Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra

Halloran, John T.; Bilmes, Jeff; Noble, William Stafford

doi:10.1021/acs.jproteome.6b00290

Cited by 10 publications

(14 citation statements)

References 29 publications

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“…The incorporation of conditional dependence and independence is similar to that in BNs. DBNs have been widely used to model longitudinal data across many scientific domains, including speech [18, 19], biological [11, 20, 21], or economic sequences [22, 23].…”

Section: Methodsmentioning

confidence: 99%

Dynamic interaction network inference from longitudinal microbiome data

et al. 2019

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Background Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data. Results Here, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions. The extended CGBayesNets package is freely available under the MIT Open Source license agreement. The source code and documentation can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public . Conclusions We propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors. Electronic supplementary material The online version of this article (10.1186/s40168-019-0660-3) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Dynamic interaction network inference from longitudinal microbiome data

et al. 2019

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“…Using the aligned taxa, we next attempted to learn graphical models that provide information about the causal impacts of taxa and clinical or demographic variables on other taxa. For this, we used Dynamic Bayesian Networks (DBNs) which have been widely used to model sequential data, including speech [16, 17], biological [18, 19, 10], or economic sequences [20, 21]. A DBN is a directed acyclic graph where, at each time slice (or time instance), nodes correspond to random variables of interest (e.g., taxa, post-conceptional age, or Nugent score) and directed edges correspond to their conditional dependencies in the graph [22].…”

Section: Methodsmentioning

confidence: 99%

Dynamic interaction network inference from longitudinal microbiome data

Lugo-Martinez

Ruiz-Perez

Narasimhan

et al. 2018

Preprint

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Background Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies researchers collect longitudinal data with the goal of understanding not just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data. Results Here we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions. Conclusions We propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological $ These authors have contributed equally to this work. relationships within the microbiome and between taxa and clinical factors.

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“…The second is the quick and easy use of PSM recalibration by newly developed search algorithms. For instance, while Percolator was initially adopted by many established database-search scoring algorithms after its introduction (e.g., Mascot, 5 XCorr, 10,23,34 and X!Tandem 47 ), Percolator analysis has since been rapidly adopted by more recent search algorithms near their initial development (e.g., MS-GF+, 12 XCorr p -values, 19 DRIP, 14 and combined res-ev p -values 31 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

Urban

Rocke

et al. 2020

Preprint

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In proteomic analysis pipelines, machine learning post-processors play a critical role in improving the accuracy of shotgun proteomics analysis. Most often performed in a semi-supervised manner, such post-processors accept the peptide-spectrum matches (PSMs) and corresponding feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSM scores based on the resulting trained parameters, often leading to significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as SVMs, gradient boosted decision trees, and linear discriminant analysis. In contrast, the powerful learning capabilities of deep models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep neural networks (DNNs) significantly improve the recalibration of shotgun proteomics data compared to the most accurate and widely used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that DNNs are able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed p-value scoring functions.

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Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra

Cited by 10 publications

References 29 publications

Dynamic interaction network inference from longitudinal microbiome data

Dynamic interaction network inference from longitudinal microbiome data

Dynamic interaction network inference from longitudinal microbiome data

Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

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