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  • 1
    Keywords: GROWTH ; PROTEIN ; DIFFERENTIATION ; DNA ; DATABASE ; INTERFACE ; W2H
    Abstract: In high throughput sequence analysis, it is often necessary to combine the results of contemporary bioinformatics tools, because no individual tool alone computes all the requested information. ESTAnnotator is a tool for the high throughput annotation of expressed sequence tags (ESTs) by automatically running a collection of bioinformatics applications. In the first step, a quality check is performed and repeats, vector parts and low quality sequences are masked. Then successive steps of database searching and EST clustering are performed. Already known transcripts present within mRNA and genomic DNA reference databases are identified. Subsequently, tools for the clustering of anonymous ESTs, and for further database searches at the protein level, are applied. Finally, the outputs of each individual tool are gathered and the relevant results presented in a descriptive summary. ESTAnnotator was already successfully applied for the systematic identification and characterisation of novel human genes involved in cartilage/bone formation, growth, differentiation and homeostasis. ESTAnnotator is available at http://genome.dkfz-heidelberg.de, contact: genome@dkfz.de
    Type of Publication: Journal article published
    PubMed ID: 12824401
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  • 2
    Keywords: Germany ; TOOL ; PROTEIN ; COMPLEX ; COMPLEXES ; DNA ; INTERVENTION ; PREDICTION ; WEB ; QUESTIONNAIRE ; ANNOTATION ; GENOME DATABASE ; PROTEIN DATA ; RESOURCE ; SEQUENCE DATABASE
    Abstract: The Helmholtz Network for Bioinformatics (HNB) is a joint venture of eleven German bioinformatics research groups that offers convenient access to numerous bioinformatics resources through a single web portal. The 'Guided Solution Finder' which is available through the HNB portal helps users to locate the appropriate resources to answer their queries by employing a detailed, tree-like questionnaire. Furthermore, automated complex tool cascades ('tasks'), involving resources located on different servers, have been implemented, allowing users to perform comprehensive data analyses without the requirement of further manual intervention for data transfer and re-formatting. Currently, automated cascades for the analysis of regulatory DNA segments as well as for the prediction of protein functional properties are provided
    Type of Publication: Journal article published
    PubMed ID: 14734319
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  • 3
    Keywords: Germany ; CLASSIFICATION ; SUPPORT ; SYSTEM ; SYSTEMS ; TOOL ; GENE ; GENOME ; GENOME SEQUENCE ; validation ; QUALITY ; SEQUENCE ; SEQUENCES ; PERFORMANCE ; VECTOR ; NUMBER ; DATABASE ; PRODUCT ; bioinformatics ; XENOPUS ; INTERFACE ; PREDICTION ; PROJECT ; SEQUENCE-ANALYSIS ; support vector machines ; IMPLEMENTATION ; SUBSET ; FRAMEWORK ; PRODUCTS ; XENOPUS-LAEVIS ; SCALE ; ANNOTATION ; GO
    Abstract: Background: The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions. Results: We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was benchmarked with a large dataset. An organism-wise cross-validation was performed to define confidence estimates, resulting in an average precision of 80% for 74% of all test sequences. The validation results show that the prediction performance was organism-independent and could reproduce the annotation of other automated systems as well as high-quality manual annotations. We applied our trained classification system to Xenopus laevis sequences, yielding functional annotation for more than half of the known expressed genome. Compared to the currently available annotation, we provided more than twice the number of contigs with good quality annotation, and additionally we assigned a confidence value to each predicted GO term. Conclusions: We present a complete automated annotation system that overcomes many of the usual problems by applying a controlled vocabulary of Gene Ontology and an established classification method on large and well-described sequence data sets. In a case study, the function for Xenopus laevis contig sequences was predicted and the results are publicly available at ftp://genome.dkfz-heidelberg.de/pub/agd/gene_association.agd_Xenopus
    Type of Publication: Journal article published
    PubMed ID: 15333146
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  • 4
    Keywords: EXPRESSION ; proliferation ; CELL-PROLIFERATION ; Germany ; INFORMATION ; screening ; GENE ; GENE-EXPRESSION ; GENES ; GENOME ; PROTEIN ; PROTEINS ; gene expression ; ASSAY ; DATABASE ; bioinformatics ; INTERFACE ; PROJECT ; INTEGRATION ; FEATURES ; RE ; cell proliferation ; FULL-LENGTH HUMAN ; HUMAN CDNAS ; ASSAYS ; genomic ; NORTHERN
