distance.py</strong> uses the reactions.tsv file from compare_padmet.py to create a dendrogram using the R package pvclust. It has been used in the article to create the metabolic dendrogram.</p> <p> – <strong>flux_analysis.py</strong> runs the flux balance analyse with cobra package on an already defined reaction. It needs to set in the SBML the value ’objective_coefficient’ to 1.</p> <p> – <strong>get_pwy_from_rxn.py</strong> from a file containing a list of reaction, it returns the pathways where these reactions are involved.</p> <p> – <strong>padmet_stats.py</strong> creates a PADMET stats file (named padlet_stats.tsv) containing the number of pathways, reactions, genes and compounds inside the one or several PADMET files.</p> <p> – <strong>pathway_production.py</strong> compares 1-n PADMET objects to show the pathway input/output for them.</p> <p> – <strong>prot2genome.py</strong> contains function to search a genome using protein sequences and Gene-Protein-Reaction associations. It is used in the structural search step of AuCoMe.</p> <p> – <strong>report_network.py</strong> creates reports of a PADMET file, and it writes three TSV files (all metabolites.tsv, all_pathways.tsv, and all_reactions.tsv).</p> <p> – <strong>visu_network.py</strong> allows to visualize a metabolic network on a compounds perspectives.</p> <p> – <strong>visu_path.py</strong> allows to visualize a pathway in PADMET network.<br> <br> – <strong>visu_similarity_gsmn.py</strong> visualize similarity between metabolic networks using MDS.</p> <p>* <strong>management</strong> this subdirectory contains 5 files:<br> – <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>manual_curation.py</strong> updates a PadmetSpec object by filling specific forms. It either creates new reaction(s) to PADMET file, or it adds/removes reaction(s) from a PadmetRef.</p> <p> – <strong>padmet_compart.py</strong> for a given PADMET file, it checks and updates compartment.</p> <p> – <strong>padmet_medium.py</strong> for a given set of compounds representing the growth medium (or seeds), it creates two reactions in order to maintain consistency of the network for flux analysis.</p> <p> – <strong>relation_curation.py</strong> for a given PADMET file, it adds or removes relations between nodes.</p> <p> </p> <p> </p> <p>3/ Content of the <strong>datasets</strong> subdirectory<br> It contains a files and four repertories.</p> <p>* <strong>metacyc 23.5.padmet</strong> the version 23.5 of the <a href="https://metacyc.org/">MetaCyc</a> database in the PADMET format. It was used by AuCoMe to reconstruct all the metabolic networks. Hence metacyc 23.5.padmet is required to reproduce the article results.</p> <p>3.1/ Content of the <strong>algal</strong>, <strong>bacterial</strong>, and <strong>fungal</strong> directories<br> These three directories are composed of 8 subdirectories and a Supplemental Table (respectively Table S1, Table S2 and Table S3 in bacterial, fungal and algal directories):<br> * <strong>FASTA</strong> contains the proteome of each species as a FASTA file.</p> <p>* <strong>cleaned_GBKs</strong> for each species, it contains the annotated genome, with the protein sequences in a GenBank format file.</p> <p>* <strong>dictionaries</strong> for some species, genes needed to be renamed for compatibility reasons. This folder contains CSV files with the mapping between the old names of genes and the new ones.</p> <p>* <strong>annotated_DATs</strong> contains a subdirectory per species with all the output files from Pathway Tools v23.5, without any post-treatment, in the DAT format.</p> <p>* <strong>annotated_PADMETs</strong> for each species, it contains a metabolic network of the draft reconstruction step of AuCoMe, in the PADMET format.<br> <br> * <strong>final_PADMETs</strong> for each species, it contains a metabolic network generated by the AuCoMe workflow, at the PADMET format.</p> <p>* <strong>final_SBMLs</strong> for each species, it contains a metabolic network generated by the AuCoMe workflow, in the SBML format.</p> <p>* <strong>panmetabolism</strong> is composed of 7 files describing the final metabolic networks:<br> – <strong>genes.tsv</strong> contains, for each organism, the list of genes and the associated reactions.</p> <p> – <strong>metabolites.tsv</strong> contains the list of metabolites present in the panmetabolism. Then, for each metabolite and for each organism, it lists the reactions that produced this compound and the reactions that consumed it.<br> <br> – <strong>pathways.tsv</strong> contains the list of pathways present in the panmetabolism. For each pathway and for each organism, it indicates the number of reactions present in this pathway, and the names of these reactions.<br> <br> – <strong>reactions.tsv</strong> contains the list of reactions present in the panmetabolism. Then for each reaction, it indicates whether or not it belongs to an organism. If a reaction is found in a species, the genes associated with the reaction are also listed.