Addressing Sequencing Errors in Phylogenetics
The impact of sequencing errors on phylogenetic analysis and the strategies to mitigate these errors through probabilistic modeling and next-generation sequencing technologies. This research delves into gene evolution, convergent evolution, mutation rate variation, and divergence time estimation in the context of error-prone data. Discover how incorporating more information from sequencing data can refine phylogenetic reconstructions.
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Presentation Transcript
ACCOUNTING FOR SEQUENCING ERROR IN PHYLOGENETICS Gregg Thomas Indiana University Society of Systematic Biologists Conference, Ann Arbor, MI 05.21.15
EVOLUTION PHYLOGENETICS GENOMICS 2
EVOLUTION PHYLOGENETICS GENOMICS Gene family evolution Convergent evolution Mutation rate variation Divergence time estimation 3
EVOLUTION Error prone! PHYLOGENETICS GENOMICS Gene family evolution Convergent evolution Mutation rate variation Divergence time estimation 4
EVOLUTION Error prone! PHYLOGENETICS GENOMICS Gene copy number error Gene family evolution Convergent evolution Mutation rate variation Divergence time estimation 5
EVOLUTION Error prone! PHYLOGENETICS GENOMICS Gene copy number error Gene family evolution Convergent evolution Mutation rate variation Divergence time estimation Base calling errors 6
ACCOUNTINGFORERRORBYPROBABILISTIC MODELING Incorporate more information from next generation sequencing into the phylogenetic reconstruction process 7
NEXTGENERATIONSEQUENCEDATAISERROR PRONE Quality Score Hu et al, 2012 8
NEXTGENERATIONSEQUENCEDATAISERROR PRONE Quality Score Philippe et al, 2012 Hu et al, 2012 9
PHYLOGENETICRECONSTRUCTIONSSTARTWITH OBSERVEDSTATEPROBABILITIESOF1OR0 A T C G A 1 0 0 0 A T C G G 0 0 0 1 A T C G A 1 0 0 0 10
NEXTGENERATIONSEQUENCINGGIVESUSMORE INFORMATION A T C G A 1 0 0 0 Aligned reads G G Assembly A T C G A 1 0 0 0 11
MODELOBSERVEDSTATESPROBABILISTICALLY A T C G A 0.8 0 0.1 0.1 A T C G G 0.6 0 0 0.4 A T C G A 0.9 0.1 0 0 12
CAPTURINGUNCERTAINTYFROM NGS Aligned reads G Assembly 13
CAPTURINGUNCERTAINTYFROM NGS Aligned reads G Assembly No quality information 14
CAPTURINGUNCERTAINTYFROM NGS Each read has a quality score Aligned reads G Assembly 15
CAPTURINGUNCERTAINTYFROM NGS Each read has a quality score Aligned reads G Assembly Map reads back to assembly and compile quality information 16
CAPTURINGUNCERTAINTYFROMNGSDATA The probability a given base is an error: ? ? = 10 ? 10 ? !? = 1 ? ? 17
CAPTURINGUNCERTAINTYFROMNGSDATA The probability a given base is an error: ? ? = 10 ? 10 ? !? = 1 ? ? The probability of each base at a position, given a set of reads: ? = ?,?,?,? ? log? ? ?? ?? ? log? !? ?? ??= ? ? ? ? = ?=1 18
CONCLUSIONS Currently benchmarking on simulated data Will add quality metric to all NGS assemblies Focus on phylogenetic reconstructions 19
ACKNOWLEDGEMENTS Colleagues: James Pease Simo Zhang Fabio Mendes Rafael Guerrero Jeff Adrion Ben Rosenzweig Advisor: Matt Hahn Committee: Haixu Tang Elizabeth Housworth GCMS Training Grant 20
