Description

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 their 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 2x100bp reads were obtained from each end of a cDNA fragment. This RNA-Seq protocol does not specify the coding strand. Reads were aligned to the mm9 human reference genome using TopHat, a program specifically designed to align RNA-Seq reads and discover splice junctions de novo. Cufflinks, a de novo transcript assembly and quantification software package, was run on the TopHat alignments to discover and quantifiy novel transcripts and to obtain trascript expression estimates based on the UCSC annotation. All sequence files, alignments, gene and transcript models and expression estimates files are available for download

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:

Alignments
The Alignments (BAM file) view shows reads aligned to the genome. Alignments are colored by cell type.
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.
Transcript Models
Transcript models generated by Cufflinks (version 1.0.3) based on the aligned RNA-Seq reads

Methods

Experimental Procedures

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. 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. 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 ChIPSeq DNA genomic DNA kit (Illumina). Paired-end libraries were size-selected around 200bp (fragment length). Libraries were sequenced with the Illumina HiSeq according to the manufacturer's recommendations. Paired-end reads of 100bp 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 that known ENSEMBL version 63 splice junctions were supplied. After mapping reads to the genome and identifying splice junctions, the data was further analyzed using the transcript assembly and quantification software Cufflinks (version 1.0.3). Cufflinks was run in quantification mode on the ENSEMBL version 63 mouse genome annotation to obtain expression estimates on the gene and transcript levle, and in Reference Annotation Based Transcript (RABT) assembly and quantification mode to obtain both candidate novel transcript models and expression estimates for them and the already annotated ones.

Downloadable Files

The following files can be found on the downloads page.

Raw Reads, Alignments and Library Characteristics:

*.fastq - raw sequence files in fastq format with phred33 quality scores
*.unique.BigWig - read density files (RawSignal view, BigWig format; "plus" and "minus" versions provided for stranded data) with only uniquely mappable reads represented
*.BigWig - read density files (RawSignal view, BigWig format; "plus" and "minus" versions provided for stranded data) with multi-reads included (with weight scaled according to the number of locations they map to)
*.bam - all alignments in SAM/BAM format (Alignments view)
*.bam.bai bam file index

Expression Estimates and Transcript Models (Cufflinks):

*RABT.Transcripts.gtf - a gtf file containing transcript models and expression estimates in FPKM (Fragments Per Kilobase per Million reads) produced by Cufflinks in reference-guided de novo mode on the UCSC Known Genes annotation
*ENSEMBL63.Transcripts.gtf - a gtf file containing transcript models and expression estimates in FPKM (Fragments Per Kilobase per Million reads) produced by Cufflinks for the ENSEMBL63 annotation

Credits

Wold Group: Brian Williams, Georgi Marinov, Diane Trout, Lorian Schaeffer, Gordon Kwan, Katherine Fisher, Gilberto De Salvo, Ali Mortazavi, Henry Amrhein, Brandon King

Contacts: Georgi Marinov (data coordination/informatics/experimental). Diane Trout (informatics) and Brian Williams (experimental).

References

Mortazavi A, Williams BA, McCue K, Schaeffer L, and Wold BJ. Mapping and quantifying mammalian transcriptomes by RNA-Seq Nature Methods. 2008 Jul; 5(7):621-628.

Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome Genome Biology. 2009 Mar; 10:R25.

Trapnell C, Pachter L, Salzberg SL.. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009 May; 25(9):1105-11.

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnology. 2010 May; 28(5):511-5.

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 here.