    Abstract: LIFEdb (http://www.LIFEdb.de) integrates data from large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. New features of LIFEdb include (i) an updated user interface with enhanced query capabilities, (ii) a configurable output table and the option to download search results in XML, (iii) the integration of data from cell-based screening assays addressing the influence of protein-overexpression on cell proliferation and (iv) the display of the relative expression ('Electronic Northern') of the genes under investigation using curated gene expression ontology information. LIFEdb enables researchers to systematically select and characterize genes and proteins of interest, and presents data and information via its user-friendly web-based interface
    Type of Publication: Journal article published
    PubMed ID: 16381901
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  • 5
    Keywords: CELLS ; EXPRESSION ; Germany ; MODEL ; INFORMATION ; SYSTEM ; DISEASE ; GENE ; GENES ; GENOME ; HYBRIDIZATION ; microarray ; TISSUE ; DISCOVERY ; TARGET ; IDENTIFICATION ; PATTERNS ; DNA microarray ; DESIGN ; DATABASE ; TARGETS ; DIFFERENTIAL EXPRESSION ; EXPRESSED SEQUENCE TAGS ; biomarker ; THERAPEUTIC TARGETS ; EST ; KNOWLEDGEBASE ; TIGR GENE INDEXES ; Tissue ontology ; Tissue slims ; Tissue synonym library ; Tissue type ; Tissue-distribution pattern ; Tissue-distribution profiles
    Abstract: Tissue-distribution profiles are crucial for understanding the characteristics of cells and tissues in terms of their differential expression of genes. Most of the currently available resources for tissue-distribution profiles are either specialized for a few particular organisms, tissue types and disease stages or do not consider the "tissue ontology" levels for the calculation of the tissue-distribution profiles. Therefore, we have developed "TissueDistributionDBs", a repository of tissue-distribution profiles based on the expressed sequence tags (ESTs) data extracted from the UniGene database by employing "Tissue Ontology" available at BRENDA. To overcome the occurrence of the natural language variations in the EST's source tissue-type terms, we have generated a "tissue synonym library" and standardized these tissue-type terms by cross-referencing to the controlled vocabulary for tissue-type terms available at BRENDA "Tissue Ontology". Furthermore, we have provided a quantitative expression for genes among the tissue types at various anatomical levels by constructing "tissue slims". Concurrently, the expression among tissue types is used for tissue-distribution calculations. The resulting output profiles can be queried by the Sequence Retrieval System (SRS) and are currently available for 20 different model organisms. We benchmarked our database system against the Swissprot database using a set of 40 different tissue types. This database system is useful for the understanding of the tissue-specific expression patterns of genes, which have implications for the identification of possible new therapeutic drug targets, in gene discovery, and in the design and analysis of micro-arrays. TissueDistributionDBs can be accessed via the World Wide Web (www) at "http://genius.embnet.dkfz-heidelberg.de/menu/tissue_db/"
    Type of Publication: Journal article published
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  • 6
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    BMC Bioinformatics 4 (), Art. Nr.: 39- 
    Keywords: COMBINATION ; Germany ; human ; CDNA ; CLONING ; GENE ; DNA ; FLOW ; BIOLOGY ; SEQUENCE ; SEQUENCES ; FORM ; IDENTIFICATION ; DATABASE ; HUMAN GENOME ; bioinformatics ; US ; exon-intron structure ; INTERFACE ; PREDICTION ; W2H ; WEB
    Abstract: Background: In the last years several high-throughput cDNA sequencing projects have been funded worldwide with the aim of identifying and characterizing the structure of complete novel human transcripts. However some of these cDNAs are error prone due to frameshifts and stop codon errors caused by low sequence quality, or to cloning of truncated inserts, among other reasons. Therefore, accurate CDS prediction from these sequences first require the identification of potentially problematic cDNAs in order to speed up the posterior annotation process.Results: cDNA2Genome is an application for the automatic high-throughput mapping and characterization of cDNAs. It utilizes current annotation data and the most up to date databases, especially in the case of ESTs and mRNAs in conjunction with a vast number of approaches to gene prediction in order to perform a comprehensive assessment of the cDNA exon-intron structure. The final result of cDNA2Genome is an XML file containing all relevant