cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.25213754
title: Newly qualified social workers' readiness in providing services to families and children

description: The goal of the study was to describe and explore newly qualified social workers (NQSW) s’ experiences regarding their work readiness to provide social work services to families and children in the Gauteng province. The research followed a qualitative research approach, and interpretivism paradigm that was underpinned by applied research. The research focused on exploring and describing the experiences of NQSWs readiness to provide services to children and families. An instrumental case study design was employed. The study population involved NQSW who provide services to children and families. Non-probability sampling was used, specifically purposive sampling, where 9 participants for the study were recruited, as this number of participants was found sufficient to reach data saturation. Semi structured interviews were used and guided by the interview schedule, as a data collection method. Thematic analysis was used for data analysis, using an inductive and latent approach for coding.
DOI: 10.6084/m9.figshare.26992900
title: Additional file 4 of The genome and transcriptome of the snail Biomphalaria sudanica s.l.: immune gene diversification and highly polymorphic genomic regions in an important African vector of Schistosoma mansoni

description: Additional file 4: Supplementary Table 3. Summary table of the 919 tRNA genes that were predicted by tRNAscan-SE 2.0 v2.0.9 [124] representing 24 tRNA types annotated in the Biomphalaria sudanica genome.
DOI: 10.6084/m9.figshare.26992903
title: Additional file 5 of The genome and transcriptome of the snail Biomphalaria sudanica s.l.: immune gene diversification and highly polymorphic genomic regions in an important African vector of Schistosoma mansoni

description: Additional file 5: Supplementary Table 4. Complete list showing the genome positions and prediction scores of 919 tRNA genes predicted in the Biomphalaria sudanica genome by tRNAscan-SE 2.0 v2.0.9 [124].
DOI: 10.6084/m9.figshare.26992927
title: Additional file 13 of The genome and transcriptome of the snail Biomphalaria sudanica s.l.: immune gene diversification and highly polymorphic genomic regions in an important African vector of Schistosoma mansoni

description: Additional file 13: Supplementary Table 12.Summary of the enriched gene ontology (GO) terms found in the protein families that significantly expanded or contracted in the CAFE 5 [69] analysis. For each group in the REVIGO [74] TreeMap summary (see Supplementary File 5) it is specified their member GO terms ID description, and the semantic similarity measurements calculated by REVIGO: Frequency of the term in the UniProt database (uniport.org/, database as of May 25th 2022), its semantical Uniqueness in the whole list and their Dispensability if the term was nested with another GO term. In the latter case (dispensable terms), the GO term number it is nested with is reported, or displayed as “-” otherwise.
DOI: 10.6084/m9.figshare.26992900.v1
title: Additional file 4 of The genome and transcriptome of the snail Biomphalaria sudanica s.l.: immune gene diversification and highly polymorphic genomic regions in an important African vector of Schistosoma mansoni

description: Additional file 4: Supplementary Table 3. Summary table of the 919 tRNA genes that were predicted by tRNAscan-SE 2.0 v2.0.9 [124] representing 24 tRNA types annotated in the Biomphalaria sudanica genome.
DOI: 10.6084/m9.figshare.26992954
title: Additional file 22 of The genome and transcriptome of the snail Biomphalaria sudanica s.l.: immune gene diversification and highly polymorphic genomic regions in an important African vector of Schistosoma mansoni

description: Additional file 22: Supplementary Table 21. BLASTp matches for the 1047 most diverse genes identified in the Biomphalaria sudanica genome.
DOI: 10.6084/m9.figshare.26993002
title: Additional file 1 of Particle-attached bacteria act as gatekeepers in the decomposition of complex phytoplankton polysaccharides

description: Additional file 1: Supplementary tables.
DOI: 10.6084/m9.figshare.26993002.v1
title: Additional file 1 of Particle-attached bacteria act as gatekeepers in the decomposition of complex phytoplankton polysaccharides

description: Additional file 1: Supplementary tables.
DOI: 10.6084/m9.figshare.26992954.v1
title: Additional file 22 of The genome and transcriptome of the snail Biomphalaria sudanica s.l.: immune gene diversification and highly polymorphic genomic regions in an important African vector of Schistosoma mansoni

