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7 results on '"Lalansingh, CM"'

1. Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples (Nature Communications, (2020), 11, 1, (4748), 10.1038/s41467-020-18151-y)

Author

Bailey, MH, Meyerson, WU, Dursi, LJ, Wang, LB, Dong, G, Liang, WW, Weerasinghe, A, Li, S, Li, Y, Kelso, S, Akbani, R, Anur, P, Buchanan, A, Chiotti, K, Covington, K, Creason, A, Ding, L, Ellrott, K, Fan, Y, Foltz, S, Getz, G, Hale, W, Haussler, D, Hess, JM, Hutter, CM, Kandoth, C, Kasaian, K, Kasapi, M, Larson, D, Leshchiner, I, Letaw, J, Ma, S, McLellan, MD, Men, Y, Mills, GB, Niu, B, Peto, M, Radenbaugh, A, Reynolds, SM, Saksena, G, Sofia, H, Stewart, C, Struck, AJ, Stuart, JM, Wang, W, Weinstein, JN, Wheeler, DA, Wong, CK, Xi, L, Ye, K, Bieg, M, Boutros, PC, Buchhalter, I, Butler, AP, Chen, K, Chong, Z, Drechsel, O, Jonathan Dursi, L, Eils, R, Espiritu, SMG, Fulton, RS, Gao, S, Gelpi, JLL, Gerstein, MB, Gonzalez, S, Gut, IG, Hach, F, Heinold, MC, Hinton, J, Hu, T, Huang, V, Huang, Y, Hutter, B, Jones, DR, Jung, J, Jäger, N, Kim, HL, Kleinheinz, K, Kumar, S, Kumar, Y, Lalansingh, CM, Letunic, I, Livitz, D, Ma, EZ, Maruvka, YE, Mashl, RJ, Menzies, A, Milovanovic, A, Nielsen, MM, Ossowski, S, Bailey, MH, Meyerson, WU, Dursi, LJ, Wang, LB, Dong, G, Liang, WW, Weerasinghe, A, Li, S, Li, Y, Kelso, S, Akbani, R, Anur, P, Buchanan, A, Chiotti, K, Covington, K, Creason, A, Ding, L, Ellrott, K, Fan, Y, Foltz, S, Getz, G, Hale, W, Haussler, D, Hess, JM, Hutter, CM, Kandoth, C, Kasaian, K, Kasapi, M, Larson, D, Leshchiner, I, Letaw, J, Ma, S, McLellan, MD, Men, Y, Mills, GB, Niu, B, Peto, M, Radenbaugh, A, Reynolds, SM, Saksena, G, Sofia, H, Stewart, C, Struck, AJ, Stuart, JM, Wang, W, Weinstein, JN, Wheeler, DA, Wong, CK, Xi, L, Ye, K, Bieg, M, Boutros, PC, Buchhalter, I, Butler, AP, Chen, K, Chong, Z, Drechsel, O, Jonathan Dursi, L, Eils, R, Espiritu, SMG, Fulton, RS, Gao, S, Gelpi, JLL, Gerstein, MB, Gonzalez, S, Gut, IG, Hach, F, Heinold, MC, Hinton, J, Hu, T, Huang, V, Huang, Y, Hutter, B, Jones, DR, Jung, J, Jäger, N, Kim, HL, Kleinheinz, K, Kumar, S, Kumar, Y, Lalansingh, CM, Letunic, I, Livitz, D, Ma, EZ, Maruvka, YE, Mashl, RJ, Menzies, A, Milovanovic, A, Nielsen, MM, and Ossowski, S

Abstract

The original version of this Article omitted from the author list the 9th author Yize Li, who is from the ‘The McDonnell Genome Institute at Washington University, St. Louis, MO 63108, USA and Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA’. This has been corrected in both the PDF and HTML versions of the Article.

