cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.19761343.v1
title: Chromium Next GEM Single Cell 3’ RNA-sequencing of tumor cell line co-cultured with PBMC and treated with trifluridine or DMSO control

description: <strong>Chromium Next GEM Single Cell 3’</strong> <strong>RNA-sequencing</strong>. 
<br>
HCT116 were seeded in 6-well Nunc plates (50,000 cells/3mL/well) and precultured for 24 h before PBMCs were added at a 1:8 ratio. Co-cultures were treated with DMSO vehicle (0.1%) or FTD (3mM) for 12 h or 72 h. MACS Dead Cell Removal Kit (Miltenyi Biotec, Gladbach, DEU) was performed according to the manufacturer’s instructions on cells treated for 72 h to increase the viability of the samples before RNA-sequencing. The viability of the samples treated for 12 h was not subjected to Dead Cell Removal as the viability was already sufficient. All samples were washed in PBS with 0.04% BSA (2x1mL). 
<br>
Chromium Next GEM Single Cell 3’ library preparation and RNA-sequencing were performed by the SNP&SEQ Technology Platform (National Genomics Infrastructure (NGI), Science for Life Laboratory, Uppsala University, Sweden).
DOI: 10.17044/scilifelab.19923161
title: Taxonomic and functional diversity of aquatic heterotrophs is sustained by dissolved organic matter chemodiversity

description: With this study, we advance the understanding of how specific aquatic heterotrophic bacteria act as central agents in the microbial loop. We use cultivated model communities developed from lake bacterioplankton under selection (i) in coculture with the cyanobacterium Microcystis aeruginosa (ii) growing on DOM produced from an axenic culture of M. aeruginosa, and (iii) growing in ambient lake DOM. After dilutions and serial transfer, cultivated model communities were established and characterized with a powerful combination of genome-resolved metagenomics and high-resolution untargeted metabolite analysis. This repository contains all metagenome-assembled genomes (MAGs) used in the study. The study is currently in bioRxiv https://www.biorxiv.org/content/10.1101/2022.03.21.485019v1
<br>
Each entry in the database corresponds to a MAG assembled from a cultured model community from Lake Erken. We had 33 cultures, 5 originated from inoculation on spent media from axenic M. aeruginosa (DOM$$_bin-**.fasta, where $$ is the numnber of the culture and ** is the MAG number), 21 cultures originated from filtered sterilized lake water (ERK$$_bin-**.fasta, where $$ is the numnber of the culture and ** is the MAG number) and 7 cultures were established as co-cultures with M. aeruginosa (CO$$_bin-**.fasta, where $$ is the numnber of the culture and ** is the MAG number) or to a MAG assembled from Lake Erken (Erken$$_bin-**.fasta, where $$ is name of the sample and ** is the MAG number).
<br>
DOI: 10.7483/opendata.cms.ku6a.d63g
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.19923161.v1
title: Taxonomic and functional diversity of aquatic heterotrophs is sustained by dissolved organic matter chemodiversity

description: With this study, we advance the understanding of how specific aquatic heterotrophic bacteria act as central agents in the microbial loop. We use cultivated model communities developed from lake bacterioplankton under selection (i) in coculture with the cyanobacterium Microcystis aeruginosa (ii) growing on DOM produced from an axenic culture of M. aeruginosa, and (iii) growing in ambient lake DOM. After dilutions and serial transfer, cultivated model communities were established and characterized with a powerful combination of genome-resolved metagenomics and high-resolution untargeted metabolite analysis. This repository contains all metagenome-assembled genomes (MAGs) used in the study. The study is currently in bioRxiv https://www.biorxiv.org/content/10.1101/2022.03.21.485019v1
<br>
Each entry in the database corresponds to a MAG assembled from a cultured model community from Lake Erken. We had 33 cultures, 5 originated from inoculation on spent media from axenic M. aeruginosa (DOM$$_bin-**.fasta, where $$ is the numnber of the culture and ** is the MAG number), 21 cultures originated from filtered sterilized lake water (ERK$$_bin-**.fasta, where $$ is the numnber of the culture and ** is the MAG number) and 7 cultures were established as co-cultures with M. aeruginosa (CO$$_bin-**.fasta, where $$ is the numnber of the culture and ** is the MAG number) or to a MAG assembled from Lake Erken (Erken$$_bin-**.fasta, where $$ is name of the sample and ** is the MAG number).
<br>
DOI: 10.7483/opendata.cms.4ke1.qpw7
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.20014820
title: Genotype data for analysis of chronic kidney disease in boxer dogs

