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394 results for “Genomic Diversity”
The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high-levels of lineage-specific diversity driven by recombination
<p><em>Aspergillus fumigatus </em>is a deadly agent of human fungal disease, where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on <em>de novo</em> assemblies offer a promising alternative to reference-based genomics, with the potential to address the full genetic repertoire of a species. Here, we use a combination of population genomics, phylogenomics, and pan-genomics to assess population structure and recombination frequency, phylogenetically structured gene presence-absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes in <em>A. fumigatus</em>. We provide evidence for three distinct populations of <em>A. fumigatus</em>, structured by both gene variation (SNPs and indels) and distinct gene presence-absence variation with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism, hinting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Despite low levels of outcrossing, <em>A. fumigatus</em> demonstrated a large pan-genome including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference based approach for evaluating intraspecific variation, and the power of combined genomic approaches to elucidate population structure, genetic diversity, and the putative ecological drivers of clinically relevant fungi.</p> <p>Accompanying manuscript is available as preprint at <a href="https://dx.doi.org/10.1101/2021.12.12.472145">https://dx.doi.org/10.1101/2021.12.12.472145</a> </p> <p>Lotus A. Lofgren, Brandon S. Ross, Robert A. Cramer, Jason E. Stajich. Combined Pan-, Population-, and Phylo-Genomic Analysis of <em>Aspergillus fumigatus</em> Reveals Population Structure and Lineage-Specific Diversity bioRxiv 2021.12.12.472145; doi: https://doi.org/10.1101/2021.12.12.472145</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Geochemical, physicochemical, and genomic data from a continental-scale survey of microbial diversity in Antarctic soils (2003-2023)
This data package offers comprehensive insights into Antarctic soil microbial diversity and composition. From 2003 to 2023, a total of 186 samples were collected from diverse locations spanning the Antarctic Peninsula to East Antarctica, representing a wide range of environmental gradients and climatic conditions. Soils were stored at -20°C to preserve their integrity for downstream analyses. This data package integrates cultivation-independent sequencing of prokaryotic and fungal communities alongside a robust cultivation-dependent culture collection to enable direct comparisons across microbial diversity assessment methods. Accompanying geochemical, physicochemical, and environmental parameters provide critical context for biogeographical analyses, offering a valuable resource for studying microbial adaptations and community dynamics in extreme Antarctic environments.
Diversity in the Expressed Genomic Host Response to Myocardial Infarction - Validation Dataset
<p>External validation was performed by separately hierarchically clustering 934 patients with STEMI in an independent cohort[1] into 2 groups (232 and 702 individuals) based on Illumina HT12v4-profiled PBMC expression (median time 21 hour between cardiac catheterization and blood sampling). Probes with most variable expression intensities (SD≥0.5, 216 probes, excluding ribosomal genes) were used. From the 20 most differentially expressed genes in the discovery cohort described in the manuscript Toma et al. 2022 [2], 19 were available in the validation cohort.</p> <p>Column names include the Illumina identifyer and the mapped gene name as used in the discovery cohort. Values are log2-transformed, quantile-normalized, batch-corrected values, see also [1] for methodological details.</p> <p>Acknowledgement:</p> <p>This work is supported by LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by means of the Free State of Saxony within the framework of the excellence initiative.</p> <p> </p> <p>1) Teren A, Kirsten H, Beutner F, Scholz M, Holdt LM, Teupser D, Gutberlet M, Thiery J, Schuler G, Eitel I. Alteration of multiple leukocyte gene expression networks is linked with magnetic resonance markers of prognosis after acute st-elevation myocardial infarction. <em>Scientific Reports</em>. 2017;7:41705</p> <p>2) Toma A, dos Santos C, Burzyńska B, Góra M, Kiliszek M, Stickle N, Kirsten H, Kosyakovsky L, Wang B, van Diepen S, Epelman S, Szekely Y, Marshall JC, Billia F, Lawler PR (2022), Diversity in the Expressed Genomic Host Response to Myocardial Infarction, submitted.</p>
"Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" - Genomes and annotation
<p>File-dump associated with the manuscript:</p> <p>"<strong>Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" (Not yet published)</strong></p> <p>MGVs refer to the viral genome database in the publication: https://www.nature.com/articles/s41564-021-00928-6 </p> <p> </p> <p>Following uploaded:</p> <p>File 1: VOG Markers in vOTUs/vMAGs and MGV genomes</p> <p>File 2: Viral Tree Newick file with vOTUs/vMAGs and MGV genomes</p> <p>File 3: All vOTUs/vMAGs genomes</p> <p>File 4: Master table annotation of vOTUs/vMAGs</p> <p>File 5: Centenarian bacterial isolate proviruses</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 3,076 Campylobacter jejuni isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 2,794-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/4/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 3,076 <em>Campylobacter jejuni </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of Sequence Type [ST]). In total, 476 different STs are represented in this dataset, with ST21, ST50, ST48, ST45 and ST257 being the most represented ones and, together, corresponding to 29.1% of the dataset.