Published
2020

2. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Author

Bailey, MH, Meyerson, WU, Dursi, LJ, Wang, LB, Dong, G, Liang, WW, Weerasinghe, A, Li, S, Kelso, S, Akbani, R, Anur, P, Buchanan, A, Chiotti, K, Covington, K, Creason, A, Ding, L, Ellrott, K, Fan, Y, Foltz, S, Getz, G, Hale, W, Haussler, D, Hess, JM, Hutter, CM, Kandoth, C, Kasaian, K, Kasapi, M, Larson, D, Leshchiner, I, Letaw, J, Ma, S, McLellan, MD, Men, Y, Mills, GB, Niu, B, Peto, M, Radenbaugh, A, Reynolds, SM, Saksena, G, Sofia, H, Stewart, C, Struck, AJ, Stuart, JM, Wang, W, Weinstein, JN, Wheeler, DA, Wong, CK, Xi, L, Ye, K, Bieg, M, Boutros, PC, Buchhalter, I, Butler, AP, Chen, K, Chong, Z, Drechsel, O, Jonathan Dursi, L, Eils, R, Espiritu, SMG, Fulton, RS, Gao, S, Gelpi, JLL, Gerstein, MB, Gonzalez, S, Gut, IG, Hach, F, Heinold, MC, Hinton, J, Hu, T, Huang, V, Huang, Y, Hutter, B, Jones, DR, Jung, J, Jäger, N, Kim, HL, Kleinheinz, K, Kumar, S, Kumar, Y, Lalansingh, CM, Letunic, I, Livitz, D, Ma, EZ, Maruvka, YE, Mashl, RJ, Menzies, A, Milovanovic, A, Nielsen, MM, Ossowski, S, Paramasivam, N, Bailey, MH, Meyerson, WU, Dursi, LJ, Wang, LB, Dong, G, Liang, WW, Weerasinghe, A, Li, S, Kelso, S, Akbani, R, Anur, P, Buchanan, A, Chiotti, K, Covington, K, Creason, A, Ding, L, Ellrott, K, Fan, Y, Foltz, S, Getz, G, Hale, W, Haussler, D, Hess, JM, Hutter, CM, Kandoth, C, Kasaian, K, Kasapi, M, Larson, D, Leshchiner, I, Letaw, J, Ma, S, McLellan, MD, Men, Y, Mills, GB, Niu, B, Peto, M, Radenbaugh, A, Reynolds, SM, Saksena, G, Sofia, H, Stewart, C, Struck, AJ, Stuart, JM, Wang, W, Weinstein, JN, Wheeler, DA, Wong, CK, Xi, L, Ye, K, Bieg, M, Boutros, PC, Buchhalter, I, Butler, AP, Chen, K, Chong, Z, Drechsel, O, Jonathan Dursi, L, Eils, R, Espiritu, SMG, Fulton, RS, Gao, S, Gelpi, JLL, Gerstein, MB, Gonzalez, S, Gut, IG, Hach, F, Heinold, MC, Hinton, J, Hu, T, Huang, V, Huang, Y, Hutter, B, Jones, DR, Jung, J, Jäger, N, Kim, HL, Kleinheinz, K, Kumar, S, Kumar, Y, Lalansingh, CM, Letunic, I, Livitz, D, Ma, EZ, Maruvka, YE, Mashl, RJ, Menzies, A, Milovanovic, A, Nielsen, MM, Ossowski, S, and Paramasivam, N

Abstract

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Published
2020

3. A community effort to create standards for evaluating tumor subclonal reconstruction

Author

Salcedo, A, Tarabichi, M, Espiritu, SMG, Deshwar, AG, David, M, Wilson, NM, Dentro, S, Wintersinger, JA, Liu, LY, Ko, M, Sivanandan, S, Zhang, H, Zhu, K, Ou Yang, T-H, Chilton, JM, Buchanan, A, Lalansingh, CM, P'ng, C, Anghel, CV, Umar, I, Lo, B, Zou, W, Simpson, JT, Stuart, JM, Anastassiou, D, Guan, Y, Ewing, AD, Ellrott, K, Wedge, DC, Morris, Q, Van Loo, P, Boutros, PC, Jha, A, Huang, T, Yang, T-P, Peifer, M, Sahinalp, C, Malikic, S, Vazquez-Garcia, I, Mustonen, V, Yang, H-T, Lee, K-R, Ji, Y, Sengupta, S, Rudewicz, J, Nikolski, M, Schaeverbeke, Q, Yuan, K, Markowetz, F, Macintyre, G, Cmero, M, Chaudhary, B, Leshchiner, I, Livitz, D, Getz, G, Loher, P, Yu, K, Wang, W, Zhu, H, Salcedo, A, Tarabichi, M, Espiritu, SMG, Deshwar, AG, David, M, Wilson, NM, Dentro, S, Wintersinger, JA, Liu, LY, Ko, M, Sivanandan, S, Zhang, H, Zhu, K, Ou Yang, T-H, Chilton, JM, Buchanan, A, Lalansingh, CM, P'ng, C, Anghel, CV, Umar, I, Lo, B, Zou, W, Simpson, JT, Stuart, JM, Anastassiou, D, Guan, Y, Ewing, AD, Ellrott, K, Wedge, DC, Morris, Q, Van Loo, P, Boutros, PC, Jha, A, Huang, T, Yang, T-P, Peifer, M, Sahinalp, C, Malikic, S, Vazquez-Garcia, I, Mustonen, V, Yang, H-T, Lee, K-R, Ji, Y, Sengupta, S, Rudewicz, J, Nikolski, M, Schaeverbeke, Q, Yuan, K, Markowetz, F, Macintyre, G, Cmero, M, Chaudhary, B, Leshchiner, I, Livitz, D, Getz, G, Loher, P, Yu, K, Wang, W, and Zhu, H

