Rationale for the Mouse ENCODE project
Our knowledge of the function of genomic DNA sequences comes from three
basic approaches. Genetics uses changes in behavior or structure of a cell or organism
in response to changes in DNA sequence to infer function of the altered sequence.
Biochemical approaches monitor states of histone modification, binding of specific
transcription factors, accessibility to DNases and other epigenetic features along
genomic DNA. In general, these are associated with gene activity, but the precise
relationships remain to be established. The third approach is evolutionary, using
comparisons among homologous DNA sequences to find segments that are evolving
more slowly or more rapidly than expected given the local rate of neutral change. These
are inferred to be under negative or positive selection, respectively, and we interpret
these as DNA sequences needed for a preserved (negative selection) or adaptive
(positive selection) function.
The ENCODE project aims to discover all the DNA sequences associated with
various epigenetic features, with the reasonable expectation that these will also be
functional (best tested by genetic methods). However, it is not clear how to relate these
results with those from evolutionary analyses. The mouse ENCODE project aims to
make this connection explicitly and with a moderate breadth. Assays identical to those
being used in the ENCODE project are performed in cell types in mouse that are similar
or homologous to those studied in the human project. Thus we will be able to discover
which epigenetic features are conserved between mouse and human, and we can
examine the extent to which these overlap with the DNA sequences under negative
selection. The contribution of DNA with a function preserved in mammals versus
that with a function in only one species will be discovered. The results will have a
significant impact on our understanding of the evolution of gene regulation.
Reference transcriptome measurements with RNA-seq
RNA-seq is a method for mapping and quantifying the transcriptome of any organism that has a genomic DNA sequence assembly (Mortazavi et al., 2008).
RNA-seq is performed by reverse-transcribing an RNA sample into cDNA, followed by high-throughput DNA sequencing,
which was done here on the Illumina HiSeq sequencer. The transcriptome measurements shown on these tracks were performed on
polyA selected RNA from
total cellular RNA. PolyA-selected RNA was fragmented
by magnesium-catalyzed hydrolysis and then converted into cDNA by random priming and amplified. Paired-end 2x100 bp reads were obtained from each end of a cDNA fragment.
Reads were aligned to the mm9 human reference genome using TopHat (Trapnell et al., 2009), a program specifically designed to align RNA-seq reads and discover splice junctions de novo.
All sequence and alignments files are available on the downloads page.
Display Conventions and Configuration
This track is a multi-view composite track that contains multiple data types
(views). For each view, there are multiple subtracks that
display individually on the browser. Instructions for configuring multi-view
tracks are here.
The following views are in this track:
- The Alignments (BAM file) view shows reads aligned to the genome. Alignments are colored by cell type. See the Bowtie Manual (Langmead et al., 2009)
for information about the SAM Bowtie output (including other tags) and the
SAM Format Specification for information on
the SAM/BAM file format.
- Raw Signal
- Density graph (wiggle) of signal enrichment based on a normalized aligned
read density (Read Per Million, RPM). The RPM measure assists in visualizing the relative amount of a given transcript across multiple samples. This is used to display all reads in this track.
- Signal (Unique Reads)
- Density graph (wiggle) of signal enrichment based on processed data. This is used to display uniquely mapped reads in this track.
Additional views are available on the Downloads page.
Cells were grown according to the approved ENCODE cell culture protocols.
Cells were lysed in RLT buffer (Qiagen RNEasy kit), and processed on RNEasy midi columns according to the manufacturer's protocol, with the inclusion of the "on-column" DNAse digestion step to remove residual genomic DNA. A quantity of 75 µgs of total RNA was selected twice with oligo-dT beads (Dynal) according to the manufacturer's
protocol to isolate mRNA from each of the preparations. A quantity of 100 ngs of mRNA was
then processed according to the protocol in Mortazavi et al. (2008), and prepared for sequencing on the Illumina GAIIx or HiSeq platforms
according to the protocol for the ChIP-Seq DNA genomic DNA kit (Illumina). Paired-end libraries were size-selected around 200 bp (fragment length).
Libraries were sequenced with the Illumina HiSeq according to the manufacturer's recommendations. Paired-end reads of 100 bp length were obtained
Data Processing and Analysis
Reads were mapped to the reference mouse genome (version mm9 with or without the Y chromosome, depending on the sex of the cell line,
and without the random chromosomes in all cases) using TopHat (version 1.3.1). TopHat was used with default settings with the exception of specifying an empirically determined mean inner-mate distance and supplying known ENSEMBL version 63 splice junctions.
Wold Group: Brian Williams, Georgi Marinov, Diane Trout, Lorian Schaeffer, Gordon Kwan, Katherine Fisher, Gilberto De Salvo, Ali Mortazavi, Henry Amrhein, Brandon King
Georgi Marinov (data coordination/informatics/experimental),
Diane Trout (informatics) and
Brian Williams (experimental)
Langmead B, Trapnell C, Pop M, Salzberg SL.
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Genome Biol. 2009;10(3):R25.
PMID: 19261174; PMC: PMC2690996
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B.
Mapping and quantifying mammalian transcriptomes by RNA-Seq.
Nat Methods. 2008 Jul;5(7):621-8.
Trapnell C, Pachter L, Salzberg SL.
TopHat: discovering splice junctions with RNA-Seq.
Bioinformatics. 2009 May 1;25(9):1105-11.
PMID: 19289445; PMC: PMC2672628
Data Release Policy
Data users may freely use ENCODE data, but may not, without prior
consent, submit publications that use an unpublished ENCODE dataset until
nine months following the release of the dataset. This date is listed in
the Restricted Until column, above. The full data release policy
for ENCODE is available