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Dgaston dec-06-2012
This document provides an overview of RNA-Seq analysis. It begins with considerations for RNA-Seq experiments such as computational requirements. It then describes the general RNA-Seq analysis workflow including short-read alignment, transcript reconstruction, abundance estimation, visualization, and statistics. The document focuses on explaining the "Tuxedo" analysis pipeline which includes Bowtie, Tophat, Cufflinks, Cuffmerge, Cuffdiff and CummeRbund. It provides examples of commands for each step and discusses alternative tools. The document concludes with resources for further information on RNA-Seq analysis.
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Dgaston dec-06-2012
- 1. Bioinformatics: Intro toRNA-Seq Analysis Integrated Learning Session Daniel Gaston, PhD Dr. Karen Bedard Lab, Department of Pathology December 6th, 2012
- 2. Overview Introduction Considerations for RNA-Seq Computational Resources/Options Analysis of RNA-Seq Data Principle of analyzing RNA-Seq General RNA-Seq analysis pipeline “Tuxedo” pipeline Alternative tools Resources http://www.slideshare.net/DanGaston
- 3. Before You Start:Considerations for RNA- Seq Analysis Next-Generation Sequencing experiments generate a lot of raw data 25-40 GB/sample/replicate for most transcriptomes/tissue types/cell lines/conditions Require more computational resources than many labs routinely have available for analyse data At minimum several processing “cores” (8 minimum) Large amount of RAM (16GB+) Large amount of disk storage space for intermediate and final results files in addition to raw FastQ files Can be a significant amount of time per sample (days to week)
- 4. Computational Options Local (Large workstation or cluster) Remote Computer/Cluster (ComputeCanada/ACENet) Cloud Services Amazon Web Services Cloud/Local Bioinformatics „Portals” Galaxy Chipster GenomeSpace CloudBioLinux CloudMan BioCloudCentral (Interface to CloudMan, CloudBioLinux, etc)
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- 6. So I Ranan RNA-Seq Experiment. Now What? Need to go from raw “read” data to gene expression data We now have: De-multiplexed fastq files for each individual sample and replicate We want lists of: Differentially expressed genes/transcripts Potentially novel genes/transcripts Potentially novel splice junctions Potential fusion events Organize your data, programs, and additional resources (discussed later)
- 7. What is theRaw Data A single lane of Illumina HiSeq 2000 sequencing produces ~ 250 – 300 million “reads” of sequencing Can be paired or single-end sequencing (paired-end preferred) Various sequencing lengths (number of sequencing cycles) 2x50bp, 2x75bp, 2x100bp, 2x150bp most common Cost versus amount of usable data True raw data is actually image data with colour intensities that are then converted into text (A, C, G, T and quality scores) called FastQ
- 8. FastQ @M00814:1:000000000-A2472:1:1101:14526:1866 1:N:0:1 TGGAACATGCGTGCGNAGCCGAAAGTGTGTCCCCACTTTCATATGAAGAAAGAC + ?????BBBBB9?+<+#,,6C>CAEEHHHFFHHHHFEHHHHHHHHHGHHHHHHHHHH FASTA format file with a header line, sequence line, and quality scores for every base in the sequenced read In Paired-End Sequencing one file for each “end” of sequencing (Primer 1 and Primer 2) Qualities scores are encoded with a single character representing a number. Most common encoding scheme is called Phred33. Old Illumina software used Phred64 but current generation does not. (Illumina 1.3 – 1.7 is Phred64) Often needs to be set explicitly in alignment programs
- 9. General Analysis Pipeline Short-Read Alignment Transcript Reconstruction Abundance/Expression Visualization / Statisticss
- 10. What is Short-ReadAlignment? Paired-End Reads Section of Reference Chromosome
- 11. What’s Special AboutRNA-Seq Normally distance between paired-reads and size of insertions both constrained With RNA-Seq the source is mRNA, not genomic DNA Mapping to a reference genome, not transcriptome Need to account for introns, pairs can be much further apart than expected
- 12. Transcript Reconstruction: Intron/Exon Junctions Exon1 Exon 2 Exon 3
