Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RNA-seq analysis of contextual fear conditioning

ABSTRACT: This dataset constitutes the first RNA-seq study of gene expression following contextual fear conditioning in the mouse hippocampus. 15 total samples were analyzed, including animals trained in a contexual conditioning paradigm (FC) and controls. Tissue was collected at 30 minutes (FC), as well as 30 minutes after testing for retrieval of the memory (RT). Testing was performed at 24 hours after training over a 5-minute interval. Animals that were handled but not trained were dissected at the same time of day to control for variations due to circadian rhythms (CC3). The protocol was repeated over the course of 5 days to obtain 9 animals (2 hippocampi) per group, so that 5 independent FC experiments were represented in each time point and all animals for each group were dissected at the exactly the same time of day.

ORGANISM(S): Mus musculus

SUBMITTER: Lucia Peixoto

PROVIDER: E-GEOD-63412 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets.

Peixoto Lucia L Risso Davide D Poplawski Shane G SG Wimmer Mathieu E ME Speed Terence P TP Wood Marcelo A MA Abel Ted T

Nucleic acids research 20150721 16

The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or 'batch effects' can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic wh ...[more]

PMID: 26202970

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Dataset Information

RNA-seq analysis of contextual fear conditioning

Publications

How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets.

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