information obtained in the process. This XML output can easily be used for further analysis such us program pipelines, or the integration of results into databases. The web interface to cDNA2Genome also presents this data in HTML, where the annotation is additionally shown in a graphical form. cDNA2Genome has been implemented under the W3H task framework which allows the combination of bioinformatics tools in tailor-made analysis task flows as well as the sequential or parallel computation of many sequences for large-scale analysis.Conclusions: cDNA2Genome represents a new versatile and easily extensible approach to the automated mapping and annotation of human cDNAs. The underlying approach allows sequential or parallel computation of sequences for high-throughput analysis of cDNAs
    Type of Publication: Journal article published
    PubMed ID: 12964951
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  • 7
    Keywords: Germany ; CLASSIFICATION ; INFORMATION ; TOOL ; SITE ; CLONING ; GENOME ; PROTEIN ; PROTEINS ; TIME ; SEQUENCE ; SIGNAL ; VARIANTS ; ASSAY ; DATABASE ; LOCALIZATION ; PREDICTION ; SELECTION ; REJECTION ; SEQUENCE-ANALYSIS ; HUMAN GENES ; FUNCTIONAL GENOMICS ; CDNAS ; FEATURES ; PROGRAM ; RE ; VARIANT ; assembly ; databases ; ANNOTATION ; CPG ISLANDS ; FULL-LENGTH HUMAN ; ASSAYS ; HIGH-THROUGHPUT ; TESTS ; GENOMIC DNA ; genomic ; SIGNALS ; E ; SET ; transcriptome ; POLYADENYLATION
    Abstract: Background: The German cDNA Consortium has been cloning full length cDNAs and continued with their exploitation in protein localization experiments and cellular assays. However, the efficient use of large cDNA resources requires the development of strategies that are capable of a speedy selection of truly useful cDNAs from biological and experimental noise. To this end we have developed a new high-throughput analysis tool, CAFTAN, which simplifies these efforts and thus fills the gap between large-scale cDNA collections and their systematic annotation and application in functional genomics. Results: CAFTAN is built around the mapping of cDNAs to the genome assembly, and the subsequent analysis of their genomic context. It uses sequence features like the presence and type of PolyA signals, inner and flanking repeats, the GC-content, splice site types, etc. All these features are evaluated in individual tests and classify cDNAs according to their sequence quality and likelihood to have been generated from fully processed mRNAs. Additionally, CAFTAN compares the coordinates of mapped cDNAs with the genomic coordinates of reference sets from public available resources ( e. g., VEGA, ENSEMBL). This provides detailed information about overlapping exons and the structural classification of cDNAs with respect to the reference set of splice variants. The evaluation of CAFTAN showed that is able to correctly classify more than 85% of 5950 selected "known protein-coding" VEGA cDNAs as high quality multi-or single-exon. It identified as good 80.6% of the single exon cDNAs and 85% of the multiple exon cDNAs. The program is written in Perl and in a modular way, allowing the adoption of this strategy to other tasks like EST-annotation, or to extend it by adding new classification rules and new organism databases as they become available. We think that it is a very useful program for the annotation and research of unfinished genomes. Conclusion: CAFTAN is a high-throughput sequence analysis tool, which performs a fast and reliable quality prediction of cDNAs. Several thousands of cDNAs can be analyzed in a short time, giving the curator/scientist a first quick overview about the quality and the already existing annotation of a set of cDNAs. It supports the rejection of low quality cDNAs and helps in the selection of likely novel splice variants, and/or completely novel transcripts for new experiments
    Type of Publication: Journal article published
    PubMed ID: 17064411
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  • 8
    Keywords: CANCER ; CELLS ; EXPRESSION ; tumor ; CELL ; Germany ; TOOL ; CLONING ; GENE ; GENE-EXPRESSION ; GENES ; GENOME ; DIFFERENTIATION ; BIOLOGY ; SEQUENCE ; SEQUENCES ; IDENTIFICATION ; PROGRESSION ; PATTERNS ; gene expression ; genetics ; DATABASE ; REGION ; REGIONS ; heredity ; CHRONIC LYMPHOCYTIC-LEUKEMIA ; RE ; microbiology ; ENGLAND ; MicroRNAs ; PROFILE ; biotechnology ; GENOMES ; FETAL
