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Sample GSM2901342

Query DataSets for GSM2901342

Status

Public on Apr 01, 2018

Title

CLIP_Neurod1_P56_rep4

Sample type

SRA

Source name

Cerebellar granule cells

Organism

Mus musculus

Characteristics

cell type: Cerebellar granule cells
age: post-natal day 56
genotype: Neurod1-Cre; Pabpc1-cTag

Extracted molecule

total RNA

Extraction protocol

cTag-PAPERCLIP on dissected P56 cerebella. Mouse monoclonal anti-GFP clones 19F7 and 19C8 (Heiman et al., 2008) were used for immunoprecipitation.
The PAPERCLIP procedure was performed as previously described (Hwang et al., 2016). Individual cTag-PAPERCLIP libraries were multiplexed and sequenced on MiSeq (Illumina) to obtain 75-nt single-end reads. A barcode sequence was removed from the reads, so the final length of reads is 68-nt.

Library strategy

OTHER

Library source

transcriptomic

Library selection

other

Instrument model

Illumina MiSeq

Description

PABPC1-bound RNA
neurod1_rep4
NeuroD1_Pcp2_clusters.txt

Data processing

Library strategy: cTag-PAPERCLIP
Analysis of cTag-PAPERCLIP datasets: The processing of raw reads was performed using the CIMS software package as previously described (Moore et al., 2014). Raw reads were filtered based on quality score. Filtered reads with the exact sequence were collapsed into one. Poly(A) sequence at the 3′ end was then trimmed using CutAdapt (Martin, 2011). Only reads that are at least 25-nt in length were mapped to mm10 reference genome. Mapping was performed using Novoalign (Novocraft) without trimming. Reads mapping to the same genomic positions without distinct barcodes were further collapsed into a single read as previously described (Moore et al., 2014). CIMS software package was then used to cluster overlapping collapsed reads from all biological replicates (for each condition) and to determine the number of reads in each cluster. To define a list of high confidence clusters we used the following criteria: clusters had to contain reads from all replicates in each condition, the number of reads in a cluster had to be at least 10% of gene total, clusters had to be located within 20 kb from 3′ ends annotated by RefSeq, clusters that were located upstream of a stretch of 6 or more adenines were excluded due to potential internal priming of the reverse transcription primer. To identify differences in APA between different conditions, Fisher’s exact test was performed comparing the ratio between reads in a particular cluster and the sum of reads in all other clusters in a gene in the two conditions. The output p-values were adjusted for multiple hypotheses testing using the Benjamini-Hochberg method.
Analysis of RNA-seq datasets: RNA-sequencing reads were mapped to the mouse genome (mm10) using Novoalign (Novocraft), reads overlapping genes were counted using HTSeq-count (Anders et al., 2015).
Genome_build: mm10
Supplementary_files_format_and_content: Columns in tab-delimited files for CLIP datasets represent the following: name_cluster (name of a CLIP cluster), id_gene (Refseq gene ID), chr_cluster (chromosome where cluster is located), start_cluster (cluster start), end_cluster (cluster end), strand_cluster (strand), name2 (gene symbol) and number of reads from each replicate in a given cluster.
Supplementary_files_format_and_content: Tab-delimited text files for RNA-seq datasets represent Refseq gene ID and number of reads overlapping with exons.

Submission date

Dec 22, 2017

Last update date

Apr 01, 2018

Contact name

Saša Jereb

Organization name

The Rockefeller University

Department

Laboratory of Molecular Neuro-Oncology

Lab

Robert B. Darnell Lab