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Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Distinguishing between canonical and non-canonical tRNA genes reveals that Thermococcaceae adhere to the standard archaeal tRNA gene set
<p><strong>Abstract</strong></p> <p>Automated genome annotation is an essential tool for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed – for selected genomes – in the Genomic tRNA database (GtRNAdb). Given the central role of tRNA in molecular biology, the accuracy and proper application of tRNAscan-SE is important for both interpretation of the output, and continued improvement of the software. Here, we report a manual annotation of the predicted tRNA gene sets for 20 complete genomes from the archaeal taxon Thermococcaceae. According to GtRNAdb, these 20 genomes contain a number of putative deviations from the standard set of canonical tRNA genes in Archaea. However, manual annotation reveals that only one represents a true divergence; the other instances are either (i) non-canonical tRNA genes resulting from the integration of horizontally transferred genetic elements, or CRISPR-Cas activity, or (ii) attributable to errors in the input DNA sequence. To distinguish between canonical and non-canonical archaeal tRNA genes, we recommend using a combination of automated pseudogene detection by tRNAscan-SE and the tRNAscan-SE isotype score, greatly reducing manual annotation efforts and leading to improved predictions of tRNA gene sets in Archaea.</p> <p> </p> <p><strong>Repository contents</strong></p> <p><strong>01_workflow_tRNAscanSE_predictions_210archaea.html </strong>contains the workflow and graphical output for tRNA gene set predictions in 20 Thermococcaceae genomes and 210 archaeal genomes. Files 03 to 06 below are the files quoted in this workflow.</p> <p><strong>02_workflow_tRNAscanSE_predictions_210archaea.Rmd </strong>contains the markdown file associated with 01_workflow_tRNAscanSE_predictions_210archaea.html above.</p> <p><strong>03_thermo_trnas_GtRNAdb.txt</strong><strong> </strong>contains the predicted tRNA gene sets of 20 Thermococcaceae genomes as listed on GtRNAdb (Data Release 19 (June 2021)).</p> <p><strong>04_Archaea_genome_list.txt </strong>contains the details of all 217 archaeal genomes listed on GtRNAdb (Data Release 19 (June 2021)). The seven genomes for which the NCBI genome sequences were no longer available are indicated by #### preceding the name.</p> <p><strong>05_thermo_tRNAs_genome.txt</strong><strong> </strong>contains the predicted tRNA gene sets of 20 Thermococcaceae genomes as predicted by locally run tRNAscan-SE (version 2.0.6), with standard settings for Archaea (option -A). To display the output, options -H and --detail were added. We note that pseudogene detection is active under these conditions.</p> <p><strong>06_Archaea_210_GtRNAdb_tRNAs.txt </strong>contains the predicted tRNA gene sets of the 210 archaeal genomes as listed on GtRNAdb (Data Release 19 (June 2021)).</p> <p><strong>07_Archaea_210genomes_tRNAs.txt</strong> contains the predicted tRNA gene sets of the 210 archaeal genomes as predicted by locally run tRNAscan-SE (version 2.0.6), with standard settings for Archaea (option -A). To display the output, options -H and --detail were added. We note that pseudogene detection is active under these conditions.</p> <p><strong>08_NCBI_genomes.zip</strong> contains the NCBI GenBank genome sequence files used in this study. These include the 20 Thermococcaceae genomes, the wider 210 archaeal genomes, and several others of interest. </p> <p><strong>09_phylogeny.tar.zip</strong> contains the data used to draw a phylogenetic tree for the 20 Thermococcaceae organisms. The folder includes a file listing the details of all data in the folder (Readme.md), a workflow file (workflow_UndinMarkers_v2.md), and data folders.</p> <p> </p> <p><strong>Notes</strong></p> <p>The extended TIGRFAM database referred to in the phylogenetic tree construction process can be found at <a href="https://zenodo.org/record/3839790#.YjByaVzMI3g">https://zenodo.org/record/3839790#.YjByaVzMI3g</a></p> <p>The perl script used during phylogenetic tree construction, catfasta2phyml.pl, is available in the GitHub repository <a href="https://github.com/nylander/catfasta2phyml">https://github.com/nylander/catfasta2phyml</a></p> <p>tRNAscan-SE is a freely available resource available online (<a href="http://lowelab.ucsc.edu/tRNAscan-SE/">http://lowelab.ucsc.edu/tRNAscan-SE/</a>)</p> <p>GtRNAdb is a publicly accessible resource available online (<a href="http://gtrnadb.ucsc.edu/">http://gtrnadb.ucsc.edu/</a>)</p> <p>NCBI is a publicly accessible resource available online (<a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov/</a>)</p> <p>rrnDB is a publicly accessible resource available online (<a href="https://rrndb.umms.med.umich.edu/">https://rrndb.umms.med.umich.edu/</a>)</p> <p>BLAST is a publicly accessible resource available online (<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi">https://blast.ncbi.nlm.nih.gov/Blast.cgi</a>)</p>
