cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.c.3643814_d6
title: Additional file 2: Table S1. of Codon bias and the folding dynamics of the cystic fibrosis transmembrane conductance regulator

description: SNPs effects on CFTR RSCU The SNPs with significant â  RSCU values are marked grey. Depending on database coverage the dbSNP database (rs#) and CFTR mutation database identifiers for SNPs were used. (XLSX 30Â kb)
DOI: 10.6084/m9.figshare.c.3643829_d1
title: Additional file 3: of Gene-specific DNA methylation profiles and LINE-1 hypomethylation are associated with myocardial infarction risk

description: Supplemental Figures S1, S2, S3, and S4. Figure S1. quantile-quantile plot, EPICOR overall subjects. Figure S2. quantile-quantile plot, EPICOR men. Figure S3. quantile-quantile plot, EPICOR women. Figure S4. locations of ZBTB12 and LINE-1 CpG sites investigated by Sequenom MassARRAY. CpGs (in red) investigated within ZBTB12-DMR, LINE-1, and flanking primers (upper case: complementary to DNA; lower case: T7-promoter sequence and 10mer tag). CpG sites that could not be tested individually due to MassARRAY technology constrains, but had to be tested jointly with neighboring CpGs as a single unit, are underlined: the methylation level is the cumulative value of all the sites within the CpG unit. (ZIP 91 kb)
DOI: 10.6084/m9.figshare.c.3643832_d3
title: Additional file 3: of Longitudinal analysis of the peripheral B cell repertoire reveals unique effects of immunization with a new influenza virus strain

description: Sequencing and repertoire mining metrics with ImmunediveRsity [ 30 ] by vaccination trial and by individual. (XLSX 23 kb)
DOI: 10.6084/m9.figshare.c.3643895_d1
title: Additional file 2: of A systematic review of mental health outcome measures for young people aged 12 to 25Â years

description: Mental health outcome measures used with young people 12 to 25 years [78â  248]. (XLSX 46 kb)
DOI: 10.6084/m9.figshare.c.3643904_d3
title: Additional file 1: Script S1. of Mining kidney toxicogenomic data by using gene co-expression modules

description: R script used to generate modules by using the iterative signature algorithm. (ZIP 15173 kb)
DOI: 10.6084/m9.figshare.c.3643904_d4
title: Additional file 3: Script S2. of Mining kidney toxicogenomic data by using gene co-expression modules

description: R script used to convert Affymetrix probe ids to human gene IDs. (ZIP 3 kb)
DOI: 10.6084/m9.figshare.c.3643904_d5
title: Additional file 5: Table S3. of Mining kidney toxicogenomic data by using gene co-expression modules

description: List of chemical exposures associated with 137 co-expression modules. (XLSX 80 kb)
DOI: 10.6084/m9.figshare.c.3643904_d6
title: Additional file 6: Table S4. of Mining kidney toxicogenomic data by using gene co-expression modules

description: List of the AKI-relevant genes. (XLSX 74 kb)
DOI: 10.6084/m9.figshare.c.3643904_d9
title: Additional file 12: Table S9. of Mining kidney toxicogenomic data by using gene co-expression modules

description: List of nodes in 158 AKI-relevant sub networks and their associated topological properties. (XLSX 19 kb)
DOI: 10.6084/m9.figshare.c.3643904_d10
title: Additional file 7: Table S5. of Mining kidney toxicogenomic data by using gene co-expression modules

description: List of chemical exposures that map to modules in module cluster 7 and module cluster 11. (XLSX 16 kb)
DOI: 10.6084/m9.figshare.c.3643904_d14
title: Additional file 16: Table S12. of Mining kidney toxicogenomic data by using gene co-expression modules

description: Complete list of publications supporting gene-to-function relationship in the 158 AKI-relevant sub-network genes (AKI-SN). (XLSX 17 kb)
DOI: 10.6084/m9.figshare.c.3643904_d13
title: Additional file 4: Table S2. of Mining kidney toxicogenomic data by using gene co-expression modules

