cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3146710.v14
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 1: qPCR validations]

description: <b>DESCRIPTION</b><br>qPCR was used to assess the enrichment of ChIP and MeDIP experiments and to validate the integrity of sequencing libraries<br><br><b>FILES</b><br>Validation of ChIP-seq library enrichment:<br><b>20130426-CA1-1h-K27ac-K79me3-K4me3-H3-lib_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K27ac, H3K79me3, H3K4me3 or H3. The target genes were NeuN, Chd4, GAPDH, InterG and cFOS.<br><b>20130715-ACCnaiveK76me3K27ac_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from ACC neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K79me3 or H3K27ac. The target genes were NeuN, Chd4 and GAPDH.<br><b>20131125-CA1-Naive_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K27ac, H3K27me3, H3K79me3, H3K4me3, H3 or H3K4me1. The target genes were TFF1, GAPDH, InterG and NeuN.<br><b>20150122-CA1-K9ac_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from ACC neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K9ac. The target genes were cFosF2R2, cFosF3R3, GAPDH, InterG and NeuN.<br><br>Validation of MedIP-seq library enrichment:<br><b>20150729-MeDIP-LIB-ACC_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of MedIP-seq libraries for samples from ACC taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (CS). The target genes were Cobl, Loxhd1, Vrk1 and GAPDHenh (-).<br><b>20150802-ACC-lib_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of MedIP-seq libraries for samples from ACC taken 1 hour (1h) or 4 weeks (4w) after naïve (N), context (C) or context-shock (CS). The target genes were Fgfr2, Nova1 and Reelin.<br><br><b>DATA GENERATION</b><br>qPCR experiments were run on Roche LightCycler 480, and the machine was used to export the Ct values. The resulting output was imported into pyQPCR (version 0.10dev) using dilutions of inputs as standards, along with the following parameters:<br>Calculation type: Absolute quantification<br>Confidence interval: 90.00%<br>Machine: Roche LightCycler 480<br>Maximum E(Ct): 0.30<br>Minimum Ct: 35.00<br><br>
DOI: 10.6084/m9.figshare.3146692.v7
title: [Data Record 4]: DHPTMs

description: This file contains a table for each analysis of Differential Histone Post-translational modifications. Each table contains the following information:<br>1) coordinates of the target regions (chromosome, start and end positions, with strand information included for analyses for marks H3K4me3, H3K9ac, H3K27me3 and H3K79me3 based on transcription start sites and gene regions, and no strand information for marks H3K4me1 and H3K27ac based on peaks)<br>2) baseMean - average number of reads in region across all samples<br>3) log2FoldChange - log2 fold change between number of reads in treatment condition and in control condition<br>4) lfcSE - standard error of the log2 fold change<br>5) stat - Wald statistic used to calculate the p-value<br>6) pvalue - p-value associated with a particular region<br>7) padj - p-value adjusted using Benjamini & Hochberg adjustment<br><br><b>TABLE DESCRIPTION</b><br>List of all differential histone post-translational modifications (DHPTMs). Each tab of this document contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context only (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context only (C) to context shock (CS) mice. The columns 'chromosome', DHPTM start', 'DHPTM end' and 'Strand' contain the position in the genome where the DHPTM was detected (mm10 assembly). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peaks, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS (peak-based tests were run as strand-independant, so for such tests column 'Strand' contains '.' for all entries). Column 'baseMean' shows the average normalised read counts in each DHPTM and column 'logFC' shows the fold change of the comparison between the conditions in log 2. Columns 'pvalue' and 'p-adjust' contain the pvalue and the adjusted p-value according to the FDR test.<br>
DOI: 10.6084/m9.figshare.3146695.v8
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: DMRs]

description: <b>DESCRIPTION</b><br>This data record contains tables of differentially methylated regions (DMRs), where each table contains different compared conditions for each time point (1h or 4w), tissue (CA1 or ACC) and cell type (Neu or Non). Tables with the suffix "_raw" contain all DMRs for adjusted p-value threshold of 0.5, and all other tables contain annotated DMRs for adjusted p-value 0.1 in C-CS tests or for adjusted p-value 0.05 in N-C and N-CS tests. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context only (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context only (C) to context shock (CS) mice.<br><b><br>DATA REPOSITORIES</b><br>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the DMRs (i.e. positions of DMRs) under category "DMR-DATA". Sample names include compared conditions (e.g. CxCS compares context with context-shock), time-point, tissue and cell-type separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>For the annotated DMRs, the tables contain columns "Gene name", "Gene type", "Transcript ID" and "Gene structure", corresponding to the gene name, type (protein_coding, long non-coding, miRNA etc.), ENSEMBL transcript ID and structure (intronic, downstream, etc.) for the nearest gene to each DMR. For all tables, The columns “chromosome”, “DMR start” and “DMR end” contain the position in the genome where the DMR was detected (mm10 assembly). The column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DMRs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3146740).<br><br>
DOI: 10.6084/m9.figshare.3146689
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: Predicted CRM regions]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>
DOI: 10.6084/m9.figshare.3146695.v9
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 7: DMRs]

