Data partitioning and correction for ascertainment bias reduce the uncertainty of placental mammal divergence times inferred from the morphological clock

O’Leary et al. (Research Article, 8 February 2013, p. 662) examined mammalian relationships and divergence times and concluded that a single placental ancestor crossed the Cretaceous-Paleogene (K-Pg) boundary. This conclusion relies on phylogenetic analyses that fail to discriminate between homology and homoplasy and further implies virus-like rates of nucleotide substitution in early Paleocene placentals.

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Molecular phylogeny and divergence times of Malagasy tenrecs: Influence of data partitioning and taxon sampling on dating analyses

BMC Evolutionary Biology ◽

10.1186/1471-2148-8-102 ◽

2008 ◽

Vol 8 (1) ◽

pp. 102 ◽

Cited By ~ 49

Author(s):

Céline Poux ◽

Ole Madsen ◽

Julian Glos ◽

Wilfried W de Jong ◽

Miguel Vences

Keyword(s):

Molecular Phylogeny ◽

Data Partitioning ◽

Divergence Times ◽

Taxon Sampling

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Development of Microsatellite Loci Exhibiting Reverse Ascertainment Bias and a Sexing Marker for use in Emperor Geese (Chen Canagica)

Avian Biology Research ◽

10.3184/175815617x14969254461396 ◽

2017 ◽

Vol 10 (4) ◽

pp. 201-210 ◽

Cited By ~ 1

Author(s):

Meg C. Gravley ◽

George K. Sage ◽

Joel A. Schmutz ◽

Sandra L. Talbot

Keyword(s):

Microsatellite Loci ◽

Statistical Power ◽

Ascertainment Bias ◽

Next Generation Sequencing Technology ◽

Branta Bernicla ◽

Black Brant ◽

Expected Heterozygosity ◽

Cackling Geese ◽

The 1960S ◽

Goose Species

The Alaskan population of Emperor Geese ( Chen canagica) nests on the Yukon–Kuskokwim Delta in western Alaska. Numbers of Emperor Geese in Alaska declined from the 1960s to the mid-1980s and since then, their numbers have slowly increased. Low statistical power of microsatellite loci developed in other waterfowl species and used in previous studies of Emperor Geese are unable to confidently assign individual identity. Microsatellite loci for Emperor Goose were therefore developed using shotgun amplification and next-generation sequencing technology. Forty-one microsatellite loci were screened and 14 were found to be polymorphic in Emperor Geese. Only six markers – a combination of four novel loci and two loci developed in other waterfowl species – are needed to identify an individual from among the Alaskan Emperor Goose population. Genetic markers for identifying sex in Emperor Geese were also developed. The 14 novel variable loci and 15 monomorphic loci were screened for polymorphism in four other Arctic-nesting goose species, Black Brant ( Branta bernicla nigricans), Greater White-fronted ( Anser albifrons), Canada ( B. canadensis) and Cackling ( B. hutchinsii) Goose. Emperor Goose exhibited the smallest average number of alleles (3.3) and the lowest expected heterozygosity (0.467). Greater White-fronted Geese exhibited the highest average number of alleles (4.7) and Cackling Geese the highest expected heterozygosity (0.599). Six of the monomorphic loci were variable and able to be characterised in the other goose species assayed, a predicted outcome of reverse ascertainment bias. These findings fail to support the hypothesis of ascertainment bias due to selection of microsatellite markers.

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Faculty Opinions recommendation of Variable locus length in the human genome leads to ascertainment bias in functional inference for non-coding elements.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1157594.617784 ◽

2009 ◽

Author(s):

Casey Bergman

Keyword(s):

Human Genome ◽

Ascertainment Bias ◽

Functional Inference

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JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

Journal of Biological System ◽

10.1142/s0218339017400022 ◽

2017 ◽

Vol 25 (04) ◽

pp. 587-603 ◽

Cited By ~ 2

Author(s):

YUSUKE ASAI ◽

HIROSHI NISHIURA

Keyword(s):

Real Time ◽

Ebola Virus ◽

Virus Disease ◽

Critical Role ◽

Estimation Method ◽

Case Fatality ◽

Ascertainment Bias ◽

Joint Estimation ◽

Ebola Virus Disease ◽

Strong Impact

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

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Bayes Estimation of Species Divergence Times and Ancestral Population Sizes Using DNA Sequences From Multiple Loci

Genetics ◽

10.1093/genetics/164.4.1645 ◽

2003 ◽

Vol 164 (4) ◽

pp. 1645-1656 ◽

Cited By ~ 3

Author(s):

Bruce Rannala ◽

Ziheng Yang

Keyword(s):

