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opencc-by-4.0Aug 2024View details →

Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry

Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry.  Dataset includes 1)  model and analysis, and 2)  supplemental data.  In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file.    Acknowledgments We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory.  We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231).  Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9  1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA. 2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA. 3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106 4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA 5Department of Natural Resources and the Environment, University of New Hampshire; Durham, NH, USA 03824 6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ, 85721, USA 7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France 8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210 9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA. 10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA. 11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA. 12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia 13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA

opencc-by-4.0May 2024View details →

RNA-seq dataset for Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism

Data to be used along with <a href="https://github.com/neelroop/asd-development-coexpression-2013">code</a> from 2013 paper that was originally on a site hosted at UCLA, but may no longer be accessible.

opencc-by-nc-sa-4.0Aug 2024View details →

BacSPaD: A robust bacterial strains' pathogenicity resource based on integrated and curated genomic metadata

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5,502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface, accessible at<a href="https://bacspad.altrabio.com/"> </a><a href="https://bacspad.altrabio.com/">https://bacspad.altrabio.com</a>.

opencc-by-4.0May 2023View details →

From genomics to integrative species delimitation? The case study of the Indo-Pacific Pocillopora corals

With the advent of genomics, sequencing thousands of loci from hundreds of individuals now appears feasible at reasonable costs, allowing complex phylogenies to be resolved. This is particularly relevant for cnidarians, for which insufficient data is available due to the small number of currently available markers and obscures species boundaries. Difficulties in inferring gene trees and morphological incongruences further blur the study and conservation of these organisms. Yet, can genomics alone be used to delimit species? Here, focusing on the coral genus Pocillopora, whose colonies play key roles in Indo-Pacific reef ecosystems but have challenged taxonomists for decades, we explored and discussed the usefulness of multiple criteria (genetics, morphology, biogeography and symbiosis ecology) to delimit species of this genus. Phylogenetic inferences, clustering approaches and species delimitation methods based on genome-wide single-nucleotide polymorphisms (SNP) were first used to resolve Pocillopora phylogeny and propose genomic species hypotheses from 356 colonies sampled across the Indo-Pacific (western Indian Ocean, tropical southwestern Pacific and south-east Polynesia). These species hypotheses were then compared to other lines of evidence based on genetic, morphology, biogeography and symbiont associations. Out of 21 species hypotheses delimited by genomics, 13 were strongly supported by all approaches, while six could represent either undescribed species or nominal species that have been synonymised incorrectly. Altogether, our results support (1) the obsolescence of macromorphology (i.e., overall colony and branches shape) but the relevance of micromorphology (i.e., corallite structures) to refine Pocillopora species boundaries, (2) the relevance of the mtORF (coupled with other markers in some cases) as a diagnostic marker of most species, (3) the requirement of molecular identification when species identity of colonies is absolutely necessary to interpret results, as morphology can blur species identification in the field, and (4) the need for a taxonomic revision of the genus Pocillopora. These results give new insights into the usefulness of multiple criteria for resolving Pocillopora, and more widely, scleractinian species boundaries, and will ultimately contribute to the taxonomic revision of this genus and the conservation of its species.   This deposit contains the data related to Oury N, Noël C, Mona S, Aurelle D, Magalon H (2023) From genomics to integrative species delimitation? The case study of the Indo-Pacific Pocillopora corals. Mol Phylogenet Evol 107803. doi:10.1016/j.ympev.2023.107803 See 0_README.txt for more content details.

opencc-by-4.0Jul 2020View details →

Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)   CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.

opencc-by-4.0Nov 2023View details →

SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae

ParametersForce field: AMBER ff19SBWater type: TIP3PIons: NaCl  Ligand topology force field: GAFF2Temperature: 298kPressure: 1 barminimization step:  20000 on  5 nanosecondsinitial velocity is changed by changing "ntx" and "ig"C2: ntx = 5 , ig = 8C3: ntx = 2 , ig = 5 Uploads- 1. Zip file of all 3 main files2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)3. Zip file of C14. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)5. Zip file of C26. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)7. Zip file of C38. Zip and unzip file of Initial PDB of complex prior to MD simulation with Post MD PDB (C1, C2, C3)9. Zip file of topology files for C1, C2, and C3

opencc-zeroJan 2024View details →

High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers

In species with large and complex genomes such as conifers, dense linkage maps are a useful for supporting genome assembly and laying the genomic groundwork at the structural, populational and functional levels. However, most of the 600+ extant conifer species still lack extensive genotyping resources, which hampers the development of high-density linkage maps. In this study, we developed a linkage map relying on 21,570 SNP makers in Sitka spruce (Picea sitchensis [Bong.] Carr.), a long-lived conifer from western North America that is widely planted for productive forestry in the British Isles. We used a single-step mapping approach to efficiently combine RAD-Seq and genotyping array SNP data for 528 individuals from two full-sib families. As expected for spruce taxa, the saturated map contained 12 linkages groups with a total length of 2,142 cM. The positioning of 5,414 unique gene coding sequences allowed us to compare our map with that of other Pinaceae species, which provided evidence for high levels of synteny and gene order conservation in this family. We then developed an integrated map for P. sitchensis and P. glauca based on 27,052 makers and 11,609 gene sequences. Altogether, these two linkage maps, the accompanying catalog of 286,159 SNPs and the genotyping chip developed herein opens new perspectives for a variety of fundamental and more applied research objectives, such as for the improvement of spruce genome assemblies, or for marker-assisted sustainable management of genetic resources in Sitka spruce and related species.