A re-analysis of an existing Drosophila melanogaster dataset reveals a new set of genes involved in post-mating response
<p>The figures and tables presented here are part of a manuscript submitted for publication by Chloe J. Bennett and Rodolfo Aramayo entitled:</p> <p><strong>"A re-Analysis of an existing <em>Drosophila melanogaster</em> dataset reveals a new set of genes involved in post-mating response"</strong></p> <p><strong>Abstract</strong></p> <p>RNA sequencing (RNA-seq) is a commonly used method to identify changes in gene expression between two conditions. The analysis of RNA-seq output is complicated, with the possibility of getting different results from the same raw data. We developed and deployed four parallel pipelines to reanalyze an existing dataset of two female Drosophila melanogaster tissue types before and after mating. The Drosophila post-mating response (PMR) is a well-characterized suite of changes that occur after mating, accompanied by a flux in gene expression. In comparing our study with the previous analysis of this dataset, we find our results to be more stringent, though we do identify a number of significant genes not found before. We also found variation among our own separate experiments, with gene-to-transcript isoform number and index building playing important roles in outcome. Finally, we identified a set of genes found by our pipeline that were not identified by the previous study and proposed potential roles for these genes in post-mating biology. Together, this work presents a critique of current RNA-seq analysis techniques and proposes multiple workflow adjustments that can increase the sensitivity, specificity, and stringency of differential gene expression studies.</p>
Data from 'Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience'
<p>Aquatic deoxygenation has been flagged as an overlooked but key factor driving mass bleaching-induced coral mortality as oxygen supplies lower to concentrations that can elicit an aerobic metabolic crisis i.e., hypoxia. Surprisingly little is known of the fundamental hypoxia responsive gene set inventory corals possess to respond to deoxygenation. It is unclear whether variation in gene copy number across species exist that potentially affect gene expression with subsequent differences in the effectiveness of a given stress response. Here, we used an ortholog-based meta-analysis to investigate how hypoxia gene inventories differed amongst coral species to assess putative copy number variation (CNV) across 24 coral protein sets from species with a sequenced genome that span corals from the robust and complex clade. We found approximately a third of the investigated genes exhibited copy number differences, and these differences were species-specific rather than the robust-complex split.</p> <p>Zipped folders of OrthoFinder results:</p> <p>'Results_Feb16' contains results including all 24 coral species from 7 genera (<em>Acropora, Pocillopora, Stylophora, Montastrea, Montipora, Obricella, Porites</em>).</p> <p>'gene_sets_acropora_acuminata_only' contains results including just one species per genera with <em>Acropora acuminata</em>.</p> <p>'gene_sets_acropora_cytherea_only' contains results including just one species per genera with <em>Acropora cytherea</em>.</p> <p>'gene_sets_acropora_digitifera_only' contains results including just one species per genera with <em>Acropora digitifera</em>.</p> <p> </p> <p>Results and Interpretations from these analyses are published open access here: <a href="https://doi.org/10.3389/fmars.2022.834332">https://doi.org/10.3389/fmars.2022.834332</a></p> <p>Full citation: Alderdice R, Hume BCC, Kühl M, Pernice M, Suggett DJ, Voolstra CR. Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience. Front Mar Sci. 2022;9. doi:10.3389/fmars.2022.834332</p> <p>Scripts are available here: <a href="https://zenodo.org/record/6396671#.YoYpoS8RoZg">https://github.com/didillysquat/alderdice_2021</a></p>
