Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 6: DHPTMs]

<b><u>DESCRIPTION</u></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><u>TABLES</u></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><u><b>DATA GENERATION</b></u><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: support files]

This repository contains annotation files for mouse genome assembly mm10, including chromosome sizes, gene and exon annotations, mappability file and the R package chequeR used to generate enrichment statistics for ChIP-seq data. The files are used in the analyses of ChIP-, MeDIP- and RNA-seq data from mouse, as described by the run_scientific_data_analysis.pdf in the scripts repository (https://dx.doi.org/10.6084/m9.figshare.3490613)<br

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: Pre-processing]

This repository contains the scripts and files used to generate the alignment files used for downstream analyses, the bigWig files used to visualise the data and various Quality Control (QC) results used to address the quality of the various samples.<br><div><br></div><div>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt</div

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 6: RNA-seq]

<u><b>DESCRIPTION<br></b>Experiment type</u> - Expression profiling by high throughput sequencing<br><u>Design</u> - Gene and exon expression changes in two distinct mouse brain regions (CA1, ACC) and three time-points (00H for naive, 01H for 1 hour, 04W for 4 weeks) before and after contextual learning (NAI for naive, CON for context, SHC for context shock).<br><br><b><u>DATA REPOSITORIES</u></b><br><u>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)</u><br>Contains RNA-seq data under category "RNASEQ-DATA". Sample names include conditioning, time-point, tissue and replicate number (1 to 5; samples without numbers are merged replicates) separated by dashes. bigWig files can also be downloaded for local visualisation.<br><u>Gene Expression Omnibus (GEO)</u><br>Data is available on GEO under the accession number GSE74966 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74966), containing SRA and bigwig files to download for all 29 RNA-seq samples.<br><br><b><u>DATA GENERATION</u></b><br>Alignment and visualisation data was generate using the pre-processing code found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153193).<br><u><b><br></b></u

Lindsaea orbiculata Mett.

原著和名: エダウチホングウシダ科名: ホングウシダ科 = Lindsaeaceae採集地: 三重県 尾鷲市 (紀伊 尾鷲市)採集日: 1962/11/1採集者: 萩庭丈壽整理番号: JH043649国立科学博物館整理番号: TNS-VS-99364

Unused

<u><b>Description</b></u><br>A group of mice was trained and tested for memory retrieval 30 minutes, 24 hours and 4 weeks after context (C) or context shock (CS) exposure (10, 14 and 10 mice per condition, respectively). The motion of mice was tracked and the percentage of freezing (i.e. percentage of total testing time the mice were recorded as frozen) was calculated using the Video Freeze ® automated monitoring system (Med Associates). The freezing percentages is available as a table in Data Record X, separated by tested time after exposure and conditioning.<br

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: CRMs]

This repository contains the positive and negative training sets generated from the ChIP-seq transcription factor and histone post-translational modification data. Such sets of data were run as input to the RFECS software. RFECS is a tailor-made random forest model for making cis-regulatory module (CRM) predictions. The random forest model used for making the predictions is also available in this repository as a binary MATLAB file.<div><br></div><div>The references below explain specifically how to (1) access a MATLAB file and (2), how to use the current model file to predict the CRMs. For more details about the RFECS algorithm itself, take a look at reference (3).<br></div

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: MeDIP-seq]

<b><u>DESCRIPTION<br></u></b><u>Experiment type</u> - Methylation profiling by high throughput sequencing<br><u>Design</u> - Chromatin modification changes in two distinct mouse brain regions (CA1, ACC), two cell-types (NEU for neurons, NON for non-neurons) and three time-points (00H for naive, 01H for 1 hour, 04W for 4 weeks) before and after contextual learning (NAI for naive, CON for context, SHC for context shock).<br><br><u><b>DATA REPOSITORIES</b></u><br><u>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)</u><br>Contains MedIP-seq data under category "MEDIP-DATA". Sample names include conditioning, time-point, tissue, cell type and replicate number (1 or 2; samples without numbers are merged replicates) separated by dashes. bigWig files can also be downloaded for local visualisation.<br><u>Gene Expression Omnibus (GEO)</u><br>Data is available on GEO under the accession number GSE74965 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74965), containing SRA and bigWig files to download for all 40 MedIP-seq samples.<br><br><u><b>DATA GENERATION</b></u><br>Alignment and visualisation data was generate using the pre-processing code found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153193).<br><b><u><br></u></b

Additional file 1: of Oasis 2: improved online analysis of small RNA-seq data

Oasis2-Suppl-Material.docx: This file contains supplementary material and figures as well. (DOCX 125 kb