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403 results for “Landscape Genomics”
Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes
<p>We use computational models based on Direct Coupling Analysis - DCA - trained on PFAM domains of distant distant homologues to accurately predict the polymorphisms segregating in a panel of 61,157 <em>Escherichia coli </em>genomes.</p> <p>We show that the genetic context (<em>i.e. </em>the rest of the protein sequence) strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. Our study also suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions.</p> <p>Please refer to the README file for additional information on the structure of this dataset.</p> <p>Code to analyse this dataset is available at https://github.com/GiancarloCroce/DCA_polymorphism_Ecoli.</p> <p> </p>
The genomic and transcriptional landscape of primary central nervous system lymphoma
<p>Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Despite extensive research, the molecular alterations leading to PCNSL have not been fully elucidated. In order to provide a comprehensive description of the genomic and transcriptional landscape of PCNSL, we here performed whole-genome and transcriptome sequencing and integrative analysis of 51 lymphomas presenting in the CNS, including 42 EBV-negative PCNSL, 6 secondary CNS lymphomas (SCNSL) and 3 EBV+ CNSL and matched controls. The results were compared to an independent validation cohort of 31 FFPE CNSL specimens (PCNSL, n = 19; SCNSL, n = 9; EBV+ CNSL, n = 3) and 36 systemic DLBCL cases outside the CNS.</p> <p>This repository contains tab separated value text files:</p> <p>- Radke_et_al_supplementary_somatic_CNVs.tsv (somatic copy number variations predicted by ACEseq)<br> - Radke_et_al_supplementary_somatic_indels.tsv (somatic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_indels_exonic.tsv (somatic exonic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_mutations_integrated.tsv (table of gene by patients, stating which mutations were observed) <br> - Radke_et_al_supplementary_somatic_mutations_integr_integrated_including_kataegis_counts.tsv (table of gene by patients, stating which mutations were observed, including the count of mutations falling into kataegis hotspots) <br> - Radke_et_al_supplementary_somatic_SNVs.tsv (somatic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_exonic_functional.tsv (somatic exonic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_rescued_by_TiNDA (mutations initially classified as germline, but likely tumor mutations based on VAF modelling by TiNDA)<br> - Radke_et_al_supplementary_somatic_SVs.tsv (somatic structural variations predicted by the DKFZ Sophia workflow)<br> - Radke_et_al_supplementary_RNAseq_numReads_CNSLs.tsv (RNAseq read counts calculated by the DKFZ RNAseq workflow)</p> <p>This repository contains the raw unedited images from the manuscript:</p> <p>- Radke_et_al_Main_Figure_1c_BCL6.tif (raw unedited image for Main Figure 1c - BCL6)<br> - Radke_et_al_Main_Figure_1c_CD10.tif (raw unedited image for Main Figure 1c - CD10)<br> - Radke_et_al_Main_Figure_1c_MUM1.tif (raw unedited image for Main Figure 1c - MUM1)<br> - Radke_et_al_Supplementary_Figure_1a_CD20.tif (raw unedited image for Supplementary Figure 1a - CD20)<br> - Radke_et_al_Supplementary_Figure_1a_EBV.tif (raw unedited image for Supplementary Figure 1a - EBV)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(FFPE).tif (raw unedited image for Supplementary Figure 1a - HE (FFPE))<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_1.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 1)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_2.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 2)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_3.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 3)<br> - Radke_et_al_Supplementary_Figure_1a_Ki67.tif (raw unedited image for Supplementary Figure 1a - Ki67)<br> - Radke_et_al_Supplementary_Figure_1b_EBV_PCR.pptx (raw unedited image for Supplementary Figure 1b - EBV PCR )<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_1.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 1)<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_2.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 2)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-033.tif (raw unedited image for Supplementary Figure 7h - PD-L1 LS-033)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-031.tif (raw unedited image for Supplemnetary Figure 7h - PD-L1 LS-031)</p>