Differential abundance and gene set enrichment in plasma of cancer patients versus controls
<p>Version update: Human Protein Atlas v23 used for gene set enrichment analysis (instead of Human Protein Atlas v18)</p> <p><br>DESeq2 differential abundance output for genes with q < 0.05 and |log<sub>2</sub> fold change| > 1 in cancer vs control plasma samples:</p> <ul> <li><em>differentialabundance_pancancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)</li> <li><em>differentialabundance_threecancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls) <ul> <li>Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.</li> </ul> </li> </ul> <p>Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):</p> <ul> <li><em>customgenesets.txt</em>: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data. <ul> <li>Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set</li> </ul> </li> <li><em>GSEA_pancancer.txt</em> & <em>GSEA_threecancer.txt</em>: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively <ul> <li>Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in <em>customgenesets.txt)</em>; pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls</li> </ul> </li> </ul>
Enrichment of gene sets altered in dram1 mutant zebrafish larvae
<p>Enrichment of gene sets altered in dram1 mutant zebrafish larvae.</p> <p>A. Gene Ontology categories significantly over and underrepresented in the significant genes differentially regulated between <em>dram1</em><sup>∆19n/∆19n </sup>PBS-injected mutants compared to <em>dram1</em><sup>+/+</sup> larvae.</p> <p>B. Gene sets from the MSigDB C2 database significantly positively correlated to the <em>dram1</em><sup>∆19n/∆19n </sup>mutants transcriptome compared to <em>dram1</em><sup>+/+</sup> larvae.</p> <p>C. Gene sets from the MSigDB C2 database significantly negatively correlated to the <em>dram1</em><sup>∆19n/∆19n </sup>mutants transcriptome compared to <em>dram1</em><sup>+/+</sup> larvae.</p>
Data set: Genome scale CRISPR Cas9a knockout screen reveals genes that controls glioblastoma susceptibility to the alkylating agent temozolomide (TMZ).
<p>Glioblastoma is the deadliest of all primary brain tumor with a very poor survival outcome. Alkylating agent such as temozolomide is used as a mainstay drug used in treating of glioblastoma patients including radiation, tumor treating field and surgery. However, this drug offers little to no benefit for the glioblastoma patients. Hence, the objective of the generation of this data is to understand and unravel genes that controls glioblastoma susceptibility and resistance to temozolomide using an unbiased genome scale CRISPR Cas9a knockout screen</p>
Data platform (genotyping data set) related to ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 "The role of vitamin D gene polymorphisms and its receptors in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis".
<p><strong>Data platform </strong><strong>(genotyping dataset)</strong> <strong>related to the ERDF postdoctoral project No. </strong><strong>1.1.1.2/VIAA/4/20/718</strong><strong> “</strong><strong>The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis</strong><strong>”.</strong></p> <p><strong>About the project and gathered data:</strong></p> <p>The dataset contains genotyping data on 289 sex-balanced samples (approximately 60% women / 40% men)) were created at the the multiple sclerosis (MS) Clinic of the Latvian Maritime Medical Center (LMMC) in 2011 (disease duration of 1-51 years); the collection was updated within the framework of the ERDF MS project (2017-2020) and replenished during the ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 “The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis” (2021-2023).</p> <p>For the <strong>Genotyping dataset </strong>relevant information for each patient from the MS disease cohort, referring to proteasomal gene genetic variations (microsatellites and SNPs): (HSMS006 <em>(PSMA6),</em> HSMS602 <em>(FAM177A1),</em> HSMS701 <em>(KIAA0391)</em>, HSMS702 <em>(KIAA0391)</em> HSMS801 <em>(KIAA0391)</em>, rs11543947<em>(PSMB5), </em>rs2277460 (mi110), rs1048990 (mi8)<em> (PSMA6),</em> rs1048990 (mi8)<em> (PSMA6),</em> rs2295826/rs2295827<em>(PSMC6),</em> rs2348071 <em>(PSMA3),</em> rs2071543, rs9357155 <em>(PSMB8),</em> rs17587<em>(PSMB9),</em> rs74421874 <em>(PSMD9); </em>rs9275596 from HLA region; vitamin D-related genes (VDR and GC) polymorphisms: rs2228570, rs1544410, rs7975232, rs731236 (<em>VDR</em>) and rs7041, rs4588 <em>(GC).</em></p>
