A
Alexander F. Auch
Researcher at University of Tübingen
Publications - 18
Citations - 10764
Alexander F. Auch is an academic researcher from University of Tübingen. The author has contributed to research in topics: Metagenomics & Genomics. The author has an hindex of 15, co-authored 18 publications receiving 8863 citations.
Papers
More filters
Journal ArticleDOI
Genome sequence-based species delimitation with confidence intervals and improved distance functions
TL;DR: Despite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty, so it is crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes.
Journal ArticleDOI
MEGAN analysis of metagenomic data
TL;DR: MEGAN, a new computer program that allows laptop analysis of large metagenomic data sets, is introduced and provides graphical and statistical output for comparing different data sets.
Journal ArticleDOI
Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison
TL;DR: This work investigates state-of-the-art methods for inferring whole-genome distances in their ability to mimic DDH and finds that some distance formulas are very robust against missing fractions of genomic information.
Journal ArticleDOI
Metagenomics to paleogenomics: large-scale sequencing of mammoth DNA.
Hendrik N. Poinar,Carsten Schwarz,Ji Qi,Beth Shapiro,Ross D. E. MacPhee,Bernard Buigues,Alexei Tikhonov,Daniel H. Huson,Lynn P. Tomsho,Alexander F. Auch,Markus Rampp,Webb Miller,Stephan C. Schuster +12 more
TL;DR: The high percentage of endogenous DNA recoverable from this single mammoth would allow for completion of its genome, unleashing the field of paleogenomics.
Journal ArticleDOI
Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs
TL;DR: An overview over the modifications that can be applied to distance methods based in high-scoring segment pairs (HSPs) or maximally unique matches (MUMs) and that need to be documented is provided.