opencc-by-4.0May 2024View details →

Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance

The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance." 1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 Nat Genet), Signac (Stuart et al. 2021 Nat Methods), ArchR (Granja et al. 2021 Nat Genet), and Cicero (Pliner et al. 2018 Mol Cell). pgBoost_scores.tsv.gz contains linking predictions made by pgBoost. constituent_method_scores.tsv.gz contains linking predictions made by constituent methods. **NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)** Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets. *Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 Mol Cell). NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates. 2) GWAS-derived evaluation SNP-gene link evaluation set. gwas_evaluation.tsv: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 Nature Genetics based on fine-mapping results from Kanai et al. medrxiv (see Methods: Evaluation data sets of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.

opencc-zeroApr 2022View details →

Phylogenomics and biogeography of Torreya (Taxaceae) – Integrating data from three organelle genomes, morphology, and fossils and a practical method for reducing missing data from RAD-seq

Restriction site-associated DNA sequencing (RAD-seq) enables obtaining thousands of genetic markers for phylogenomic studies. However, RAD-seq data are subject to allele dropout (ADO) due to polymorphisms at enzyme cutting sites. We developed a new pipeline, RADADOR, to mitigate the ADO in outgroups by recovering missing loci from previously published transcriptomes in our study of a gymnosperm genus Torreya. Using the supplemented RAD-seq data in combination with plastome and mitochondrial gene sequences, morphology, and fossil records, we reconstructed the phylogenetic and biogeographic histories of the genus and test hypotheses on diversity anomaly in eastern Asian-North American floristic disjunction. Our results showed that our pipeline recovered many loci missing from the outgroup, and the improved data yielded a more robust phylogeny for Torreya. Using the fossilized-birth-death model and divergence-extinction-cladogenesis method we resolved detailed biogeographic history of Torreya that suggested a Jurassic origin in the Laurasia and differential speciation and extinction among continents accounting for the modern diversity anomaly biased toward Eastern Asia (EA). The history also supported a vicariance origin of the modern Torreya from a widespread ancestor in EA and NA in the mid-Eocene, cross-Beringia exchange in the early Paleogene before the vicariant isolation, in contrast to the "Out of NA" pattern common to gymnosperms and in contrast to the "Out of EA" hypothesis previously proposed for the genus. Furthermore, we observed phylogenetic discordance between the nuclear and plastid phylogenies on T. jackii, suggesting differential lineage sorting of plastid genomes among Torreya species or plastid genome capture in T. jackii.

opencc-by-4.0May 2022View details →

Integrated data-driven reannotation of the Kluyveromyces marxianus genome reveals an expanded protein coding repertoire

Supplementary data for Fenton et al. 2022.  <table> <tbody> <tr> <td>Supplementary Table</td> <td>ID</td> <td>Table Description</td> </tr> <tr> <td>supplementary table 1</td> <td>S1</td> <td>Transcript Start Site (TSS) metrics</td> </tr> <tr> <td>supplementary table 2</td> <td>S2</td> <td>Polyadenylation Site (PAS) metrics</td> </tr> <tr> <td>supplementary table 3</td> <td>S3</td> <td>NTE candidates </td> </tr> <tr> <td>supplementary table 4</td> <td>S4</td> <td>MTS candidates</td> </tr> <tr> <td>supplementary table 5</td> <td>S5</td> <td>iORFs candidates</td> </tr> <tr> <td>supplementary table 6</td> <td>S6</td> <td>uORFs candidates</td> </tr> <tr> <td>supplementary table 7</td> <td>S7</td> <td>ouORFs candidates</td> </tr> <tr> <td>supplementary table 8</td> <td>S8</td> <td>aORFs candidates</td> </tr> <tr> <td>supplementary table 9</td> <td>S9</td> <td>tRNA copy numbers</td> </tr> <tr> <td>supplementary table 10</td> <td>S10</td> <td>novel gene periodicity scores</td> </tr> <tr> <td>supplementary table 11</td> <td>S11</td> <td>description of novel genes</td> </tr> <tr> <td>supplementary table 12</td> <td>S12</td> <td>comparison of published genomes</td> </tr> <tr> <td>supplementary table 13</td> <td>S13</td> <td>table corrections</td> </tr> <tr> <td>supplementary table 14</td> <td>S14</td> <td>start codon corrections</td> </tr> <tr> <td>supplementary table 15</td> <td>S15</td> <td>Genes with splicing (at least one intron)</td> </tr> </tbody> </table>

opencc-zeroJun 2024View details →

Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change

Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (Fraxinus pennsylvanica) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.

opencc-by-4.0Oct 2022View details →

Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences

Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., CWC27 and CLUAP1), ultraviolet protection (e.g., ERCC8), and pigmentation (e.g. PDE4D) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.

publicApr 2022View details →

Phylogenomics and biogeography of Torreya (Taxaceae) – Integrating data from three organelle genomes, morphology, and fossils and a practical method for reducing missing data from RAD-seq

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publicJan 2024View details →

High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers

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publicJun 2024View details →

Integrating genomics and biogeography to unravel the origin of a mountain biota: The case of a reptile endemicity hotspot in Arabia

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