The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.
The LS-APGD/Orbitrap system, while still in the preliminary stages of development, offers highly accurate and precise isotope ratio results that suggest a potential paradigm shift in the world of isotope ratio analysis. Furthermore, the portability of the LS-APGD as an elemental ion source, combined with the small size and smaller operating demands of the Orbitrap, suggests that the instrumentation is capable of being field-deployable.
Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organism carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov chain (CTMC) and works well for small state spaces. The computations slow down considerably for larger state spaces (e.g., space of codons), because current methodology relies on exponentiating CTMC infinitesimal rate matrices-an operation whose computational complexity grows as the size of the CTMC state space cubed. In this work, we introduce a new approach, based on a CTMC technique called uniformization, which does not use matrix exponentiation for phylogenetic stochastic mapping. Our method is based on a new Markov chain Monte Carlo (MCMC) algorithm that targets the distribution of trait histories conditional on the trait data observed at the tips of the tree. The computational complexity of our MCMC method grows as the size of the CTMC state space squared. Moreover, in contrast to competing matrix exponentiation methods, if the rate matrix is sparse, we can leverage this sparsity and increase the computational efficiency of our algorithm further. Using simulated data, we illustrate advantages of our MCMC algorithm and investigate how large the state space needs to be for our method to outperform matrix exponentiation approaches. We show that even on the moderately large state space of codons our MCMC method can be significantly faster than currently used matrix exponentiation methods.
Phylogenetic recombination detection is a fundamental task in bioinformatics and evolutionary biology. Most of the computational tools developed to attack this important problem are not integrated into the growing suite of R packages for statistical analysis of molecular sequences. Here, we present an R package, rbrothers, that makes a Bayesian multiple change-point model, one of the most sophisticated model-based phylogenetic recombination tools, available to R users. Moreover, we equip the Bayesian change-point model with a set of pre- and post- processing routines that will broaden the application domain of this recombination detection framework. Specifically, we implement an algorithm that forms the set of input trees required by multiple change-point models. We also provide functionality for checking Markov chain Monte Carlo convergence and creating estimation result summaries and graphics. Using rbrothers, we perform a comparative analysis of two Salmonella enterica genes, fimA and fimH, that encode major and adhesive subunits of the type 1 fimbriae, respectively. We believe that rbrothers, available at R-Forge: http://evolmod.r-forge.r-project.org/, will allow researchers to incorporate recombination detection into phylogenetic workflows already implemented in R.
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