description: Additional file 22: Supplementary Table 21. BLASTp matches for the 1047 most diverse genes identified in the Biomphalaria sudanica genome.
DOI: 10.6084/m9.figshare.26993029
title: Additional file 7 of New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

description: Supplementary material 7.
DOI: 10.6084/m9.figshare.26993029.v1
title: Additional file 7 of New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

description: Supplementary material 7.
DOI: 10.6084/m9.figshare.26992888.v1
title: Additional file 4 of Weathered granites and soils harbour microbes with lanthanide-dependent methylotrophic enzymes

description: Additional file 4: Supplementary Data 2. Multiple alignment of lanmodulin.
DOI: 10.6084/m9.figshare.26993053
title: Additional file 8 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 8: Table S5A. Functional profiles of the metagenome-assembled genes with KEGG ortholog annotation in porcine intestinal tracts. Table S5B Functional profiles of the metagenome-assembled genes with eggNOG ortholog annotation in porcine intestinal tracts. Table S5C List of KEGG orthologs differentially abundant in porcine jejunum segment between LT and LW samples. Table S5D Functional terms annotated with KEGG pathway in porcine jejunal segment. Table S5E List of KEGG pathways differentially abundant in porcine jejunal segment between LT and LW samples. Table S5F Lipid metabolites identified in porcine jejunal contents. Table S5G Differential lipid metabolites in porcine jejunal contents between LT and LW piglets. Table S5H The distribution of the metagenome-assembled genes on bacteria at different taxonomy levels. Table S5I Functional terms annotated with KEGG pathway in porcine colonic segment. Table S5J List of KEGG pathways differentially abundant in porcine colonic segment between LT and LW samples.
DOI: 10.6084/m9.figshare.26993053.v1
title: Additional file 8 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 8: Table S5A. Functional profiles of the metagenome-assembled genes with KEGG ortholog annotation in porcine intestinal tracts. Table S5B Functional profiles of the metagenome-assembled genes with eggNOG ortholog annotation in porcine intestinal tracts. Table S5C List of KEGG orthologs differentially abundant in porcine jejunum segment between LT and LW samples. Table S5D Functional terms annotated with KEGG pathway in porcine jejunal segment. Table S5E List of KEGG pathways differentially abundant in porcine jejunal segment between LT and LW samples. Table S5F Lipid metabolites identified in porcine jejunal contents. Table S5G Differential lipid metabolites in porcine jejunal contents between LT and LW piglets. Table S5H The distribution of the metagenome-assembled genes on bacteria at different taxonomy levels. Table S5I Functional terms annotated with KEGG pathway in porcine colonic segment. Table S5J List of KEGG pathways differentially abundant in porcine colonic segment between LT and LW samples.
DOI: 10.6084/m9.figshare.26993056
title: Additional file 9 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 9: Table S6A. List of canonical cell type-specific markers in porcine liver tissues. Table S6B The proportions of the 13 major cell types in porcine liver tissues. Table S6C The zonation-specific genes in porcine periportal and central hepatocytes. Table S6D KEGG analysis of the periportal hepatocyte-specific genes. Table S6E KEGG analysis of the central hepatocyte-specific genes. Table S6F Differentially expressed genes between LT and LW piglets in porcine periportal hepatocytes. Table S6G KEGG analysis of differentially expressed genes in porcine periportal hepatocytes between LT and LW piglets. Table S6H Lipid metabolites identified in porcine liver tissues. Table S6I Differential lipid metabolites in porcine liver tissues between LT and LW piglets.
DOI: 10.6084/m9.figshare.26993041.v1
title: Additional file 4 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 4: Table S1A. The list of canonical cell type-specific markers. Table S1B The cluster-specific genes identified for 40 major cell clusters.
DOI: 10.6084/m9.figshare.26993044.v1
title: Additional file 5 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 5: Table S2A. The proportions of the 28 major clusters in porcine intestinal tracts. Table S2B List of canonical cell type-specific markers in porcine intestinal tracts. Table S2C The proportions of the 14 major cell types in porcine intestinal tracts. Table S2D The enterocyte-enriched genes identified in porcine intestinal tracts. Table S2E KEGG analysis of the enterocyte-enriched genes identified in porcine intestinal tracts.
DOI: 10.6084/m9.figshare.26993056.v1
title: Additional file 9 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 9: Table S6A. List of canonical cell type-specific markers in porcine liver tissues. Table S6B The proportions of the 13 major cell types in porcine liver tissues. Table S6C The zonation-specific genes in porcine periportal and central hepatocytes. Table S6D KEGG analysis of the periportal hepatocyte-specific genes. Table S6E KEGG analysis of the central hepatocyte-specific genes. Table S6F Differentially expressed genes between LT and LW piglets in porcine periportal hepatocytes. Table S6G KEGG analysis of differentially expressed genes in porcine periportal hepatocytes between LT and LW piglets. Table S6H Lipid metabolites identified in porcine liver tissues. Table S6I Differential lipid metabolites in porcine liver tissues between LT and LW piglets.
DOI: 10.6084/m9.figshare.26993059