    Abstract: Background: MicroRNAs (miRNAs) are a novel class of gene expression regulators implicated in cancer biology. Neuroblastoma (NB) is an embryonal tumour consisting of neural crest-derived undifferentiated cells and is characterised by variable clinical courses ranging from spontaneous regression to therapy-resistant progression. Recent advances identified a subset of miRNAs with putative function in NB biology. However, the full repertoire of miRNAs expressed in NBs is not available. Results: We describe miRNA profiles of 13 NB specimens and 2 NB cell lines as determined by miRNA cloning. A total of 3153 sequences were sequenced and analysed by a miRNA prediction tool (miRpredict). Our library covered 27% miRNAs known to date. 39 reads corresponding to 25 individual sequences were classified as novel miRNAs, including miRNA* species of 10 known miRNAs. Expression of 5 new miRNA* forms and 8 individual sequences was supported by Northern blotting. Most of the novel miRNA genes are not related to each other and do not share homology with the annotated sequences in the public miRNA database, but they are conserved within mammals or have close homologues in primates genomes. Conclusion: We provide evidence for 29 new miRNA and miRNA-like sequences (24 novel sequences and 5 miRNAs discovered initially in other species). Some of these newly identified sequences reside within frequently altered chromosomal regions in NB tumours and may play a role in NB biology
    Type of Publication: Journal article published
    PubMed ID: 18230126
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  • 9
    Keywords: COMBINATION ; Germany ; ALGORITHM ; TOOL ; GENE ; MOTIFS ; TRANSCRIPTION FACTOR ; BINDING ; SEQUENCES ; DISCOVERY ; IDENTIFICATION ; PROMOTER ; DATABASE ; REGION ; INTERFACE ; PREDICTION ; W2H ; FRAMEWORK ; ANNOTATION ; Motif discovery
    Abstract: There are many tools available for the prediction of potential promoter regions and the transcription factor binding sites (TFBS) harboured by them. Unfortunately, these tools cannot really avoid the prediction of vast amounts of false positives, the greatest problem in promoter analysis. The combination of different methods and algorithms has shown an improvement in prediction accuracy for similar biological problems such as gene prediction. The web-tool presented here uses this approach to perform an exhaustive integrative analysis, identification and annotation of potential promoter regions. The combination of methods employed includes searches in different experimental promoter databases to identify promoter regions and their orthologs, use of TFBS databases and search tools, and a phylogenetic footprinting strategy, combining multiple alignment of genomic sequences together with motif discovery tools that were tested previously in order to get the best method combination. The pipeline is available for academic users at the HUSAR open server "http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar/" . It integrates all of this information and identifies among the huge number of TFBS predictions those, which are more likely to be potentially functional
    Type of Publication: Journal article published
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  • 10
    Keywords: CANCER ; SYSTEM ; DISEASE ; RISK ; GENOME ; PROTEIN ; ASSOCIATION ; polymorphism ; VARIANTS ; MUTATION ; PATTERN ; ANNOTATION ; TOOLS ; ENHANCERS
    Abstract: BACKGROUND: Some single nucleotide polymorphisms (SNPs) are known to modify the risk of developing certain diseases or the reaction to drugs. Due to next generation sequencing methods the number of known human SNPs has grown. Not all SNPs lead to a modified protein, which may be the origin of a disease. Therefore, the recognition of functional SNPs is needed. Because most SNP annotation tools look for SNPs which lead to an amino acid exchange or a premature stop, we designed a new tool called AASsites which searches for SNPs which modify splicing. RESULTS: AASsites uses several gene prediction programs and open reading frame prediction to compare the wild type (wt) and the variant gene sequence. The results of the comparison are combined by a handmade rule system to classify a change in splicing as "likely, probable, unlikely". Having received good results from tests with SNPs known for changing the splicing pattern we checked 80,000 SNPs from the human genome which are located near splice sites for their ability to change the splicing pattern of the gene and hereby result in a different protein. We identified 301 "likely" and 985 "probable" classified SNPs with such characteristics. Within this set 33 SNPs are described in the ssSNP Target database to cause modified splicing. CONCLUSIONS: With AASsites single SNPs can be checked for those causing splice modifications. Screening 80,000 known human SNPs we detected about 1,200 SNPs which probably modify splicing. AASsites is available at http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar using any web browser.
    Type of Publication: Journal article published
    PubMed ID: 21992029
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