description: <b>DESCRIPTION</b><br>This data record contains tables of differentially methylated regions (DMRs), where each table contains different compared conditions for each time point (1h or 4w), tissue (CA1 or ACC) and cell type (Neu or Non). Tables with the suffix "_raw" contain all DMRs for adjusted p-value threshold of 0.5, and all other tables contain annotated DMRs for adjusted p-value 0.1 in C-CS tests or for adjusted p-value 0.05 in N-C and N-CS tests. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context only (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context only (C) to context shock (CS) mice.<br><b><br>DATA REPOSITORIES</b><br>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the DMRs (i.e. positions of DMRs) under category "DMR-DATA". Sample names include compared conditions (e.g. CxCS compares context with context-shock), time-point, tissue and cell-type separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>For the annotated DMRs, the tables contain columns "Gene name", "Gene type", "Transcript ID" and "Gene structure", corresponding to the gene name, type (protein_coding, long non-coding, miRNA etc.), ENSEMBL transcript ID and structure (intronic, downstream, etc.) for the nearest gene to each DMR. For all tables, The columns “chromosome”, “DMR start” and “DMR end” contain the position in the genome where the DMR was detected (mm10 assembly). The column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DMRs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3146740).<br><br>
DOI: 10.6084/m9.figshare.3146665
title: Entropy in volunteer evolutionary computing experiments

description: <br>An evolutionary algorithm is a search method that tries to keep the balance between two forces: exploration and exploitation. Exploration keeps the search process alive, looking for solutions in zones where it had not before, while exploitation changes the best solutions to find the global optimum.<br>This balance is kept by diversity, represented by entropy: if entropy is high, exploration can proceed at the same time exploitation breaks through. <br>Besides, in volunteer computing experiments where people visiting a web page contribute to an evolutionary algorithm it is difficult to find a model of performance, and, even more so, find what minimum amount of intervention must be made to make the whole experiment run apace and finish early. It is obvious that more users will make the simulation faster, but global number of participants (represented by single IPs) has proved not to be a predictor for total time since, for instance, these users could operate sequentially and not at the same time, but neither maximum number of IPs in a single minute is a good predictor.<br>It is clear, however, that diversity is always correlated with performance: good diversity means, in general, that the algorithm will finish soon. <br>So in these figures we measured "compression" diversity for two different aspects, and we did so for every successfully finished evolutionary algorithm. First, the string of IPs that were contributing every minute, and second, the sequence of cache (that is, the number of elements kept in the pool, capped to 32) sizes for every experiment, These strings were compressed and the compression ratio used as entropy. <br>The figures show a good and so far unmeasured correlation, better for IPs per minute, worse for cache sizes. However, this latter is the one in which experiment design can have a bigger influence, so it will probably be used in the future for improving the evolutionary algorithms. <br>All data and sources can be downloaded from http://github.com/JJ/splash-volunteer (data branch)<br>
DOI: 10.6084/m9.figshare.3146689.v9
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 3: CRMs]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>
DOI: 10.6084/m9.figshare.3146665.v1
title: Entropy in volunteer evolutionary computing experiments

description: <br>An evolutionary algorithm is a search method that tries to keep the balance between two forces: exploration and exploitation. Exploration keeps the search process alive, looking for solutions in zones where it had not before, while exploitation changes the best solutions to find the global optimum.<br>This balance is kept by diversity, represented by entropy: if entropy is high, exploration can proceed at the same time exploitation breaks through. <br>Besides, in volunteer computing experiments where people visiting a web page contribute to an evolutionary algorithm it is difficult to find a model of performance, and, even more so, find what minimum amount of intervention must be made to make the whole experiment run apace and finish early. It is obvious that more users will make the simulation faster, but global number of participants (represented by single IPs) has proved not to be a predictor for total time since, for instance, these users could operate sequentially and not at the same time, but neither maximum number of IPs in a single minute is a good predictor.<br>It is clear, however, that diversity is always correlated with performance: good diversity means, in general, that the algorithm will finish soon. <br>So in these figures we measured "compression" diversity for two different aspects, and we did so for every successfully finished evolutionary algorithm. First, the string of IPs that were contributing every minute, and second, the sequence of cache (that is, the number of elements kept in the pool, capped to 32) sizes for every experiment, These strings were compressed and the compression ratio used as entropy. <br>The figures show a good and so far unmeasured correlation, better for IPs per minute, worse for cache sizes. However, this latter is the one in which experiment design can have a bigger influence, so it will probably be used in the future for improving the evolutionary algorithms. <br>All data and sources can be downloaded from http://github.com/JJ/splash-volunteer (data branch)<br>
DOI: 10.6084/m9.figshare.3146695.v11
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 7: DMRs]