The primate Major Histocompatibility Complex: Sets of posterior trees from BEAST2 for the whole-class multi-gene alignments
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Data for: "Generative prediction of causal gene sets responsible for complex traits"
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The primate Major Histocompatibility Complex: Sets of posterior trees from BEAST2 for each gene group and region
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Data set 1 for CARBON AND GENE FLOW MEDIATED BY VIRUS LIFE
<p>Experimental data sets used for manuscripts associated with coccolithovirus infection of <em>Emiliania huxleyi</em>. Flow cytometry data; expression data of genes associated with photophysiology, fatty acid metabolism and sulphur cycling.</p> <p>Please contact Willie Wilson (wilwil@sahfos.ac.uk) for further information.</p> <p> </p>
Data set 2 for CARBON AND GENE FLOW MEDIATED BY VIRUS LIFE
<p>Experimental data sets used for manuscripts associated with coccolithovirus infection of <em>Emiliania huxleyi; </em>and grazing of virus-infected <em>Emiliania huxleyi. </em>Data includes: Flow cytometry; photophysiology, carbon/nitrogen, fatty acid metabolism, sulphur cycling and cell volume.</p> <p>Please contact Willie Wilson (wilwil@sahfos.ac.uk) for further information.</p>
R data set: The Cancer Genome Atlas Gene Expression data
<p>This compound data set comprises the following information from the The Cancer Genome Atlas:</p> <ul> <li>RNA-Seq counts for 60483 genes across 11093 samples</li> <li>HuEx 1.0 ST gene expression data for 18632 genes across 1211 samples</li> <li>clinical indicators for 11160 patients</li> </ul> <p>All gene expression data is annotated across ENSEMBL, ENTREZ and symbols. Samples are annotated by TCGA barcodes.</p> <p>To read the data set into R (requires 6 GB of RAM) use:</p> <p>tcga <- readRDS("tcga.rds")</p>
Set of 4 597 baits designed in collaboration with RapidGenomics (Gainesville, Florida, USA) to capture the identified low‐ to single‐copy nuclear genes (LSCN).
<p>This dataset presents a set of 4,597 baits designed in collaboration with RapidGenomics (Gainesville, Florida, USA) to capture the identified low- to single-copy nuclear genes (LSCN) in our study. These baits were instrumental in our research on the evolutionary relationships of the Neotropical magnolias based on plastome and nuclear phylogenomics. The data provided are crucial for understanding the methodology and results of our study.</p>
SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis
<h1>SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis</h1> <div> </div> <div>SaintGSE is an artificial intelligence model designed to predict human gene-pathway relationships using large-scale differentially expressed gene (DEG) datasets. By leveraging an autoencoder and the SAINT transformer model, SaintGSE overcomes challenges in gene expression analysis, such as data scarcity, model compatibility, and interpretability. This project fine-tuned codes from the SAINT project (https://github.com/somepago/saint), licensed under the Apache License 2.0. </div> <div> </div> <div> </div> <h2>Key Features</h2> <div> </div> <div> * AI-Driven Pathway Prediction: Uses autoencoders and the SAINT model to analyze gene expression data and predict related signaling pathways.</div> <div> </div> <div> * Osteoarthritis Study: Applied to osteoarthritis (OA) to identify key pathways and potential therapeutic targets.</div> <div> </div> <div> * Explainability: Utilizes Shapley additive explanations (SHAP) to interpret model predictions and identify influential genes.</div> <div> </div> <div> </div> <h2>Installation</h2> <div> </div> <div>Before installation, we recommend to build a conda environment from the attached yml file and activate it.</div> <div>Our code has been tested with python=3.8 on linux.</div> <div> </div> <div>```</div> <div>$ cd /path/to/SaintGSE</div> <div>$ conda env create -f saintgse_env.yml</div> <div>$ conda activate saintgse_env</div> <div>```</div> <div> </div> <div>After downloading all the files, please extract the contents of all compressed directories by running the following command in your terminal:</div> <div> </div> <div>```</div> <div>$ find . -name "*.tar.gz" -exec tar -xzvf {} \;</div> <div>$ rm *.tar.gz</div> <div>```</div> <div> </div> <div>Once the file structure is formed as follows, the preparation for using SaintGSE is complete.