description: Genotype data of 254 boxer samples were used for the Bayesian analysis of chronic kidney disease (CKD). In this study, we aimed to uncover the genetic loci that contribute to canine CKD in a young boxer population by comparing the cases and controls boxers from different countries.
<br>
These genome-wide genotypes were generated with illumina 170K CanineHD BeadChip, and stored in plink format. Details for the plink bed format is available: https://www.cog-genomics.org/plink/1.9/strat. Only autosomal markers were kept for the analysis, and markers with genotyping rate &lt; 95% and minor allele frequency &lt; 1% were excluded. The coordinates of markers in CKD_254boxers.bim are based on the Canfam4 reference assembly (http://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=canFam4). The sex and phenotype information were listed in the column 5 and 6 in CKD_254boxers.fam.
DOI: 10.17044/scilifelab.20014820.v1
title: Genotype data for analysis of chronic kidney disease in boxer dogs

description: Genotype data of 254 boxer samples were used for the Bayesian analysis of chronic kidney disease (CKD). In this study, we aimed to uncover the genetic loci that contribute to canine CKD in a young boxer population by comparing the cases and controls boxers from different countries.
<br>
These genome-wide genotypes were generated with illumina 170K CanineHD BeadChip, and stored in plink format. Details for the plink bed format is available: https://www.cog-genomics.org/plink/1.9/strat. Only autosomal markers were kept for the analysis, and markers with genotyping rate &lt; 95% and minor allele frequency &lt; 1% were excluded. The coordinates of markers in CKD_254boxers.bim are based on the Canfam4 reference assembly (http://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=canFam4). The sex and phenotype information were listed in the column 5 and 6 in CKD_254boxers.fam.
DOI: 10.17632/n8d4y5j9rg.1
title: strategies of Mute Swans

description: An important factor influencing nesting strategies in birds is interspecies interaction, often shaped by competitive behaviours. The expansion of a more territorial species can intensify negative interactions, leading to increased competition with native species. This study investigates how the presence of Whooper Swans Cygnus cygnus affects the nesting strategies of Mute Swans Cygnus olor, with a particular focus on changes in nest site selection. Fieldwork was conducted at two study sites in central and southern Poland, encompassing both single-species and mixed-species nesting environments. We measured a range of spatial variables, including proximity to open water, vegetation cover, and spatial relationships between nests of competing swan species. A novel method was developed to quantify nest concealment and isolation in complex habitats, integrating multiple habitat and spatial metrics into three indices. Mixed linear regression models revealed that Mute Swans adapt their nesting strategies in the presence of Whooper Swans by selecting more concealed and isolated locations. This behaviour is interpreted as a trade-off between optimal nesting conditions and the need for protection from aggressive competitors.
DOI: 10.7483/opendata.cms.6rfi.7tib
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tb51.nu1y
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.20107199
title: Spatiotemporally controlled generation of NTPs for single-molecule studies of NTP-driven processes

description: Data supporting the findings of the paper titled "Spatiotemporally controlled generation of NTPs for single-molecule studies" in Nature Chemical Biology which describes a method for controlling NTP-driven reactions in single-molecule experiments <em>via</em> the local generation of NTPs (LAGOON) that increases the measurement throughput and enables single-turnover observations. 
The dataset contains single-molecule FRET data capturing T4 helicase DNA unwinding, nucleosome remodeling by Chd1 and the invasion of a nucleosome by the T7 RNA polymerase. Additionally, the dataset contains acoustic force spectroscopy data on E. coli RNA polymerase elongation.
DOI: 10.17044/scilifelab.20107199.v1
title: Spatiotemporally controlled generation of NTPs for single-molecule studies of NTP-driven processes

