RNA Sequencing Analyses & Mapping Uncertainty

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1 RNA Sequencing Analyses & Mapping Uncertainty Adam McDermaid 1/26

2 RNA-seq Pipelines Collection of tools for analyzing raw RNA-seq data Tier 1 Quality Check Data Trimming Tier 2 Read Alignment Assembly De-novo Reference-based Tier 3 Differential Expression Analysis Functional Enrichment Analysis Quality Check (De-novo) Assembly of Transcripts Differential Expression Analysis RNA-seq Reads Read Mapping Gene Read Count Data Trimming Transcription Unit Prediction Functional Enrichment Analysis Tier 4 Clustering & Bi-clustering Network Analyses De-novo (Bi)-Clustering Network Analysis & Modeling 2/26

3 Visualizations for Differential Gene Expression ViDGER R package Available on GitHub Visualization of Differential Gene Expression Results Cufflinks EdgeR DESeq2 Six publication-quality figures Two matrix options Currently under review in Bioinformatics 3/26

4 vsboxplot Boxplots of log-fpkm values for each condition 4/26

5 vsscatterplot log-fpkm scatterplots between two select conditions 5/26

6 vsscattermatrix Matrix option to display all pairwise scatterplot comparisons Also displays pairwise correlations and FPKM densities for each condition 6/26

7 vsdegmatrix Displays shared differentially expressed genes for each pairwise comparison 7/26

8 vsmaplot Pairwise display of log fold change compared with mean expression values Colored data points indicate significant p- values and differential expression 8/26

9 vsvolcano Pairwise display of log fold change compared with negative log p-value 9/26

10 vsvolcanomatrix Matrix option for all pairwise Volcano plots 10/26

11 vsfourway Conditional comparison relative to chosen sample (control) 11/26

12 Current Pipeline Issues Pipeline tools are not optimal Read Alignment Ambiguous Reads All downstream analyses and results are affected Quality Check (De-novo) Assembly of Transcripts RNA-seq Reads Read Mapping Gene Read Count Data Trimming Transcription Unit Prediction Differential Expression Analysis Functional Enrichment Analysis De-novo (Bi)-Clustering Network Analysis & Modeling 12/26

13 Mapping Uncertainty Read alignment may result in multiple possible alignment locations Reads aligned to reference genome may have multiple possible alignments Due to duplicate nature of genomes, especially in plant genomes When a read can be mapped to multiple locations, mapping uncertainty occurs 13/26

14 Mapping Uncertainty Occurrences Plants Highly duplicative nature of genome Animals Alternative splicing Metagenomics Sequencing of entire microbial communities simultaneously Identical genes across different species Similar, mutated or evolved genes 14/26

15 Mapping Uncertainty in Plants 55 RNA-seq datasets 5 Distinct species GB of data Average of 20% ambiguous data Species Uniquemapped Multimapped Unmapped Arabidopsis thaliana Diploid plants Vitis vinifera Solanum lycopersicum Solanum tuberosum Polyploid plants Triticum aestivum 77%~89% 55%~82% 49%~87% 55%~69% 62%~69% 8%~17% 10%~25% 6%~34% 18%~26% 18%~25% 2%~5% 8%~23% 5%~44% 12%~19% 9%~18% 15/26

16 Current Strategies Omission of ambiguous reads Underestimation Alignment to all possible locations Overestimation Best Match Proportionate assignment Best current approach Still over/underestimates 16/26

17 Solving Mapping Uncertainty Two step approach: Determine severity of mapping uncertainty in a given dataset Quantification using relevant factors Address issue of mapping ambiguous reads 17/26

18 Quantifying Mapping Uncertainty GeneQC Computational program collecting relevant information from datasets Interprets information in meaningful way to provide quantification of mapping uncertainty Maximum base-pair similarity between gene pairs Maximum proportion of shared reads between gene pairs Partitions genes into categorizations based on mapping uncertainty severity 18/26

19 C D GeneQC 0.5 A B C 97 19/26

20 D-score Allows for comparable metric of mapping uncertainty 0.07 Combines three statistics Maximum proportion of shared ambiguous reads Maximum base-pair similarity Number of gene pair interactions Normalized between 0 and 1 for each dataset i /26

21 D-score Higher D-score indicates more mapping uncertainty D-score increased alphabetically by categorization on average 21/26

Addressing Mapping Uncertainty Co-expression Modules (CEMs) Can use expression levels for known co-expressed genes (CEGs) to predict likely expression levels for the gene locations This information

22 Addressing Mapping Uncertainty Co-expression Modules (CEMs) Can use expression levels for known co-expressed genes (CEGs) to predict likely expression levels for the gene locations This information can be in turn used to determine which location is most likely for any particular ambiguous read Can use existing information to gain insight into the likelihood of the correct location for alignment 22/26

23 Heuristic Approach to Mapping Uncertainty Utilize Negative Binomial Distribution X i ~NB 2 xҧ i, s 2 i + xҧ i ҧ x i s i 2 + ҧ x i Read alignments follow such that read counts converge to CEM mean 23/26

24 Theoretical Approach to Mapping Uncertainty Hidden Markov Model True state is unknown Background information used for predictions CEM statistics Read alignment interactions 24/26

25 Projected Outcomes GeneQC Allow for quantification of mapping uncertainty over entire dataset Statistical Modeling Allow for re-alignment of ambiguous reads aligned to genes with high levels of mapping uncertainty Implement within R package for widespread use Improve reliability of all reference-based RNA-seq analyses 25/26

26 Acknowledgements Dr. Qin Ma Dr. Anne Fennell NSF-IOS NSF-OIA SDRIC 26/26

Analysis of RNA-seq Data. Feb 8, 2017 Peikai CHEN (PHD)

Analysis of RNA-seq Data. Feb 8, 2017 Peikai CHEN (PHD) Analysis of RNA-seq Data Feb 8, 2017 Peikai CHEN (PHD) Outline What is RNA-seq? What can RNA-seq do? How is RNA-seq measured? How to process RNA-seq data: the basics How to visualize and diagnose your