Kantor, Rose · Shakya, Migun · Ruth, Nelson · et al.

2,095 rows · 907 KB · fasta, tsv

A virus genome database representing 21,015 near-complete virus genomes collected from untargeted ultra-deep RNA/DNA combined sequencing of wastewater. Sequence data was provided by the CASPER consortium and raw data may be found on NCBI SRA under bioprojects PRJNA1247874 and PRJNA1198001. Data underwent read trimming, rRNA and human read removal, de novo assembly, and selection of high-quality viral contigs. Contigs were clustered at 95% identity and 85% query coverage to dereplicate. Chimera-checking required at least two independent assemblies of the same viral genome or presence of the genome in another reference database. Annotation made use of RdRpCATCH, geNomad, checkV, BLASTN against NCBI core-nt, and RNAVirHost. The RdRp fasta files contain representative RdRp sequences identified through homology to major RdRp reference databases and clustered at 90% sequence identity over 75% sequence coverage. Included sequences contain all three conserved RdRp motifs (A, B, and C) arranged in either the canonical ABC configuration or the permuted CAB configuration.

Kim, Kwanho · Lin, Zechuan · Simmons, Sean · et al.

1 files · 37 KB · pdf

This dataset contains CCS corrected HiFi long-read DNA sequencing (lrDNAseq) in FASTQ format for 100 PMDBS samples from Parkinson's patients and healthy controls. It's part of the PD5D atlas, where the same subjects were also profiled with other omics assays including genotyping, single-cell ATACseq, and spatial transcriptomics.

Kim, Kwanho · Lin, Zechuan · Simmons, Sean · et al.

1 files · 37 KB · pdf

This dataset contains raw FASTQ files from the midbrain single-nucleus RNA sequencing (snRNAseq) dataset with hybrid selection for the matching PMDBS samples from the PD5D chort. The same subjects were also profiled with other omics assays including genomic DNAseq, genotyping, single-cell ATACseq, and spatial transcriptomics.

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 37 KB · pdf

This dataset consists of raw sequencing snRNA-seq data (10x Genomics Chromium Next GEM Single Cell 3ʹ). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors.

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 36 KB · pdf

This dataset consists of raw sequencing snRNA-seq data using ParseBio Evercode Whole Transcriptome. The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a specific ParseBio barcode. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors.

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 36 KB · pdf

This dataset consists of raw sequencing snATAC-seq data (10x Genomics Chromium Next GEM Single Cell ATAC v2). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors. (edited)

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 36 KB · pdf

This dataset consists of raw sequencing snATAC-seq data (HyDrop v2). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors.

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 36 KB · pdf

This dataset consists of raw sequencing ATAC-seq data (Scale-ATAC pre-indexing followed by 10x Genomics snATAC v2). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors.

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 36 KB · pdf

This dataset consists of raw sequencing ATAC-seq data (Scale-ATAC + HyDrop v2). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocols followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108 ) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors.

Pančíková, Alexandra · Theunis, Koen · Hulselmans, Gert · et al.

1 files · 37 KB · pdf

This dataset consists of raw sequencing snRNA-seq data and snATAC-seq data (10x Genomics Chromium Next GEM Multiome ATAC/GEX). The data is part of an overall set of samples derived from postmortem midbrain (n=140), cingulate cortex (n=190) and motor cortex (n=4) of healthy donors (n=114), patients with Parkinson's disease (n=75) or patients with other neurological disorder (n=1). The protocol followed to isolate nuclei from postmortem brain samples and to prepare sequencing libraries can be found below. To increase throughput and to decrease batch effects, several donors have been pooled together into a single sequencing library. To computationally demultiplex the nuclei to their corresponding donors, cellsnp-lite (version commit: aad18644adcde853c313362a856a24245c9b91f7) followed by vireo (https://github.com/single-cell-genetics/vireo/pull/108 ) has been used. The population VCF with the donor genotypes derived from whole genome sequencing data has been used to assign nuclei back to their donors. (edited)

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.