description: List of genes associated with 137 co-expression modules. (XLSX 1398 kb)
DOI: 10.6084/m9.figshare.c.3643946_d5
title: Additional file 14: Dataset S2. of The Drosophila transcriptional network is structured by microbiota

description: Variance-stabilized read counts. Dataset S1 with a variance-stabilising transformation applied using functions provided by the DESeq library. (CSV 7395Â kb)
DOI: 10.15468/dl.f6uef3
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Anopheles litoralis King, 1932"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034001-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.c.3643946_d7
title: Additional file 4: Table S2. of The Drosophila transcriptional network is structured by microbiota

description: Gene ontology enrichment, full sets of terms and differentially expressed genes for (a) biological process enriched in axenic flies, (b) molecular function enriched in axenic flies, (c) cellular component enriched in axenic flies, (d) biological process depleted in axenic flies, (e) molecular function depleted in axenic flies, (f) cellular component depleted in axenic flies. (CSV 55Â kb)
DOI: 10.6084/m9.figshare.c.3643946_d11
title: Additional file 12: of The Drosophila transcriptional network is structured by microbiota

description: Data analysis tools. The unzipped folder contains (1) parameters used to align and count reads (align_count_index_reads.txt) and (2) an R script to replicate the data analysis. See README file and R script within folder for instructions. (ZIP 15304Â kb)
DOI: 10.6084/m9.figshare.c.3643946_d14
title: Additional file 5: Table S3. of The Drosophila transcriptional network is structured by microbiota