</p> <p>File “Cj_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Cj_profiles_wgMLST.tsv” corresponds to a tab separated file with the 2,794-loci wgMLST profiles of each solate presented in the metadata file. The files “profiles/Cj_profiles_cgMLST_95.tsv”, “profiles/Cj_profiles_cgMLST_98.tsv” and “profiles/Cj_profiles_cgMLST_100.tsv” correspond to a 1,012-loci, 987-loci and 29-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>C. jejuni</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://pubmlst.org/organisms/campylobacter-jejunicoli">PubMLST</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 3,539 samples. The majority of them are associated with the INNUENDO project (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>). The remaining ones are associated with five BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB31119">PRJEB31119</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB38253">PRJEB38253</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB40238">PRJEB40238</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4165">PRJEB4165</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA350537">PRJNA350537</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 3,076 isolates passed this curation step and were included in the final dataset. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 2,794-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/4">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 2,794-loci wgMLST profiles of the 3,076 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 1,012-loci, 987-loci and 29-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,999 Escherichia coli isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 7,601-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/5/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,999 <em>Escherichia coli </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 411 different serotypes are represented in this dataset, with O157:H7 being the most represented one, corresponding to 37.1% of the dataset.</p> <p>File “Ec_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Ec_profiles_wgMLST.tsv” corresponds to a tab separated file with the 7,601-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Ec_profiles_cgMLST_95.tsv”, “profiles/Ec_profiles_cgMLST_98.tsv” and “profiles/Ec_profiles_cgMLST_100.tsv” correspond to a 2,826-loci, 2,704-loci and 465-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>E. coli </em>genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/ecoli">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 2,688 samples associated with three BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA230969">PRJNA230969</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB27020">PRJEB27020</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA248042">PRJNA248042</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,999 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with <a href="https://github.com/B-UMMI/seq_typing">seq_typing</a> v2.2. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 7,601-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/5">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 7,601-loci wgMLST profiles of the 1,999 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 2,826-loci, 2,704-loci and 465-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,434 Salmonella enterica isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 8,558-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/8/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,434 <em>Salmonella enterica </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 125 different serotypes are represented in this dataset, with Typhimurium (including monophasic), Enteritidis and Infantis being the most represented ones and, together, corresponding to 56.2% of the dataset.</p> <p>File “Se_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Se_profiles_wgMLST.tsv” corresponds to a tab separated file with the 8,558-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Se_profiles_cgMLST_95.tsv”, “profiles/Se_profiles_cgMLST_98.tsv” and “profiles/Se_profiles_cgMLST_100.tsv” correspond to a 3,261-loci, 3,179-loci and 874-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>S. enterica</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/senterica">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,779 samples associated with four BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB16326">PRJEB16326</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB20997">PRJEB20997</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB30335">PRJEB30335</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39988">PRJEB39988</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,434 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>). wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 8,558-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/8">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31<sup>st</sup>, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 8,558-loci wgMLST profiles of the 1,434 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 3,261-loci, 3,179-loci and 874-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Bioinformatic pipeline: Genomic diversity landscape of the honey bee gut microbiota
