Munich Information Center for Protein Sequences

About: Munich Information Center for Protein Sequences is a research topic. Over the lifetime, 79 publications have been published within this topic receiving 6967 citations.

Online genomics facilities in the new millennium.

Abstract: The review begins by providing a brief typology of biological databases on the Internet, illustrated by examples of the most influential resources of each kind. We then take an insider look at one typical on-line genomic resource – the yeast genome database hosted at the Munich Information Center for Protein Sequences (MIPS) – and explain how and why it has evolved from a basic sequence repository to a multidomain knowledge base. The role of community efforts in curating and annotating genome data is discussed. The crucial role of data integration and interoperability in developing next-generation genomic facilities is underscored.

In search of genome annotation consistency: solid gene clusters and how to use them.

Abstract: Maintaining consistency in genome annotations is important for supporting many computational tasks, particularly metabolic modeling. The SEED project has implemented a process that improves annotation consistencies across microbial genomes for proteins with conserved sequences and genomic context. In this research report, we describe this process and show how this effort has resulted in improvements to microbial genome annotations in the SEED. We also compare SEED annotation consistencies with other commonly used resources such as IMG (the Joint Genome Institute’s Integrated Microbial Genomes system), RefSeq (the National Center for Biotechnology Information’s Reference Sequence Database), Swiss-Prot (the annotated protein sequence database of the Swiss Institute of Bioinformatics, European Molecular Biology Laboratory and the European Bioinformatics Institute) and TrEMBL (Translated European Molecular Biology Laboratory nucleotide sequence data Library). Our analysis indicates that manual and computational efforts are paying off for the databases where consistency is a major goal.

Plant genome annotation methods.

Abstract: Annotation of plant genomic sequences can be separated into structural and functional annotation. Structural annotation is the foundation of all genomics as without accurate gene models understanding gene function or evolution of genes across taxa can be impeded. Structural annotation is dependent on sensitive, specific computational programs and deep experimental evidence to identify gene features within genomic DNA. Functional annotation is highly dependent on sequence similarity to other known genes or proteins as the majority of initial "first-pass" functional annotation on a genomic scale is transitive. Coupling structural and functional annotation across genomes in a comparative manner promotes more accurate annotation as well as an understanding of gene and genome evolution. With the increasing availability of plant genome sequence data, the value of comparative annotation will increase. As with any new field, methodologies are evolving for genome annotation and will improve in the future.

GrailEXP and Genome Analysis Pipeline for genome annotation.

Abstract: The Gene Recognition and Analysis Internet Link (GRAIL) is one of the most widely used systems for evaluating the protein-coding potential of anonymous DNA sequences. This unit describes the use of the XGRAIL and genQuest client-server applications to locate exons in DNA sequences, to develop gene models, and to search databases for homologs. A support protocol describes how to obtain the GRAIL and genQuest client software by anonymous FTP.

Comparative genome annotation for mapping, prediction and discovery of genes

Abstract: We have used comparative genome analyses to produce annotated maps for large genomic loci. The first example is a locus on mouse chromosome 9 that is syntenic to human chromosome 15. This effort relied on draft sequences from the human genome project, our own draft sequences from mouse genomic UNA, and, more recently, from the mouse genome project. Our strategy used reiterative searches of UNA, protein, STS and EST databases, as well as genome maps. In this fashion, we were able to assemble sequence contigs over a large region that comprises 14 genes. We present our framework for data interpretation and demonstrate that unfinished sequences can be used to assemble maps of complex genomic loci with good accuracy. By focusing on one model locus, we discuss limitations and advantages of this approach and provide criteria for the implementation of automated genome annotation strategies.