Dna Sequences ◽

Gene Tree ◽

Species Tree ◽

Bayes Estimation ◽

Ancestral Population ◽

Divergence Times ◽

Gene Trees ◽

Species Divergence ◽

Multiple Loci ◽

Population Sizes

Abstract The effective population sizes of ancestral as well as modern species are important parameters in models of population genetics and human evolution. The commonly used method for estimating ancestral population sizes, based on counting mismatches between the species tree and the inferred gene trees, is highly biased as it ignores uncertainties in gene tree reconstruction. In this article, we develop a Bayes method for simultaneous estimation of the species divergence times and current and ancestral population sizes. The method uses DNA sequence data from multiple loci and extracts information about conflicts among gene tree topologies and coalescent times to estimate ancestral population sizes. The topology of the species tree is assumed known. A Markov chain Monte Carlo algorithm is implemented to integrate over uncertain gene trees and branch lengths (or coalescence times) at each locus as well as species divergence times. The method can handle any species tree and allows different numbers of sequences at different loci. We apply the method to published noncoding DNA sequences from the human and the great apes. There are strong correlations between posterior estimates of speciation times and ancestral population sizes. With the use of an informative prior for the human-chimpanzee divergence date, the population size of the common ancestor of the two species is estimated to be ∼20,000, with a 95% credibility interval (8000, 40,000). Our estimates, however, are affected by model assumptions as well as data quality. We suggest that reliable estimates have yet to await more data and more realistic models.

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Plastid phylogenomics resolves ambiguous relationships within the orchid family and provides a solid timeframe for biogeography and macroevolution

Scientific Reports ◽

10.1038/s41598-021-83664-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maria Alejandra Serna-Sánchez ◽

Oscar A. Pérez-Escobar ◽

Diego Bogarín ◽

María Fernanda Torres-Jimenez ◽

Astrid Catalina Alvarez-Yela ◽

...

Keyword(s):

Phylogenetic Relationships ◽

Divergence Times ◽

Molecular Clocks ◽

Phylogenetic Placement ◽

Plastid Genomes ◽

Phylogenetic Reconstructions ◽

A Genome ◽

Tree Models ◽

Phylogenomic Analyses ◽

First Time

AbstractRecent phylogenomic analyses based on the maternally inherited plastid organelle have enlightened evolutionary relationships between the subfamilies of Orchidaceae and most of the tribes. However, uncertainty remains within several subtribes and genera for which phylogenetic relationships have not ever been tested in a phylogenomic context. To address these knowledge-gaps, we here provide the most extensively sampled analysis of the orchid family to date, based on 78 plastid coding genes representing 264 species, 117 genera, 18 tribes and 28 subtribes. Divergence times are also provided as inferred from strict and relaxed molecular clocks and birth–death tree models. Our taxon sampling includes 51 newly sequenced plastid genomes produced by a genome skimming approach. We focus our sampling efforts on previously unplaced clades within tribes Cymbidieae and Epidendreae. Our results confirmed phylogenetic relationships in Orchidaceae as recovered in previous studies, most of which were recovered with maximum support (209 of the 262 tree branches). We provide for the first time a clear phylogenetic placement for Codonorchideae within subfamily Orchidoideae, and Podochilieae and Collabieae within subfamily Epidendroideae. We also identify relationships that have been persistently problematic across multiple studies, regardless of the different details of sampling and genomic datasets used for phylogenetic reconstructions. Our study provides an expanded, robust temporal phylogenomic framework of the Orchidaceae that paves the way for biogeographical and macroevolutionary studies.

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Pre‐filtering based summarization for data partitioning in distributed stream processing

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.6338 ◽

2021 ◽

Author(s):

Adeel Aslam ◽

Hanhua Chen ◽

Hai Jin

Keyword(s):

Stream Processing ◽

Data Partitioning ◽

Distributed Stream Processing

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Data Partitioning on Heterogeneous Multicore Platforms

2011 IEEE International Conference on Cluster Computing ◽

10.1109/cluster.2011.64 ◽

2011 ◽

Cited By ~ 5

Author(s):

Ziming Zhong ◽

Vladimir Rychkov ◽

Alexey Lastovetsky

Keyword(s):

Data Partitioning ◽

Multicore Platforms ◽

Heterogeneous Multicore

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Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-06-2016-0021 ◽

2017 ◽

Vol 10 (2) ◽

pp. 166-182 ◽

Cited By ~ 2

Author(s):

Shabia Shabir Khan ◽

S.M.K. Quadri

Keyword(s):

Pancreatic Cancer ◽

Fuzzy Inference System ◽

Fuzzy System ◽

Fuzzy Inference ◽

Patient Survival ◽

Data Partitioning ◽

Structure Identification ◽

Inference System ◽

Content Type ◽

Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

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