Browsing by Subject "Copy number variation"

Now showing 1 - 7 of 7

Open Access
The BioPAX community standard for pathway data sharing
(Nature Publishing Group, 2010-09) Demir, Emek; Cary, M. P.; Paley, S.; Fukuda, K.; Lemer, C.; Vastrik, I.; Wu, G.; D'Eustachio, P.; Schaefer, C.; Luciano, J.; Schacherer, F.; Martinez-Flores, I.; Hu, Z.; Jimenez-Jacinto, V.; Joshi-Tope, G.; Kandasamy, K.; Lopez-Fuentes, A. C.; Mi, H.; Pichler, E.; Rodchenkov, I.; Splendiani, A.; Tkachev, S.; Zucker, J.; Gopinath, G.; Rajasimha, H.; Ramakrishnan, R.; Shah, I.; Syed, M.; Anwar, N.; Babur, Özgün; Blinov, M.; Brauner, E.; Corwin, D.; Donaldson, S.; Gibbons, F.; Goldberg, R.; Hornbeck, P.; Luna, A.; Murray-Rust, P.; Neumann, E.; Reubenacker, O.; Samwald, M.; Iersel, Martijn van; Wimalaratne, S.; Allen, K.; Braun, B.; Whirl-Carrillo, M.; Cheung, Kei-Hoi; Dahlquist, K.; Finney, A.; Gillespie, M.; Glass, E.; Gong, L.; Haw, R.; Honig, M.; Hubaut, O.; Kane, D.; Krupa, S.; Kutmon, M.; Leonard, J.; Marks, D.; Merberg, D.; Petri, V.; Pico, A.; Ravenscroft, D.; Ren, L.; Shah, N.; Sunshine, M.; Tang R.; Whaley, R.; Letovksy, S.; Buetow, K. H.; Rzhetsky, A.; Schachter, V.; Sobral, B. S.; Doğrusöz, Uğur; McWeeney, S.; Aladjem, M.; Birney, E.; Collado-Vides, J.; Goto, S.; Hucka, M.; Novère, Nicolas Le; Maltsev, N.; Pandey, A.; Thomas, P.; Wingender, E.; Karp, P. D.; Sander, C.; Bader, G. D.
Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery. © 2010 Nature America, Inc. All rights reserved.
Open Access
Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci
(Cell Press, 2015) Sanders, S. J.; He, X.; Willsey, A. J.; Ercan-Sencicek, A. G.; Samocha, K. E.; Cicek, A. E.; Murtha, M. T.; Bal, V. H.; Bishop, S. L.; Dong, S.; Goldberg, A. P.; Jinlu, C.; Keaney, J. F.; Keaney III, J. F.; Mandell, J. D.; Moreno-De-Luca, D.; Poultney, C. S.; Robinson, E. B.; Smith L.; Solli-Nowlan, T.; Su, M. Y.; Teran, N. A.; Walker, M. F.; Werling, D. M.; Beaudet, A. L.; Cantor, R. M.; Fombonne, E.; Geschwind, D. H.; Grice, D. E.; Lord, C.; Lowe, J. K.; Mane, S. M.; Martin, D.M.; Morrow, E. M.; Talkowski, M. E.; Sutcliffe, J. S.; Walsh, C. A.; Yu, T. W.; Ledbetter, D. H.; Martin, C. L.; Cook, E. H.; Buxbaum, J. D.; Daly, M. J.; Devlin, B.; Roeder, K.; State, M. W.
Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1). Through analysis of de novo mutations in autism spectrum disorder (ASD), Sanders et al. find that small deletions, but not large deletions/duplications, contain one critical gene. Combining CNV and sequencing data, they identify 6 loci and 65 genes associated with ASD.
Open Access
Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal
(American Association for the Advancement of Science (A A A S), 2013) Gao J.; Aksoy, B. A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S. O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; Cerami, E.; Sander, C.; Schultz, N.