Data and code for the publication "DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Zhao et al, DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis. Genome Biology (2022). </p> <p>All sequence data have been deposited in NCBI GEO accession codes GSE183987 and GSE169497.</p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</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>
Genome data and resources on the recombination landscape and population history of the harlequin fly
<p>This dataset contains phased vcf files of <em>Chironomus riparius, </em>ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files. </p> <p>For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a></p> <ul> <li><strong>phased-vcfs: </strong>Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC.<br> <ul> <li>Hesse in Germany = MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li><strong>multihetsep-files: </strong>Created with msmc-tools. Input files for MSMC2. </li> <li><strong>RepeatMasker: </strong>Raw output of RepeatMasker run. Summary file and file with filtered <em>Cla</em>-element (a transposable element) included.<strong><br></strong></li> <li><strong>MELT: </strong>MELT ouput with added info on population and numbered insertions reflecting all 441 detected <em>Cla </em>insertions.<strong><br></strong></li> <li><strong>RepeatOBserver: </strong>Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. <strong><br></strong></li> <li><strong>MSMC2: </strong>Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. <strong><br></strong></li> <li><strong>iSMC: </strong>Recombination rate rho in 10 kb windows and 100 kb windows along the genome. <strong><br></strong></li> <li><strong>bedtools closest ismc 10 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 10 kb windows.<strong><br></strong></li> <li><strong>bedtools closest ismc 100 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 100 kb windows.</li> <li><strong>input-files figures: </strong>Supporting files needed to create figures.<strong><br></strong></li> </ul>
ScRAPv20230731: Telomere-to-telomere assemblies of 142 strains characterize the genome structural landscape in Saccharomyces cerevisiae
<p><strong><em>Saccharomyces cerevisiae </em>Reference Assembly Panel (ScRAP) v20230731 </strong>></p> <p>The haplotype-resolved and/or collapsed T2T genome assemblies for 142 <em>S. cerevisiae</em> strains isolated from diverse geographical and ecological niches.</p>
Data from: Gene flow, ancient polymorphism, and ecological adaptation shape the genomic landscape of divergence among Darwin's finches
Genomic comparisons of closely related species have identified "islands" of locally elevated sequence divergence. Genomic islands may contain functional variants involved in local adaptation or reproductive isolation and may therefore play an important role in the speciation process. However, genomic islands can also arise through evolutionary processes unrelated to speciation, and examination of their properties can illuminate how new species evolve. Here, we performed scans for regions of high relative divergence (FST) in 12 species pairs of Darwin's finches at different genetic distances. In each pair, we identify genomic islands that are, on average, elevated in both relative divergence (FST) and absolute divergence (dXY). This signal indicates that haplotypes within these genomic regions became isolated from each other earlier than the rest of the genome. Interestingly, similar numbers of genomic islands of elevated dXY are observed in sympatric and allopatric species pairs, suggesting that recent gene flow is not a major factor in their formation. We find that two of the most pronounced genomic islands contain the ALX1 and HMGA2 loci, which are associated with variation in beak shape and size, respectively, suggesting that they are involved in ecological adaptation. A subset of genomic island regions, including these loci, appears to represent anciently diverged haplotypes that evolved early during the radiation of Darwin's finches. Comparative genomics data indicate that these loci, and genomic islands in general, have exceptionally low recombination rates, which may play a role in their establishment.