</p> <p> – <strong>pvclust_reaction_dendrogram.png</strong> based on the presence/absence matrix of reactions in different species of the dataset, it computes the Jaccard distances between these species, and it applies a hierarchical clustering on these data with a complete linkage to create a dendrogram. The R package pvclust is used to create the dendrogram, with bootstrap resampling. For each node, a p-value indicates how strong the cluster is supported by data. This dendrogram is provided as a PNG picture.</p> <p><br> 3.2/ Content of the <strong>synthetic_bacterial</strong> repertory<br> The synthetic_bacterial repertory contains the Supplemental Table S4 and 32 subdirectories named Run_00, Run_01, . . . , etc, Run 31. Each subdirectory is composed of 9 files:<br> * <strong>K_12_MG1655.gbk</strong> the annotated genome of <em>E. coli K–12 MG1655</em> to which degradation of the functional and/or structural annotations was applied.</p> <p>* <strong>annotated_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the draft reconstruction step of AuCoMe in the SBML format.</p> <p>* <strong>annotated_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the draft reconstruction step of AuCoMe in the PADMET format.</p> <p>* <strong>orthology_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the orthology propagation step of AuCoMe in the SBML format.</p> <p>* <strong>orthology_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the orthology propagation step of AuCoMe in the PADMET format.<br> <br> * <strong>structural_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the structural verification step of AuCoMe in the SBML format.<br> <br> * <strong>structural_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the structural verification step of AuCoMe in the PADMET format.</p> <p>* <strong>final_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the AuCoMe workflow in the SBML format.</p> <p>* <strong>final_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the AuCoMe worflow in the PADMET format.</p> <p> </p> <p> </p> <p>4/ Content of the <strong>scripts_analyses</strong> subdirectory<br> The scripts repertory contains 12 files:</p> <p>* <strong>bacteria_random_degradation.py</strong> was used to degrade the <em>E. coli</em> K–12 MG1655 genome. The procedure for the genome degradation is described in the algorithm 1.</p> <p>* <strong>figure_2_algal_dataset.py</strong> for each species of the algal dataset, and at each AuCoMe step. This script allows to generate the figure 2D.</p> <p>* <strong>figure_2_bacterial_dataset.py</strong> for each species of the bacterial dataset, and at each AuCoMe step. This script allows to generate the figure 2B.</p> <p>* <strong>figure_2_fungal_dataset.py</strong> for each species of the fungal dataset, and at each AuCoMe step. This script allows to generate the figure 2C.</p> <p>* <strong>figure_3_degradation.py</strong> allows to generate the figure 3B from the figure_3_fmeasure_steps.tsv file (described above).</p> <p>* <strong>figure_5_mds.py</strong> allows to generate the figure 5A from two reactions.tsv files of the algal dataset (annotation-based and final).</p> <p>* <strong>figure_S4_comparison_bacteria.py</strong> computes statistics on all the 29 bacterial metabolic networks reconstructed with AuCoMe, CarveMe, gapseq and ModelSEED, it uses the mapping_modelseed_ec.tsv, soft_stat.tsv files and bacteria/networks soft directories, then it creates the Supplemental Fig. S4 and files inside the analyses/bacteria/Figure_S4_output repertory.</p> <p>* <strong>figure_S5_reference_catalog.py</strong> reads the ecocyc.padmet, kegg ecs.txt files, and the jsons bigg/, jsons modelseed/ directories, then it creates the Figure_S5_refence_ec_catalog_K12MG1655.tsv and the Supplemental Fig. S5.</p> <p>* <strong>figure_S6.py</strong> reads the Figure_S5_refence_ec_catalog_K12MG1655.tsv file and those inside the analyses/bacteria/networks_soft directories about <em>E. coli</em> K–12 MG1655, it generates the Supplemental Fig. S6.</p> <p>* <strong>figure_S7_comparison_pathway_fungi.py</strong> reads metacyc_23.5.padmet file and those inside the analyses/fungi/networks_soft directories, then it create five completion pathway species.svg pictures which composed the Supplemental Fig. S7.</p> <p>* <strong>figures_S8_S9.py</strong> reads the metacyc_23.5.padmet, and metabolic networks of <em>S. cerevisiae</em> S288 reconstructed with AuCoMe, gapseq, and YeastCyc. For all pathways of both AuCoMe and gapseq networks, it also computes their completion rates. Then it compares the results obtained with AuCoMe and gapseq on <em>S. cerevisiae</em> S288C to YeastCyc according to the completion rates of their pathways. It generates Supplemental Fig. S8 and S9.</p> <p>* <strong>figure_S12_supervenn.py</strong> allows to generate the figure S12, it reads the reactions.tsv file of the algal dataset at the final AuCoMe step, and another tabular file that contains abbreviated names of species.</p>
Genomic identification of direct seeding and evolutionary lineages by combining heterogeneous genomic resources
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High heterogeneity in genomic differentiation between phenotypically divergent songbirds: A test of mitonuclear co-introgression
