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  • 51.
    Tofigh, Ali
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sjöstrand, J.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sennblad, Bengt
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Arbestad, L.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Detecting LGTs using a novel probabilistic modelintegrating duplications, LGTs, losses, rate variation,and sequence evolution2009Manuscript (preprint) (Other academic)
    Abstract [en]

    The debate over the prevalence of lateral gene transfers (LGTs) has been intense.There is now to a large extent consensus around the view that LGT is an important evolutionary force as well as regarding its relative importance across species. This consensus relies, however, mainly on studies of individual gene families.

    Up until now, the gold standard for identifying LGTs has been phylogenetic methods where LGTs are inferred from incongruities between a species tree andan associated gene tree. Even in cases where there is evidence of LGT, several concerns have often been raised regarding the significance of the evidence. One common concern has been the possibility that other evolutionary events have caused the incongruities. Another has been the significance of the gene trees involved in the inference; there may for instance be alternative, almost equally likely, gene trees that do not provide evidence for LGT. Independently of these concerns, there has been a need for methods that can be used to quantitatively characterize the level of LGT among sets of species, but also for methods able to pinpoint where in the species tree LGTs have occurred.

    Here, we provide the first probabilistic model capturing gene duplication, LGT,gene loss, and point mutations with a relaxed molecular clock. We also provide allfundamental algorithms required to analyze a gene family relative to a given speciestree under this model. Our algorithms are based on Markov chain Monte Carlo(MCMC) methodology but build also on techniques from numerical analysis and involve dynamic programming (DP).

  • 52. Ullah, I.
    et al.
    Karthik, G. -M
    Alkodsi, A.
    Kjällquist, U.
    Stålhammar, G.
    Lövrot, J.
    Martinez, N. -F
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hautaniemi, S.
    Hartman, J.
    Bergh, J.
    Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes2018In: Journal of Clinical Investigation, ISSN 0021-9738, E-ISSN 1558-8238, Vol. 128, no 4, p. 1355-1370Article in journal (Refereed)
    Abstract [en]

    Metastatic breast cancers are still incurable. Characterizing the evolutionary landscape of these cancers, including the role of metastatic axillary lymph nodes (ALNs) in seeding distant organ metastasis, can provide a rational basis for effective treatments. Here, we have described the genomic analyses of the primary tumors and metastatic lesions from 99 samples obtained from 20 patients with breast cancer. Our evolutionary analyses revealed diverse spreading and seeding patterns that govern tumor progression. Although linear evolution to successive metastatic sites was common, parallel evolution from the primary tumor to multiple distant sites was also evident. Metastatic spreading was frequently coupled with polyclonal seeding, in which multiple metastatic subclones originated from the primary tumor and/or other distant metastases. Synchronous ALN metastasis, a well-established prognosticator of breast cancer, was not involved in seeding the distant metastasis, suggesting a hematogenous route for cancer dissemination. Clonal evolution coincided frequently with emerging driver alterations and evolving mutational processes, notably an increase in apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like-associated (APOBEC-associated) mutagenesis. Our data provide genomic evidence for a role of ALN metastasis in seeding distant organ metastasis and elucidate the evolving mutational landscape during cancer progression.

  • 53.
    Ullah, Ikram
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Parviainen, Pekka
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Species tree inference using a mixture model2015In: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719Article in journal (Refereed)
    Abstract [en]

    Species tree reconstruction has been a subject of substantial research due to its central role across biology and medicine. A species tree is often reconstructed using a set of gene trees or by directly using sequence data. In either of these cases, one of the main confounding phenomena is the discordance between a species tree and a gene tree due to evolutionary events such as duplications and losses. Probabilistic methods can resolve the discordance by co-estimating gene trees and the species tree but this approach poses a scalability problem for larger data sets.

    We present MixTreEM-DLRS: a two-phase approach for reconstructing a species tree in the presence of gene duplications and losses. In the first phase, MixTreEM, a novel structural EM algorithm based on a mixture model is used to reconstruct a set of candidate species trees, given sequence data for monocopy gene families from the genomes under study. In the second phase, PrIME-DLRS, a method based on the DLRS model ( ̊Akerborg et al., 2009), is used for selecting the best species tree. PrIME-DLRS can handle multicopy gene families since DLRS, apart from modeling sequence evolution, models gene duplication and loss using a gene evolution model (Arvestad et al., 2009).

