cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3661236
title: Generating an expression matrix for droplet single-cell RNA-seq (dscRNA-seq) data

description: This tutorial is adapted from the 'Generating an expression matrix' training session at the EBI (https://www.ebi.ac.uk/training/events/2019/single-cell-rna-seq-analysis-questions-clusters).
DOI: 10.5281/zenodo.3591856
title: Genomic Data Visualisation with JBrowse

description: The data provided here is part of the Galaxy Training Network tutorial for visualising genomic data with JBrowse.
DOI: 10.5281/zenodo.3591846
title: Old version

description: Data required for the GTN tutorial on Circos visualization in Galaxy
DOI: 10.5281/zenodo.3591855
title: Genomic Data Visualisation with JBrowse

description: The data provided here is part of the Galaxy Training Network tutorial for visualising genomic data with JBrowse.
DOI: 10.5281/zenodo.3540705
title: Dataset: Diamonds from Hadley's ggplot2 for Galaxy training

description: Sample dataset created from https://doi.org/10.5281/zenodo.3522106 by selecting carat,price,color,clarity and cut columns only. In addition color and clarity are factors with integer values so we can reuse the dataset directly with an existing workflow (taught in Galaxy 101 for everyone).
DOI: 10.5281/zenodo.3554549
title: Sequencing of ABL1 from a CML patient

description: Capture and Duplex Sequencing of the human ABL1 gene
DOI: 10.5281/zenodo.3526229
title: HIV-1 genome and annotation

description: HIV-1 genome and annotation datasets for use with Galaxy Training Materials (https://training.galaxyproject.org). This repository contains three datasets: hbx2.fa - genomic sequence of HIV-1 derived from GenBank entry K03455.1 hxb2.bed - coordinates of genomic features and drug resistance mutations hxb2.dr.bed - a subset of the annotation data containing drug resistance mutations only. Coordinates of drug resistance mutations are derived from Los Alamos National Lab HIV database data.
DOI: 10.5281/zenodo.3591845
title: Old version

description: Data required for the GTN tutorial on Circos visualization in Galaxy
DOI: 10.5281/zenodo.3540704
title: Dataset: Diamonds from Hadley's ggplot2 for Galaxy training

description: Sample dataset created from https://doi.org/10.5281/zenodo.3522106 by selecting carat,price,color,clarity and cut columns only. In addition color and clarity are factors with integer values so we can reuse the dataset directly with an existing workflow (taught in Galaxy 101 for everyone).
DOI: 10.5281/zenodo.3554548
title: Sequencing of ABL1 from a CML patient

description: Capture and Duplex Sequencing of the human ABL1 gene
DOI: 10.5281/zenodo.3526228
title: HIV-1 genome and annotation

description: HIV-1 genome and annotation datasets for use with Galaxy Training Materials (https://training.galaxyproject.org). This repository contains three datasets: hbx2.fa - genomic sequence of HIV-1 derived from GenBank entry K03455.1 hxb2.bed - coordinates of genomic features and drug resistance mutations hxb2.dr.bed - a subset of the annotation data containing drug resistance mutations only. Coordinates of drug resistance mutations are derived from Los Alamos National Lab HIV database data.
DOI: 10.5281/zenodo.3519268
title: Sample data for analysis of sequence variation in HIV

description: These are downsampled interleaved paired fastq datasets from Jair et. 2019 (https://doi.org/10.1371/journal.pone.0214820). The datasets were prepared by: Downloading original data from NCBI SRA (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA517147) Trimming contaminating Nextera adapters using trim-galore Mapping reads against nxb2 reference of HIV genome (K03455.1) with BWA MEM Restricting mapped reads to <em>pol</em> gene vicinity (K03455.1:2000-5100) Downsampling mapped data to ~10% of the original with Picard's DownsampleSam Converting BAM to Interleaved Fastq with Picard's SamToFastq Gzipping resultant interleaved paired fastq files
description: {"references": ["https://doi.org/10.1371/journal.pone.0214820"]}
DOI: 10.5281/zenodo.3519267
title: Sample data for analysis of sequence variation in HIV

description: These are downsampled interleaved paired fastq datasets from Jair et. 2019 (https://doi.org/10.1371/journal.pone.0214820). The datasets were prepared by: Downloading original data from NCBI SRA (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA517147) Trimming contaminating Nextera adapters using trim-galore Mapping reads against nxb2 reference of HIV genome (K03455.1) with BWA MEM Restricting mapped reads to <em>pol</em> gene vicinity (K03455.1:2000-5100) Downsampling mapped data to ~10% of the original with Picard's DownsampleSam Converting BAM to Interleaved Fastq with Picard's SamToFastq Gzipping resultant interleaved paired fastq files
description: {"references": ["https://doi.org/10.1371/journal.pone.0214820"]}
DOI: 10.5281/zenodo.3477564
title: RNA Seq Counts to Viz in R