MacDonald, Adam · Stratton, Jo Anne

1 files · 35 KB · pdf

We performed 10X Genomics single-cell RNAsequencing of human prepheral blood mononuclear cells from healthy control, PD and RBD patients. This dataset contains raw FASTQ files. Sequencing was performed using NovaSeq 6000 S4 PE 100bp. Reads were processed using the 10X Genomics Cell Ranger Single Cell 2.0.0 pipeline. FASTQs generated from sequencing output were aligned to the human GRCh38 reference genome using STAR algorithm 2.7.3a.

Stratton, Jo Anne · Mukherjee, Sriparna · Trudeau, Louis-Eric

1 files · 5.0 KB · pdf

We performed bulk RNAsequencing of substantia nigra from wild type and PINK1 KO mice 26-days post C.rodentium infection. This dataset contains raw fastq files from striatal cells, sorted into 4 groups namely wild type and PINK1 KO uninfected and infected mice. Sequencing was performed using NextSeq500. FASTQs generated from sequencing output were aligned to the mm10 reference genome using STAR aligner.

Stratton, Jo Anne · Mukherjee, Sriparna · Trudeau, Louis-Eric

1 files · 6.1 KB · pdf

We performed bulk RNAsequencing of striatum from wild type and PINK1 KO mice 26-days post C.rodentium infection. This dataset contains raw fastq files from striatal cells, sorted into 4 groups namely wild type and PINK1 KO uninfected and infected mice. Sequencing was performed using NextSeq500. FASTQs generated from sequencing output were aligned to the mm10 reference genome using STAR aligner.

Recinto, Sherilyn · Stratton, Jo Anne

1 files · 6.1 KB · pdf

We performed 10X Genomics single-cell RNAsequencing of human iSPC-derived monocytes and macrophages in vitro. Cells were treated with 500 ng/mL lipopolysaccharide (LPS) and 50 ng/mL interleukin-1 beta (IL1b) for 24 hours. This dataset contains raw FASTQ files from myeloid cells, sorted into 4 groups namely monocytes (Mono) and Macrophages (Mac) non-stimulated (NS) and LPS+IL1b-stimulated cells. Sequencing was performed using NovaSeq 6000 S4 PE 100bp. Reads were processed using the 10X Genomics Cell Ranger Single Cell 2.0.0 pipeline. FASTQs generated from sequencing output were aligned to the human GRCh38 reference genome using STAR algorithm 2.7.3a.

Recinto, Sherilyn · Stratton, Jo Anne · Pei, Jessica · et al.

2 files · 6.0 KB · pdf

We performed 10X Genomics single-cell RNAsequencing of colonic lamina propria cells from wild type and LRRK2 G2019S mice following 1-week post C. rodentium infection. The cells were pooled from 3 mice per group of both sexes at 8-12 weeks of age. This dataset contains raw FASTQ files from mouse colonic lamina propria, sorted into 4 groups namely wild type and LRRK2 G2019S uninfected and infected mice. Sequencing was performed using NovaSeq 6000 S4 PE 100bp. Reads were processed using the 10X Genomics Cell Ranger Single Cell 2.0.0 pipeline. FASTQs generated from sequencing output were aligned to the mouse GRCm38 reference genome using STAR algorithm 2.7.3a.

Recinto, Sherilyn · Stratton, Jo Anne

1 files · 5.7 KB · pdf

We performed 10X Genomics single-cell RNA sequencing of colonic lamina propria cells from wild type and PINK1 KO mice following either 1-week or 2-weeks post C. rodentium infection. The cells were pooled from 3 mice per group of both sexes at 8-12 weeks of age. This dataset contains raw FASTQ files from mouse colonic lamina propria, sorted into 8 groups namely wild type and PINK1 KO uninfected and infected mice at 1- or 2-weeks post-infection. Sequencing was performed using NovaSeq 6000 S4 PE 100bp. Reads were processed using the 10X Genomics Cell Ranger Single Cell 2.0.0 pipeline. FASTQs generated from sequencing output were aligned to the mouse GRCm38 reference genome using STAR algorithm 2.7.3a.

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.