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      Integrated Analysis of Germline and Somatic Variants in Ovarian Cancer

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          We report the first large-scale exome-wide analysis of the combined germline-somatic landscape in ovarian cancer. Here we analyze germline and somatic alterations in 429 ovarian carcinoma cases and 557 controls. We identify 3,635 high confidence, rare truncation and 22,953 missense variants with predicted functional impact. We find germline truncation variants and large deletions across Fanconi pathway genes in 20% of cases. Enrichment of rare truncations is shown in BRCA1, BRCA2, and PALB2. Additionally, we observe germline truncation variants in genes not previously associated with ovarian cancer susceptibility ( NF1, MAP3K4, CDKN2B, and MLL3). Evidence for loss of heterozygosity was found in 100% and 76% of cases with germline BRCA1 and BRCA2 truncations respectively. Germline-somatic interaction analysis combined with extensive bioinformatics annotation identifies 237 candidate functional germline truncation and missense variants, including 2 pathogenic BRCA1 and 1 TP53 deleterious variants. Finally, integrated analyses of germline and somatic variants identify significantly altered pathways, including the Fanconi, MAPK, and MLL pathways.

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          Most cited references 44

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          Is Open Access

          iRefIndex: A consolidated protein interaction database with provenance

          Background Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping. Results We present a method to generate a key for a protein interaction record and a key for each participant protein. These keys may be generated by anyone using only the primary sequence of the proteins, their taxonomy identifiers and the Secure Hash Algorithm. Two interaction records will have identical keys if they refer to the same set of identical protein sequences and taxonomy identifiers. We define records with identical keys as a redundant group. Our method required that we map protein database references found in interaction records to current protein sequence records. Operations performed during this mapping are described by a mapping score that may provide valuable feedback to source interaction databases on problematic references that are malformed, deprecated, ambiguous or unfound. Keys for protein participants allow for retrieval of interaction information independent of the protein references used in the original records. Conclusion We have applied our method to protein interaction records from BIND, BioGrid, DIP, HPRD, IntAct, MINT, MPact, MPPI and OPHID. The resulting interaction reference index is provided in PSI-MITAB 2.5 format at . This index may form the basis of alternative redundant groupings based on gene identifiers or near sequence identity groupings.
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            PolyPhred: automating the detection and genotyping of single nucleotide substitutions using fluorescence-based resequencing.

            Fluorescence-based sequencing is playing an increasingly important role in efforts to identify DNA polymorphisms and mutations of biological and medical interest. The application of this technology in generating the reference sequence of simple and complex genomes is also driving the development of new computer programs to automate base calling (Phred), sequence assembly (Phrap) and sequence assembly editing (Consed) in high throughput settings. In this report we describe a new computer program known as PolyPhred that automatically detects the presence of heterozygous single nucleotide substitutions by fluorescencebased sequencing of PCR products. Its operations are integrated with the use of the Phred, Phrap and Consed programs and together these tools generate a high throughput system for detecting DNA polymorphisms and mutations by large scale fluorescence-based resequencing. Analysis of sequences containing known DNA variants demonstrates that the accuracy of PolyPhred with single pass data is >99% when the sequences are generated with fluorescent dye-labeled primers and approximately 90% for those prepared with dye-labeled terminators.
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              A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1).

              We conducted a three-stage genome-wide association study (GWAS) of breast cancer in 9,770 cases and 10,799 controls in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. In stage 1, we genotyped 528,173 SNPs in 1,145 cases of invasive breast cancer and 1,142 controls. In stage 2, we analyzed 24,909 top SNPs in 4,547 cases and 4,434 controls. In stage 3, we investigated 21 loci in 4,078 cases and 5,223 controls. Two new loci achieved genome-wide significance. A pericentromeric SNP on chromosome 1p11.2 (rs11249433; P = 6.74 x 10(-10) adjusted genotype test, 2 degrees of freedom) resides in a large linkage disequilibrium block neighboring NOTCH2 and FCGR1B; this signal was stronger for estrogen-receptor-positive tumors. A second SNP on chromosome 14q24.1 (rs999737; P = 1.74 x 10(-7)) localizes to RAD51L1, a gene in the homologous recombination DNA repair pathway. We also confirmed associations with loci on chromosomes 2q35, 5p12, 5q11.2, 8q24, 10q26 and 16q12.1.

                Author and article information

                Nat Commun
                Nat Commun
                Nature communications
                26 March 2014
                22 July 2014
                : 5
                : 3156
                [1 ]The Genome Institute, Washington University in St. Louis, MO 63108, USA
                [2 ]Brown School Master of Public Health Program, Washington University in St. Louis, St. Louis, MO 63130, USA
                [3 ]Department of Computer Science, Brown University, Providence, RI 02912, USA
                [4 ]Department of Genetics, Washington University in St. Louis, MO 63108, USA
                [5 ]Department of Mathematics, Washington University in St. Louis, MO 63108, USA
                [6 ]Oregon Health & Science University, Portland, OR 97239, USA
                [7 ]Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA
                [8 ]Department of Pediatrics, Washington University in St. Louis, MO 63108, USA
                [9 ]Department of Medicine, Washington University in St. Louis, MO 63108, USA
                [10 ]The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
                Author notes
                [# ]Corresponding Author: Li Ding, Ph.D, The Genome Institute, Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63108, ding@ 123456genome.wustl.edu

                These authors contributed equally to this work.


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