title: Additional file 10 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 10: Table S7A. List of canonical cell type-specific markers in porcine muscle tissues. Table S7B The cell type-specific genes identified in porcine muscle. Table S7C The proportions of the 12 major cell types in porcine muscle tissues. Table S7D KEGG analysis of the significantly up-expressed genes in oxidative myofibers. Table S7E KEGG analysis of the significantly up-expressed genes in glycolytic myofibers. Table S7F KEGG analysis of the significantly up-expressed genes in intermuscular fat cells. Table S7G Differentially expressed genes between LT and LW piglets in porcine IMF subtypes. Table S7H KEGG analysis of differentially expressed genes in porcine IMF subtypes between LT and LW piglets. Table S7I The expression levels of identified genes in each cluster of FAP subpopulation. Table S7J Differentially expressed genes in porcine FAP subtypes between LT and LW piglets. Table S7K Differentially expressed genes in porcine endothelial cells between LT and LW piglets.
DOI: 10.6084/m9.figshare.26993059.v1
title: Additional file 10 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 10: Table S7A. List of canonical cell type-specific markers in porcine muscle tissues. Table S7B The cell type-specific genes identified in porcine muscle. Table S7C The proportions of the 12 major cell types in porcine muscle tissues. Table S7D KEGG analysis of the significantly up-expressed genes in oxidative myofibers. Table S7E KEGG analysis of the significantly up-expressed genes in glycolytic myofibers. Table S7F KEGG analysis of the significantly up-expressed genes in intermuscular fat cells. Table S7G Differentially expressed genes between LT and LW piglets in porcine IMF subtypes. Table S7H KEGG analysis of differentially expressed genes in porcine IMF subtypes between LT and LW piglets. Table S7I The expression levels of identified genes in each cluster of FAP subpopulation. Table S7J Differentially expressed genes in porcine FAP subtypes between LT and LW piglets. Table S7K Differentially expressed genes in porcine endothelial cells between LT and LW piglets.
DOI: 10.6084/m9.figshare.26993041
title: Additional file 4 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 4: Table S1A. The list of canonical cell type-specific markers. Table S1B The cluster-specific genes identified for 40 major cell clusters.
DOI: 10.6084/m9.figshare.26993044
title: Additional file 5 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 5: Table S2A. The proportions of the 28 major clusters in porcine intestinal tracts. Table S2B List of canonical cell type-specific markers in porcine intestinal tracts. Table S2C The proportions of the 14 major cell types in porcine intestinal tracts. Table S2D The enterocyte-enriched genes identified in porcine intestinal tracts. Table S2E KEGG analysis of the enterocyte-enriched genes identified in porcine intestinal tracts.
DOI: 10.6084/m9.figshare.26993047
title: Additional file 6 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 6: Table S3A. Differentially expressed genes between LT and LW piglets in small intestinal epithelial cells. Table S3B KEGG analysis of differently expressed genes between LT and LW piglets in small intestinal epithelial cells. Table S3C Lipid metabolites identified in porcine serum. Table S3D Differential lipid metabolites in porcine serum between LT and LW piglets. Table S3E Differentially expressed genes between LT and LW piglets in large intestinal epithelial cells. Table S3F KEGG analysis of differently expressed genes between LT and LW piglets in large intestinal epithelial cells. Table S3G Differentially expressed genes between LT and LW piglets in large intestinal Paneth cells. Table S3H KEGG analysis of differently expressed genes between LT and LW piglets in large intestinal Paneth cells. Table S3I The cell composition of porcine colon tissues.
DOI: 10.6084/m9.figshare.26993047.v1
title: Additional file 6 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 6: Table S3A. Differentially expressed genes between LT and LW piglets in small intestinal epithelial cells. Table S3B KEGG analysis of differently expressed genes between LT and LW piglets in small intestinal epithelial cells. Table S3C Lipid metabolites identified in porcine serum. Table S3D Differential lipid metabolites in porcine serum between LT and LW piglets. Table S3E Differentially expressed genes between LT and LW piglets in large intestinal epithelial cells. Table S3F KEGG analysis of differently expressed genes between LT and LW piglets in large intestinal epithelial cells. Table S3G Differentially expressed genes between LT and LW piglets in large intestinal Paneth cells. Table S3H KEGG analysis of differently expressed genes between LT and LW piglets in large intestinal Paneth cells. Table S3I The cell composition of porcine colon tissues.
DOI: 10.6084/m9.figshare.26993050
title: Additional file 7 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 7: Table S4A. Quality control and preprocessing of metagenomic datasets. Table S4B Basic statistics of the metagenome-assembled scaftigs. Table S4C Basic statistics of the gene catalogues. Table S4D The relative abundance of intestinal microbiota at the phylum level. Table S4E The relative abundance of intestinal microbiota at the genus level. Table S4F The relative abundance of intestinal microbiota at the species level. Table S4G Comparison of the classification of jejunal microbiota between two breeds by linear discriminant analysis effect size (LefSe) method. Table S4H Comparison of the classification of colonic microbiota between two breeds by linear discriminant analysis effect size (LefSe) method. Table S4I List of genus differentially abundant in porcine jejunum between LT and LW samples. Table S4J List of genus differentially abundant in porcine colon between LT and LW samples.
DOI: 10.6084/m9.figshare.26993050.v1
title: Additional file 7 of Integrated meta-omics reveals the regulatory landscape involved in lipid metabolism between pig breeds