</div> <div> </div> <div>```</div> <div>.</div> <div>├── datasets</div> <div>│ ├── AE_100cycle_model.pth</div> <div>│ ├── AE_enrichment.tsv</div> <div>│ ├── AEshap</div> <div>│ │ ├── shap_values_latent_dim_1.csv</div> <div>│ │ ├── shap_values_latent_dim_2.csv</div> <div>│ │ ├── shap_values_latent_dim_3.csv</div> <div>│ │ ├── ...</div> <div>│ │ ├── ...</div> <div>│ │ └── shap_values_latent_dim_256.csv</div> <div>│ ├── bestmodels</div> <div>│ │ └── binary</div> <div>│ │ ├── Chronic_Myeloid_Leukemia</div> <div>│ │ │ └── testrun</div> <div>│ │ │ └── saint_gse_model.pth</div> <div>│ │ │── ...</div> <div>│ │ └── Selencompound_Biosynthesis</div> <div>│ │ └── testrun</div> <div>│ │ └── saint_gse_model.pth</div> <div>│ ├── gene_list.pkl</div> <div>│ ├── MGI_Gene_Model_Coord.tsv</div> <div>│ └── pathway_list_in_DEG.txt</div> <div>```</div> <div> </div> <div> <p>The code in this dataset is also accessible via GitHub. You can find the GitHub repository at the following link:</p> <p><a href="https://github.com/MSjeon27/SaintGSE" target="_new" rel="noopener">https://github.com/MSjeon27/SaintGSE</a></p> </div> <div> </div> <h2>DEG dataset preparation</h2> <div>Prior to SaintGSE analysis, prepare DEG data to be used as input in .tsv format as follows. In the column, the official gene symbol of DEGs is located, and the row adds the log2 fold change value in each DEG group. An example is as follows.</div> <div> </div> <div>```</div> <div>LAP3 CD99 HS3ST1 MAD1L1 LASP1 SNX11</div> <div>'mock-6' vs 'LPS-6' -1.3 0 2.4 0 0.7 0</div> <div>'mock-6' vs 'EBOV-6' -1.3 0 2.3 0 0.6 0</div> <div>```</div> <div> </div> <h2>Usage</h2> <div> </div> <h3>Step 0. Preprocessing the input DEG (from pyDESeq2 result)</h3> <div> </div> <div>Currently, SaintGSE has the function of converting mouse genes into human genes. The preprocessing code serves to change the human or mouse DEG data into the format used for SaintGSE.</div> <div> </div> <div>* human DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div>* mouse DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --org mouse --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div> </div> <h3>Step 1. Training SaintGSE for a target pathway</h3> <div> </div> <div>SaintGSE can be used to analyze new gene expression datasets for pathway prediction:</div> <div> </div> <div>```</div> <div>$ SaintGSE.py --pathway 'Proteins Involved in Osteoarthritis' --pretrain</div> <div>```</div> <div> </div> <div> </div> <h3>Step 2. Prediction through SaintGSE</h3> <div> </div> <div>```</div> <div>$ SaintGSE.py --predict Preprocessed_fc.tsv --pathway 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The results of the predictions are as follows.</div> <div> </div> <div>```</div> <div>tensor([[1.]], device='cuda:0')</div> <div>```</div> <div> </div> <div>This indicates that your DEG data is related to the target signaling path.</div> <div> </div> <div> </div> <div> </div> <h3>Step 3. Interpretation the result of SaintGSE (Get Relative SHAP contribution for each DEGs)</h3> <div>```</div> <div>$ Interpret.py -d Preprocessed_fc.tsv -p 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The result of interpretation produces the following files for each sample.</div> <div> </div> <div>```</div> <div><Sample_name>_<target_pathway>_significant_gene_shap.csv</div> <div>```</div> <div> </div> <div>This represents the relative SHAP contribution for each gene in the DEG data. In the subsequent analysis, it is recommended to focus on the genes with the relative SHAP contributions in the top 35% to 50% as we suggested in the paper, depending on the number of DEGs.</div> <div> </div> <div> </div> <h2>How to Cite</h2> <div> </div> <div>If you use this model or repository in your research, please cite it as follows:</div> <div> </div> <div>```</div> <div>Jeon, MS & Nam, JH et al., "SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis," 2024. GitHub repository. Available at: https://github.com/MSjeon27/SaintGSE</div> <div>```</div> <div> </div> <div>For more information or any questions regarding citation, feel free to contact us (msjeon27@cau.ac.kr).</div> <p> </p>
Automated cell annotation in scRNA-seq data using unique marker gene sets