<p>This data-set describes the full bioinformatic pipeline used to analyze 54 metagenomic samples of the honey bee gut microbiota. Each sample was isolated from an individual honey bee, and all samples originate from two colonies of the Engel laboratory at the University of Lausanne, Switzerland. The full raw data-set is available from the sequence-read archive: SRP150166.</p> <p>A publication based on this analysis is currently under review, with the title: "Genomic diversity landscape of the honey bee gut microbiota", and an upload to Biorxiv is also underway.</p> <p>The data-set contains tar-balls for the different main workflows of the analysis. Dowload and unpack to view the contents (tar -zxvf filename.tar.gz). For each workflow, all directories contain README.txt files, describing the contents of the directory. Due to size constraints, some intermediate files have been omitted, and some workflows are demonstrated for a subset of the data. However, the full analysis can be reproduced from the raw data, using the provided scripts.</p> <p>Scripts are included within workflow directories, and are also provided as a separate tar-ball for convenience. All perl-scripts come with documentation, which can be viewed by typing: "perl script_name.pl -h". For R scripts, the usage is indicated as a comment in the top lines of each script. Note that many of the scripts require specific input-files to be present in the run-directory. Their usage is demonstrated within the workflow directories in bash-scripts (*.sh). Commands used for generating plots and some statistics are given within workflow directories in text-files "R.commands" when applicable.</p> <p>Aside from custom code, the pipeline also utilizes various open-source Software packages, which are detailed in the file "software_dependencies.txt". Note, while many of the scripts will run fast on any computer, some steps of the pipeline are computationally demanding, and will require significant computing time, as well as storage space. When scripts are known to be time-consuming, this is indicated in the script help message.</p> <p> </p> <p> </p> <p> </p>
Data and Code for "Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements"
<p>This is a release of the gitlab repository "meta-methylome" (https://gitlab.com/okartal/meta-methylome.git) that, in addition to the code, also contains the resulting genomic data.</p> <p>Extract the directory on the command line using</p> <pre><code class="language-bash">$ tar -xhzvf meta-methylome.tar.gz</code></pre> <p>to preserve the symbolic links.</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1, dated using <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with nodes contain the mean and variance of tsdate's posterior distribution on node time. To access these values, we can use:</p> <pre><code>import json node = ts.node(10000) metadata_dict = json.loads(node.metadata) print("The mean of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["mn"])) print("The variance of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["vr"]))</code></pre> <p>Age estimates for each variant site can be derived from the mean of the age estimates of the upper and lower bounding nodes of the oldest mutation associated with a site. tsdate includes <a href="https://tsdate.readthedocs.io/en/latest/python-api.html?highlight=sites_time_from_ts#tsdate.sites_time_from_ts">a function to find the age estimates of all sites in the tree sequence</a>:</p> <pre><code>import tsdate site_times = tsdate.sites_time_from_ts(ts, node_selection='arithmetic')</code></pre> <p>This returns a numpy array which has a length equal to the number of sites.</p> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
Supplementary phylogenetic data for Rouïl et. al. 2020 "The protector within: Comparative genomics of APSE phages across aphids reveals rampant recombination and diverse toxin arsenals"
<p>Supplementary phylogenetic data for Rouïl <em>et. al.</em> 2020 "The protector within: Comparative genomics of APSE phages across aphids reveals rampant recombination and diverse toxin arsenals"</p> <p> </p> <p>The data set consists of the following sub-directories:</p> <p>1) "APSE_conserved_proteins_alns": Single-copy conserved genes codon sequences and alignments in FASTA format.</p> <p>2) "APSE_phylogeny": Files used for APSE phylogenetic and recombination analyses.</p> <p>3) "APSE_reannotations": GenBank-formatted files of the assemblies and re-annotations of APSE phages. Newly-sequenced phages deposited at the European nucleotide Archive are also included. ***New in this version***</p> <p>4) "APSE_toxin_lyzozyme": Files used for APSE toxin-cassette and lyzozyme-related gene phylogenies.</p> <p>5) "Arsenophonus_PHASTER": PHASTER phage annotation output files organised by organisim and contig/scaffold.</p> <p>6) "Hamiltonella_drafts": Newly-sequenced low-coverage draft <em>Hamiltonella</em> genomes in FASTA format.</p> <p>7) "Hamiltonella_phylogeny": files used for <em>Hamiltonella</em> phylogenetic analysis.</p> <p> </p> <p>See enclosed README.txt file for more details.</p> <p> </p> <p>* ver. 1.1.1: Updated annotations for APSE genomes including inteins missing in previous annotation files.</p>
Genome size influences plant growth and biodiversity responses to nutrient fertilization in diverse grassland communities
Experiments comparing diploids with polyploids and in single grassland sites show that nitrogen and/or phosphorus availability influences plant growth and community composition dependent on genome size; specifically plants with larger genomes grow faster under nutrient enrichments relative to those with smaller genomes. However, it is unknown if these effects are specific to particular site localities with speciifc plant assemblages, climates, and historical contingencies. To determine the generality of genome size dependent growth responses to nitrogen and phosphorus fertilisation, we combined genome size and species abundance data from 27 coordinated grassland nutrient addition experiments in the Nutrient Network that occur in the Northern Hemisphere across a range of climates and grassland communities. We found that after nitrogen treatment, species with larger genomes generally increased more in cover compared to those with smaller genomes, potentially due to a release from nutrient limitation. Responses were strongest for C3 grasses and in less seasonal, low precipitation environments, indicating that genome size effects on water-use-efficiency modulates genome size-nutrient interactions. Cumulatively the data suggest that genome size is informative and improves predictions of species’ success in grassland communities.
