version /1/2011 Source code Linux x86_64 binary Mac OS X x86_64 binary

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1 Cufflinks RNA-Seq analysis tools - Getting Started 1 of :42 Cufflinks Transcript assembly, differential expression, and differential regulation for RNA-Seq Site Map Home Getting started Manual How Cufflinks works FAQ News and updates New releases and related tools will be announced through the mailing list Getting Help Releases Questions about Cufflinks should be sent to tophat.cufflinks@gmail.com. Please do not technical questions to Cufflinks contributors directly. version 0.3 6/1/2011 Source code Linux x86_64 binary Mac OS X x86_64 binary Related Tools TopHat: Alignment of short RNA-Seq reads Bowtie: Ultrafast short read alignment Publications Trapnell C, Williams BA, Pertea G, Mortazavi AM, Kwan G, van Baren MJ, Salzberg SL, Wold B, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation Nature Biotechnology doi: /nbt.1621 Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias Genome Biology doi: /gb r22 Contributors Cole Trapnell Adam Roberts Geo Pertea Brian Williams Ali Mortazavi Gordon Kwan Jeltje van Baren

2 Cufflinks RNA-Seq analysis tools - Getting Started 2 of :42 Links Steven Salzberg Barbara Wold Lior Pachter Berkeley LMCB UMD CBCB Wold Lab

3 ufflinks RNA-Seq analysis tools - Getting Started 3 of :42 Getting started Setting up Cufflinks Install quick-start Test the installation Common uses of the Cufflinks package Discovering novel genes and transcripts Identifying differentially expressed and regulated genes Install quick-start Installing a pre-compiled binary release In order to make it easy to install Cufflinks, we provide a few binary packages to save users from occasionally frustrating process of building Cufflinks, which requires that you install the Boost libraries. To use the binary packages, simply download the appropriate one for your machine, untar it, and make sure the cufflinks,cuffdiff and cuffcompare binaries are in a directory in your PATH environment variable. Building Cufflinks from source In order to build Cufflinks, you must have the Boost C++ libraries (version 38 or higher) installed on your system. See below for instructions on installing Boost. Installing Boost Download Boost and the bjam build engine. Unpack bjam and add it to your PATH. Unpack the Boost tarball and cd to the Boost source directory. This directory is called the BOOST_ROOT in some Boost installation instructions. 4. Build Boost. Note that you can specify where to put Boost with the --prefix option. The default Boost installation directory is /usr/local. Take note of the boost installation directory, because you will need to tell the Cufflinks installer where to find Boost later on. If you are on Mac OS X, type (all on one line): bjam --prefix=<your_boost_install_directory> --toolset=darwin architecture=x86 address_model=32_64 link=static runtime-link=static --layout=versioned stage install If you are on a 32-bit Linux system, type (all on one line): bjam --prefix=<your_boost_install_directory> --toolset=gcc architecture=x86 address_model=32 link=static runtime-link=static stage install If you are on a 64-bit Linux system, type (all on one line): bjam --prefix=<your_boost_install_directory> --toolset=gcc architecture=x86 address_model=64 link=static runtime-link=static stage install Installing the SAM tools 4. Download the SAM tools Unpack the SAM tools tarball and cd to the SAM tools source directory. Build the SAM tools by typing make at the command line. Choose a directory into which you wish to copy the SAM tools binary, the included library libbam.a, and the library

4 ufflinks RNA-Seq analysis tools - Getting Started 4 of :42 headers. A common choice is /usr/local/. 5. Copy libbam.a to the lib/ directory in the folder you've chosen above (e.g./usr/local/lib/) 6. Create a directory called "bam" in the include/ directory (e.g. /usr/local/include/bam) 7. Copy the headers (files ending in.h) to the include/bam directory you've created above (e.g./usr/local /include/bam) 8. Copy the samtools binary to some directory in your PATH. Building Cufflinks Unpack the Cufflinks source tarball: tar zxvf cufflinks tar.gz Change to the Cufflinks directory: cd cufflinks Configure Cufflinks. If Boost is installed somewhere other than /usr/local, you will need to tell the installer where to find it using the --with-boost option. Specify where to install Cufflinks using the --prefix option../configure --prefix=/path/to/cufflinks/install --with-boost=/path/to/boost If you see any errors during configuration, verify that you are using Boost version 38 or higher, and that the directory you specified via --with-boost contains the boost header files and libraries. See the Boost Getting started page for more details. If you copied the SAM tools binaries to someplace other than /usr/local/, you may need to supply the --with-bam configuration option. Finally, make and install Cufflinks. make make install Testing the installation Download the test data In the directory where you placed the test file, type: cufflinks./test_data.sam You should see the following output: [bam_header_read] EOF marker is absent. The input is probably truncated. [bam_header_read] invalid BAM binary header (this is not a BAM file). File./test_data.sam doesn't appear to be a valid BAM file, trying SAM... [13:23:15] Inspecting reads and determining fragment length distribution. > Processed 1 loci. [*************************] 100% > Map Properties: > Total Map Mass: > Read Type: 75bp paired-end > Fragment Length Distribution: Gaussian (default) > Estimated Mean: > Estimated Std Dev: [13:23:15] Assembling transcripts and estimating abundances. > Processed 1 loci. [*************************] 100% Verify that the file transcripts.gtf is in the current directory and looks like this (your file will have GTF attributes, omitted here for clarity) test_chromosome Cufflinks exon test_chromosome Cufflinks exon test_chromosome Cufflinks exon

