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26 results on '"Jones, Steven"'

2. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data

Author

Birol, Inanc, Raymond, Anthony, Jackman, Shaun D., Pleasance, Stephen, Coope, Robin, Taylor, Greg A., Yuen, Macaire Man Saint, Keeling, Christopher I., Brand, Dana, Vandervalk, Benjamin P., Kirk, Heather, Pandoh, Pawan, Moore, Richard A., Zhao, Yongjun, Mungall, Andrew J., Jaquish, Barry, Yanchuk, Alvin, Ritland, Carol, Boyle, Brian, Bousquet, Jean, Ritland, Kermit, MacKay, John, Bohlmann, Jörg, and Jones, Steven J.M.

Published
2013
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6. De novo transcriptome assembly with ABySS

Author

Birol, Inanç, Jackman, Shaun D., Nielsen, Cydney B., Qian, Jenny Q., Varhol, Richard, Stazyk, Greg, Morin, Ryan D., Zhao, Yongjun, Hirst, Martin, Schein, Jacqueline E., Horsman, Doug E., Connors, Joseph M., Gascoyne, Randy D., Marra, Marco A., and Jones, Steven J. M.

Published
2009

15. ntEdit: scalable genome sequence polishing

Author

Warren, René L, primary, Coombe, Lauren, additional, Mohamadi, Hamid, additional, Zhang, Jessica, additional, Jaquish, Barry, additional, Isabel, Nathalie, additional, Jones, Steven J M, additional, Bousquet, Jean, additional, Bohlmann, Joerg, additional, and Birol, Inanç, additional

Published
2019
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18. MAVIS: merging, annotation, validation, and illustration of structural variants

Author

Reisle, Caralyn, primary, Mungall, Karen L, additional, Choo, Caleb, additional, Paulino, Daniel, additional, Bleile, Dustin W, additional, Muhammadzadeh, Amir, additional, Mungall, Andrew J, additional, Moore, Richard A, additional, Shlafman, Inna, additional, Coope, Robin, additional, Pleasance, Stephen, additional, Ma, Yussanne, additional, and Jones, Steven J M, additional

Published
2018
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21. A collaborative filtering-based approach to biomedical knowledge discovery.

Author

Lever, Jake, Gakkhar, Sitanshu, Gottlieb, Michael, Rashnavadi, Tahereh, Lin, Santina, Siu, Celia, Smith, Maia, Jones, Martin R, Krzywinski, Martin, and Jones, Steven J M

Subjects
DATA mining, TEXT mining, SINGULAR value decomposition, BIOINFORMATICS
Abstract

Motivation: The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. Results: We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another wellperforming system using several predicted associations. [ABSTRACT FROM AUTHOR]

Published
2018
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22. MAVIS: merging, annotation, validation, and illustration of structural variants.

Author

Reisle, Caralyn, Mungall, Karen L, Choo, Caleb, Paulino, Daniel, Bleile, Dustin W, Muhammadzadeh, Amir, Mungall, Andrew J, Moore, Richard A, Shlafman, Inna, Coope, Robin, Pleasance, Stephen, Ma, Yussanne, and Jones, Steven J M

Subjects
GENETIC disorders, CANCER genetics, TRANSCRIPTOMES, BIOINFORMATICS, GENOMICS
Abstract

Summary Reliably identifying genomic rearrangements and interpreting their impact is a key step in understanding their role in human cancers and inherited genetic diseases. Many short read algorithmic approaches exist but all have appreciable false negative rates. A common approach is to evaluate the union of multiple tools increasing sensitivity, followed by filtering to retain specificity. Here we describe an application framework for the rapid generation of structural variant consensus, unique in its ability to visualize the genetic impact and context as well as process both genome and transcriptome data. Availability and implementation http://mavis.bcgsc.ca Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2019
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23. ALEA: a toolbox for allele-specific epigenomics analysis

Author

Younesy, Hamid, primary, Möller, Torsten, additional, Heravi-Moussavi, Alireza, additional, Cheng, Jeffrey B., additional, Costello, Joseph F., additional, Lorincz, Matthew C., additional, Karimi, Mohammad M., additional, and Jones, Steven J. M., additional

Published
2013
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