Genomic landscape of introgression from the ghost lineage in a gobiid fish uncovers the generality of forces shaping hybrid genomes
<p>Extinct lineages can leave legacies in the genomes of extant lineages through ancient introgressive hybridization. The patterns of genomic survival of these extinct lineages provide insight into the role of extinct lineages in current biodiversity. However, our understanding of the genomic landscape of introgression from extinct lineages remains limited due to challenges associated with locating the traces of unsampled "ghost" extinct lineages without ancient genomes. Herein, we conducted population genomic analyses on the East China Sea (ECS) lineage of <em>Chaenogobius annularis</em>, which was suspected to have originated from ghost introgression, with the aim of elucidating its genomic origins and characterizing its landscape of introgression. By combining phylogeographic analysis and demographic modeling, we demonstrated that the ECS lineage originated from ancient hybridization with an extinct ghost lineage. Forward simulations based on the estimated demography indicated that the statistic <em>γ</em> of the HyDe analysis can be used to distinguish the differences in local introgression rates in our data. Consistent with introgression between extant organisms, we found reduced introgression from extinct lineage in regions with low-recombination rates and with functional importance, thereby suggesting a role of linked selection that has eliminated the extinct lineage in shaping the hybrid genome. Moreover, we identified enrichment of repetitive elements in regions associated with ghost introgression, which was hitherto little-known but was also observed in the reanalysis of published data on introgression between extant organisms. Overall, our findings underscore the unexpected similarities in the characteristics of introgression landscapes across different taxa, even in cases of ghost introgression.</p>
Selection shapes the genomic landscape of introgressed ancestry in a pair of sympatric sea urchin species
<p>A growing number of recent studies have demonstrated that introgression is common across the tree of life. However, we still have a limited understanding of the fate and fitness consequence of introgressed variation at the whole-genome scale across diverse taxonomic groups. Here, we implemented a phylogenetic hidden Markov model to identify and characterize introgressed genomic regions in a pair of well-diverged, non-sister sea urchin species: <em>Strongylocentrotus</em> <em>pallidus</em> and <em>S. droebachiensis</em>. Despite the old age of introgression, a sizable fraction of the genome (1% - 5%) exhibited introgressed ancestry, including numerous genes showing signals of historical positive selection that may represent cases of adaptive introgression. One striking result was the overrepresentation of hyalin genes in the identified introgressed regions despite observing considerable overall evidence of selection against introgression. There was a negative correlation between introgression and chromosome gene density, and two chromosomes were observed with considerably reduced introgression. Relative to the non-introgressed genome-wide background, introgressed regions had significantly reduced nucleotide divergence (<em>d</em><sub>XY</sub>) and overlapped fewer protein-coding genes, coding bases, and genes with a history of positive selection. Additionally, genes residing within introgressed regions showed slower rates of evolution (<em>d</em><sub>N</sub>, <em>d</em><sub>S</sub>, <em>d</em><sub>N</sub>/<em>d</em><sub>S</sub>) than random samples of genes without introgressed ancestry. Overall, our findings are consistent with widespread selection against introgressed ancestry across the genome and suggest that slowly evolving, low-divergence genomic regions are more likely to move between species and avoid negative selection following hybridization and introgression.</p>
Data from: Genomic landscapes of divergence among island bird populations: evidence of parallel adaptation but at different loci?
<p>When populations colonise new environments they may be exposed to novel selection pressures but also suffer from extensive genetic drift due to founder effects, small population sizes, and limited interpopulation gene flow. Genomic approaches enable us to study how these factors drive divergence, and disentangle neutral effects from differentiation at specific loci due to selection. Here, we investigate patterns of genetic diversity and divergence using whole-genome resequencing (> 22X coverage) in Berthelot's pipit (<em>Anthus berthelotii</em>), a passerine endemic to the islands of three north Atlantic archipelagos. Strong environmental gradients, including in pathogen pressure, across populations in the species range, make it an excellent system in which to explore traits important in adaptation and/or incipient speciation. Firstly, we quantify how genomic divergence accumulates across the speciation continuum, i.e., among Berthelot's pipit populations, between subspecies across archipelagos, and between Berthelot's pipit and its mainland ancestor, the tawny pipit (<em>Anthus campestris</em>). Across these colonisation timeframes (2.1 million – <em>ca.</em> 8,000 years ago), we identify highly differentiated loci within genomic islands of divergence and conclude that the observed distributions align with expectations for non-neutral divergence. Characteristic signatures of selection are identified in loci associated with craniofacial/bone and eye development, metabolism, and immune response between population comparisons. Interestingly, we find limited evidence for repeated divergence of the same loci across the colonisation range but do identify different loci putatively associated with the same biological traits in different populations, likely due to parallel adaptation. Incipient speciation across these island populations, in which founder effects and selective pressures are strong, may therefore be repeatedly associated with morphology, metabolism, and immune defence.</p>