- 13. Transcript Reconstruction: Alternative Splicing Exon1 Exon 2 Exon 3
- 14. Transcript Reconstruction: Novel Exon/TranscriptIdentification Exon1 Exon 2 Exon X Exon 3
- 15. Transcript Reconstruction: Fusion Transcripts Exon1 Exon 2 Exon 3 Gene 2 Exon 4
- 16. Transcript Reconstruction: Differential Expression Sample 1 Sample 2
- 17. What else canwe look for? Combine with ChiP-Seq to differentiate various levels of regulation Integrative analyses to identify common elements (micro-RNA, transcription factors, molecular pathways, protein-DNA interactions) Combine with whole-exome or whole-genome sequencing Allele-specific expression Allelic imbalance LOH Large genomic rearrangements/abnormalities
- 18. Caution Need to differentiate between real data and artifacts Differentiate between biologically meaningful data and “noise” Sample selection, experimental design, biological replication (not technical replication), and robust statistical methods are important Looking at your data “by eye” is useful, but needs to be backed up by stats Avoid experimenter bias Try and be holistic in your analyses
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- 20. “Tuxedo” Analysis Pipeline Bowtie Tophat Cufflinks Cufflinks Cuffcompare Cuffmerge Cuffdiff CummeRbund
- 21. What you needbefore you begin The individual programs Reference genome (hg19/GRCh37) FASTA file of whole genome, each chromosome is a sequence entry Bowtie2 Index files for reference genome Index files are compressed representations of the genome that allow assembly to the reference efficiently and in parallel Gene/Transcript annotation reference (UCSC, Ensembl, ENCODE, etc) Gives information about the location of genes and important features such as location of introns, exons, splice junctions, etc
- 22. Step 0: Bowtie Bowtie forms the core of TopHat for short-read alignment Initial mapping of subset of reads (~5 million) to a reference transcriptome to estimate inner-distance mean/median and standard deviation for tophat This info can be retrieved from the library prep stage but is actually better to estimate from your final data Sample command-line: bowtie –x /path/to/transcriptome_ref.fa –q –phred33 –local –p 8 -1 read1.fastq -2 read2.fastq –S output.sam
- 23. Step 1: Tophat Tophat is a short-read mapper capable of aligning reads to a reference genome and finding exon-exon junctions Can be provided a list of known junctions, do de novo junction discovery, or both Also has an option to find potential fusion-gene transcripts Sample command-line: tophat –p 8 –G gene_annotations.gtf –r inner_distance –mate-std-dev std_dev –o Output.dir /path/to/bowtie2indexes/genome read1.fastq read2.fastq
- 24. About TopHat Options -o: The path/name of a directory in which to place all of the TopHat output files -G path to and name of an annotation file so TopHat can be aware of known junctions Reference Genome: Given as path and “base name.” If reference genome saved as: /genomes/hg19/genome.fa then the relevant path and basename would be /genomes/hg19/genome Inner Distance = Fragment size – (2 x read length)
- 25. TopHat: Additional options --no-mixed --b2-very-sensitive --fusion-search Running above options on 6 processing cores on one sample took ~26 hours
- 26. Step 2: Cufflinks Cufflinks performs gene and transcript discovery Many possible options No novel discovery, use only a reference group of transcripts de novo mode (shown below, beginner‟s default) Mixed Reference-Guided Assembly and de novo discovery. Options for more robust normalization methods and error correction Sample command-line: cufflinks –p 8 –o Cufflinks.out/ accepted_hits.bam
- 27. Step 3: Cuffmerge Merges sample assemblies, estimate abundances, clean up transcriptome Sample command-line: cuffmerge –g gene_annotations.gtf –s /path/to/genome.fa –p 8 text_list_of_assemblies.txt
- 28. Step 4: Cuffdiff Calculates expression levels of transcripts in samples Estimates differential expression between samples Calculates significance value for difference in expression levels between samples Also groups together transcripts that all start from same start site. Identify genes under transcriptional/post-transcriptional regulation Sample command-line: cuffdiff –o Output.dir/ –b /path/to/genome.fa –p 8 –L Cond1,Cond2 –u merged.gtf cond1.bam cond2.bam
