Dataset Information

Increasing the Time Resolution of Single-Molecule Experiments with Bayesian Inference.

ABSTRACT: Many time-resolved single-molecule biophysics experiments seek to characterize the kinetics of biomolecular systems exhibiting dynamics that challenge the time resolution of the given technique. Here, we present a general, computational approach to this problem that employs Bayesian inference to learn the underlying dynamics of such systems, even when they are much faster than the time resolution of the experimental technique being used. By accurately and precisely inferring rate constants, our Bayesian inference for the analysis of subtemporal resolution dynamics approach effectively enables the experimenter to super-resolve the poorly resolved dynamics that are present in their data.

SUBMITTER: Kinz-Thompson CD

PROVIDER: S-EPMC5984981 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

Increasing the Time Resolution of Single-Molecule Experiments with Bayesian Inference.

Kinz-Thompson Colin D CD Gonzalez Ruben L RL

Biophysical journal 20180101 2

Many time-resolved single-molecule biophysics experiments seek to characterize the kinetics of biomolecular systems exhibiting dynamics that challenge the time resolution of the given technique. Here, we present a general, computational approach to this problem that employs Bayesian inference to learn the underlying dynamics of such systems, even when they are much faster than the time resolution of the experimental technique being used. By accurately and precisely inferring rate constants, our ...[more]

PMID: 29401427

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Project description:The dataset in this Proteome Exchange entry are specific to the WTC11 benchmarking samples described in the "IsoBayes" publication (Bollon et al.) Motivation: Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Results: Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. Availability and implementation: IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Dataset Information

Increasing the Time Resolution of Single-Molecule Experiments with Bayesian Inference.

Publications

Increasing the Time Resolution of Single-Molecule Experiments with Bayesian Inference.

Similar Datasets

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