Supplementary Data - Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions
<p><strong>File: 50K_GenotypesEU_raw_UHOH_SoySNP50K.csv.tgz </strong></p> <p>Genotyping data of SoySNP50k SNP array of 170 European soybean varieties.</p> <p>The array includes 51.955 SNP markers.</p> <p>Genotypes of each variety are in columns and each row is a SNP marker. Naming of markers follows the annotation of the soybean genome.</p> <p><strong>File: EUvarieties_infos.csv </strong></p> <p>Description of European varieties</p> <p>Contains variety name, country of origin, EU region and maturity group assignment.</p> <p> </p> <p><strong>File: Supplementary_Data_Haupt_Schmid.xlsx</strong></p> <p>Additional data derived from data analysis. Description of data contained within file (Worksheet "Summary")</p> <p> </p>
The genomic landscapes of desert birds form over multiple time scales
<p><span>Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, which could indicate that multiple spatial models better explain divergence in different portions of the genome. We test whether alternative geographic predictors of intraspecific differentiation vary across the genome in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we characterized the genomic landscapes across species and modeled five predictors that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across the ten species, influenced by varying levels of population structuring and admixture between deserts. General dissimilarity matrix modeling indicated that the best-fit models differed from the whole genome and partitions along the genome. Almost all of the historical and contemporary mechanisms were important in explaining genetic distance, particularly historical and contemporary environment, while contemporary abundance, position of the barrier to gene flow, and distance explained relatively less. Individual species have significantly different patterns of genomic variation. These results illustrate that the genomic landscape of differentiation was influenced by alternative geographic factors operating on different portions of the genome.</span></p>
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Evolution of the correlated genomic variation landscape across a divergence continuum in the genus Castanopsis
<p>The heterogeneous landscape of genomic variation has been well documented in population genomic studies. However, disentangling the intricate interplay of evolutionary forces influencing the genetic variation landscape over time remains challenging. In this study, we assembled a chromosome-level genome for <em>Castanopsis eyrei</em> and sequenced the whole genomes of 276 individuals from 12 <em>Castanopsis</em> species, spanning a broad divergence continuum. We found highly correlated genomic variation landscapes across these species. Furthermore, variations in genetic diversity and differentiation along the genome were strongly associated with recombination rates and gene density. These results suggest that long-term linked selection and conserved genomic features have contributed to the formation of a common genomic variation landscape. By examining how correlations between population summary statistics change throughout the species divergence continuum, we determined that background selection alone does not fully explain the observed patterns of genomic variation; the effects of recurrent selective sweeps must be considered. We further revealed that extensive gene flow has significantly influenced patterns of genomic variation in <em>Castanopsis</em> species. The estimated admixture proportion correlated positively with recombination rate and negatively with gene density, supporting a scenario of selection against gene flow. Additionally, putative introgression regions exhibited strong signals of positive selection, an enrichment of functional genes, and reduced genetic burdens, indicating that adaptive introgression has played a role in shaping the genomes of hybridizing species. This study provides insights into how different evolutionary forces have interacted in driving the evolution of the genomic variation landscape.</p>
Constructing a high-density linkage map to infer the genomic landscape of recombination rate variation in European Aspen (Populus tremula)
<p>Data sets and files for linkage map construction and for inferring recombination rate variation in <em>Populus tremula</em>. Associated scripts for analyses can be found at <a href="https://github.com/parkingvarsson/Recombination_rate_variation">https://github.com/parkingvarsson/Recombination_rate_variation</a> </p>
Data from: Genomic landscapes of divergence among island bird populations: evidence of parallel adaptation but at different loci?
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Optimizing sampling design for landscape genomics
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Data from: Gene flow, ancient polymorphism, and ecological adaptation shape the genomic landscape of divergence among Darwin's finches
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Genomic landscape of introgression from the ghost lineage in a gobiid fish uncovers the generality of forces shaping hybrid genomes
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Selection shapes the genomic landscape of introgressed ancestry in a pair of sympatric sea urchin species
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Allen Brain Atlas
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International Brain Laboratory public data
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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.