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Transcriptome profiling for genes transcriptionally regulated by Smchd1 in lymphoma cell lines.

GSE64099 Mus musculus (mouse) RNA-seq (gene expression) 7 samples Submitted 2015/03/13 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 7 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
7 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Purpose: The aim of this study is to identify genes that are under the transcriptional control of the epigenetic modifier Smchd1 in mouse lymphoma cell lines. Methods: Total RNA was extracted using QIAGEN RNeasy Minikit from sorted lymphoma cell lines derived from mice either wild-type or null for Smchd1. 1µg total RNA was used to generate sequencing libraries for whole transcriptome analysis with Illumina’s TruSeq RNA Sample Preparation Kit v2 as per standard protocols.
Read the full summary ↓
Libraries were sequenced on HiSeq 2000 with Illumina TruSeq SBS Kit v3-HS reagents as either 100 bp single-end or paired-end reads at the Australian Genome Research Facility (AGRF), Melbourne. Reads were aligned to the mouse reference genome mm10 and mapped to known genomic features at the gene level using the Rsubread package (version 1.10.5) (Liao et al. 2013). Mapped reads were then summarized into gene-level counts using FeatureCounts (Liao et al. 2014).
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE64099_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA270115 and SRA study SRP051084. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 7 more — browse all 7 samples with per-sample file links →

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