| 1. |
- Ali, Raja Hashim, 1985-, et al.
(författare)
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Burnin estimation and convergence assessment in Bayesian phylogenetic inference
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Convergence assessment and burnin estimation are central concepts in Markov chain Monte Carlo algorithms. Studies on eects, statistical properties, and comparisons between dierent convergence assessment methods have been conducted during the past few decades. However, not much work has been done on the eect of convergence diagnostic on posterior distribution of tree parameters and which method should be used by researchers in Bayesian phylogenetics inference. In this study, we propose and evaluate two novel burnin estimation methods that estimate burnin using all parameters jointly. We also consider some other popular convergence diagnostics, evaluate them in light of parallel chains and quantify the eect of burnin estimates from various convergence diagnostics on the posterior distribution of trees. We motivate the use of convergence diagnostics to assess convergence and estimate burnin in Bayesian phylogenetics inference and found out that it is better to employ convergence diagnostics rather than remove a xed percentage as burnin. We concluded that the last burnin estimator using eective sample size appears to estimate burnin better than all other convergence diagnostics.
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| 2. |
- Ali, Raja Hashim, 1985-
(författare)
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From genomes to post-processing of Bayesian inference of phylogeny
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Life is extremely complex and amazingly diverse; it has taken billions of years of evolution to attain the level of complexity we observe in nature now and ranges from single-celled prokaryotes to multi-cellular human beings. With availability of molecular sequence data, algorithms inferring homology and gene families have emerged and similarity in gene content between two genes has been the major signal utilized for homology inference. Recently there has been a significant rise in number of species with fully sequenced genome, which provides an opportunity to investigate and infer homologs with greater accuracy and in a more informed way. Phylogeny analysis explains the relationship between member genes of a gene family in a simple, graphical and plausible way using a tree representation. Bayesian phylogenetic inference is a probabilistic method used to infer gene phylogenies and posteriors of other evolutionary parameters. Markov chain Monte Carlo (MCMC) algorithm, in particular using Metropolis-Hastings sampling scheme, is the most commonly employed algorithm to determine evolutionary history of genes. There are many softwares available that process results from each MCMC run, and explore the parameter posterior but there is a need for interactive software that can analyse both discrete and real-valued parameters, and which has convergence assessment and burnin estimation diagnostics specifically designed for Bayesian phylogenetic inference.In this thesis, a synteny-aware approach for gene homology inference, called GenFamClust (GFC), is proposed that uses gene content and gene order conservation to infer homology. The feature which distinguishes GFC from earlier homology inference methods is that local synteny has been combined with gene similarity to infer homologs, without inferring homologous regions. GFC was validated for accuracy on a simulated dataset. Gene families were computed by applying clustering algorithms on homologs inferred from GFC, and compared for accuracy, dependence and similarity with gene families inferred from other popular gene family inference methods on a eukaryotic dataset. Gene families in fungi obtained from GFC were evaluated against pillars from Yeast Gene Order Browser. Genome-wide gene families for some eukaryotic species are computed using this approach.Another topic focused in this thesis is the processing of MCMC traces for Bayesian phylogenetics inference. We introduce a new software VMCMC which simplifies post-processing of MCMC traces. VMCMC can be used both as a GUI-based application and as a convenient command-line tool. VMCMC supports interactive exploration, is suitable for automated pipelines and can handle both real-valued and discrete parameters observed in a MCMC trace. We propose and implement joint burnin estimators that are specifically applicable to Bayesian phylogenetics inference. These methods have been compared for similarity with some other popular convergence diagnostics. We show that Bayesian phylogenetic inference and VMCMC can be applied to infer valuable evolutionary information for a biological case – the evolutionary history of FERM domain.
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| 3. |
- Ali, Raja Hashim, 1985-, et al.
(författare)
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GenFamClust : an accurate, synteny-aware and reliable homology inference algorithm
- 2016
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Ingår i: BMC Evolutionary Biology. - : Springer Science and Business Media LLC. - 1471-2148. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: Homology inference is pivotal to evolutionary biology and is primarily based on significant sequence similarity, which, in general, is a good indicator of homology. Algorithms have also been designed to utilize conservation in gene order as an indication of homologous regions. We have developed GenFamClust, a method based on quantification of both gene order conservation and sequence similarity. Results: In this study, we validate GenFamClust by comparing it to well known homology inference algorithms on a synthetic dataset. We applied several popular clustering algorithms on homologs inferred by GenFamClust and other algorithms on a metazoan dataset and studied the outcomes. Accuracy, similarity, dependence, and other characteristics were investigated for gene families yielded by the clustering algorithms. GenFamClust was also applied to genes from a set of complete fungal genomes and gene families were inferred using clustering. The resulting gene families were compared with a manually curated gold standard of pillars from the Yeast Gene Order Browser. We found that the gene-order component of GenFamClust is simple, yet biologically realistic, and captures local synteny information for homologs. Conclusions: The study shows that GenFamClust is a more accurate, informed, and comprehensive pipeline to infer homologs and gene families than other commonly used homology and gene-family inference methods.