description: Gene membership of transcriptional modules. The table shows gene identifiers and names in FlyBase format, the module to which the gene was assigned, and whether the module exhibited significantly altered network structure (differential coexpression, i.e., TRUE) between axenic flies and gnotobiotic flies. Significant differential coexpression can indicate changes within a module or between two modules, see Fig.Â 1. (CSV 457Â kb)
DOI: 10.6084/m9.figshare.c.3643949_d1
title: Additional file 10: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: R code for extracting histone modification track signal near TSS for epigenetic modeling of gene expression method. Program written in R that calculates histone modification track signal in 100 bp bins spanning 1 kbp upstream and downstream of the TSS. Takes multiple .bigwig files of histone modification epigenomic tracks and the output of Additional file 9 as input. (R 5 kb)
DOI: 10.6084/m9.figshare.c.3643949_d3
title: Additional file 17: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: K562 epigenetic modeling of gene expression dataset. Expression modeling dataset used for epigenetic modeling of gene expression analysis. Each row corresponds to a TSS and contains information on positions of PQS within 2 kbp of the TSS as well as â  bestbinâ   epigenomic track signals for DNase hypersensitivity and histone modifications. This file is an output of Additional file 11 and is taken as input by Additional file 19. (CSV 4722 kb)
DOI: 10.6084/m9.figshare.c.3643949_d4
title: Additional file 16: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: HUVEC epigenetic modeling of gene expression dataset. Expression modeling dataset used for epigenetic modeling of gene expression analysis. Each row corresponds to a TSS and contains information on positions of PQS within 2 kbp of the TSS as well as â  bestbinâ   epigenomic track signals for DNase hypersensitivity and histone modifications. This file is an output of Additional file 11 and is taken as input by Additional file 19. (CSV 3089 kb)
DOI: 10.6084/m9.figshare.c.3643949_d5
title: Additional file 18: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: NHEK epigenetic modeling of gene expression dataset. Expression modeling dataset used for epigenetic modeling of gene expression analysis. Each row corresponds to a TSS and contains information on positions of PQS within 2 kbp of the TSS as well as â  bestbinâ   epigenomic track signals for DNase hypersensitivity and histone modifications. This file is an output of Additional file 11 and is taken as input by Additional file 19. (CSV 2268 kb)
DOI: 10.6084/m9.figshare.c.3643949_d6
title: Additional file 4: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: Differentially expressed genes in Werner Syndrome. List of genes that are differentially expressed in human fibroblasts in Werner Syndrome. (CSV 116 kb)
DOI: 10.6084/m9.figshare.c.3643949_d7
title: Additional file 8: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: R code for mapping PQS to TSS for epigenetic modeling of gene expression method. Program written in R that identifies all PQS within 2 kbp of TSS, calculates relative distance between PQS and TSS, identifies whether PQS are located on sense or antisense strand. Takes Additional file 1 and the output of Additional file 8 as input. (R 13 kb)
DOI: 10.6084/m9.figshare.c.3643949_d8
title: Additional file 19: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: R code for implementing epigenetic modeling of gene expression and analyzing PQS transcriptional effects. Program written in R that takes Additional files 12, 13, 14, 15, 16, 17, 18 as inputs and trains a Bayesian linear regression model upon epigenomic data to predict gene expression levels. Models are then applied to gene sets not used for training, and the subsequent sorting of modeling prediction errors by PQS position is used to calculate PQS transcriptional effects. Also conducts statistical bootstrapping simulations to estimate the FDR for this analysis. (R 44 kb)
DOI: 10.6084/m9.figshare.c.3643949_d9
title: Additional file 2: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: R code used for mapping genomic PQS to known TSS. Program written in R, used for mapping PQS motifs to known TSS. Takes GENCODE annotations in .csv format and Additional file 1 as input. (R 7 kb)
DOI: 10.6084/m9.figshare.c.3643949_d11
title: Additional file 13: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: H1-hESC epigenetic modeling of gene expression dataset. Expression modeling dataset used for epigenetic modeling of gene expression analysis. Each row corresponds to a TSS and contains information on positions of PQS within 2 kbp of the TSS as well as â  bestbinâ   epigenomic track signals for DNase hypersensitivity and histone modifications. This file is an output of Additional file 11 and is taken as input by Additional file 19. (CSV 6250 kb)
DOI: 10.6084/m9.figshare.c.3643949_d10
title: Additional file 7: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: R code for CAGE data processing for epigenetic modeling of gene expression method. Program written in R that takes CAGE data in .gtf format and the output of Additional file 6 as input, merges rows with GENCODE annotation table (output of Additional file 6), calculates aggregate expression of all transcripts originating from a given TSS, and identifies TSS for each gene with the highest expression. (R 4 kb)
DOI: 10.6084/m9.figshare.c.3643949_d12
title: Additional file 21: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: R code for PQS motif analysis in BS and WS differentially-expressed genes via SOM method. Program written in R for conducting self-organizing map classification of PQS motifs and analyzing enrichment of certain types of motifs in genes differentially expressed in BS and WS. (R 11 kb)
DOI: 10.6084/m9.figshare.c.3643949_d13
title: Additional file 14: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: HeLa-S3 epigenetic modeling of gene expression dataset. Expression modeling dataset used for epigenetic modeling of gene expression analysis. Each row corresponds to a TSS and contains information on positions of PQS within 2 kbp of the TSS as well as â  bestbinâ   epigenomic track signals for DNase hypersensitivity and histone modifications. This file is an output of Additional file 11 and is taken as input by Additional file 19. (CSV 4263 kb)
DOI: 10.6084/m9.figshare.c.3643949_d14
title: Additional file 3: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: Mapped coordinates of all PQS within 2 kbp of known TSS. Genomic coordinates and gene localization of all PQS within 2 kbp of known TSS. Each row contains data on a PQS motif and its host gene. PQS entries are repeated multiple times if a given motif is within 2 kbp of multiple TSS. Genes without any PQS motifs are represented as single rows with missing PQS motif data. (ZIP 11660 kb)
DOI: 10.6084/m9.figshare.c.3643949_d15
title: Additional file 22: of Relationships between putative G-quadruplex-forming sequences, RecQ helicases, and transcription