description: <b>DESCRIPTION</b><br>This data record contains tables of differentially methylated regions (DMRs), where each table contains different compared conditions for each time point (1h or 4w), tissue (CA1 or ACC) and cell type (Neu or Non). Tables with the suffix "_raw" contain all DMRs for adjusted p-value threshold of 0.5, and all other tables contain annotated DMRs for adjusted p-value 0.1 in C-CS tests or for adjusted p-value 0.05 in N-C and N-CS tests. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context only (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context only (C) to context shock (CS) mice.<br><b><br>DATA REPOSITORIES</b><br>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the DMRs (i.e. positions of DMRs) under category "DMR-DATA". Sample names include compared conditions (e.g. CxCS compares context with context-shock), time-point, tissue and cell-type separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>For the annotated DMRs, the tables contain columns "Gene name", "Gene type", "Transcript ID" and "Gene structure", corresponding to the gene name, type (protein_coding, long non-coding, miRNA etc.), ENSEMBL transcript ID and structure (intronic, downstream, etc.) for the nearest gene to each DMR. For all tables, The columns “chromosome”, “DMR start” and “DMR end” contain the position in the genome where the DMR was detected (mm10 assembly). The column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DMRs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3146740).<br><br>
DOI: 10.6084/m9.figshare.3146692
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 6: DHPTMs]

description: <b>DESCRIPTION</b><br>This data record contains tables of all differential histone post-translational modifications (DHPTMs), where each table contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peak regions, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context (C) to context shock (CS) mice.<br><br><b>TABLES</b><br>The table contains columns with HPTM coordinates and differential occupation statistics. Columns “chromosome”, “DHPTM start”, “DHPTM end” and “Strand” contain the position in the genome where the DHPTM was detected (mm10 assembly), where peak-based tests (marks H3K27ac and H3K4me1) were run as strand-independent, so for such tests column “Strand” contains “.” for all entries. Column “baseMean” shows the average normalised read counts in each DHPTM. Column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br>
DOI: 10.6084/m9.figshare.3146656
title: PhiSpy prophage predictions

description: Fasta format files of prophage predictions from 11,278 Bacterial and Archaeal genomes. Each fasta file is labeled with the NCBI taxonomy ID of the organism and the name of the organism, and each entry in the fasta file is one prophage prediction.<br>There are 9,086 genome files and each file has the prophages predicted for a different genome
DOI: 10.6084/m9.figshare.3146695.v10
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 9: DMRs]

description: <b>DESCRIPTION</b><br>This data record contains tables of differentially methylated regions (DMRs), where each table contains different compared conditions for each time point (1h or 4w), tissue (CA1 or ACC) and cell type (Neu or Non). Tables with the suffix "_raw" contain all DMRs for adjusted p-value threshold of 0.5, and all other tables contain annotated DMRs for adjusted p-value 0.1 in C-CS tests or for adjusted p-value 0.05 in N-C and N-CS tests. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context only (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context only (C) to context shock (CS) mice.<br><b><br>DATA REPOSITORIES</b><br>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the DMRs (i.e. positions of DMRs) under category "DMR-DATA". Sample names include compared conditions (e.g. CxCS compares context with context-shock), time-point, tissue and cell-type separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>For the annotated DMRs, the tables contain columns "Gene name", "Gene type", "Transcript ID" and "Gene structure", corresponding to the gene name, type (protein_coding, long non-coding, miRNA etc.), ENSEMBL transcript ID and structure (intronic, downstream, etc.) for the nearest gene to each DMR. For all tables, The columns “chromosome”, “DMR start” and “DMR end” contain the position in the genome where the DMR was detected (mm10 assembly). The column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DMRs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3146740).<br><br>
DOI: 10.6084/m9.figshare.3146689.v10
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: CRMs]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>