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Data sets for phylogenomic analyses in: Ant backbone phylogeny resolved by modelling compositional heterogeneity among sites in genomic data
<p>Ants are the most ubiquitous and ecologically dominant arthropods on Earth, and understanding their phylogeny is crucial for deciphering their character evolution, species diversification, and biogeography. Although recent genomic data have shown promise in clarifying intrafamilial relationships across the tree of ants, inconsistencies between molecular datasets have also emerged. Here I re-examine the most comprehensive published Sanger-sequencing and genome-scale datasets of ants using model comparison methods that model among-site compositional heterogeneity to understand the sources of conflict in phylogenetic studies. My results under the best-fitting model, selected on the basis of Bayesian cross-validation and posterior predictive model checking, identify contentious nodes in ant phylogeny whose resolution is <a>modelling-dependent. </a>I show that the Bayesian infinite mixture CAT model outperforms empirical finite mixture models (C20, C40 and C60) and that, under the best-fitting CAT-GTR+G4 model, the enigmatic <a><em>Martialis</em> </a><em>heureka</em> is sister to all ants except Leptanillinae, rejecting the more popular hypothesis supported under worse-fitting models, that place it as sister to Leptanillinae. These analyses resolve a lasting controversy in ant phylogeny and highlight the significance of model comparison and adequate modelling of among-site compositional heterogeneity in reconstructing the deep phylogeny of insects.</p>
Data for: (Epi)genomic adaptation driven by fine geographical scale environmental heterogeneity after recent biological invasions
<p><span>Elucidating processes and mechanisms involved in rapid local adaptation to varied environments is a poorly understood but crucial component in management of invasive species. Recent studies have proposed that genetic and epigenetic variation could both contribute to ecological adaptation, yet it remains unclear on the interplay between these two components underpinning rapid adaptation in wild animal populations. To assess their respective contributions to local adaptation, we explored epigenomic and genomic responses to environmental heterogeneity in eight recently colonized ascidian (<em>Ciona intestinalis</em>) populations at a relatively fine geographical scale. Based on MethylRADseq data, we detected strong patterns of local environment-driven DNA methylation divergence among populations, significant epigenetic isolation by environment (IBE), and a large number of local environment-associated epigenetic loci. Meanwhile, multiple genetic analyses based on single nucleotide polymorphisms (SNPs) showed genomic footprints of </span><span>divergent selection</span><span>. </span><span>In addition, for five genetically similar populations, we detected significant methylation divergence and local environment-driven methylation patterns, indicating strong effects of local environments on epigenetic variation. From a functional perspective, a majority of functional genes, gene ontology (GO) terms, and biological pathways were largely specific to one of these two types of variation, suggesting partial independence between epigenetic and genetic adaptation. The methylation quantitative trait loci (mQTL) analysis showed that the genetic variation explained only 18.67% of methylation variation, further confirming the autonomous relationship between these two types of variation. Altogether, we highlight the complementary interplay of genetic and epigenetic variation involved in local adaptation, which may jointly promote populations' rapid adaptive capacity and successful invasions in different environments. The findings here provide valuable insights into interactions between invaders and local environments to allow invasive species to rapidly spread, thus contributing to better prediction of invasion success and development of management strategies.</span></p>
Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes
<p><strong>Supplemental files contain all necessary datasets to reproduce the analysis and figures in "Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes" Code and additional files can be found at https://github.com/FowlerLab/VEP-calibrations</strong></p>
Data for: Genomic signals of local adaptation across climatically heterogenous habitats in an invasive tropical fruit fly (Bactrocera tryoni)