    We evaluate MixTreEM-DLRS using synthetic and biological data, and compare its performance to a recent genome-scale species tree reconstruction method PHYLDOG (Boussau et al., 2013) as well as to a fast parsimony-based algorithm Duptree (Wehe et al., 2008). Our method is competitive with PHYLDOG in terms of accuracy and runs significantly faster and our method outperforms Duptree in accuracy. The analysis constituted by MixTreEM without DLRS may also be used for selecting the target species tree, yielding a fast and yet accurate algorithm for larger data sets. MixTreEM is freely available at http://prime.scilifelab.se/mixtreem.

  • 54.
    Åkerborg, Örjan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sennblad, Bengt
    Arvestad, Lars
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Simultaneous Bayesian gene tree reconstruction and reconciliation analysis2009In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 106, p. 5714-5719Article in journal (Refereed)
    Abstract [en]

    We present GSR, a probabilistic model integrating gene duplication, sequence evolution, and a relaxed molecular clock for substitution rates, that enables genomewide analysis of gene families. The gene duplication and loss process is a major cause for incongruence between gene and species tree, and deterministic methods have been developed to explain such differences through tree reconciliations. Although probabilistic methods for phylogenetic inference have been around for decades, probabilistic reconciliation methods are far less established. Based on our model, we have implemented a Bayesian analysis tool, PrIME-GSR, for gene tree inference that takes a known species tree into account. Our implementation is sound and we demonstrate its utility for genomewide gene-family analysis by applying it to recently presented yeast data. We validate PrIME-GSR by comparing with previous analyses of these data that take advantage of gene order information. In a case study we apply our method to the ADH gene family and are able to draw biologically relevant conclusions concerning gene duplications creating key yeast phenotypes. On a higher level this shows the biological relevance of our method. The obtained results demonstrate the value of a relaxed molecular clock. Our good performance will extend to species where gene order conservation is insufficient.

  • 55.
    Åkerborg, Örjan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sennblad, Bengt
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Birth-death prior on phylogeny and speed dating2008In: BMC Evolutionary Biology, ISSN 1471-2148, E-ISSN 1471-2148, Vol. 8, no 1, p. 77-Article in journal (Refereed)
    Abstract [en]

    Background: In recent years there has been a trend of leaving the strict molecular clock in order to infer dating of speciations and other evolutionary events. Explicit modeling of substitution rates and divergence times makes formulation of informative prior distributions for branch lengths possible. Models with birth-death priors on tree branching and auto-correlated or iid substitution rates among lineages have been proposed, enabling simultaneous inference of substitution rates and divergence times. This problem has, however, mainly been analysed in the Markov chain Monte Carlo (MCMC) framework, an approach requiring computation times of hours or days when applied to large phylogenies.

    Results: We demonstrate that a hill-climbing maximum a posteriori (MAP) adaptation of the MCMC scheme results in considerable gain in computational efficiency. We demonstrate also that a novel dynamic programming (DP) algorithm for branch length factorization, useful both in the hill-climbing and in the MCMC setting, further reduces computation time. For the problem of inferring rates and times parameters on a fixed tree, we perform simulations, comparisons between hill-climbing and MCMC on a plant rbcL gene dataset, and dating analysis on an animal mtDNA dataset, showing that our methodology enables efficient, highly accurate analysis of very large trees. Datasets requiring a computation time of several days with MCMC can with our MAP algorithm be accurately analysed in less than a minute. From the results of our example analyses, we conclude that our methodology generally avoids getting trapped early in local optima. For the cases where this nevertheless can be a problem, for instance when we in addition to the parameters also infer the tree topology, we show that the problem can be evaded by using a simulated-annealing like (SAL) method in which we favour tree swaps early in the inference while biasing our focus towards rate and time parameter changes later on.

    Conclusion: Our contribution leaves the field open for fast and accurate dating analysis of nucleotide sequence data. Modeling branch substitutions rates and divergence times separately allows us to include birth-death priors on the times without the assumption of a molecular clock. The methodology is easily adapted to take data from fossil records into account and it can be used together with a broad range of rate and substitution models.

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