5 ufflinks RNA-Seq analysis tools - Getting Started 5 of :42 Common uses of the Cufflinks package Discovering novel genes and transcripts RNA-Seq is a powerful technology for gene and splice variant discovery. You can use Cufflinks to help annotate a new genome or find new genes and splice isoforms of known genes in even well-annotated genomes. Annotating genomes is a complex and difficult process, but we outline a basic workflow that should get you started here. The workflow also excludes examples of the commands you'd run to implement each step in the workflow. Suppose we have RNA-Seq reads from human liver, brain, and heart. Map the reads for each tissue to the reference genome We recommend that you use TopHat to map your reads to the reference genome. For this example, we'll assume you have paired-end RNA-Seq data. You can map reads as follows: tophat -r 50 -o tophat_brain /seqdata/indexes/hg19 brain_fq brain_fq tophat -r 50 -o tophat_liver /seqdata/indexes/hg19 liver_fq liver_fq tophat -r 50 -o tophat_heart /seqdata/indexes/hg19 heart_fq heart_fq The commands above are just examples of how to map reads with TopHat. Please see the TopHat manual for more details on RNA-Seq read mapping. Run Cufflinks on each mapping file The next step is to assemble each tissue sample independently using Cufflinks. Assemble each tissue like so: cufflinks -o cufflinks_brain tophat_brain/accepted_hits.bam cufflinks -o cufflinks_liver tophat_liver/accepted_hits.bam cufflinks -o cufflinks_heart tophat_liver/accepted_hits.bam Merge the resulting assemblies assemblies.txt: cufflinks_brain/transcripts.gtf cufflinks_liver/transcripts.gtf cufflinks_heart/transcripts.gtf Now run the merge script: cuffmerge -s /seqdata/fastafiles/hg19/hg19.fa assemblies.txt The final, merged annotation will be in the file merged_asm/merged.gtf. At this point, you can use your favorite browser to explore the structure of your genes, or feed this file into downstream informatic analyses, such as a search for orthologs in other organisms. You can also explore your samples with Cuffdiff and identify genes that are significantly differentially expressed between the three conditions. See the workflows below for more details on how to do this. 4. (optional) Compare the merged assembly with known or annotated genes If you want to discover new genes in a genome that has been annotated, you can use cuffcompare to sort out what is new in your assembly from what is already known. Run cuffcompare like this: cuffcompare -s /seqdata/fastafiles/hg19/hg19.fa -r known_annotation.gtf merged_asm/merged.gtf Cuffcompare will produce a number of output files that you can parse to select novel genes and isoforms. Identifying differentially expressed and regulated genes

6 Cufflinks RNA-Seq analysis tools - Getting Started 6 of :42 There are two workflows you can choose from when looking for differentially expressed and regulated genes using the Cufflinks package. The first workflow is simpler and is a good choice when you aren't looking for novel genes and transcripts. This workflow requires that you not only have a reference genome, but also a reference gene annotation in GFF format (GFF3 or GTF2 formats are accepted, see details here). The second workflow, which includes steps to discover new genes and new splice variants of known genes, is more complex and requires more computing power. The second workflow can use and augment a reference gene annotation GFF if one is available. Differential analysis without gene and transcript discovery Map the reads for each condition to the reference genome We recommend that you use TopHat to map your reads to the reference genome. For this example, we'll assume you have paired-end RNA-Seq data. Suppose you have RNA-Seq from a knockdown experiment where you have two biological replicates of a mock condition as a control and two replicates of your knockdown. Note: Cuffdiff will work much better if you map your replicates independently, rather than pooling the replicates from one condition into a single set of reads. Note: While an GTF of known transcripts is not strictly required at this stage, providing one will improve alignment sensitivity, and ultimately, the accuracy of Cuffdiff's analysis. You can map reads as follows: tophat -r 50 -G annotation.gtf -o tophat_mock_rep1 /seqdata/indexes/hg19 \ mock_rep1_fq mock_rep1_fq tophat -r 50 -G annotation.gtf -o tophat_mock_rep2 /seqdata/indexes/hg19 \ mock_rep2_fq mock_rep2_fq tophat -r 50 -G annotation.gtf -o tophat_knockdown_rep1 /seqdata/indexes/hg19 \ knockdown_rep1_fq knockdown_rep1_fq tophat -r 50 -G annotation.gtf -o tophat_knockdown_rep2 /seqdata/indexes/hg19 \ knockdown_rep2_fq knockdown_rep2_fq Run Cuffdiff Take the annotated transcripts for your genome (as GFF or GTF) and provide them to cuffdiff along with the BAM files from TopHat for each replicate: cuffdiff annotation.gtf mock_repbam,mock_repbam \ knockdown_repbam,knockdown_repbam Differential analysis with gene and transcript discovery Complete steps 1-3 in "Discovering novel genes and transcripts", above Follow the protocol for gene and transcript discovery listed above. Be sure to provide TopHat and the assembly merging script with an reference annotation if one is available for your organism, to ensure the highest possible quality of differential expression analysis. Run Cuffdiff Take the merged assembly from produced in step 3 of the discovery protocol and provide it to cuffdiff along with the BAM files from TopHat: cuffdiff merged_asm/merged.gtf liverbam,liverbam brainbam,brainbam As shown above, replicate BAM files for each conditions must be given as a comma separated list. If you put spaces between replicate files instead of commas, cuffdiff will treat them as independent conditions. This research was supported in part by NIH grants R01-LM06845 and R01-GM083873, NSF grant CCF and the Miller Institute for Basic Research in Science at UC Berkeley. Administrator: Cole Trapnell. Design by David Herreman