description: Additional file 7: Table S4A. Quality control and preprocessing of metagenomic datasets. Table S4B Basic statistics of the metagenome-assembled scaftigs. Table S4C Basic statistics of the gene catalogues. Table S4D The relative abundance of intestinal microbiota at the phylum level. Table S4E The relative abundance of intestinal microbiota at the genus level. Table S4F The relative abundance of intestinal microbiota at the species level. Table S4G Comparison of the classification of jejunal microbiota between two breeds by linear discriminant analysis effect size (LefSe) method. Table S4H Comparison of the classification of colonic microbiota between two breeds by linear discriminant analysis effect size (LefSe) method. Table S4I List of genus differentially abundant in porcine jejunum between LT and LW samples. Table S4J List of genus differentially abundant in porcine colon between LT and LW samples.
DOI: 10.25345/c5v11vx89
title: MassIVE MSV000095830 - MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation
title: Mass spectrometry imaging (MSI) has gained popularity in large-scale clinical analyses due to its high sensitivity, specificity and throughput. However, global profiling experiments are often still restricted to LC-MS/MS analyses that lack spatial localization due to low throughput methods for on-tissue peptide confirmation. Parallel LC-MS/MS confirmation of extracted peptides is often utilized with MALDI-MSI; however, integration and analysis of the two data sets once again presents a bottleneck in throughput. Further, the incorporation of histological stains is critical for mapping identified peptides of interest back to relevant tissue regions, yet many software generated for this are not readily accessible to those lacking coding capabilities. Here, we present a novel platform termed MSIght, which automates the integration of these multiple modalities into an accessible and modular platform. Histological stains of tissue sections are co-registered to their respective MSI data sets to improve spatial localization and resolution of identified peptides. LC-MS/MS data of extracted peptides is used to confirm putative MSI identifications and generate confident MS images in an untargeted manner. This platform enables large-scale clinical cohorts to utilize MSI for global proteomic profiling to uncover novel biomarkers in a spatial manner, thus widely expanding the utility of MSI in clinical discovery.
DOI: 10.6084/m9.figshare.26993155
title: Additional file 3 of Transcriptome-wide association studies associated with Crohn’s disease: challenges and perspectives

