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Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors.

ABSTRACT: Calibration is the rate-determining step in every molecular clock analysis and, hence, considerable effort has been expended in the development of approaches to distinguish good from bad calibrations. These can be categorized into a priori evaluation of the intrinsic fossil evidence, and a posteriori evaluation of congruence through cross-validation. We contrasted these competing approaches and explored the impact of different interpretations of the fossil evidence upon Bayesian divergence time estimation. The results demonstrate that a posteriori approaches can lead to the selection of erroneous calibrations. Bayesian posterior estimates are also shown to be extremely sensitive to the probabilistic interpretation of temporal constraints. Furthermore, the effective time priors implemented within an analysis differ for individual calibrations when employed alone and in differing combination with others. This compromises the implicit assumption of all calibration consistency methods, that the impact of an individual calibration is the same when used alone or in unison with others. Thus, the most effective means of establishing the quality of fossil-based calibrations is through a priori evaluation of the intrinsic palaeontological, stratigraphic, geochronological and phylogenetic data. However, effort expended in establishing calibrations will not be rewarded unless they are implemented faithfully in divergence time analyses.

SUBMITTER: Warnock RC

PROVIDER: S-EPMC4262156 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors.

Warnock Rachel C M RC Parham James F JF Joyce Walter G WG Lyson Tyler R TR Donoghue Philip C J PC

Proceedings. Biological sciences 20150101 1798

Calibration is the rate-determining step in every molecular clock analysis and, hence, considerable effort has been expended in the development of approaches to distinguish good from bad calibrations. These can be categorized into a priori evaluation of the intrinsic fossil evidence, and a posteriori evaluation of congruence through cross-validation. We contrasted these competing approaches and explored the impact of different interpretations of the fossil evidence upon Bayesian divergence time ...[more]

PMID: 25429012

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Project description:BackgroundThe flood of genomic data to help build and date the tree of life requires automation at several critical junctures, most importantly during sequence assembly and alignment. It is widely appreciated that automated alignment protocols can yield inaccuracies, but the relative impact of various sources error on phylogenomic analysis is not yet known. This study employs an updated mammal data set of 5162 coding loci sampled from 90 species to evaluate the effects of alignment uncertainty, substitution models, and fossil priors on gene tree, species tree, and divergence time estimation. Additionally, a novel coalescent likelihood ratio test is introduced for comparing competing species trees against a given set of gene trees.ResultsThe aligned DNA sequences of 5162 loci from 90 species were trimmed and filtered using trimAL and two filtering protocols. The final dataset contains 4 sets of alignments - before trimming, after trimming, filtered by a recently proposed pipeline, and further filtered by comparing ML gene trees for each locus with the concatenation tree. Our analyses suggest that the average discordance among the coalescent trees is significantly smaller than that among the concatenation trees estimated from the 4 sets of alignments or with different substitution models. There is no significant difference among the divergence times estimated with different substitution models. However, the divergence dates estimated from the alignments after trimming are more recent than those estimated from the alignments before trimming.ConclusionsOur results highlight that alignment uncertainty of the updated mammal data set and the choice of substitution models have little impact on tree topologies yielded by coalescent methods for species tree estimation, whereas they are more influential on the trees made by concatenation. Given the choice of calibration scheme and clock models, divergence time estimates are robust to the choice of substitution models, but removing alignments deemed problematic by trimming algorithms can lead to more recent dates. Although the fossil prior is important in divergence time estimation, Bayesian estimates of divergence times in this data set are driven primarily by the sequence data.

Project description:High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Dataset Information

Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors.

Publications

Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors.

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