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| 4. |
- Ali, Raja Hashim, et al.
(författare)
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Quantitative synteny scoring improves homology inference and partitioning of gene families
- 2013
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 14, s. S12-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Clustering sequences into families has long been an important step in characterization of genes and proteins. There are many algorithms developed for this purpose, most of which are based on either direct similarity between gene pairs or some sort of network structure, where weights on edges of constructed graphs are based on similarity. However, conserved synteny is an important signal that can help distinguish homology and it has not been utilized to its fullest potential. Results: Here, we present GenFamClust, a pipeline that combines the network properties of sequence similarity and synteny to assess homology relationship and merge known homologs into groups of gene families. GenFamClust identifies homologs in a more informed and accurate manner as compared to similarity based approaches. We tested our method against the Neighborhood Correlation method on two diverse datasets consisting of fully sequenced genomes of eukaryotes and synthetic data. Conclusions: The results obtained from both datasets confirm that synteny helps determine homology and GenFamClust improves on Neighborhood Correlation method. The accuracy as well as the definition of synteny scores is the most valuable contribution of GenFamClust.
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| 5. |
- Ali, Raja Hashim, 1985-, et al.
(författare)
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VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Motivation: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical diculties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters.Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines. Availability: VMCMC is available for Java SE 6+ under the New BSD License. Executable jar les, tutorial manual and source code can be downloaded from https://bitbucket.org/rhali/visualmcmc/.
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| 6. |
- Ali, Raja Hashim, et al.
(författare)
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VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces
- 2017
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Ingår i: Bmc Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Background: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical difficulties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters. Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines. Conclusions: VMCMC is a free software available under the New BSD License. Executable jar files, tutorial manual and source code can be downloaded from https://bitbucket. org/rhali/visualmcmc/.
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| 7. |
- Alneberg, Johannes
(författare)
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Bioinformatic Methods in Metagenomics
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Microbial organisms are a vital part of our global ecosystem. Yet, our knowledge of them is still lacking. Direct sequencing of microbial communities, i.e. metagenomics, have enabled detailed studies of these microscopic organisms by inspection of their DNA sequences without the need to culture them. Furthermore, the development of modern high- throughput sequencing technologies have made this approach more powerful and cost-effective. Taken together, this has shifted the field of microbiology from previously being centered around microscopy and culturing studies, to largely consist of computational analyses of DNA sequences. One such computational analysis which is the main focus of this thesis, aims at reconstruction of the complete DNA sequence of an organism, i.e. its genome, directly from short metagenomic sequences.This thesis consists of an introduction to the subject followed by five papers. Paper I describes a large metagenomic data resource spanning the Baltic Sea microbial communities. This dataset is complemented with a web-interface allowing researchers to easily extract and visualize detailed information. Paper II introduces a bioinformatic method which is able to reconstruct genomes from metagenomic data. This method, which is termed CONCOCT, is applied on Baltic Sea metagenomics data in Paper III and Paper V. This enabled the reconstruction of a large number of genomes. Analysis of these genomes in Paper III led to the proposal of, and evidence for, a global brackish microbiome. Paper IV presents a comparison between genomes reconstructed from metagenomes with single-cell sequenced genomes. This further validated the technique presented in Paper II as it was found to produce larger and more complete genomes than single-cell sequencing.
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| 8. |
- Angleby, Helen, et al.
(författare)
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Forensic Informativity of similar to 3000bp of Coding Sequence of Domestic Dog mtDNA
- 2014
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Ingår i: Journal of Forensic Sciences. - : Wiley. - 0022-1198 .- 1556-4029. ; 59:4, s. 898-908
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Tidskriftsartikel (refereegranskat)abstract
- The discriminatory power of the noncoding control region (CR) of domestic dog mitochondrial DNA alone is relatively low. The extent to which the discriminatory power could be increased by analyzing additional highly variable coding regions of the mitochondrial genome (mtGenome) was therefore investigated. Genetic variability across the mtGenome was evaluated by phylogenetic analysis, and the three most variable similar to 1kb coding regions identified. We then sampled 100 Swedish dogs to represent breeds in accordance with their frequency in the Swedish population. A previously published dataset of 59 dog mtGenomes collected in the United States was also analyzed. Inclusion of the three coding regions increased the exclusion capacity considerably for the Swedish sample, from 0.920 for the CR alone to 0.964 for all four regions. The number of mtDNA types among all 159 dogs increased from 41 to 72, the four most frequent CR haplotypes being resolved into 22 different haplotypes.
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| 10. |
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