| 1. |
- 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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| 2. |
- 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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| 3. |
- Ali, Raja Hashim, et al.
(författare)
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A graph-based approach for improving the homologyinference in multiple sequence alignments
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Multiple sequence alignment (MSA) is ubiquitous in evolutionary studies and other areas ofbioinformatics. In nearly all cases MSAs are taken to be a known and xed quantity on which toperform downstream analysis despite extensive evidence that MSA accuracy and uncertainty aectsresults. Mistakes in the MSA are known to cause a wide range of problems for downstream evolutionaryinference, ranging from false inference of positive selection to long branch attraction artifacts. The mostpopular approach to dealing with this problem is to remove (lter) specic columns in the MSA thatare thought to be prone to error, either through proximity to gaps or through some scoring function.Although popular, this approach has had mixed success and several studies have even suggested thatltering might be detrimental to phylogenetic studies. Here we present a dierent approach to dealingwith MSA accuracy and uncertainty through a graph-based approach implemented in the freely availablesoftware Divvier. The aim of Divvier is to identify clusters of characters that have strong statisticalevidence of shared homology, based on the output of a pair hidden Markov model. These clusters canthen be used to either lter characters out the MSA, through a process we call partial ltering, or torepresent each of the clusters in a new column, through a process we call divvying up. We validateour approach through its performance on real and simulated benchmarks, nding Divvier substantiallyoutperforms all other ltering software for treating MSAs by retaining more true positive homology callsand removing more false positive homology calls. We also nd that Divvier, in contrast to other lteringtools, can alleviate long branch attraction artifacts induced by MSA and reduces the variation in treeestimates caused by MSA uncertainty.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- 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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| 7. |
- Ali, Raja Hashim, et al.
(författare)
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Identifying Clusters of High Confidence Homologies in Multiple Sequence Alignments
- 2019
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Ingår i: Molecular biology and evolution. - : OXFORD UNIV PRESS. - 0737-4038 .- 1537-1719. ; 36:10, s. 2340-2351
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Tidskriftsartikel (refereegranskat)abstract
- Multiple sequence alignment (MSA) is ubiquitous in evolution and bioinformatics. MSAs are usually taken to be a known and fixed quantity on which to perform downstream analysis despite extensive evidence that MSA accuracy and uncertainty affect results. These errors are known to cause a wide range of problems for downstream evolutionary inference, ranging from false inference of positive selection to long branch attraction artifacts. The most popular approach to dealing with this problem is to remove (filter) specific columns in the MSA that are thought to be prone to error. Although popular, this approach has had mixed success and several studies have even suggested that filtering might be detrimental to phylogenetic studies. We present a graph-based clustering method to address MSA uncertainty and error in the software Divvier (available at https://github.com/simonwhelan/Divvier), which uses a probabilistic model to identify clusters of characters that have strong statistical evidence of shared homology. These clusters can then be used to either filter characters from the MSA (partial filtering) or represent each of the clusters in a new column (divvying). We validate Divvier through its performance on real and simulated benchmarks, finding Divvier substantially outperforms existing filtering software by retaining more true pairwise homologies calls and removing more false positive pairwise homologies. We also find that Divvier, in contrast to other filtering tools, can alleviate long branch attraction artifacts induced by MSA and reduces the variation in tree estimates caused by MSA uncertainty.
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| 8. |
- 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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| 9. |
- Ali, Raja Hashim, et al.
(författare)
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Tracing the evolution of FERM domain of Kindlins
- 2014
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Ingår i: Molecular Phylogenetics and Evolution. - : Elsevier. - 1055-7903 .- 1095-9513. ; 80, s. 193-204
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Tidskriftsartikel (refereegranskat)abstract
- Kindlin proteins represent a novel family of evolutionarily conserved FERM domain containing proteins (FDCPs) and are members of B4.1 superfamily. Kindlins consist of three conserved protein homologs in vertebrates: Kindlin-1, Kindlin-2 and Kindlin-3. All three homologs are associated with focal adhesions and are involved in Integrin activation. FERM domain of each Kindlin is bipartite and plays a key role in Integrin activation. A single ancestral Kindlin protein can be traced back to earliest metazoans, e.g., to Parazoa. This protein underwent multiple rounds of duplication in vertebrates, leading to the present Kindlin family. In this study, we trace phylogenetic and evolutionary history of Kindlin FERM domain with respect to FERM domain of other FDCPs. We show that FERM domain in Kindlin homologs is conserved among Kindlins but amount of conservation is less in comparison with FERM domain of other members in B4.1 superfamily. Furthermore, insertion of Pleckstrin Homology like domain in Kindlin FERM domain has important evolutionary and functional consequences. Important residues in Kindlins are traced and ranked according to their evolutionary significance. The structural and functional significance of high ranked residues is highlighted and validated by their known involvement in Kindlin associated diseases. In light of these findings, we hypothesize that FERM domain originated from a proto-Talin protein in unicellular or proto-multicellular organism and advent of multi-cellularity was accompanied by burst of FDCPs, which supported multi-cellularity functions required for complex organisms. This study helps in developing a better understanding of evolutionary history of FERM domain of FDCPs and the role of FERM domain in metazoan evolution.
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| 10. |
- Bogusz, Marcin, et al.
(författare)
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Examining sequence alignments using a model-based approach
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Multiple sequence alignment (MSA) is a commonly performed procedure required for a number of evolutionary and comparative analyses. The common two-step process of sequence alignment followed by statistical phylogenetic inference depends on MSA quality. MSA is computationally difficult and as a result in many cases sequence alignments contain regions of spurious homologies. These errors in the alignment affect downstream results, so choosing an accurate MSA is critical. Researchers often face the problem of choosing an aligner out of many multiple sequence alignment methods (MSAMs). This choice is often based on the results of benchmarks with various popular methods claiming high accuracy scores. These methods compete to obtain the highest scores in the commonly used sum-of-pairs benchmark—which accounts for a fraction of the true homologies recovered—ignoring the fraction of introduced false positive homologies. Furthermore, these benchmarks do not account for the fact that some homologies are more difficult to recover than the others. We take a probabilistic model-based approach to examine the quality of pairwise homologies returned by four popular MSAMs. We use pair-hidden Markov models to break down alignment columns into pairs and obtain distributions of pairwise posterior scores for these aligners. Basing our results on a structural benchmark and a simulation study, we find that MSAMs appear to return a sample from a confidence set defined by high posterior probabilities. Furthermore, we find that the reference alignment contains low pairwise posterior portions of pairwise homologies which cannot be expected to be recovered by any MSAM. Finally, we look at several possible test statistics, with and without the need for reference alignments, and ultimately suggest using positive predictive value (PPV) and mean posterior probability for MSA evaluation.
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