Search

Your search keyword '"Layer, Ryan"' showing total 192 results
Start Over Author "Layer, Ryan"
192 results on '"Layer, Ryan"'

2. Experience Report: Standards-Based Grading at Scale in Algorithms

Author

Chen, Lijun, Grochow, Joshua A., Layer, Ryan, and Levet, Michael

Subjects
Computer Science - Computers and Society, 97-02, 97D40, 97D60, 97P20, K.3.2
Abstract

We report our experiences implementing standards-based grading at scale in an Algorithms course, which serves as the terminal required CS Theory course in our department's undergraduate curriculum. The course had 200-400 students, taught by two instructors, eight graduate teaching assistants, and supported by two additional graders and several undergraduate course assistants. We highlight the role of standards-based grading in supporting our students during the COVID-19 pandemic. We conclude by detailing the successes and adjustments we would make to the course structure., Comment: This is an extended version of our paper, which will appear in ITiCSE 2022

Published
2022
Full Text
View/download PDF

3. Implications of noncoding regulatory functions in the development of insulinomas

Author

Ramos-Rodríguez, Mireia, Subirana-Granés, Marc, Norris, Richard, Sordi, Valeria, Fernández, Ángel, Fuentes-Páez, Georgina, Pérez-González, Beatriz, Berenguer Balaguer, Clara, Raurell-Vila, Helena, Chowdhury, Murad, Corripio, Raquel, Partelli, Stefano, López-Bigas, Núria, Pellegrini, Silvia, Montanya, Eduard, Nacher, Montserrat, Falconi, Massimo, Layer, Ryan, Rovira, Meritxell, González-Pérez, Abel, Piemonti, Lorenzo, and Pasquali, Lorenzo

Published
2024
Full Text
View/download PDF

4. A complete reference genome improves analysis of human genetic variation

Author

Aganezov, Sergey, Yan, Stephanie M, Soto, Daniela C, Kirsche, Melanie, Zarate, Samantha, Avdeyev, Pavel, Taylor, Dylan J, Shafin, Kishwar, Shumate, Alaina, Xiao, Chunlin, Wagner, Justin, McDaniel, Jennifer, Olson, Nathan D, Sauria, Michael EG, Vollger, Mitchell R, Rhie, Arang, Meredith, Melissa, Martin, Skylar, Lee, Joyce, Koren, Sergey, Rosenfeld, Jeffrey A, Paten, Benedict, Layer, Ryan, Chin, Chen-Shan, Sedlazeck, Fritz J, Hansen, Nancy F, Miller, Danny E, Phillippy, Adam M, Miga, Karen H, McCoy, Rajiv C, Dennis, Megan Y, Zook, Justin M, and Schatz, Michael C

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Genetic Variation, Genome, Human, Genomics, Humans, Reference Standards, Sequence Analysis, DNA, General Science & Technology
Abstract

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 million base pairs of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome for clinical and functional study. We show how this reference universally improves read mapping and variant calling for 3202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of variants per sample in previously unresolved regions, showcasing the promise of the T2T-CHM13 reference for evolutionary and biomedical discovery. Simultaneously, this reference eliminates tens of thousands of spurious variants per sample, including reduction of false positives in 269 medically relevant genes by up to a factor of 12. Because of these improvements in variant discovery coupled with population and functional genomic resources, T2T-CHM13 is positioned to replace GRCh38 as the prevailing reference for human genetics.

Published
2022

5. Mapping and characterization of structural variation in 17,795 human genomes

Author

Abel, Haley J, Larson, David E, Regier, Allison A, Chiang, Colby, Das, Indraniel, Kanchi, Krishna L, Layer, Ryan M, Neale, Benjamin M, Salerno, William J, Reeves, Catherine, Buyske, Steven, Matise, Tara C, Muzny, Donna M, Zody, Michael C, Lander, Eric S, Dutcher, Susan K, Stitziel, Nathan O, and Hall, Ira M

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, Generic health relevance, Alleles, Case-Control Studies, Epigenesis, Genetic, Female, Gene Dosage, Genetic Variation, Genetics, Population, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Male, Molecular Sequence Annotation, Quantitative Trait Loci, Racial Groups, Software, Whole Genome Sequencing, NHGRI Centers for Common Disease Genomics, General Science & Technology
Abstract

