Information theory-based methods have been shown to be sensitive and specific for pre- dicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for ge...Information theory-based methods have been shown to be sensitive and specific for pre- dicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for genome-scale mutation analysis and provide evidence that the soft- ware predicts variants affecting mRNA splicing. Individual information contents （in bits） of refer- ence and variant splice sites are compared and significant differences are annotated and prioritized. The software has been implemented for CLC-Bio Genomics platform. Annotation indicates the context of novel mutations as well as common and rare SNPs with splicing effects. Potential natural and cryptic mRNA splicing variants are identified, and null mutations are distinguished from leaky mutations. Mutations and rare SNPs were predicted in genomes of three cancer cell lines （U2OS, U251 and A431）, which were supported by expression analyses. After filtering, tractable numbers of potentially deleterious variants are predicted by the software, suitable for further laboratory investigation. In these cell lines, novel functional variants comprised 6-17 inactivating mutations, 1 5 leaky mutations and 6-13 cryptic splicing mutations. Predicted effects were validated by RNA-seq analysis of the three aforementioned cancer cell lines, and expression microarray analysis of SNPs in HapMap cell lines.展开更多
The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functi...The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functional units often corresponding to subpathways in the KEGG pathway maps, are designed for better automation of genome interpretation. Each KEGG module is represented by a simple Boolean expression of KEGG Orthology （KO） identifiers （K numbers）, enabling automatic evaluation of the completeness of genes in the genome. Here we focus on metabolic functions and introduce reaction modules for improving annotation and signature modules for inferring metabolic capacity. We also describe how genome annotation is performed in KEGG using the manually created KO database and the computationaUy generated SSDB database. The resulting KEGG GENES database with KO （K number） annotation is a reference sequence database to be compared for automated annotation and interpretation of newly determined genomes.展开更多
基金support from Natural Sciences and Engineering Research Council (Discovery Grant 371758-2009) (PKR)Canadian Breast Cancer Foundation(PKR)+1 种基金Compute Canada (PKR),Canadian Foundation for Innovation,Canada Research Chairs,MITACS Accelerate(BCS)the Ontario Graduate Scholarship Programs (BCS) and Cytognomix Inc
文摘Information theory-based methods have been shown to be sensitive and specific for pre- dicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for genome-scale mutation analysis and provide evidence that the soft- ware predicts variants affecting mRNA splicing. Individual information contents （in bits） of refer- ence and variant splice sites are compared and significant differences are annotated and prioritized. The software has been implemented for CLC-Bio Genomics platform. Annotation indicates the context of novel mutations as well as common and rare SNPs with splicing effects. Potential natural and cryptic mRNA splicing variants are identified, and null mutations are distinguished from leaky mutations. Mutations and rare SNPs were predicted in genomes of three cancer cell lines （U2OS, U251 and A431）, which were supported by expression analyses. After filtering, tractable numbers of potentially deleterious variants are predicted by the software, suitable for further laboratory investigation. In these cell lines, novel functional variants comprised 6-17 inactivating mutations, 1 5 leaky mutations and 6-13 cryptic splicing mutations. Predicted effects were validated by RNA-seq analysis of the three aforementioned cancer cell lines, and expression microarray analysis of SNPs in HapMap cell lines.
文摘The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functional units often corresponding to subpathways in the KEGG pathway maps, are designed for better automation of genome interpretation. Each KEGG module is represented by a simple Boolean expression of KEGG Orthology （KO） identifiers （K numbers）, enabling automatic evaluation of the completeness of genes in the genome. Here we focus on metabolic functions and introduce reaction modules for improving annotation and signature modules for inferring metabolic capacity. We also describe how genome annotation is performed in KEGG using the manually created KO database and the computationaUy generated SSDB database. The resulting KEGG GENES database with KO （K number） annotation is a reference sequence database to be compared for automated annotation and interpretation of newly determined genomes.