description: PQS motif classifications from SOM method. PQS motif classifications from self-organizing map analysis. Number of nearest multidimensional cluster is given as the last column in the dataset. (CSV 8064 kb)
DOI: 10.6084/m9.figshare.c.3643988_d1
title: Additional file 1: of Frequent down-regulation of ABC transporter genes in prostate cancer

description: Significant changes (p
DOI: 10.6084/m9.figshare.c.3643997_d2
title: MOESM9 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 9. Metadata regarding the operational conditions of the reactors. Metadata regarding the operational parameters of the reactors including pH, methane yield and volatile fatty acid concentration.
DOI: 10.6084/m9.figshare.c.3643997_d1
title: MOESM5 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 5. Functional characterization of the GBs according to SEED. (“SEED” worksheet) GBs were annotated using SEED subsystem and for each category (row 1) the subcategories are reported (row 2). Numbers refer to the subsystem feature counts. The SEED categories are reported in columns highlighted in grey (C, R, X, AF, AH, AL, AP, AX, BN, BR, BV, BZ, CF, CJ, CO, CV, DG, DN, DT, DW, DZ, EH, ER, EW, FH, FL, FN). In columns at right of each category are reported the results for the subcategories (for example, columns D-Q refer to the “super-category” “C”). Rows 110–114 report the median, the third and the first quartile results for each column. In red and in blue are highlighted the GBs having high and low numbers of subsystem feature counts (those in the third and first quartile of each category). (“hypergeometric” worksheet) For each GB and each SEED functional category the P value obtained from hypergeometric distribution is reported.
DOI: 10.6084/m9.figshare.c.3643997_d4
title: MOESM2 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 2. Gene annotation. Annotation of the genes identified in the assembly using COG, KEGG and Pfam databases.
DOI: 10.6084/m9.figshare.c.3643997_d6
title: MOESM7 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 7. Functional characterization of the GBs according to KEGG. (“KEGG” worksheet) GBs were annotated using KEGG. Numbers refer to the genes identified on each GB for each KEGG pathway. The KEGG pathways are reported in columns (C–EZ), the GBs are reported in rows (2–107). (“hypergeometric” worksheet) For each GB and each KEGG pathway, the P value obtained from hypergeometric distribution is reported.
DOI: 10.6084/m9.figshare.c.3643997_d5
title: MOESM6 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 6. Functional characterization of the GBs according to COG. (“COG_gene_numb” worksheet) GBs were annotated using COG. Numbers refer to the genes identified on each GB for each COG category. The COG categories are reported in columns (C–AA), the GBs are reported in rows (2–107). (“COG_perc” worksheet) Percentages of genes belonging to COG categories are calculated with respect to the total number of COG results for each GB (note that some genes belong to more than one COG category). In red and green are highlighted, for each COG category, the GBs having the 10 highest and the 10 lowest percentages. (“hypergeometric” worksheet). For each GB and each COG functional category, the P value obtained from hypergeometric distribution is reported.
DOI: 10.6084/m9.figshare.c.3643997_d7
title: MOESM8 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 8. Database resources used for functional characterization of the GBs. Functional processes of the GBs analyzed in the AD community are reported in the first column. The SEED subsystems, KEGG pathway maps, COG categories and Pfam domains used are reported in columns 3 and 4.
DOI: 10.6084/m9.figshare.c.3643997_d8
title: MOESM10 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 10. Suggested viewer: FigTree http://tree.bio.ed.ac.uk/software/figtree/ . Newick format of the file representing the microbial tree of life. The tree reports the 106 GBs of the AD microbial community together with other 3,737 microbial genomes. The file was obtained with PhyloPhlAn using 400 broadly conserved proteins used to extract phylogenetic signal.
DOI: 10.6084/m9.figshare.c.3643997_d9
title: MOESM4 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 4. Input files used for the network Representation of the Biogas Functional Organization (NRBFO). Functional analysis of the microbial community begins with the identification of the GBs enriched in SEED subsystem counts (ranked among the top one eighth of each SEED category). Starting from the entire list of SEED sub-categories (Data Set 6) 56 were selected (the more relevant for the characterization of the biogas community) and the GBs with high number of subsystem counts were identified (“node abundance” worksheet). The subsystem categories were compared in order to identify if they shared the same GBs and the couples with three or more common genome bins were identified (“edge_size_connections” worksheet). The number of GBs identified for each category was used to set the node size and the number of GBs shared was used to set the edge size (Fig. 2).
DOI: 10.6084/m9.figshare.c.3643997_d10
title: MOESM3 of Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy

