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Udo Stenzel
Researcher at Max Planck Society
Publications - 24
Citations - 12077
Udo Stenzel is an academic researcher from Max Planck Society. The author has contributed to research in topics: Ancient DNA & Genome. The author has an hindex of 21, co-authored 24 publications receiving 10823 citations.
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Improved base calling for the Illumina Genome Analyzer using machine learning strategies
TL;DR: Ibis (Improved base identification system), an accurate, fast and easy-to-use base caller that significantly reduces the error rate and increases the output of usable reads is presented.
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Targeted high-throughput sequencing of tagged nucleic acid samples
TL;DR: PTS is introduced, a simple, inexpensive and flexible barcoding technique that can be used for parallel sequencing any number and type of double-stranded nucleic acid samples that fully mobilizes the resources of the 454 technology for targeted sequencing.
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Patterns of coding variation in the complete exomes of three Neandertals
Sergi Castellano,Genís Parra,Federico Sánchez-Quinto,Fernando Racimo,Martin Kuhlwilm,Martin Kircher,Susanna Sawyer,Qiaomei Fu,Anja Heinze,Birgit Nickel,Jesse Dabney,Michael Siebauer,Louise White,Hernán A. Burbano,Gabriel Renaud,Udo Stenzel,Carles Lalueza-Fox,Marco de la Rasilla,Antonio Rosas,Pavao Rudan,Dejana Brajković,Željko Kućan,Ivan Gušić,Michael V. Shunkov,A.P. Derevianko,Bence Viola,Matthias Meyer,Janet Kelso,Aida M. Andrés,Svante Pääbo +29 more
TL;DR: It is shown that genetic diversity among Neandertals was remarkably low, and that they carried a higher proportion of amino acid-changing (nonsynonymous) alleles inferred to alter protein structure or function than present-day humans.
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PatMaN: rapid alignment of short sequences to large databases.
TL;DR: A tool suited for searching for many short nucleotide sequences in large databases, allowing for a predefined number of gaps and mismatches, using a non-deterministic automata matching algorithm on a keyword tree of the search strings.
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leeHom: adaptor trimming and merging for Illumina sequencing reads
TL;DR: An algorithm is introduced that removes the adaptors and reconstructs the original DNA sequences using a Bayesian maximum a posteriori probability approach, which is faster, and provides a more accurate reconstruction of the original sequence for both simulated and ancient DNA data sets, than other approaches.