description: Data supporting the findings of the paper titled "Spatiotemporally controlled generation of NTPs for single-molecule studies" in Nature Chemical Biology which describes a method for controlling NTP-driven reactions in single-molecule experiments <em>via</em> the local generation of NTPs (LAGOON) that increases the measurement throughput and enables single-turnover observations. 
The dataset contains single-molecule FRET data capturing T4 helicase DNA unwinding, nucleosome remodeling by Chd1 and the invasion of a nucleosome by the T7 RNA polymerase. Additionally, the dataset contains acoustic force spectroscopy data on E. coli RNA polymerase elongation.
DOI: 10.17044/scilifelab.20120150
title: Whole genome analyses based on single, field collected spores of the AMF F.geosporum

description: The item contains data and figures supporting the publication with the same name. In this study we sequenced nuclei and assembled genomes for two spores from an arbuscular mycorrhizal fungi morphotypically identified as Funneliforms geosporum. The spores were collected at the Kunsgängen nature reserve just south of Uppsala, Sweden.
<br>
The file "Spore nuclie scatter plots and MDA results for ..." contains information on the spores from which nuclei were sorted in this project including FACS scatterplot, well positions, MDA, PCR screening for bacteria and fungi as well as a list of which nuclei were selected for sequening. 
<br>
The zipped folder "Alignments for species identification.zip" contains alignments and phylogenetic trees for there reference genes (Fox, LSU and Tor) used for species identification of the morphotyped spores agains known species.
<br>
The file "Alignment of MAT contigs from all funneliformis.phy" contains one contig from each of the four Funneliformis genome assemblies with a gene homologous to one of the genes in the mating type locus identified in Rhizophagus irregulars, gene bank accession number KT946687
<br>
The file "Alignment of MAT contigs from all funneliformis extraction.fasta" contains the region from each of the four contigs that was extracted from the above alignment and used to Blast all single nuclei assemblies for potential mating type locus homologues.
<br>
The two excel files "Fig 3a..." and "Fig 3b..." contains the FunAnnotate output for gene families CAZyme and MEROPS and calculations for Fig 3a and b.
<br>
DOI: 10.7483/opendata.cms.9h68.l99d
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20120150.v1
title: Whole genome analyses based on single, field collected spores of the AMF F.geosporum

description: The item contains data and figures supporting the publication with the same name. In this study we sequenced nuclei and assembled genomes for two spores from an arbuscular mycorrhizal fungi morphotypically identified as Funneliforms geosporum. The spores were collected at the Kunsgängen nature reserve just south of Uppsala, Sweden.
<br>
The file "Spore nuclie scatter plots and MDA results for ..." contains information on the spores from which nuclei were sorted in this project including FACS scatterplot, well positions, MDA, PCR screening for bacteria and fungi as well as a list of which nuclei were selected for sequening. 
<br>
The zipped folder "Alignments for species identification.zip" contains alignments and phylogenetic trees for there reference genes (Fox, LSU and Tor) used for species identification of the morphotyped spores agains known species.
<br>
The file "Alignment of MAT contigs from all funneliformis.phy" contains one contig from each of the four Funneliformis genome assemblies with a gene homologous to one of the genes in the mating type locus identified in Rhizophagus irregulars, gene bank accession number KT946687
<br>
The file "Alignment of MAT contigs from all funneliformis extraction.fasta" contains the region from each of the four contigs that was extracted from the above alignment and used to Blast all single nuclei assemblies for potential mating type locus homologues.
<br>
The two excel files "Fig 3a..." and "Fig 3b..." contains the FunAnnotate output for gene families CAZyme and MEROPS and calculations for Fig 3a and b.
<br>
DOI: 10.17044/scilifelab.20171347
title: References and test datasets for the Cactus pipeline