description: Additional file 3. Taxonomy assignment and characteristics of the GBs. The taxonomy of the GBs identified was determined using different methods and a taxonomic assignment was suggested considering the results obtained. In columns (A–V) are reported: (A) the acronym of the GBs as reported in the main text (Sp = Spirochetes; Sy = Synergistetes; Th = Thermotogae; Pr = Proteobacteria; Fi = Firmicutes; Te = Tenericutes; Ac = Actinobacteria; Ba = Bacteroidetes; Tm = TM7 phylum; Eu = Euryarchaeota) the Phylum was determined using Phylophlan and, secondly, the results obtained from BLASTP search versus nr databases filtered using MEGAN; (B) the tentative taxonomic assignment; (C) domain of the genome bin; (D) phylum; (E) taxonomic level considered for the name assignment (the result obtained using Phylopythia was used when more than 50 % of the genome bin sequence was assigned to the same taxonomic group); (F) confidence for taxonomic assignment obtained using Phylophlan; (G–I) domain, phylum, class determined using Phylophlan; (J) taxonomic assignment determined using Phylopythia; (K) percentage of the genome assigned as reported in “J”; (L) taxonomic level reported in “J”; (M) number of genes having BLASTP e-value lower than 1*E-5; (N) average similarity for BLASTP results; (O) number of genes having BLASTN e-value lower than 1*E-5; (P) average similarity for BLASTN results; (Q) the species having the highest number of best match in BLASTP column “M”; (R) taxonomy assignment obtained using RDP classifier on the 16S rRNA gene, similarity, contig where the 16S gene was identified; (S) total length of the scaffolds assigned to the genome bin; (T) number of scaffolds, (U) scaffolds N50, (V) scaffolds N90, (W) average scaffolds length, (X) number of contigs determined after splitting scaffolds on stretched of 10 or more unknown bases “N”, (Y) contigs N50, (Z) contigs N90, (AA) average contigs length, (AB) number of protein encoding genes identified using SEED subsystem; (AC) number of protein encoding genes identified using Prodigal; (AD) total number of essential genes identified, (AE) univocal number of essential genes (removed those in multiple copies); (AF) estimated completeness of the GB; (AG) average number of essential genes in phylum 1. Bold text in columns (A, U, Y, Z, AA, AF) refers to GBs that satisfy the Human Microbiome Project quality criteria; (AH) estimated contamination level determined considering the univocal number of essential genes and the total number of essential genes in multiple copies; (AI) estimated completeness of the GB determined using CheckM software; (AJ) estimated level of contamination determined using CheckM software.
DOI: 10.15468/dl.d3476q
title: Occurrence Download

description: A dataset containing 590 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((130.6807 -0.93756,130.63934 -0.9541,130.64451 -0.98822,130.72646 -1.00682,130.79159 -0.98408,130.85052 -0.97064,130.8854 -0.92499,130.88069 -0.88262,130.82575 -0.89047,130.74728 -0.91872,130.6807 -0.93756))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 590 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034002-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.c.3644060_d1
title: Additional file 5: of Lower IgG somatic hypermutation rates during acute dengue virus infection is compatible with a germinal center-independent B cell response

description: Reads, clonal groups, and lineages numbers per individual and condition. The table shows the number of raw and used sequences as well as the number of clonotypes and lineages per individual. (XLS 30 kb)