Bayesian Methods in Biological Sequence Analysis

Liu, Jun S.; Logvinenko, Tanya

doi:10.1002/9780470061619.ch3

Cited by 3 publications

(3 citation statements)

References 89 publications

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“…BaySeq Tool. The method applied in the baySeq R/ Bioconductor package (version 1.16.0; Hardcastle and Kelly, 2010) uses prior information to fit a nonlinear model that quantifies biases and uncertainties in the estimates (Liu and Logvinenko, 2003). It also assumes an NB distribution for the data and derives an empirically determined prior distribution from the entire data set by maximum likelihood (Hardcastle and Kelly, 2010), represented as follows:…”

Section: Statistical Analysesmentioning

confidence: 99%

RNA sequencing differential gene expression analysis of isolated perfused bovine udders experimentally inoculated with Streptococcus agalactiae

Sbardella

Weller

Fonseca

et al. 2019

Journal of Dairy Science

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The aim of this study was to elucidate the differential gene expression in the RNA sequencing transcriptome of isolated perfused udders collected from 4 slaughtered Holstein × Zebu crossbred dairy cows experimentally inoculated with Streptococcus agalactiae. We studied 3 different statistical tools (edgeR, baySeq, and Cuffdiff 2). In summary, 2 quarters of each udder were experimentally inoculated with Strep. agalactiae and the other 2 were used as a control. Mammary tissue biopsies were collected at times 0 and 3 h after infection. The total RNA was extracted and sequenced on an Illumina HiSeq 2000 (Illumina Inc., San Diego, CA). Transcripts were assembled from the reads aligned to the bovine UMD 3.1 reference genome, and the statistical analyses were performed using the previously mentioned tools (edgeR, baySeq, and Cuffdiff 2). Finally, the identified genes were submitted to pathway enrichment analysis. A total of 1,756, 1,161, and 3,389 genes with differential gene expression were identified when using edgeR, baySeq, and Cuffdiff 2, respectively. A total of 122 genes were identified by the overlapping of the 3 methods; however, only the platelet activation presented a significantly enriched pathway. From the results, we suggest the FCER1G, GNAI2, ORAI1, and VASP genes shared among the 3 methods in this pathway for posterior biological validation.

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Section: Statistical Analysesmentioning

confidence: 99%

RNA sequencing differential gene expression analysis of isolated perfused bovine udders experimentally inoculated with Streptococcus agalactiae

Sbardella

Weller

Fonseca

et al. 2019

Journal of Dairy Science

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“…Bayesian inference procedures and algorithms have revolutionized the field of computational biology (Liu and Logvinenko, 2003) due to the development of computationally-intensive simulation-based methods such as Markov chain Monte Carlo (MCMC), which are available in software such as WinBUGS (Lunn et al, 2000), and has led to the adoption of increasingly complex models in many situations.…”

Section: Introductionmentioning

confidence: 99%

Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis

Nur

Allingham²,

Rousseau

et al. 2009

Computational Statistics & Data Analysis

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The focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an important role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov models for DNA sequence segmentation; (ii) to examine the impact of replacing the standard Dirichlet prior with a mixture Dirichlet prior; and (iii) to propose and illustrate a more comprehensive approach to sensitivity analysis, using importance sampling. It is obtained that (i) the posterior estimates obtained under a Bayesian hidden Markov model are indeed sensitive to the specification of the prior distributions; (ii) compared with the standard Dirichlet prior, the mixture Dirichlet prior is more flexible, less sensitive to the choice of hyperparameters and less constraining in the analysis, thus improving posterior estimates; and (iii) importance sampling was computationally feasible, fast and effective in allowing a richer sensitivity analysis.

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“…In fact, the use of an Approximate 91 Bayesian Computation (ABC) approach, discussed below, is particularly well suited to 92 these kinds of problems [16]. There are many examples of Bayesian methods being used 93 to analyse bioinformatics data and systems biology models [17], including in sequence 94 analysis [18], gene microarray data [19] and in models of genetic oscillators [20] and 95 DNA network dynamics [21]. There are a number of models that take a systems biology 96 approach towards understanding physiology, particularly oxygen transport and blood 97 flow, including the previously mentioned BrainSignals [2,4,6] and BrainPiglet [5,6] 98 models, the Aubert-Costalat model [22], and work by Fantini [23][24][25][26] and Orlowski and 99 Payne [27,28] where Bayesian parameter estimation could also be applied but has yet to 100 be.…”

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confidence: 99%

A Bayesian framework for the analysis of systems biology models of the brain

Russell-Buckland

Barnes

Tachtsidis

2018

Preprint

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responses between a healthy and a vascular compromised brain to a drop in oxygenation. We then use experimental data that demonstrates the healthy brain response to an increase in arterial CO2 and fit our brain model predictions to the measurements. In both instances we show that our approach provides more information about the overlap between healthy and unhealthy brain states than a fitting process that provides a single value parameter estimate. Introduction 1 Systems biology models are used to understand complex biological and physiological 2 systems comprised of large numbers of individual elements that give rise to emergent 3 behaviours. These complex systems are dependent on both the properties of the whole 4 network and on the individual elements [1]. This inherent complexity within the models 5 can lead to difficulties in determining how best to interpret information obtained 6 through their use.7At University College London, the family of BrainSignals models (and the 8 BRAINCIRC model on which they are based) are used to understand the brain's 9 dynamics via a systems biology approach. They bring together a number of 10 mathematical models relating to different aspects of blood circulation, oxygen transport 11 and oxygen metabolism within the brain in order to develop a more complete model that 12 can be used alongside experimental data to simulate physiological phenomena of the 13 brain, such as autoregulation and neural activation. This allows us to understand how 14 our measurements are linked to specific brain physiological and metabolic mechanisms. 15 All of the models were developed to reproduce broadband near-infrared spectroscopy 16 (NIRS) measurements of brain tissue concentration changes of haemoglobin (oxygenation 17 and haemodynamics) and cytochrome-c-oxidase (mitochondrial metabolism) and vary 18 in their complexity and scope. Table 1 compares the number of reactions, equations, 19 relations, reactions, variables and parameters in three different models. The first model 20 developed was the 'BRAINCIRC' model in 2005 [2]. This built on an earlier circulatory 21 model by Ursino and Lodi [3] and combined models for the biophysics of the circulatory 22 system, the brain metabolic biochemistry and the function of vascular smooth muscle. 23This model was succeeded by the 'BrainSignals' model [4], which simplified the previous 24 'BRAINCIRC' model and added a submodel of mitochondrial metabolism. A number of 25 additional versions were then developed from this, such as the 'BrainPiglet' model [5] 26 which was developed to to simulate the physiological and metabolic processes of the 27 piglet brain often used as the neonatal preclinical model. It involved modifying the 28 default values for 11 of the 107 parameters used and was extended to include simulated 29 measurements for magnetic resonance spectroscopy values that included brain tissue 30 lactate and ATP production, measurements of which are available in piglet studies. 31 This was extended in BrainPiglet v2 to incorporate the effects of ce...

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Bayesian Methods in Biological Sequence Analysis

Cited by 3 publications

References 89 publications

RNA sequencing differential gene expression analysis of isolated perfused bovine udders experimentally inoculated with Streptococcus agalactiae

RNA sequencing differential gene expression analysis of isolated perfused bovine udders experimentally inoculated with Streptococcus agalactiae

Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis

A Bayesian framework for the analysis of systems biology models of the brain

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