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Nikolaos Alachiotis
Researcher at University of Twente
Publications - 66
Citations - 1406
Nikolaos Alachiotis is an academic researcher from University of Twente. The author has contributed to research in topics: Population & Computer science. The author has an hindex of 15, co-authored 55 publications receiving 1086 citations. Previous affiliations of Nikolaos Alachiotis include Carnegie Mellon University & Foundation for Research & Technology – Hellas.
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SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes
TL;DR: It is shown that an increase of sample size results in more precise detection of positive selection and the ability to analyze substantially larger sample sizes by using SweeD leads to more accurate sweep detection.
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OmegaPlus: a scalable tool for rapid detection of selective sweeps in whole-genome datasets.
TL;DR: OmegaPlus, an open-source tool for rapid detection of selective sweeps in whole-genome data based on linkage disequilibrium, is introduced, which is up to two orders of magnitude faster than existing programs for this purpose and also exhibits up toTwo Orders of magnitude smaller memory requirements.
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Time and memory efficient likelihood-based tree searches on phylogenomic alignments with missing data
TL;DR: This work presents and implements a generally applicable mechanism that allows for reducing memory footprints of likelihood-based [maximum likelihood (ML) or Bayesian) phylogenomic analyses proportional to the amount of missing data in the alignment.
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A survey of methods and tools to detect recent and strong positive selection
TL;DR: This survey presents and discusses summary statistics and software tools, and classify them based on the selective sweep signature they detect, i.e., SFS-based vs. LD-based, as well as their capacity to analyze whole genomes or just subgenomic regions.
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RAiSD detects positive selection based on multiple signatures of a selective sweep and SNP vectors
TL;DR: RAiSD (Raised Accuracy in Sweep Detection), an open-source software that implements a novel, to the authors' knowledge, and parameter-free detection mechanism that relies on multiple signatures of a selective sweep via the enumeration of SNP vectors, is presented.