The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics. © 2013 American Association for the Advancement of Science.
Open Access
Inter-varietal structural variation in grapevine genomes
(Wiley-Blackwell Publishing Ltd., 2016) Cardone, M. F.; D'Addabbo, P.; Alkan C.; Bergamini, C.; Catacchio, C. R.; Anaclerio, F.; Chiatante, G.; Marra, A.; Giannuzzi, G.; Perniola, R.; Ventura M.; Antonacci, D.
Grapevine (Vitis vinifera L.) is one of the world's most important crop plants, which is of large economic value for fruit and wine production. There is much interest in identifying genomic variations and their functional effects on inter-varietal, phenotypic differences. Using an approach developed for the analysis of human and mammalian genomes, which combines high-throughput sequencing, array comparative genomic hybridization, fluorescent inï¿½situ hybridization and quantitative PCR, we created an inter-varietal atlas of structural variations and single nucleotide variants (SNVs) for the grapevine genome analyzing four economically and genetically relevant table grapevine varieties. We found 4.8 million SNVs and detected 8% of the grapevine genome to be affected by genomic variations. We identified more than 700 copy number variation (CNV) regions and more than 2000 genes subjected to CNV as potential candidates for phenotypic differences between varieties
Open Access
Paralog-specific gene copy number discovery within segmental duplications
(2019-09) Doğru, Emre
With the advancing technology in genome sequencing and analysis, it has become evident that the structural variations are the main source of alteration in human genome. Despite their signi cance in understanding disease susceptibility, there is no algorithm yet to nd all types and sizes of structural variations at once. Structural variation discovery remained problematic since they often overlap with the segmental duplications, nearly identical segments of DNA that appear more than once in the genome. Researchers often excluded these regions that made up 5% of the genome because of the complexity it brings to their studies. Only few of them are working in these regions, however, they require a special sequence alignment le where reads are mapped to multiple locations. Here, we present ParaCoND to discover paralog speci c gene copy number within segmental duplications using a sequence alignment le with unique mapping. We utilize the singly unique nucleotides (SUN) that distinguish paralogs from each other in the sequence alignment of the duplicated regions. Our method is based on read depth and is limited to detect only duplications and deletions. We computed the absolute copy numbers of genes using only read depth of SUN. Furthermore, we also computed the paralog speci c absolute copy numbers for genes residing in the same segmental duplication.
Open Access
Polishing copy number variant calls on exome sequencing data VIA deep learning
(2021-07) Özden, Furkan
Accurate and eﬃcient detection of copy number variants (CNVs) is of critical importance due to their signiﬁcant association with complex genetic diseases. Although algorithms that use whole genome sequencing (WGS) data provide sta-ble results with mostly-valid statistical assumptions, copy number detection on whole exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data is cost eﬃcient, compact and is relatively ubiquitous. The bottleneck is primarily due to non-contiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sam-ple batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data and learns to correct the copy number variations reported by any oﬀ-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can eﬃciently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model con-sistently improves the performance independent from (i) sequencing technology,(ii) exome capture kit and (iii) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.
Open Access
Whole-genome shotgun sequence CNV detection using read depth
(Humana Press, 2018) Kahveci, Fatma; Alkan, Can; Bickhart, D. M.
With the developments in high-throughput sequencing (HTS) technologies, researchers have gained a powerful tool to identify structural variants (SVs) in genomes with substantially less cost than before. SVs can be broadly classified into two main categories: balanced rearrangements and copy number variations (CNVs). Many algorithms have been developed to characterize CNVs using HTS data, with focus on different types and size range of variants using different read signatures. Read depth (RD) based tools are more common in characterizing large (>10 kb) CNVs since RD strategy does not rely on the fragment size and read length, which are limiting factors in read pair and split read analysis. Here we provide a guideline for a user friendly tool for detecting large segmental duplications and deletions that can also predict integer copy numbers for duplicated genes.