<p class="MsoNormal"><span>Local</span> <span>adaptation</span> plays a key role in the successful establishment of pest populations in new environments by enabling them to tolerate novel biotic and abiotic conditions experienced outside their native range. However, the <span>genomic underpinnings of such adaptive responses</span> remain unclear, especially for agriculturally important pests. We investigate<span>d</span> <span>population </span>genomic signatures in the tropical/subtropical Queensland fruit fly, <em>Bactrocera tryoni</em>, which has <span>an </span>expanded range <span>encompassing</span> temperate and arid zones in Australia, <span>and</span> tropical <span>zones in the Pacific Islands</span>. Using reduced representation sequencing data from 28 populations, we detected allele frequency shifts associated with the <span>native/invasive </span>status of populations and <span>identified</span> environmental factors that <span>have likely </span>drive<span>n</span> population differentiation. We also determined that precipitation, temperature<span>,</span> and geographic variables explain allelic shifts across the distribution range of <em>B. tryoni</em>. <span>We found</span> spatial heterogeneity in signatures of local adaptation across various climatic conditions in invaded areas. Specifically, disjunct invasive populations in the tropical <span>Pacific Islands</span> and arid zones of Australia <span>were characterised by</span> multiple significantly differentiated single nucleotide polymorphisms (SNPs), some of which were associated with genes with well-understood function in environmental stress (e.g., heat and desiccation) response. However, invasive populations in southeast Australian temperate zones show<span>ed</span> higher gene flow with the native range <span>and </span>lacked a strong local <span>adaptive signal</span>. These results suggest that population connectivity with the native range has <span>differentially </span>affect<span>ed</span> <span>local adaptive patterns in different</span> invasive populations. Overall, our findings provide insights into the evolutionary underpinnings of invasion success of an important horticultural pest in climatically distinct environments.</p>
Data for: Genomic signals of local adaptation across climatically heterogenous habitats in an invasive tropical fruit fly (Bactrocera tryoni)
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Data for: (Epi)genomic adaptation driven by fine geographical scale environmental heterogeneity after recent biological invasions
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Data sets for phylogenomic analyses in: Ant backbone phylogeny resolved by modelling compositional heterogeneity among sites in genomic data
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Data from: Patterns of genomic heterogeneity in a classic field cricket mosaic hybrid zone
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Data from: Demographic history and genomic diversity and divergence in blue tit populations across heterogeneous environments
<p>Understanding the genomic processes underlying local adaptation is a central aim of modern evolutionary biology. This task requires identifying footprints of local selection but also estimating spatio-temporal variation in population demography and variation in recombination rate and diversity along the genome. Here, we investigated these parameters in blue tit populations inhabiting deciduous <i>versus</i> evergreen forests and insular <i>versus</i> mainland areas, in the context of a previously described strong phenotypic differentiation. Neighboring population pairs of deciduous and evergreen habitats were weakly genetically differentiated (<i>F</i><sub>ST</sub> = 0.004 on average), nevertheless with a statistically significant effect of habitat type on the overall genetic structure. This low differentiation was consistent with the strong and long-lasting gene flow between populations, inferred by demographic modeling. In turn, insular and mainland populations were moderately differentiated (<i>F</i><sub>ST</sub> = 0.08 on average), in line with the inference of moderate ancestral migrations, followed by isolation since the end of the last glaciation. Effective population sizes were overall large, yet smaller on the island than on the mainland. Weak and non-parallel footprints of divergent selection between deciduous and evergreen populations were consistent with their high connectivity and the probable polygenic nature of local adaptation in these habitats. In turn, stronger footprints of divergent selection were identified between long isolated insular <i>versus</i> mainland birds, and were more often found in regions of low recombination as expected from theory. Lastly, we identified a genomic inversion on the mainland, spanning 2.8Mb. These results provide insights into the demographic history and genetic architecture of local adaptation in blue tit populations at multiple geographic scales.</p>
Genomic insights into adaptation to heterogeneous environments for the ancient relictual Circaeaster agrestis (Circaeasteraceae, Ranunculales)
