M
Michael Remmert
Researcher at Ludwig Maximilian University of Munich
Publications - 14
Citations - 15765
Michael Remmert is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Multiple sequence alignment & Bacterial outer membrane. The author has an hindex of 11, co-authored 14 publications receiving 12954 citations. Previous affiliations of Michael Remmert include Max Planck Society & Center for Integrated Protein Science Munich.
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Journal ArticleDOI
Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega
Fabian Sievers,Andreas Wilm,David Dineen,Toby J. Gibson,Kevin Karplus,Weizhong Li,Rodrigo Lopez,Hamish McWilliam,Michael Remmert,Johannes Söding,Julie D. Thompson,Desmond G. Higgins +11 more
TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
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HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
TL;DR: An open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM–based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/).
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Fast and accurate automatic structure prediction with HHpred
TL;DR: Three fully automated versions of the HHpred server that participated in the community‐wide blind protein structure prediction competition CASP8 are described, each with the combination of usability, short response times and a model accuracy that is competitive with those of the best servers in CASP 8.
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The MPI Bioinformatics Toolkit for protein sequence analysis
TL;DR: The MPI Bioinformatics Toolkit is an interactive web service which offers access to a great variety of public and in-house bioinformatic tools grouped into different sections that support sequence searches, multiple alignment, secondary and tertiary structure prediction and classification.
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Identification of plant microRNA homologs
TL;DR: A computational identification approach is presented that is able to identify candidate miRNA homologs in any set of sequences, given a query miRNA, based on a sequence similarity search step followed by a set of structural filters.