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Lars Arvestad

Researcher at Science for Life Laboratory

Publications -  53
Citations -  3437

Lars Arvestad is an academic researcher from Science for Life Laboratory. The author has contributed to research in topics: Tree (data structure) & Phylogenetic tree. The author has an hindex of 22, co-authored 53 publications receiving 3037 citations. Previous affiliations of Lars Arvestad include SERC Reliability Corporation & Karolinska Institutet.

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The Norway spruce genome sequence and conifer genome evolution.

TL;DR: The draft assembly of the 20-gigabase genome of Norway spruce (Picea abies), the first available for any gymnosperm, is presented, revealing numerous long (>10,000 base pairs) introns, gene-like fragments, uncharacterized long non-coding RNAs and short RNAs, which opens up new genomic avenues for conifer forestry and breeding.
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Bayesian gene/species tree reconciliation and orthology analysis using MCMC

TL;DR: This is the first successful introduction of this type of probabilistic methods, which flourish in phylogeny analysis, into reconciliation and orthology analysis, and develops a Bayesian analysis based on MCMC which facilitates approximation of an a posteriori distribution for reconciliations.
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Evolution After Gene Duplication: Models, Mechanisms, Sequences, Systems, and Organisms

TL;DR: Here, gene duplication is examined across levels of biological organization in an attempt to create a unified picture of the mechanistic process by which gene duplication can have played a role in generating biodiversity.
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Simultaneous Bayesian gene tree reconstruction and reconciliation analysis.

TL;DR: A probabilistic model integrating gene duplication, sequence evolution, and a relaxed molecular clock for substitution rates that enables genomewide analysis of gene families and is able to draw biologically relevant conclusions concerning gene duplications creating key yeast phenotypes is presented.
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BESST - Efficient scaffolding of large fragmented assemblies

TL;DR: A comprehensive comparison of BESST against the most popular stand-alone scaffolders on a large variety of datasets concludes that information sources other than the quantity of links, as is commonly used, can provide useful information about genome structure when scaffolding.