<p>Investigating the interaction between environmental heterogeneity and local adaptation is critical to understand the evoutionary history of a species, providing the premise for studying the response of organisms to rapid cimate change. However, for most species how exacty the spatial heterogeneity promotes popuation divergence and how genomic variations contribute to adaptive evolution remain poorly understood.</p> <p>We examine the contributions of geographica and environmental variables to population divergence of the relictual, alpine herb <i>Circaeaster agrestis</i>, as wel as genetic basis of local adaptation using RAD-seq and plastome data.</p> <p><span>We detected</span> significant genetic structure with <span>an extraordinary disequilibrium of genetic diversity</span><span> among regions</span>, and signals of isolation-by-distance along with isolation-by-resistance. The populations were estimated to begin diverging in the late Miocene, along with a possible ancestral distribution of the Hengduan Mountains and adjacent regions. Both environmental gradient and <span>redundancy </span>analyses revealed significant association between genetic variation and temperature variables<i>.</i> <span><span>Genome</span></span><span><span>‐</span></span><span><span>environment association analyses identified 16 putatively adaptive loci mainly related </span></span>mainly <span><span>to biotic and abiotic stress resistance.</span></span></p> <p><span>Our genome wide data provide new insights into the important roe of </span>environmenta heterogeneity<span> in shaping genetic structure, and access the footprints of ocal adaptation in an </span>ancient reictual species, informing future conservation efforts<span>.</span></p> <div> <div class="gtx-trans-icon"> </div> </div>
Data from: Sun skink landscape genomics: assessing how microevolutionary processes shape genetic and phenotypic diversity across a heterogeneous and fragmented landscape
Incorporating genomic data sets into landscape genetic analyses allows for powerful insights into population genetics, explicitly geographical correlates of selection, and morphological diversification of organisms across the geographical template. Here, we utilize an integrative approach to examine gene flow and detect selection, and we relate these processes to genetic and phenotypic population differentiation across South-East Asia in the common sun skink, Eutropis multifasciata. We quantify the relative effects of geographic and ecological isolation in this system and find elevated genetic differentiation between populations from island archipelagos compared to those on the adjacent South-East Asian continent, which is consistent with expectations concerning landscape fragmentation in island archipelagos. We also identify a pattern of isolation by distance, but find no substantial effect of ecological/environmental variables on genetic differentiation. To assess whether morphological conservatism in skinks may result from stabilizing selection on morphological traits, we perform FST–PST comparisons, but observe that results are highly dependent on the method of comparison. Taken together, this work provides novel insights into the manner by which micro-evolutionary processes may impact macro-evolutionary scale biodiversity patterns across diverse landscapes, and provide genomewide confirmation of classic predictions from biogeographical and landscape ecological theory.
Data from: The impact of selection, gene flow and demographic history on heterogeneous genomic divergence: threespine sticklebacks in divergent environments
Heterogeneous genomic divergence between populations may reflect selection, but should also be seen in conjunction with gene flow and drift, particularly population bottlenecks. Marine and freshwater threespine stickleback (Gasterosteus aculeatus) populations often exhibit different lateral armor plate morphs. Moreover, strikingly parallel genomic footprints across different marine-freshwater population pairs are interpreted as parallel evolution and gene reuse. Nevertheless, in some geographic regions like the North Sea and Baltic Sea different patterns are observed. Freshwater populations in coastal regions are often dominated by marine morphs, suggesting that gene flow overwhelms selection, and genomic parallelism may also be less pronounced. We used RAD sequencing for analyzing 28,888 SNPs in two marine and seven freshwater populations in Denmark, Europe. Freshwater populations represented a variety of environments: river populations accessible to gene flow from marine sticklebacks and large and small isolated lakes with and without fish predators. Sticklebacks in an accessible river environment showed minimal morphological and genome-wide divergence from marine populations, supporting the hypothesis of gene flow overriding selection. Allele frequency spectra suggested bottlenecks in all freshwater populations, and particularly two small lake populations. However, genomic footprints ascribed to selection could nevertheless be identified. No genomic regions were consistent freshwater-marine outliers, and parallelism was much lower than in other comparable studies. Two genomic regions previously described to be under divergent selection in freshwater and marine populations were outliers between different freshwater populations. We ascribe these patterns to stronger environmental heterogeneity among freshwater populations in our study as compared to most other studies, although the demographic history involving bottlenecks should also be considered in the interpretation of results.
