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Michael Remmert

Ludwig-Maximilians-Universität München

Publishes on Genomics and Phylogenetic Studies, RNA and protein synthesis mechanisms, Machine Learning in Bioinformatics. 22 papers and 20.1k citations.

22Publications
20.1kTotal Citations

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Top publicationsby citations

Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega
Fabian Sievers, Andreas Wilm, David Dineen et al.|Molecular Systems Biology|2011
Cited by 16.2kOpen Access

Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.

Fast and accurate automatic structure prediction with HHpred
Andrea Hildebrand, Michael Remmert, A. Biegert et al.|Proteins Structure Function and Bioinformatics|2009
Cited by 482Open Access

Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

The MPI Bioinformatics Toolkit for protein sequence analysis
A. Biegert, Claudine Mayer, Michael Remmert et al.|Nucleic Acids Research|2006
Cited by 275Open Access

The MPI Bioinformatics Toolkit is an interactive web service which offers access to a great variety of public and in-house bioinformatics tools. They are grouped into different sections that support sequence searches, multiple alignment, secondary and tertiary structure prediction and classification. Several public tools are offered in customized versions that extend their functionality. For example, PSI-BLAST can be run against regularly updated standard databases, customized user databases or selectable sets of genomes. Another tool, Quick2D, integrates the results of various secondary structure, transmembrane and disorder prediction programs into one view. The Toolkit provides a friendly and intuitive user interface with an online help facility. As a key feature, various tools are interconnected so that the results of one tool can be forwarded to other tools. One could run PSI-BLAST, parse out a multiple alignment of selected hits and send the results to a cluster analysis tool. The Toolkit framework and the tools developed in-house will be packaged and freely available under the GNU Lesser General Public Licence (LGPL). The Toolkit can be accessed at http://toolkit.tuebingen.mpg.de.

Identification of plant microRNA homologs
Cited by 147

Abstract Summary: MicroRNAs (miRNAs) are a recently discovered class of non-coding RNAs that regulate gene and protein expression in plants and animals. MiRNAs have so far been identified mostly by specific cloning of small RNA molecules, complemented by computational methods. We present a computational identification approach that is able to identify candidate miRNA homologs in any set of sequences, given a query miRNA. The approach is based on a sequence similarity search step followed by a set of structural filters. Availability: microHARVESTER is offered as a web-service and additionally as source code upon request at Contact: dezulian@informatik.uni-tuebingen.de