General Information
| Description | Annotation of biological reaction networks |
| Homepage | http://3d-alignment.eu/metannogen/ |
Publications
M: annotation of biological reaction networks
Motivation:
Semantic annotations of the biochemical entities constituting a biological reaction network are indispensable to create biologically meaningful networks. They further heighten efficient exchange, reuse and merging of existing models which concern present-day systems biology research more often. Two types of tools for the reconstruction of biological networks currently exist: (i) several sophisticated programs support graphical network editing and visualization. (ii) Data management systems permit reconstruction and curation of huge networks in a team of scientists including data integration, annotation and cross-referencing. We seeked ways to combine the advantages of both approaches.
Results:
Metannogen, which was previously developed for network reconstruction, has been considerably improved. From now on, Metannogen provides sbml import and annotation of networks created elsewhere. This permits users of other network reconstruction platforms or modeling software to annotate their networks using Metannogen's advanced information management. We implemented word-autocompletion, multipattern highlighting, spell check, brace-expansion and publication management, and improved annotation, cross-referencing and team work requirements. Unspecific enzymes and transporters acting on a spectrum of different substrates are efficiently handled. The network can be exported in sbml format where the annotations are embedded in line with the miriam standard. For more comfort, Metannogen may be tightly coupled with the network editor such that Metannogen becomes an additional view for the focused reaction in the network editor. Finally, Metannogen provides local single user, shared password protected multiuser or public access to the annotation data.
Availability:
Metannogen is available free of charge at: http://www.bioinformatics.org/strap/metannogen/ or http://3d-alignment.eu/metannogen/.
Contact:
christoph.gille@charite.de
Supplementary information:
Supplementary data are available at Bioinformatics online.
M: an improved SNP calling algorithm for Illumina BeadArray data
Summary:
Genotype calling from high-throughput platforms such as Illumina and Affymetrix is a critical step in data processing, so that accurate information on genetic variants can be obtained for phenotype–genotype association studies. A number of algorithms have been developed to infer genotypes from data generated through the Illumina BeadStation platform, including GenCall, GenoSNP, Illuminus and CRLMM. Most of these algorithms are built on population-based statistical models to genotype every SNP in turn, such as GenCall with the GenTrain clustering algorithm, and require a large reference population to perform well. These approaches may not work well for rare variants where only a small proportion of the individuals carry the variant. A fundamentally different approach, implemented in GenoSNP, adopts a single nucleotide polymorphism (SNP)-based model to infer genotypes of all the SNPs in one individual, making it an appealing alternative to call rare variants. However, compared to the population-based strategies, more SNPs in GenoSNP may fail the Hardy–Weinberg Equilibrium test. To take advantage of both strategies, we propose a two-stage SNP calling procedure, named the modified mixture model (M3), to improve call accuracy for both common and rare variants. The effectiveness of our approach is demonstrated through applications to genotype calling on a set of HapMap samples used for quality control purpose in a large case–control study of cocaine dependence. The increase in power with M3 is greater for rare variants than for common variants depending on the model.
Availability:
M3 algorithm: http://bioinformatics.med.yale.edu/group.
Contact:
name@bio.com; hongyu.zhao@yale.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
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