- 29. Cuffdiff Output FPKM values for genes, isoforms, CDS, and groups of genes from same Transcription Start Site for each condition FPKM is the normalized “expression value” used in RNA-Seq Count files of above As above but on a per replicate basis Differential expression test results for genes, CDS, primary transcripts, spliced transcripts on a per sample (condition) comparison basis (Each possible X vs Y comparison unless otherwise specified) Includes identifiers, expression levels, expression difference values, p-values, q-values, and yes/no significance field Differential splicing tests, differential coding output, differential promoter use
- 30. Step 5: CummeRbund(R) Trapnell et al., 2012
- 31. Visualization Trapnell et al., 2012
- 32. Help! Command X failed Keep calm Don‟t blame the computer Check input files and formats Google/SeqAnswers/Biostars Results looks “weird” Check the raw data Re-check the commands you used RNA-Seq analysis is an experiment: Maintain good records of what you did, like any other experiment
- 33. Alternative tools Alternative short-read alignment BWA -> Can not align RNA-Seq data GSNAP STAR -> Requires minimum of 30GB of RAM Alternative transcript reconstruction STAR Scripture Alternative Expression/Abundance Estimation DESeq DEXSeq edgeR
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- 35. Software Websites TopHat http://tophat.cbcb.umd.edu Cufflinks http://cufflinks.cbcb.umd.edu STAR http://gingeraslab.cshl.edu/STAR/ Scripture http://www.broadinstitute.org/software/scripture/ Bioconductor http://www.bioconductor.org/ DEXSeq DESeq edgeR Blah
- 36. Additional Resources Differential gene and transcript expression analysis of RNA-Seq Experiments with TopHat and Cufflinks (2012) Nature Protocols. 7(3) www.biostars.org (Q&A site) SeqAnswers Forum GENCODE Gene Annotations http://www.gencodegenes.org/ ftp://ftp.sanger.ac.uk/pub/gencode TopHat / Illumina iGenomes References and Annotation Files: http://tophat.cbcb.umd.edu/igenomes.html
- 37. Acknowledgements Dalhousie University Dr. Graham Dellaire Dr. Karen Bedard Montgomery Lab Dr. Chris McMaster Stanford Dr. Andrew Orr Dr. Stephen Montgomery Dr. Conrad Fernandez BHCRI CRTP Skills Dr. Marissa Leblanc Acquisition Program Mat Nightingale Bedard Lab IGNITE
- 38. Experimental Data forGenes of Interest
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- 43. NCI Pathway InteractionDatabase
- 44. The Cancer GenomeAtlas Identify cancer subtypes, actionable driver mutations, personalized/genomic/precision medicine More than $275 million in funding from NIH Multiple research groups around the world 20 cancer types being studied 205 publications from the research network since late 2008
- 45. The Cancer GenomeAtlas
- 46. The Cancer GenomeAtlas
- 47. The Cancer GenomeAtlas
- 48.
- 49. What is UNIX? UNIX and UNIX-Like are a family of computer operating systems originally developed at AT&T‟s Bell Labs Apple OS X and iOS (UNIX) Linux (UNIX-Like)
- 50. Intro The terminal (command-line) isn‟t THAT scary. Maintaining a Linux environment can be challenging, but most of these analyses can also be done in an OS X environment Installing software can sometimes be cumbersome and confusing, however many standard bioinformatics programs and software libraries are fairly easy to set-up Working with the programs from the command-line will often give you a better appreciation for what the program does and what it requires
- 51. Terms to Know Path: The location of a directory, file, or command on the computer. Example: /Users/dan (OS X home directory)
- 52. The Commands YouNeed to Know ls: Lists the files in the current directory. Directories (folders) are just a special type of file themselves cd: Change directory pwd: View the full path of the directory you are currently in cat: Displays the contents of a file on the terminal screen head / tail : Displays the top or bottom contents of a file to the screen respectively