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

Published
2020

7. Supplementary Tables from NPEPPS Is a Druggable Driver of Platinum Resistance

Author

Jones, Robert T., primary, Scholtes, Mathijs, primary, Goodspeed, Andrew, primary, Akbarzadeh, Maryam, primary, Mohapatra, Saswat, primary, Feldman, Lily Elizabeth, primary, Vekony, Hedvig, primary, Jean, Annie, primary, Tilton, Charlene B., primary, Orman, Michael V., primary, Romal, Shahla, primary, Deiter, Cailin, primary, Kan, Tsung Wai, primary, Xander, Nathaniel, primary, Araki, Stephanie P., primary, Joshi, Molishree, primary, Javaid, Mahmood, primary, Clambey, Eric T., primary, Layer, Ryan, primary, Laajala, Teemu D., primary, Parker, Sarah J., primary, Mahmoudi, Tokameh, primary, Zuiverloon, Tahlita C.M., primary, Theodorescu, Dan, primary, and Costello, James C., primary

Published
2024
Full Text
View/download PDF

8. Data from NPEPPS Is a Druggable Driver of Platinum Resistance

Author

Jones, Robert T., primary, Scholtes, Mathijs, primary, Goodspeed, Andrew, primary, Akbarzadeh, Maryam, primary, Mohapatra, Saswat, primary, Feldman, Lily Elizabeth, primary, Vekony, Hedvig, primary, Jean, Annie, primary, Tilton, Charlene B., primary, Orman, Michael V., primary, Romal, Shahla, primary, Deiter, Cailin, primary, Kan, Tsung Wai, primary, Xander, Nathaniel, primary, Araki, Stephanie P., primary, Joshi, Molishree, primary, Javaid, Mahmood, primary, Clambey, Eric T., primary, Layer, Ryan, primary, Laajala, Teemu D., primary, Parker, Sarah J., primary, Mahmoudi, Tokameh, primary, Zuiverloon, Tahlita C.M., primary, Theodorescu, Dan, primary, and Costello, James C., primary

Published
2024
Full Text
View/download PDF

9. Supplementary Figures from NPEPPS Is a Druggable Driver of Platinum Resistance

Author

Jones, Robert T., primary, Scholtes, Mathijs, primary, Goodspeed, Andrew, primary, Akbarzadeh, Maryam, primary, Mohapatra, Saswat, primary, Feldman, Lily Elizabeth, primary, Vekony, Hedvig, primary, Jean, Annie, primary, Tilton, Charlene B., primary, Orman, Michael V., primary, Romal, Shahla, primary, Deiter, Cailin, primary, Kan, Tsung Wai, primary, Xander, Nathaniel, primary, Araki, Stephanie P., primary, Joshi, Molishree, primary, Javaid, Mahmood, primary, Clambey, Eric T., primary, Layer, Ryan, primary, Laajala, Teemu D., primary, Parker, Sarah J., primary, Mahmoudi, Tokameh, primary, Zuiverloon, Tahlita C.M., primary, Theodorescu, Dan, primary, and Costello, James C., primary

Published
2024
Full Text
View/download PDF

12. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder

Author

Werling, Donna M, Brand, Harrison, An, Joon-Yong, Stone, Matthew R, Zhu, Lingxue, Glessner, Joseph T, Collins, Ryan L, Dong, Shan, Layer, Ryan M, Markenscoff-Papadimitriou, Eirene, Farrell, Andrew, Schwartz, Grace B, Wang, Harold Z, Currall, Benjamin B, Zhao, Xuefang, Dea, Jeanselle, Duhn, Clif, Erdman, Carolyn A, Gilson, Michael C, Yadav, Rachita, Handsaker, Robert E, Kashin, Seva, Klei, Lambertus, Mandell, Jeffrey D, Nowakowski, Tomasz J, Liu, Yuwen, Pochareddy, Sirisha, Smith, Louw, Walker, Michael F, Waterman, Matthew J, He, Xin, Kriegstein, Arnold R, Rubenstein, John L, Sestan, Nenad, McCarroll, Steven A, Neale, Benjamin M, Coon, Hilary, Willsey, A Jeremy, Buxbaum, Joseph D, Daly, Mark J, State, Matthew W, Quinlan, Aaron R, Marth, Gabor T, Roeder, Kathryn, Devlin, Bernie, Talkowski, Michael E, and Sanders, Stephan J

Subjects
Biological Sciences, Genetics, Intellectual and Developmental Disabilities (IDD), Brain Disorders, Pediatric, Autism, Biotechnology, Mental Health, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Autism Spectrum Disorder, Female, Genetic Predisposition to Disease, Genome, Genome-Wide Association Study, Humans, INDEL Mutation, Male, Polymorphism, Single Nucleotide, Protein Isoforms, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