description: dataset for GTN tutorial "RNA Seq Counts to Viz in R"
DOI: 10.5281/zenodo.3482616
title: Dataset for Training Material - Galaxy Workflow - Analyse unaligned ncRNAs

description: Input dataset for Galaxy Training Material for the Analyze unaligned ncRNAs workflow. See https://github.com/galaxyproject/training-material for more information.
description: {"references": ["Kalvari, Ioanna, et al. \"Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families.\" Nucleic acids research 46.D1 (2017): D335-D342.", "Cunningham, Fiona, et al. \"Ensembl 2019.\" Nucleic acids research 47.D1 (2018): D745-D751."]}
DOI: 10.5281/zenodo.3482615
title: Dataset for Training Material - Galaxy Workflow - Analyse unaligned ncRNAs

description: Input dataset for Galaxy Training Material for the Analyze unaligned ncRNAs workflow. See https://github.com/galaxyproject/training-material for more information.
description: {"references": ["Kalvari, Ioanna, et al. \"Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families.\" Nucleic acids research 46.D1 (2017): D335-D342.", "Cunningham, Fiona, et al. \"Ensembl 2019.\" Nucleic acids research 47.D1 (2018): D745-D751."]}
DOI: 10.5281/zenodo.3405119
title: Proteomic characterization of human exhaled breath condensate.

description: datasets from 3 studies, for In-depth proteomics characterization of exhaled breath condensate (EBC). 1) Lacombe M. et al, 2018 2) Muccilli V. et al, 2015 3) Bredberg A. et al, 2012
DOI: 10.5281/zenodo.3477563
title: RNA Seq Counts to Viz in R

description: dataset for GTN tutorial "RNA Seq Counts to Viz in R"
DOI: 10.5281/zenodo.3386291
title: A Galaxy-based training resource for single-cell RNA-seq quality control and analyses

description: This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.
DOI: 10.5281/zenodo.3351375
title: STOC indicator trend - Galaxy Training Network

description: Example file for the tutorial STOC indicator trend
DOI: 10.5281/zenodo.3382053
title: Training Data for "Making sense of a newly assembled genome"

description: The data provided here is part of the Galaxy Training Network tutorial for Making sense of a newly assembled genome. This data was sourced from NCBI on 2019-08-29
DOI: 10.5281/zenodo.3405118
title: Proteomic characterization of human exhaled breath condensate.

description: datasets from 3 studies, for In-depth proteomics characterization of exhaled breath condensate (EBC). 1) Lacombe M. et al, 2018 2) Muccilli V. et al, 2015 3) Bredberg A. et al, 2012
DOI: 10.5281/zenodo.3386290
title: A Galaxy-based training resource for single-cell RNA-seq quality control and analyses

description: This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.
DOI: 10.5281/zenodo.3270536
title: ATAC-Seq (training data)

description: Training dataset for a Galaxy ATAC-seq tutorial.
DOI: 10.5281/zenodo.3339190
title: STOC population trend - Galaxy training material

description: Galaxy training material example file https://galaxyproject.github.io/training-material/
DOI: 10.5281/zenodo.3351374
title: STOC indicator trend - Galaxy Training Network

description: Example file for the tutorial STOC indicator trend
DOI: 10.5281/zenodo.3382052
title: Training Data for "Making sense of a newly assembled genome"

description: The data provided here is part of the Galaxy Training Network tutorial for Making sense of a newly assembled genome. This data was sourced from NCBI on 2019-08-29
DOI: 10.5281/zenodo.3339189
title: STOC population trend - Galaxy training material

description: Galaxy training material example file https://galaxyproject.github.io/training-material/
DOI: 10.5281/zenodo.3269404
title: Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"

description: Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6), http://dx.doi.org/10.1093/gigascience/giy057). The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.
DOI: 10.5281/zenodo.3054169
title: Training data for 'Exome sequencing data analysis' tutorial (Galaxy Training Material)

description: The data used in this tutorial are a subset of the data published previously in Training material for the course "Exome analysis with GALAXY". Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru! Specifically, you may need the following datasets for following the tutorial: <strong>Raw sequencing reads</strong> https://zenodo.org/record/3243160/files/father_R1.fq.gz https://zenodo.org/record/3243160/files/father_R2.fq.gz https://zenodo.org/record/3243160/files/mother_R1.fq.gz https://zenodo.org/record/3243160/files/mother_R2.fq.gz https://zenodo.org/record/3243160/files/proband_R1.fq.gz https://zenodo.org/record/3243160/files/proband_R2.fq.gz <strong>Premapped sequencing reads</strong> https://zenodo.org/record/3243160/files/mapped_reads_father.bam https://zenodo.org/record/3243160/files/mapped_reads_mother.bam https://zenodo.org/record/3243160/files/mapped_reads_proband.bam <strong>Reference sequence (human chromosome 8)</strong> https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.
DOI: 10.5281/zenodo.3269403
title: Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"

