scispace - formally typeset
T

Thomas Ludwig

Researcher at French Institute of Health and Medical Research

Publications -  200
Citations -  10834

Thomas Ludwig is an academic researcher from French Institute of Health and Medical Research. The author has contributed to research in topics: Computer science & File system. The author has an hindex of 26, co-authored 178 publications receiving 10306 citations. Previous affiliations of Thomas Ludwig include Information Technology University & University of Strasbourg.

Papers
More filters
Journal ArticleDOI

RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees

TL;DR: This paper presents the latest release of the program RAxML-III for rapid maximum likelihood-based inference of large evolutionary trees which allows for computation of 1.000-taxon trees in less than 24 hours on a single PC processor.
Journal ArticleDOI

Assemble: an interactive graphical tool to analyze and build RNA architectures at the 2D and 3D levels

TL;DR: Assemble is an intuitive graphical interface to analyze, manipulate and build complex 3D RNA architectures that provides several advanced and unique features within the framework of a semi-automated modeling process that can be performed by homology and ab initio with or without electron density maps.
Journal ArticleDOI

Syros metasomatic tourmaline: evidence for very high-δ11B fluids in subduction zones

TL;DR: In this paper, the B isotopic composition of dravitic tourmaline within these blackwalls was investigated in situ by secondary ion mass spectrometry, and the results showed that the tourmalines are unusually heavy, with d 11 B values exceeding þ18‰ in all investigated samples.
Book ChapterDOI

RAxML-OMP: an efficient program for phylogenetic inference on SMPs

TL;DR: RAxML-OMP, an efficient OpenMP-parallelization for Symmetric Multi-Processing machines (SMPs) based on the sequential program RAxML (Randomized Axelerated Maximum Likelihood), scales particularly well on the AMD Opteron architecture and achieves even super-linear speedups for large datasets due to improved cache-efficiency and data locality.