Chromosomal-level genome assembly of the scimitar‐horned oryx: insights into diversity and demography of a species extinct in the wild
<p>Captive populations provide a valuable insurance against extinctions in the wild. However, they are also vulnerable to the negative impacts of inbreeding, selection and drift. Genetic information is therefore considered a critical aspect of conservation management. Recent developments in sequencing technologies have the potential to improve the outcomes of management programmes; however, the transfer of these approaches to applied conservation has been slow. The scimitar‐horned oryx (<i>Oryx dammah)</i> is a North African antelope that has been extinct in the wild since the early 1980s and is the focus of a large‐scale and long‐term reintroduction project. To enable the selection of suitable founder individuals, facilitate post‐release monitoring and improve captive breeding management, comprehensive genomic resources are required. Here, we used 10X Chromium sequencing together with Hi‐C contact mapping to develop a chromosomal‐level genome assembly for the species. The resulting assembly contained 29 chromosomes with a scaffold N50 of 100.4 Mb, and displayed strong chromosomal synteny with the cattle genome. Using resequencing data from six additional individuals, we demonstrated relatively high genetic diversity in the scimitar‐horned oryx compared to other mammals, despite it having experienced a strong founding event in captivity. Additionally, the level of diversity across populations varied according to management strategy. Finally, we uncovered a dynamic demographic history that coincided with periods of climate variation during the Pleistocene. Overall, our study provides a clear example of how genomic data can uncover valuable insights into captive populations and contributes important resources to guide future management decisions of an endangered species.</p>
Overcoming the Dichotomy: New Insights into the Genomic Diversity of Open and Isolated European Populations
<p>The dataset includes autosomal data of 227 individuals of 9 Italian populations (Aosta, Benetutti, Carloforte, Lessinia Cimbrians, North Sardinia, Sappada, Sauris, Sulcis Iglesiente and Timau) genotyper with the GenoChip 2.0.</p>
Functional genomics and co-occurrence in a diverse tropical tree genus: The roles of drought and defense related genes
<p>Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co-occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well-characterized by the set of functional traits generally measured by ecologists. To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co-occurrence in the hyper-diverse genus <em>Ficus</em>. Our study focused on over 800 genes related to drought and defense, providing detailed information on how these genes may contribute to the diversity of <em>Ficus</em> species. We find widespread and consistent evidence of the importance of defense gene dissimilarity in co-occurring species, providing genetic support for what would be expected under the Janzen-Connell mechanism. We also find that drought-related gene sequence similarity is related to <em>Ficus</em> co-occurrence, indicating that similar responses to drought promote co-occurrence. We provide the first detailed functional genomic evidence of how drought- and defense-related genes simultaneously contribute to the local co-occurrence in a hyper-diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co-occurrence in hyper-diverse tropical tree genera.</p>
One to host them all: genomics of the diverse bacterial endosymbionts of the spider Oedothorax gibbosus
<p>Bacterial endosymbionts of the groups <em>Wolbachia</em>, <em>Cardinium </em>and <em>Rickettsiaceae </em>are well-known for their diverse effects on their arthropod hosts, ranging from mutualistic relationships to reproductive phenotypes. Here, we analyzed a unique system in which the dwarf spider <em>Oedothorax gibbosus</em> is co-infected with up to five different endosymbionts affiliated with <em>Wolbachia</em>, ‘<em>Candidatus </em>Tisiphia’ (formerly Torix group <em>Rickettsia</em>), <em>Cardinium, </em>and <em>Rhabdochlamydia</em>. Using short-read genome sequencing data, we show that the endosymbionts are heterogeneously distributed among <em>O. gibbosus</em> populations and are frequently found co-infecting spider individuals. To study this intricate host-endosymbiont system on a genome resolved level, we used long-read sequencing to reconstruct closed genomes of the <em>Wolbachia</em>, ‘<em>Ca. </em>Tisiphia’ and <em>Cardinium </em>endosymbionts. We provide insights in the ecology and evolution of the endosymbionts and shed light on the interactions with their spider host. We detected high quantities of transposable elements in all endosymbiont genomes and provide evidence that ancestors of the <em>Cardinium</em>, ‘<em>Ca. </em>Tisiphia’ and <em>Wolbachia </em>endosymbionts have co-infected the same hosts in the past. Our findings contribute to broadening our knowledge about endosymbionts infecting one of the largest animal phyla on earth, and show the usefulness of transposable elements as an evolutionary “contact-tracing” tool.</p>