<p>Single-cell RNA sequencing has revolutionized the study of cellular heterogeneity, yet accurate cell type annotation remains a significant challenge. Inconsistent labels, technological variability, and limitations in transferring annotations from reference datasets hinder precise annotation. This study presents a novel approach for accurate cell type annotation in scRNA-seq data using unique marker gene sets. By manually curating cell type names and markers from 280 publications, we verified marker expression profiles across these datasets and unified nomenclatures to consistently identify 166 cell types and subtypes. Our customized algorithm, which builds on the AUCell method, achieves accurate cell labeling at single-cell resolution and surpasses the performance of reference-based tools like Azimuth, especially in distinguishing closely related subtypes. To enhance accessibility and practical utility for researchers, we have also developed a user-friendly application that automates the cell typing process, enabling efficient verification and supporting comprehensive downstream analyses. The desktop application can be accessed at <a href="https://omnibusx.com/">https://omnibusx.com</a>.</p>
Summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI WGS data set
<p>These are the summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI Whole-Genome Sequencing data set from the European subcohort (n=1,017).</p> <p>Case - control definitions:<br>Ex (file: DeCOI_EUR_Ex_RVAS.tsv.gz): 272 cases (required mechanical ventilation or died because of COVID-19 = WHO score 6-10); 362 controls (not hospitalized = WHO score 1-3)<br>B1 (file: DeCOI_EUR_B1_RVAS.tsv.gz): 655 cases (at least hospitalized = WHO score 4-10); 362 controls (not hospitalized = WHO score 1-3)</p> <p>Column description:<br>CHROM: Chromosome of the respective gene<br>ID: Column of the format: [Ensemble Gene ID].[mask which was used to select variants].[upper alle frequency cut-off]<br>ALLELE0: The reference allele<br>ALLELE1: The alternative allele and the effect allele - here all variants which pass allele frequency and mask filters (see ID-column)<br>A1FREQ: Total frequency of Allele 1<br>A1FREQ_CASES: Frequency of Allele 1 in cases<br>A1FREQ_CONTROLS: Frequency of Allele 1 in controls<br>N: Count of individuals that were used in the analysis<br>N_CASES: Count of cases that were used in the analysis<br>N_CONTROLS: Count of controls that were used in the analysis<br>TEST: Test-mode of regenie - here an additive model was used<br>BETA: Estimated effect size given as beta<br>SE: Standard error of BETA<br>LOG10P: Negative decadic logarithm of the p-value</p> <p>Method:<br>Please refer to our accompanying manuscript for a detailed description. We conducted gene-based collapsing analysis using regenie 3.2.4 in logistic regression mode (without step 1). Covariates used were sex, age, age*age, age*sex and the first 10 principal components derived from common variants.</p> <p>Contact for further information:<br>dac_decoi_hostgenetics@listen.uni-bonn.de</p>
Phylogenomic analyses using a new 1013-gene Vitaceae bait-set support major groups of North American Vitis
<p class="p1">A set of newly designed Vitaceae baits targeting 1013 genes was employed to explore phylogenetic relationships among North American <em>Vitis</em>. Eurasian <em>Vitis</em> taxa including <em>Vitis vinifera</em> were found to be nested within North American <em>Vitis</em> subgenus <em>Vitis</em>. North American <em>Vitis</em> subgenus <em>Vitis</em> can be placed into nine main groups: the Monticola group, the Occidentales group, the Californica group, the Vinifera group (introduced from Eurasia), the Mustangensis group, the Palmata group, the Aestivali group, the Labrusca group, and the Cinerea group. Strong cytonuclear discordances were detected in North American <em>Vitis</em>, with many species non-monophyletic in the plastid phylogeny, while monophyletic in the nuclear phylogeny. The phylogenomic analyses support recognizing four distinct species in the <em>Vitis cinerea </em>complex in North America: <em>V. cinerea</em>, <em>V. baileyana</em>, <em>V. berlandieri</em>, and <em>V. simpsonii</em>. Such treatment will better serve the conservation of wild <em>Vitis</em> diversity in North America.</p>
Phylogenomic analyses using a new 1013-gene Vitaceae bait-set support major groups of North American Vitis
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