Yuan, Guangyuan

72 rows × 13 cols · 6.0 KB · csv, fasta

11 numeric · 2 categorical

This dataset supports the findings of the manuscript "Root anatomical traits modulate the assembly and nitrogen transformation potential of root-associated microbiomes in a temperate steppe" (NPH-MS-2026-55667). It contains root traits data, bacterial 16S rRNA gene absolute abundances, functional genes relative abundances, DNA extraction metadata, and phylogenetic marker sequences for 37 plant species from a temperate steppe ecosystem. The dataset includes the following files: 1. root traits.csv - Root traits including average diameter (AD), specific root length (SRL), specific root area (SRA), root tissue density (RTD), root nitrogen content (RNC), root carbon content (RCC), carbon‑nitrogen ratio (RCN), cortex layer number (CLN), cortex thickness (CT), and the ratio of cortex thickness to root diameter (CTRD). The first column lists plant species names. 2. Absolute abundance of 16S rRNA gene.csv - Quantitative PCR (qPCR) derived absolute abundances of bacterial 16S rRNA gene copies (copies/ng DNA) across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 3. DNA extraction sample weight.csv - Fresh weight (grams) of root material used for DNA extraction for each sample, linked by SampleID to the abundance data. 4. DNA extraction concentration.csv - Qubit‑measured DNA concentrations (ng/μL) and the sample volume (μL) used for quality control, together with sample metadata. 5. 37species.fasta - DNA sequences of two chloroplast markers (matK and rbcL) for the 37 plant species included in the study. The sequences are in FASTA format with headers formatted as ">Species". These were used for host phylogeny construction and Pagel's λ analyses. 6. Quantitative PCR results of functional gene.csv - Quantitative PCR (qPCR) derived relative abundances of bacterial 16S rRNA gene and functional genes across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 7. README.md - A detailed description of each file, column headers, abbreviations, units, and any missing value codings (NA). All data are provided to ensure transparency and reproducibility of the analyses. For methodological details, please refer to the Materials and Methods section of the associated publication. These data are under embargo until the associated research article is published. After that date, they will be freely available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. During the embargo period, the metadata (title, authors, abstract) and the DOI remain publicly visible, but the data files are not accessible. For access requests before the embargo expires, please contact the corresponding author.

Budzinski, Lisa · Beenken, Anne Elisabeth · Sempert, Toni · et al.

9 rows × 1 cols · 743 B · csv, docx, zip

1 categorical

We have investigated an IgG4-RD (IgG4-RD) cohort by our multi-parameter microbiota flow cytometry approach to characterise the microbiota on single-cell level for attributes of the disease. The microbiota is isolated from stool samples and stained according to the published protocol for (a) host immunoglobulins IgA1, IgA2, IgM, IgG and (b) agglutinin binding to mannose, galactose or N-Acetyl-glucosamine surface sugar moieties. For all samples we also determined the microbiome composition by 16S rRNA (V3-V4) sequencing on the illumina MiSeq platform. We provide the raw .fcs and FASTQ files of 40 IgG4-RD patients. For comparison we additionally analysed 36 healthy donors. All .fcs files were generated on BD Influx®. The metadata is collected in the provided meta.csv. The staining parameters are summarized in provided panel.csv.

Saha, Sudipto · Raghava, Gajendra

1 files · 186 KB · zip

VICMpred: SVM-Based Prediction of Functional Proteins of Gram-Negative Bacteria Using Amino Acid Patterns and Composition VICMpred is a computational web server developed for predicting the major functional classes of Gram-negative bacterial proteins from amino acid sequences. The tool classifies Gram-negative bacterial proteins into four broad functional categories: virulence factors, information molecules, cellular process proteins, and metabolism-related proteins. VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, and class-specific tetrapeptide patterns. Web Server: https://webs.iiitd.edu.in/raghava/vicmpred/ Citation Saha, S., and Raghava, G. P. S. VICMpred: An SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, Proteomics & Bioinformatics, 4(1), 42-47, 2006. https://doi.org/10.1016/S1672-0229(06)60015-6 About the Research Functional annotation of proteins is one of the major challenges in the post-genomic era. Due to the rapid growth of protein sequence databases, experimental functional characterization of every newly discovered protein is not practical. Traditional methods such as BLAST, FASTA, and PSI-BLAST depend on sequence similarity. However, proteins with similar functions may show poor sequence similarity, making direct function prediction difficult. VICMpred was developed as a direct function prediction method for Gram-negative bacterial proteins. Instead of only predicting subcellular localization, it predicts broad biological functions directly from protein sequence features. Data Compilation: The final dataset contained 670 non-redundant Gram-negative bacterial proteins. These included 255 cellular process proteins, 60 information molecules, 285 metabolism proteins, and 70 virulence factors. Methodology: VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, PSI-BLAST similarity search, class-specific tetrapeptide patterns, and hybrid combinations of these features.