Deep-sequencing of viral genomes from treatment-naive HIV-infected persons shows positive association between intrahost genetic diversity and viral load
<p><strong><span>Background:</span></strong><span> Infection with human immunodeficiency virus type 1 (HIV) typically results from transmission of a small and genetically uniform viral population. Following transmission, the virus population becomes more diverse because of recombination and acquired mutations through genetic drift and selection. Viral intrahost genetic diversity remains a major obstacle to the cure of HIV; however, there is a disagreement whether intrahost viral genetic diversification associates positively or negatively with disease progression and progression markers. Viral load is a key progression marker and understanding its relationship to viral intrahost genetic diversity could help design future strategies for HIV monitoring and treatment.</span></p> <p><span><strong>Methods:</strong> </span><span>We analyzed deep-sequenced viral genomes from 2,650 treatment-naive HIV-infected persons to measure the intrahost genetic diversity of 2,447 genomic codon positions as calculated by Shannon entropy. We tested for associations between viral load (VL) and amino acid (AA) entropy accounting for sex, age, race, duration of infection, and HIV population structure.</span></p> <p><strong><span>Results:</span></strong><span><strong> </strong>We confirmed that the intrahost genetic diversity is highest in the <em>env</em> gene. Furthermore, we showed that mean Shannon entropy is significantly associated with VL, especially in infections of >24 months duration. We identified 16 significant associations between VL (p-value<2.0x10<sup>-5</sup>) and Shannon entropy at AA positions which in our association analysis explained 13% of the variance in VL.</span></p> <p><strong><span>Conclusions: </span></strong><span>Our results elucidate that viral intrahost genetic diversity is associated with VL and could be used as a better disease progression marker than HIV consensus sequence variants, especially in infections of longer duration. We emphasize that viral intrahost diversity should be considered when studying viral genomes and infection outcomes.</span></p>
Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. Supplementary data.
<p>Original data linked to the publication Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. It includes, Supp. Table 1: Genomic data; Supp. Table 2: Characterization data; Supp. Table 3: sugar and acids data; Supp Table Supp. Table 4: Voaltile organic compounds data; Supp Table 5: Germplasm details; Supp. table 6: Cromatographic parameters</p>
Relationship between genome-wide and MHC class I and II genetic diversity and complementarity in a nonhuman primate
<p>Although mate choice is expected to favor partners with advantageous genetic properties, the relative importance of genome-wide characteristics, such as overall heterozygosity or kinship, versus specific loci, is unknown. To disentangle genome-wide and locus-specific targets of mate choice, we must first understand congruence in global and local variation within the same individual. This study compares genetic diversity, both absolute and relative to other individuals (e.g., complementarity), assessed across the genome to that found at the major histocompatibility complex (MHC), a hyper-variable gene family integral to immune system function and implicated in mate choice across species. Using DNA from 22 captive olive baboons (<em>Papio anubis</em>), we conducted double digest restriction-site associated DNA sequencing to estimate genome-wide heterozygosity and kinship and sequenced two class I and two class II MHC loci. We found that genome-wide diversity was not associated with MHC diversity, and that diversity at class I MHC loci was not correlated with diversity at class II loci. Additionally, kinship was a significant predictor of the number of MHC alleles shared between dyads at class II loci. Our results provide further evidence of the strong selective pressures maintaining genetic diversity at the MHC in comparison to other randomly selected sites throughout the genome. Furthermore, our results indicate that class II MHC disassortative mate choice may mediate inbreeding avoidance in this population. Our study suggests that mate choice favoring genome-wide genetic diversity is not always synonymous with mate choice favoring MHC diversity, and highlights the importance of controlling for kinship when investigating MHC-associated mate choice.</p>
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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.