Published
2018

13. NPEPPS Is a Druggable Driver of Platinum Resistance

Author

Jones, Robert T., primary, Scholtes, Mathijs, additional, Goodspeed, Andrew, additional, Akbarzadeh, Maryam, additional, Mohapatra, Saswat, additional, Feldman, Lily Elizabeth, additional, Vekony, Hedvig, additional, Jean, Annie, additional, Tilton, Charlene B., additional, Orman, Michael V., additional, Romal, Shahla, additional, Deiter, Cailin, additional, Kan, Tsung Wai, additional, Xander, Nathaniel, additional, Araki, Stephanie P., additional, Joshi, Molishree, additional, Javaid, Mahmood, additional, Clambey, Eric T., additional, Layer, Ryan, additional, Laajala, Teemu D., additional, Parker, Sarah J., additional, Mahmoudi, Tokameh, additional, Zuiverloon, Tahlita C.M., additional, Theodorescu, Dan, additional, and Costello, James C., additional

Published
2024
Full Text
View/download PDF

18. LUMPY: A probabilistic framework for structural variant discovery

Author

Layer, Ryan M., Hall, Ira M., and Quinlan, Aaron R.

Subjects
Quantitative Biology - Genomics
Abstract

Comprehensive discovery of structural variation (SV) in human genomes from DNA sequencing requires the integration of multiple alignment signals including read-pair, split-read and read-depth. However, owing to inherent technical challenges, most existing SV discovery approaches utilize only one signal and consequently suffer from reduced sensitivity, especially at low sequence coverage and for smaller SVs. We present a novel and extremely flexible probabilistic SV discovery framework that is capable of integrating any number of SV detection signals including those generated from read alignments or prior evidence. We demonstrate improved sensitivity over extant methods by combining paired-end and split-read alignments and emphasize the utility of our framework for comprehensive studies of structural variation in heterogeneous tumor genomes. We further discuss the broader utility of this approach for probabilistic integration of diverse genomic interval datasets., Comment: version 2; updated for journal resubmission; includes comparison to PINDEL and analysis on NA12878 and parents

Published
2012

19. Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections

Author

Layer, Ryan M., Skadron, Kevin, Robins, Gabriel, Hall, Ira M., and Quinlan, Aaron R.

Subjects
Quantitative Biology - Genomics
Abstract

Motivation: The comparison of diverse genomic datasets is fundamental to understanding genome biology. Researchers must explore many large datasets of genome intervals (e.g., genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect: that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features is crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures such as Graphics Processing Units (GPUs) by illustrating its utility for efficient Monte-Carlo simulations measuring the significance of relationships between sets of genomic intervals.

Published
2012

22. The structural variation landscape in 492 Atlantic salmon genomes

Author

Bertolotti, Alicia C., Layer, Ryan M., Gundappa, Manu Kumar, Gallagher, Michael D., Pehlivanoglu, Ege, Nome, Torfinn, Robledo, Diego, Kent, Matthew P., Røsæg, Line L., Holen, Matilde M., Mulugeta, Teshome D., Ashton, Thomas J., Hindar, Kjetil, Sægrov, Harald, Florø-Larsen, Bjørn, Erkinaro, Jaakko, Primmer, Craig R., Bernatchez, Louis, Martin, Samuel A. M., Johnston, Ian A., Sandve, Simen R., Lien, Sigbjørn, and Macqueen, Daniel J.

Published
2020
Full Text
View/download PDF

26. Experience Report

Author

Chen, Lijun, Grochow, Joshua A., Layer, Ryan, and Levet, Michael

Subjects
FOS: Computer and information sciences, Computer Science - Computers and Society, Computers and Society (cs.CY), ComputingMilieux_COMPUTERSANDEDUCATION, 97-02, 97D40, 97D60, 97P20, K.3.2
Abstract

We report our experiences implementing standards-based grading at scale in an Algorithms course, which serves as the terminal required CS Theory course in our department's undergraduate curriculum. The course had 200-400 students, taught by two instructors, eight graduate teaching assistants, and supported by two additional graders and several undergraduate course assistants. We highlight the role of standards-based grading in supporting our students during the COVID-19 pandemic. We conclude by detailing the successes and adjustments we would make to the course structure., Comment: This is an extended version of our paper, which will appear in ITiCSE 2022

