Top 5 Lessons Learned From MAQC III/SEQC

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1 Top 5 Lessons Learned From MAQC III/SEQC Weida Tong, Ph.D Division of Bioinformatics and Biostatistics, NCTR/FDA

2 MicroArray Quality Control (MAQC) An FDA led community wide consortium effort to assess technical performance and application of genomics technologies (microarrays, GWAS and next gen sequencing) in clinic and safety evaluation. MAQC IV/SEQC Present QC and reliable use of Whole Genome Sequencing (WGS) and Targeted Gene Sequencing (TGS) in clinical application and regulatory science research 2

3 The 3rd Phase of MAQC SEquencing Quality Control (SEQC) >180 participants from 73 organizations Generated > 10Tb data and >100 billion reads Represented ~6% data in GEO (Jun, 2014) 10 Manuscripts: 3 in Nat Biotechnol, 2 in Nat Commun, 3 in Scientific Data, 2 in Genome Biology Datasets Study designs Objectives 3

4 #5: Relative measures agree well across laboratories and platforms but not for absolute measurements 6 reference samples Statistical test: Intensity: A (Lab1) vs A (Lab2) = DEGs DEG: A/B (Lab1) vs A/B (Lab2) 11 Labs 3 Platforms - Illumina - SOLiD Su et al Nat Biotechnol (2014) Bioinformatics Pipelines 4

5 #4: RNA-Seq has a better sensitivity for weakly expressed genes than microarrays The treatment effect dictates the concordance between two platforms in detecting DEGs RNA Seq agrees with qpcr for low expressed genes The concordance between the two platforms is high for the highly expressed genes, not for the weakly expressed genes Wang et al Nat Biotechnol (2014) 5

6 #3: RNA-Seq Gene Discovery Large numbers of new splice junctions were discovered and can be verified by qpcr (>80%), but their biological functions need to be further investigated 6

7 #2: Which Pipelines Should I Use? 13 pipelines: BWA Bowtie Bowtie2 GSNAP MAGIC MapSlplice Novoalign OSA RUM STAR Subread TopHat WHAM DEGs < > qpcr Mapping Quantification Normalization 278 permutations Classifiers Multiple pipeline components jointly and significantly impacted the quality of gene expression and downstream prediction performance RNA seq pipelines that produced better gene expression resulted in better prediction performance. Guidelines Guidelines Nat Methods (revision) 7

8 #1: Legacy microarrays data in the RNA-seq era: Predictive models and biomarkers RNA-Seq and microarrays were comparable for predictive models The transferability of signature genes with three modeling algorithms and three gene mappings A, B, and C Signature genes are reciprocally transferable between 2 platforms Microarray A B C k NNs NSCs SVM A B C RNA seq Microarray models can accurately predict RNA-seq profiled samples Cross platform prediction with three modeling algorithms and two gene mappings A and B RNA-seq are less accurate in predicting microarrayprofiled samples and affected both by modeling algorithm and the gene mapping complexity Microarray A B k NNs NSCs SVM A B RNA seq Su et al. Genome Biology (2014) Zhang et al. Genome Biology (2015) Working Not working 8

9 Next generation sequencing is a tool and the challenges and issues rest on its application Genome size Human (~3.2B) Microbial genome (1 10M) mrna (~27K) microrna (18 25) 9

10 MicroArray Quality Control (MAQC) An FDA led community wide consortium effort to assess technical performance and application of genomics technologies (microarrays, GWAS and next gen sequencing) in clinic and safety evaluation. MAQC IV/SEQC Present QC and reliable use of Whole Genome Sequencing (WGS) and Targeted Gene Sequencing (TGS) in clinical application and regulatory science research 10

11 Food for Thought: NGS is just a tool; the value and challenge for the tool depends on how it is used The challenges and issues are different depending on the intend application (DNA seq, microbial NGS, RNA seq, microrna and others) Recognize the evolving nature of the field Bioinformatics is a significant part of this field The results are often not able to be reproduced without the original script In silico mistakes are common in this field; need to implement a best practice guideline General rules can be followed and best practice can be developed Need to engage the research community to gain consensus for reproducibility and standard analysis protocol The FDA should play an active role to expedite the translation of basic science for regulatory application 11

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