Sikking, Maurits · Peisker, Fabian · Maatz, Henrike · et al.

1 files · 100 MB · zip

Project description: See related publication Code repository of the related publication: https://github.com/fpeisker303/IVIG_snRNA_project/ Methods use to generate the Single nuclear RNA sequencing data Endomyocardial biopsies (EMB) were taken from the right ventricular septum and collected via the internal jugular vein using a transcatheter bioptome (Cordis, Miami, FL., USA) at baseline before the IVIg treatment and at the standardized six-months follow-up timepoint of the original study (i.e., median 6.4 [5.9-7.3] months). EMB were evaluated regarding viral persistent and immunohistology markers of inflammation and fibrosis. Spare cardiac biopsies were stored at -80°C until preparation of snRNA sequencing. The isolation of cardiac nuclei and the 10x library preparation were performed at the Max Delbrück Center for Molecular Medicine following a published protocol (1) with adaptations to low-sized tissue pieces (2). In brief, 1-4-mg-sized flash-frozen cardiac biopsies were placed in a pre-cooled dish and an equally sized droplet of homogenization buffer (250 mM sucrose, 25 mM KCl, 5 mM MgCl 2 , 10 mM Tris-HCl, 1 μM DTT, 1× protease inhibitor, 0.4 U μl -1 RNaseIn, 0.2 U μl -1 SUPERaseIn and 0.1% Triton X-100 in nuclease-free water) was added. Buffer-encapsulated tissue pieces were sliced with a scalpel. The tissue pieces were then transferred to a 7-ml glass Dounce tissue grinder (Merck), and nuclei were isolated and stained with NucBlue Live ReadyProbes Reagent (Thermo Fisher Scientific). Hoechst + single nuclei were sorted via fluorescence-activated cell sorting (FACS) (BD Biosciences, FACSAria Fusion). Purity and integrity of nuclei were confirmed microscopically, and nuclei numbers were counted using a Countess II (Life Technologies) before processing with the Chromium Controller (10x Genomics) per the manufacturer's protocol. Single-nucleus 3' gene expression libraries were created using version 3.1 Chromium Single Cell Reagent Kits (10x Genomics) following the manufacturer's instructions. cDNA library quality control was performed using Bioanalyzer High Sensitivity DNA Analysis (Agilent Technologies) and a KAPA Library Quantification Kit. cDNA libraries were sequenced on an Illumina NovaSeq with a targeted read number of 30,000-50,000 reads per nucleus. Fastq files with sequencing results were processed using cellranger version 6.1.2 with the GRCh38-2020-A reference provided by 10x Genomics. References 1. Nadelmann ER, Gorham JM, Reichart D, Delaughter DM, Wakimoto H, Lindberg EL, et al. Isolation of Nuclei from Mammalian Cells and Tissues for Single-Nucleus Molecular Profiling. Curr Protoc. 2021;1(5):e132. 2. Maatz H, Lindberg EL, Adami E, López-Anguita N, Perdomo-Sabogal A, Cocera Ortega L, et al. The cellular and molecular cardiac tissue responses in human inflammatory cardiomyopathies after SARS-CoV-2 infection and COVID-19 vaccination. Nat Cardiovasc Res. 2025;4(3):330-45.