Published
2022

30. A complete reference genome improves analysis of human genetic variation

Author

Aganezov, Sergey, primary, Yan, Stephanie M., additional, Soto, Daniela C., additional, Kirsche, Melanie, additional, Zarate, Samantha, additional, Avdeyev, Pavel, additional, Taylor, Dylan J., additional, Shafin, Kishwar, additional, Shumate, Alaina, additional, Xiao, Chunlin, additional, Wagner, Justin, additional, McDaniel, Jennifer, additional, Olson, Nathan D., additional, Sauria, Michael E.G., additional, Vollger, Mitchell R., additional, Rhie, Arang, additional, Meredith, Melissa, additional, Martin, Skylar, additional, Lee, Joyce, additional, Koren, Sergey, additional, Rosenfeld, Jeffrey A., additional, Paten, Benedict, additional, Layer, Ryan, additional, Chin, Chen-Shan, additional, Sedlazeck, Fritz J., additional, Hansen, Nancy F., additional, Miller, Danny E., additional, Phillippy, Adam M., additional, Miga, Karen H., additional, McCoy, Rajiv C., additional, Dennis, Megan Y., additional, Zook, Justin M., additional, and Schatz, Michael C., additional

Published
2021
Full Text
View/download PDF

31. Editorial: Genomic Colocalization and Enrichment Analyses

Author

Kanduri, Chakravarthi, Sandve, Geir Kjetil, Hovig, Eivind, De, Subhajyoti, and Layer, Ryan M.

Subjects
Editorial, computational biology, genome annotation, colocalization analyses, Genetics, genomics, co-occurrence analyses, bioinformatics, enrichment analyses, genomic overlap
Published
2021

37. NPEPPS regulates intracellular import and sensitivity to cisplatin by interaction with volume-regulated anion channels

Author

Jones, Robert T., primary, Goodspeed, Andrew, additional, Mohapatra, Saswat, additional, Feldman, Lily Elizabeth, additional, Scholtes, Mathijs, additional, Vekony, Hedvig, additional, Jean, Annie, additional, Tilton, Charlene B., additional, Orman, Michael V., additional, Deiter, Cailin, additional, Xander, Nathaniel, additional, Araki, Stephanie, additional, Joshi, Molishree, additional, Javaid, Mahmood, additional, Clambey, Eric T., additional, Layer, Ryan, additional, Laajala, Teemu D., additional, Parker, Sarah, additional, Zuiverloon, Tahlita, additional, Theodorescu, Dan, additional, and Costello, James C., additional

Published
2021
Full Text
View/download PDF

44. The structural variation landscape in 492 Atlantic salmon genomes

Author

Bertolotti, Alicia C., primary, Layer, Ryan M., additional, Gundappa, Manu Kumar, additional, Gallagher, Michael D., additional, Pehlivanoglu, Ege, additional, Nome, Torfinn, additional, Robledo, Diego, additional, Kent, Matthew P., additional, Røsæg, Line L., additional, Holen, Matilde M., additional, Mulugeta, Teshome D., additional, Ashton, Thomas J., additional, Hindar, Kjetil, additional, Sægrov, Harald, additional, Florø-Larsen, Bjørn, additional, Erkinaro, Jaakko, additional, Primmer, Craig R., additional, Bernatchez, Louis, additional, Martin, Samuel A.M., additional, Johnston, Ian A., additional, Sandve, Simen R., additional, Lien, Sigbjørn, additional, and Macqueen, Daniel J., additional

Published
2020
Full Text
View/download PDF

49. A parallel algorithm for

Author

Layer, Ryan M. and Quinlan, Aaron R.

Subjects
Article
Abstract

The comparison of sets of genome intervals (e.g., genes, repeats, ChIP-seq peaks) is essential to genome research, especially as modern sequencing technologies enable ever larger and more complex experiments. Relationships between genomic features are commonly identified by their intersection: that is, if feature sets contain overlapping intervals then it is inferred that they share a common biological function or origin. Using this technique, researchers identify genomic regions that are common among multiple (or unique to individual) datasets. While there have been recent advances in algorithms for pairwise intersections between two sets of genomic intervals, few advances have been made to the intersection of many sets of genomic intervals. Identifying intersections among many interval sets is particularly important when attempting to distill biological insights from the massive, multi-dimensional datasets that are common to modern genome research. For such analyses, speed and efficiency are crucial given the size and sheer number of datasets involved. To solve this problem, we present a novel “slice-then-sweep” algorithm that, given N interval sets, efficiently reveals the subset of intervals that are common to all N sets. We demonstrate that our algorithm is more efficient in the sequential case and has a vastly higher capacity for parallelization with a 19x speedup over the existing algorithm.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results