description: Overview
This item contains references and test datasets for the Cactus pipeline.
Cactus (Chromatin ACcessibility and Transcriptomics  Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study. 
<br>
Test datasets
The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (*.fastq.gz), parameter files (*.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAS (Differential Analysis Subsets) for enrichment analysis.
Datasets downloaded:
- Worm and Humans: GSE98758
- Fly: GSE149339
- Mouse: GSE193393
<br>
References
One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references.
Genomic sequences and annotations were downloaded from Ensembl (Cunningham et al., 2022). The ENCODE API (Luo et al., 2020) was used to download the CHIP-Seq profiles of 2,714 Transcription Factors (TFs) (Landt et al., 2012; Boyle et al., 2014) and chromatin states in the form of 899 ChromHMM profiles (Boix et al., 2021; van der Velde et al., 2021) and 6 HiHMM profiles (Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations, allowing users to analyze only CHIP-Seq profiles relevant to their study. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021), respectively.
<br>
Documentation
More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.
DOI: 10.7483/opendata.cms.ag9h.u8r1
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.20171347.v1
title: References and test datasets for the Cactus pipeline

description: Overview
This item contains references and test datasets for the Cactus pipeline.
Cactus (Chromatin ACcessibility and Transcriptomics  Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study. 
<br>
Test datasets
The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (*.fastq.gz), parameter files (*.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAE (Differential Abundant Entries) for enrichment analysis.
Datasets downloaded:
- Worm and Humans: GSE98758
- Fly: GSE149339
- Mouse: GSE193393
<br>
References
One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references.
Genomic sequences and annotations were downloaded from the Ensembl FTP. The ENCODE API was used to download 2,714 TF CHIP-Seq profiles (Landt et al., 2012; Boyle et al., 2014) and 899 ChromHMM and 6 HiHMM chromatin state profiles (Boix et al., 2021; van der Velde et al., 2021; Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations to allow users to analyze only relevant CHIP-Seq profiles. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via respectively the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021).
<br>
Documentation
More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.
DOI: 10.17044/scilifelab.20171347.v2
title: References and test datasets for the Cactus pipeline

description: Overview
This item contains references and test datasets for the Cactus pipeline.
Cactus (Chromatin ACcessibility and Transcriptomics  Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study. 
<br>
Test datasets
The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (*.fastq.gz), parameter files (*.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAE (Differential Abundant Entries) for enrichment analysis.
Datasets downloaded:
- Worm and Humans: GSE98758
- Fly: GSE149339
- Mouse: GSE193393
<br>
References
One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references.
Genomic sequences and annotations were downloaded from the Ensembl FTP. The ENCODE API was used to download 2,714 TF CHIP-Seq profiles (Landt et al., 2012; Boyle et al., 2014) and 899 ChromHMM and 6 HiHMM chromatin state profiles (Boix et al., 2021; van der Velde et al., 2021; Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations to allow users to analyze only relevant CHIP-Seq profiles. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via respectively the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021).
<br>
Documentation
More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.
DOI: 10.7483/opendata.cms.034r.y82r
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20171347.v4
title: References and test datasets for the Cactus pipeline

description: Overview
This item contains references and test datasets for the Cactus pipeline.
Cactus (Chromatin ACcessibility and Transcriptomics  Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study. 
<br>
Test datasets
The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (*.fastq.gz), parameter files (*.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAS (Differential Analysis Subsets) for enrichment analysis.
Datasets downloaded:
- Worm and Humans: GSE98758
- Fly: GSE149339
- Mouse: GSE193393
<br>
References
One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references.
Genomic sequences and annotations were downloaded from Ensembl (Cunningham et al., 2022). The ENCODE API (Luo et al., 2020) was used to download the CHIP-Seq profiles of 2,714 Transcription Factors (TFs) (Landt et al., 2012; Boyle et al., 2014) and chromatin states in the form of 899 ChromHMM profiles (Boix et al., 2021; van der Velde et al., 2021) and 6 HiHMM profiles (Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations, allowing users to analyze only CHIP-Seq profiles relevant to their study. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021), respectively.
<br>
Documentation
More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.