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Comparison between RNASeq and Microarray for Gene Expression Analysis


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Comparison between RNASeq and Microarray for Gene Expression Analysis

  1. 1. Yaoyu E. Wang, Ph.D Center for Cancer Computational Biology, DFCI SPECSII webinar June 05, 2013
  2. 2. - Transcriptome profiling represents a static gene expression state of a biological sample across the genome - Allows for direct genomic comparisons with multiple samples to determine genes that exhibit differential expression in different state (i.e. normal vs. tumor) - Allows for hypothesis generation on molecular abnormalities and mechanisms that may contribute to the tumor phenotype - Provides information on molecular subtypes, the development of prognostic and predictive molecular signatures - Two main technologies: a. Microarray b. RNA-Sequencing (RNASeq) using next generation sequencing
  3. 3. Affymetrix GeneChip scanner
  4. 4. Blencowe B J et al. Genes Dev. 2009;23:1379-1386 Illumina HiSeq
  5. 5. .bcl files CASAVA processing •Demultiplexing •Fastq file generation •Sequencing filtering Raw files containing base calls and quality scores Illumina defined quality filters Split into Project and Sample Folders Jones_Lab ChIP_A ChIP-B Marcus_Lab RNA-SeqA RNA-SeqB RNA-SeqC Williams_Lab Exome1 Exome2 Fastq Files Fastq Files Fastq Files
  6. 6. Haas & Zody. Nature Biotechnology 28, 421–423 (2010) Using known annotations And compare to known annotations •Differential Expression •Differential Isoform Abundance •RNA editing •SNP, indel detection
  7. 7. Technology RNASeq Microarray High run-to run reproducibility Yes Yes Dynamic Range Comparable to actual transcript abundance >8000-fold Hundred fold Able to detect alternative splice site and novel isoforms Yes No De novo analysis of samples without reference genome Yes No Multiplexing Samples in one run Yes No Required amount of total RNA >100 ng ~1 ug Re-analyzable data Yes No
  8. 8. Technology RNASeq Microarray Heterogeneity of read coverage across an expressed region Yes No Well understood sources of experimental bias No Yes Data portable on a flush drive (~4G) No Yes Data is analyzable by any PC No Yes Cheaper cost per sample No(?) Yes(?)
  9. 9. RNA-Seq Experiment GEO Database
  10. 10. White paper, Illumina
  11. 11. White paper, Illumina
  12. 12. Comparing Expression Profiles from Microarrays to RNASeq n=7532 n=4537
  13. 13. Mooney M, PloSOne (2013) 10 Lymphoma (3T-cell, 7 B-cell) 4 Normal lymph node Total RNA PE100 run 50-100 million mapped reads Compare 15,092 annotated genes on chip
  14. 14. Mooney M, PloSOne (2013) T NB r=0.6; p<10-15
  15. 15. c. elegans Biological Replicates for L2 andYA stages AffyTilingArrays* Illumina RNASeq Agarwal, BMC Genomics (2010) * Covers whole c.elegans genome
  16. 16. Differential Expression genes between the L2 andYA stage Agarwal, BMC Genomics (2010)
  17. 17. RNA-Seq and tiling arrays Tiling Array Microarray Maximum Sensitivity RNASeq 11-plex RNASeq 6-plex Agarwal, BMC Genomics (2010)
  18. 18. Per Sample Microarray Illumina HiSeq 1 per Chip/Lane $670 $4,010.00 2 plex NA $2,097.50 4-plex NA $1,141.25 6-plex NA $822.50 8-plex NA $663.13 6-plex 11-plex
  19. 19. Per Sample Microarray Illumina HiSeq 1 per Chip/Lane $670 $4,010.00 2 plex NA $2,097.50 4-plex NA $1,141.25 6-plex NA $822.50 8-plex NA $663.13
  20. 20. Data Per Sample Time to download 1 Sample Time to download 100 samples Cost to Store on the Cloud per Month RNASeq 30-65GB 1 Hr 6 days $270 Microarray 30MB 5 second 8 minutes $0.30
  21. 21. -Application withUser Interface RNA-Seq analysis (i.e. Galaxy) can only handle very few samples -Knowledge of Linux server, scripting language, programming language is absolutely REQUIRED -Lack of detailed understanding in NGS technology and data leads to diverse bioinformatics tools with different characteristics LawWC ,Voom!, Bionconductor (2013)
  22. 22. The answer isYes - Transcriptome profiles generated by microarray and RNASeq are in strongly concordance - Microarray data generated in the last decades is durable - RNASeq is it offers more a lot more biological information than microarray that is re-analyzable - NGS is getting cheaper However, the devil is in the data - NGS data is a lot more expensive to store and analyze - Specialized computing infrastructure and personnel are required to take advantage of the information from NGS data