description: Additional file 3. The total significant results of GO functional analyses.
DOI: 10.6084/m9.figshare.26485174
title: QTL analysis of native Fusarium head blight and deoxynivalenol resistance in ‘D8006W’/‘Superior’, soft white winter wheat population

description: The genotyping of the D8006W/Superior winter wheat population was performed using the wheat 90K Infinium iSelect SNP array (Illumina, San Diego, CA, USA (Wang et al. 2014). The raw SNP data was filtered using Genome Studio V2011.1, and the final quality SNP marker was used for linkage map construction using MapDisto version 1.7.7. The dataset folder contained raw SNP data generated from the Illumina platform. The excel file contained the dataset for filtered SNP, linkage map, and phenotype data used in QTL analysis. Article associated with this dataset: Neupane et al. 2024 BMC Plant Biol https://doi.org/10.1186/s12870-024-05536-0
DOI: 10.6084/m9.figshare.26485174.v3
title: QTL analysis of native Fusarium head blight and deoxynivalenol resistance in ‘D8006W’/’Superior’, soft white winter wheat population

description: The genotyping of the D8006W/Superior winter wheat population was performed using the wheat 90K Infinium iSelect SNP array (Illumina, San Diego, CA, USA (Wang et al. 2014). The raw SNP data was filtered using Genome Studio V2011.1, and the final quality SNP marker was used for linkage map construction using MapDisto version 1.7.7. The dataset folder contained raw SNP data generated from the Illumina platform. The excel file contained the dataset for filtered SNP, linkage map, and phenotype data used in QTL analysis.
DOI: 10.6084/m9.figshare.26993101
title: Additional file 2 of Genome-wide detection of positive and balancing signatures of selection shared by four domesticated rainbow trout populations (Oncorhynchus mykiss)

description: Additional file 2: Table S1. Shared signatures of selection between the French lines (LB, LC and SY) and the American pooled population (HA) detected by the XP-EHH statistics. Table S2. List of ROH islands for the LB population. Table S3. List of ROH islands for the LC population. Table S4. List of ROH islands for the SY population. Table S5. List of ROH islands for the HA population. Table S6. List of significant signatures of selection identified by iHS for the LB population. Table S7. List of significant signals of selection identified by iHS for the LC population. Table S8. List of significant signals of selection identified by iHS for the SY population. Table S9. List of significant signals of selection identified by iHS for the HA population. Table S10. iHS and ROH values per population within the nine overlapping regions detected among the four populations. Table S11. List of the 253 genes included in the nine shared genomic regions. In bold, genes identified as linked to domestication in the literature. Table S12. List and functions of the 17 genes annotated in the three homozygous regions (chr2_a, chr2_c and chr15_a) shared by the four rainbow trout populations, and the 15 genes in the six other regions previously identified in the literature as signatures of selection [159, 166–183]. Table S13. List of the heterozygous regions (i.e., large regions without ROH) for the LB population. Table S14. List of the heterozygous regions (i.e., large regions without ROH) for the LC population. Table S15. List of the heterozygous regions (i.e., large regions without ROH) for the SY population. Table S16. List of the heterozygous regions (i.e., large regions without ROH) for the HA population. Table S17. List of the 29 genes included in the four heterozygous genomic regions. Table S18. Percentage of protein homology between rainbow trout and nine other vertebrate species for the genes annotated in the heterozygous chr10_a, chr13_a, chr19_a and chr19_b regions.
DOI: 10.6084/m9.figshare.26993149
title: Additional file 1 of Transcriptome-wide association studies associated with Crohn’s disease: challenges and perspectives

description: Additional file 1. The list of susceptibility genes in various tissue types screened out by different TWAS methods.