Data from: Genomic comparisons reveal biogeographic and anthropogenic impacts in the koala (Phascolarctos cinereus); a dietary-specialist species distributed across heterogeneous environments
The Australian koala is an iconic marsupial with specific dietary requirements distributed across heterogeneous environments, over a large geographic range. The distribution and genetic structure of koala populations has been heavily influenced by human actions, specifically habitat modification, hunting and translocation of koalas. There is currently limited information on population diversity and gene-flow at a species-wide scale, or with consideration to the potential impacts of local adaptation. Using species-wide sampling across heterogeneous environments, and high-density genome-wide markers (SNPs and PAVs), we show that most koala populations display levels of diversity comparable to other outbred species, except for those populations impacted by population reductions. Genetic clustering analysis and phylogenetic reconstruction reveals a lack of support for taxonomic classification of three koala sub-species, with only a single evolutionary significant unit supported. Furthermore, ~70% of genetic variance is accounted for at the individual level. The Sydney Basin region is highlighted as a unique reservoir of genetic diversity, having higher diversity levels (ie. Blue Mountains region; AvHecorr=0.20, PL%=68.6). Broad-scale population differentiation is primarily driven by an Isolation by Distance genetic structure model (49% of genetic variance), with clinal local adaptation corresponding to habitat bioregions. Signatures of selection were detected between bioregions, with no single region returning evidence of strong selection. The results of this study show that although the koala is widely considered to be a dietary-specialist species, this apparent specialisation has not limited the koala's ability to maintain gene-flow and adapt across divergent environments as long as the required food source is available.
Data from: Heterogeneous genomic differentiation in marine threespine sticklebacks: adaptation along an environmental gradient
Evolutionary divergence among populations occupying ecologically distinct environments can occur even in the face of on-going gene flow. However, the genetic underpinnings, as well as the scale and magnitude at which this differentiation occurs in marine habitats are not well understood. We investigated the patterns and degree of genomic heterogeneity in threespine sticklebacks (Gasterosteus aculeatus) by assessing genetic variability in 20 nongenic and 20 genic (associated with genes important for freshwater adaptation) microsatellite loci in samples collected from 38 locations spanning the entire Baltic Sea coast to the North Sea boundary. Population divergence (FST ≈ 0.026) and structuring (five genetic clusters) was significantly more pronounced in the genic as compared to nongenic markers (FST ≈ 0.008; no genetic clusters). Patterns of divergence in the genic markers—45% of which were identified as outliers—correlated with local differences in salinity. Yet, a strong positive correlation between divergence in genic and nongenic markers, and their association with environmental factors suggests that adaptive divergence is reducing gene flow across the genome. Apart from providing a clear demonstration of heterogeneous genomic patterns of differentiation in a marine species, the results are indicative of adaptive population structuring across the relatively young Baltic Sea in spite of ample opportunities for gene flow.
Data from: Patterns of cyto-nuclear linkage disequilibrium in Silene latifolia: genomic heterogeneity and temporal stability
Non-random association of alleles in the nucleus and cytoplasmic organelles, or cyto-nuclear linkage disequilibrium (LD), is both an important component of a number of evolutionary processes and a statistical indicator of others. The evolutionary significance of cyto-nuclear LD will depend on both its magnitude and how stable those associations are through time. Here, we use a longitudinal population genetic data set to explore the magnitude and temporal dynamics of cyto-nuclear disequilibria through time. We genotyped 135 and 170 individuals from 16 and 17 patches of the plant species Silene latifolia in Southwestern VA, sampled in 1993 and 2008, respectively. Individuals were genotyped at 14 highly polymorphic microsatellite markers and a single-nucleotide polymorphism (SNP) in the mitochondrial gene, atp1. Normalized LD (D′) between nuclear and cytoplasmic loci varied considerably depending on which nuclear locus was considered (ranging from 0.005–0.632). Four of the 14 cyto-nuclear associations showed a statistically significant shift over approximately seven generations. However, the overall magnitude of this disequilibrium was largely stable over time. The observed origin and stability of cyto-nuclear LD is most likely caused by the slow admixture between anciently diverged lineages within the species' newly invaded range, and the local spatial structure and metapopulation dynamics that are known to structure genetic variation in this system.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.