description: Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6), http://dx.doi.org/10.1093/gigascience/giy057). The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.
DOI: 10.5281/zenodo.3270535
title: ATAC-Seq (training data)

description: Training dataset for a Galaxy ATAC-seq tutorial.
DOI: 10.5281/zenodo.2600690
title: Modeled cellulase structure

description: Cellulase structure based on PDB code 7CEL, used for Galaxy molecular dynamics tutorial.
DOI: 10.5281/zenodo.3054168
title: Training data for 'Exome sequencing data analysis' tutorial (Galaxy Training Material)

description: The data used in this tutorial are a subset of the data published previously in Training material for the course "Exome analysis with GALAXY". Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru! Specifically, you may need the following datasets for following the tutorial: <strong>Raw sequencing reads</strong> https://zenodo.org/record/3243160/files/father_R1.fq.gz https://zenodo.org/record/3243160/files/father_R2.fq.gz https://zenodo.org/record/3243160/files/mother_R1.fq.gz https://zenodo.org/record/3243160/files/mother_R2.fq.gz https://zenodo.org/record/3243160/files/proband_R1.fq.gz https://zenodo.org/record/3243160/files/proband_R2.fq.gz <strong>Premapped sequencing reads</strong> https://zenodo.org/record/3243160/files/mapped_reads_father.bam https://zenodo.org/record/3243160/files/mapped_reads_mother.bam https://zenodo.org/record/3243160/files/mapped_reads_proband.bam <strong>Reference sequence (human chromosome 8)</strong> https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.
DOI: 10.5281/zenodo.2558444
title: Sample data for Permanganate sequencing

description: Sample datasets for permanganate seq data analysis (human)
DOI: 10.5281/zenodo.2600689
title: Modeled cellulase structure

description: Cellulase structure based on PDB code 7CEL, used for Galaxy molecular dynamics tutorial.
DOI: 10.5281/zenodo.1346742
title: Sacurine toy dataset - 2 samples / 2 blanks

description: <strong>Objective: </strong> Influence of age, body mass index, and gender on the urine metabolome <strong>Cohort: </strong> 183 employees from CEA <strong>LC-HRMS: </strong> LTQ-Orbitrap (negative ionization mode)
DOI: 10.5281/zenodo.2558443
title: Sample data for Permanganate sequencing

description: Sample datasets for permanganate seq data analysis (human)
DOI: 10.5281/zenodo.2553519
title: GTN_PAR-CLIP_workflow

description: Data from https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?exp=SRX105188&cmd=search&m=downloads&s=seq and ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/002/985/GCA_000002985.3_WBcel235 For Galaxy Training https://rna.usegalaxy.eu/workflows/run?id=a108b575b16e6cb9
description: {"references": ["https://www.ncbi.nlm.nih.gov/sra/SRX105188[accn]", "ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/002/985/GCA_000002985.3_WBcel235"]}
DOI: 10.5281/zenodo.1346695
title: Sacurine toy dataset - 2 samples / 2 blanks

description: <strong>Objective: </strong> Influence of age, body mass index, and gender on the urine metabolome <strong>Cohort: </strong> 183 employees from CEA <strong>LC-HRMS: </strong> LTQ-Orbitrap (negative ionization mode)
DOI: 10.5281/zenodo.2553518
title: GTN_PAR-CLIP_workflow

description: Data from https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?exp=SRX105188&cmd=search&m=downloads&s=seq and ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/002/985/GCA_000002985.3_WBcel235 For Galaxy Training https://rna.usegalaxy.eu/workflows/run?id=a108b575b16e6cb9
description: {"references": ["https://www.ncbi.nlm.nih.gov/sra/SRX105188[accn]", "ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/002/985/GCA_000002985.3_WBcel235"]}
DOI: 10.5281/zenodo.1256760
title: GeneSeqToFamily - CDS

description: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL and HomoloGene, to identify gene families and visualise syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences and provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families.
DOI: 10.5281/zenodo.1256759
title: GeneSeqToFamily - CDS

description: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL and HomoloGene, to identify gene families and visualise syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences and provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families.
DOI: 10.5281/zenodo.1256753
title: GeneSeqToFamily - Species Tree

description: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL and HomoloGene, to identify gene families and visualise syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences and provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families.
DOI: 10.5281/zenodo.1256752
title: GeneSeqToFamily - Species Tree

description: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL and HomoloGene, to identify gene families and visualise syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences and provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families.
DOI: 10.5281/zenodo.1346135
title: GeneSeqToFamily: Data

description: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL and HomoloGene, to identify gene families and visualise syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences and provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families.
DOI: 10.5281/zenodo.1256762
title: GeneSeqToFamily: Gene JSON

description: Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL and HomoloGene, to identify gene families and visualise syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences and provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families.