Aalborg, Trine

7 files · 8.0 MB · csv, gzip

README - Data for "Genomic constraint and hypervariability in tetraploid potatoes" Trine Aalborg, May 2026 The data applied in the study includes phenotypic and genotypic information on the MASPOT panel (768 F1 progeny of an 18-parent diallel cross - property of Danespo A/S). The genotypic data was generated using genotyping-by-sequencing technology as described in the paper. Following genotype calling and filtration (5-60x read depth, < 50 % missing rate, > 1 % MAF), the total SNP set includes 151,164 biallelic SNPs. Coordinates of these SNPs relative to the DMv6.1 potato reference genome are provided. In addition to genotypes across the clones, the estimated GERP score of that SNP from (Wu et al., 2023) is reported. The manuscript analyses only consider markers with reliable GERP scores (MSA alignment depth > 50, and neutral score > 2), which corresponded to 97,815 of the total 151,164 biallelic SNPs. SNPeff annotations of the markers (based on the DMv6.1 reference genome) are also appended. The phenotypes were collected across 1-2 field trials, depending on the traits, and includes a minimum of two replicates per clone from a randomized block design. There are phenotypes for eight traits: dry matter content [%], yield (hkg/ha), senescence [1-9], flesh color [1-9], tubers/plant, tuber length [mm], tuber diameter [mm], and tuber size [mm^3]. Metadata includes phenotyping year and block location of the plot as well as pedigree of the diallel offspring. File descriptions: gt_MASPOT.csv - .csv file of the non-imputed genotypic data of 151,164 SNPs for the 768 F1 clones (those with GERP scores). Columns 1-3 are SNP coordinates and SNP IDs. Column names from column 4 and onwards are clone IDs. gt_MASPOT_imputed.csv - .csv file holding the imputed (random forest, missRanger algorithm) genotypic data of 151,164 SNPs for the 768 F1 clones. gt_MASPOT_recoded.csv - .csv file of the recoded, imputed genotypic data of 151,164 SNPs for the 768 F1 clones. The SNPs are recoded from original MASPOT ref/alt allele (based on AF in the MASPOT panel) to the alternative allele = the derived allele in the 100-Solanaceae panel. pt_MASPOT.csv - .csv file holding the phenotypic data (eight traits) of the 768 F1 clones (Clone_ID). The number of observations varies across traits. Also including metadata: year of phenotyping (Year), block number (Block, Line_in_block), clone parents (Mother, Father, Family). GERP_MASPOT.csv - .csv file holding the GERP scores (including alignment depth, neutral scores, and a marker annotation based on GERP score thresholds (deleterious, neutral, hypervariable, or low quality)), SNPeff annotations, and the derived allele in the 100-Solanaceae panel from (Wu et al., 2023) [MASPOT_Alt_Allele_Is_Sol_Derived_Allele - used for recoding of the genotypes] of the MASPOT SNPs with GERP scores. snps.MASPOT_F1.vcf.gz - zipped .vcf file of the GBS MASPOT genotypic data (both discrete genotype calls and allele frequencies) called to the DMv6.1 potato reference genome. Filtered to read depth 5x, MQ > 30. A total of 160,920 biallelic SNPs. Includes the 768 F1 progeny analyzed. Literature: Wu, Y., Li, D., Hu, Y., Li, H., Ramstein, G. P., Zhou, S., et al. (2023). Phylogenomic discovery of deleterious mutations facilitates hybrid potato breeding. Cell 186, 2313-2328.e15. doi: 10.1016/j.cell.2023.04.008

Li, Xuankun · Tu, Yuezheng · Plotkin, David · et al.

5 files · 100 MB · zip

Raw molecular data for 90 newly-sequenced specimens of Eupterotidae (and related families of Lepidoptera) used to generate a molecular phylogeny for the study "Phylogeny and biogeographic history of monkey moths (Lepidoptera: Eupterotidae)" (currently under peer review as of May 2026). File names contain the sequence ID and taxonomic information for each specimen. Data files are in fastq format and have been compressed; there are two files per specimen (labelled "R1" and "R2"). Data files have been organized into five .zip files, based on family-group taxonomy, as follows: Eupterotidae: Eupterotinae (25 specimens, 50 files) Eupterotidae: Ganisa Group (15 specimens, 30 files) Eupterotidae: Janinae (27 specimens, 54 files) Eupterotidae: Striphnopteryginae (15 specimens, 30 files) Other Lepidoptera families (outgroup taxa): Anthelidae, Bombycidae, Lasiocampidae, Saturniidae (8 specimens, 16 files)

Lolicato, Fabio · Javanainen, Matti

1 files · 100 MB · zip

This repository contains the data, simulation files, and scripts used in the study "High-Throughput Characterization of Transmembrane Helix Partitioning in Membrane Domains." The repository includes: Scripts used to extract FASTA sequences from the Orientations of Proteins in Membranes database (OPM). Scripts and input files used to generate peptide systems and prepare the molecular dynamics simulations. Simulation parameter files, topology files, coordinate files required to reproduce the simulations. Final structure files ( md.gro ) for each simulation system. The deposited material is intended to ensure transparency, reproducibility, and reuse of the computational workflow and simulation datasets associated with this work.