Abstract

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Published
2020

4. The genomic and evolutionary landscapes of anaplastic thyroid carcinoma.

Author

Zeng PYF, Prokopec SD, Lai SY, Pinto N, Chan-Seng-Yue MA, Clifton-Bligh R, Williams MD, Howlett CJ, Plantinga P, Cecchini MJ, Lam AK, Siddiqui I, Wang J, Sun RX, Watson JD, Korah R, Carling T, Agrawal N, Cipriani N, Ball D, Nelkin B, Rooper LM, Bishop JA, Garnis C, Berean K, Nicolson NG, Weinberger P, Henderson YC, Lalansingh CM, Tian M, Yamaguchi TN, Livingstone J, Salcedo A, Patel K, Vizeacoumar F, Datti A, Xi L, Nikiforov YE, Smallridge R, Copland JA, Marlow LA, Hyrcza MD, Delbridge L, Sidhu S, Sywak M, Robinson B, Fung K, Ghasemi F, Kwan K, MacNeil SD, Mendez A, Palma DA, Khan MI, Shaikh M, Ruicci KM, Wehrli B, Winquist E, Yoo J, Mymryk JS, Rocco JW, Wheeler D, Scherer S, Giordano TJ, Barrett JW, Faquin WC, Gill AJ, Clayman G, Boutros PC, and Nichols AC

Subjects
Humans, Mutation genetics, Genomics, Thyroid Carcinoma, Anaplastic genetics, Thyroid Carcinoma, Anaplastic pathology, Thyroid Neoplasms genetics, Thyroid Neoplasms pathology, Adenocarcinoma
Abstract

Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations., Competing Interests: Declaration of interests All authors declare that they have no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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5. A community effort to create standards for evaluating tumor subclonal reconstruction.

Author

Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, David M, Wilson NM, Dentro S, Wintersinger JA, Liu LY, Ko M, Sivanandan S, Zhang H, Zhu K, Ou Yang TH, Chilton JM, Buchanan A, Lalansingh CM, P'ng C, Anghel CV, Umar I, Lo B, Zou W, Simpson JT, Stuart JM, Anastassiou D, Guan Y, Ewing AD, Ellrott K, Wedge DC, Morris Q, Van Loo P, and Boutros PC

Subjects
Clone Cells, Computer Simulation, DNA Copy Number Variations genetics, Gene Dosage, Genome, Humans, Mutation genetics, Neoplasms genetics, Polymorphism, Single Nucleotide genetics, Reference Standards, Algorithms, Neoplasms pathology
Abstract

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Published
2020
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6. VennDiagramWeb: a web application for the generation of highly customizable Venn and Euler diagrams.

Author

Lam F, Lalansingh CM, Babaran HE, Wang Z, Prokopec SD, Fox NS, and Boutros PC

Subjects
Documentation, Humans, Internet, User-Computer Interface, Computational Biology methods, Computer Graphics, Data Interpretation, Statistical, Software
Abstract

Background: Visualization of data generated by high-throughput, high-dimensionality experiments is rapidly becoming a rate-limiting step in computational biology. There is an ongoing need to quickly develop high-quality visualizations that can be easily customized or incorporated into automated pipelines. This often requires an interface for manual plot modification, rapid cycles of tweaking visualization parameters, and the generation of graphics code. To facilitate this process for the generation of highly-customizable, high-resolution Venn and Euler diagrams, we introduce VennDiagramWeb: a web application for the widely used VennDiagram R package. VennDiagramWeb is hosted at http://venndiagram.res.oicr.on.ca/ ., Results: VennDiagramWeb allows real-time modification of Venn and Euler diagrams, with parameter setting through a web interface and immediate visualization of results. It allows customization of essentially all aspects of figures, but also supports integration into computational pipelines via download of R code. Users can upload data and download figures in a range of formats, and there is exhaustive support documentation., Conclusions: VennDiagramWeb allows the easy creation of Venn and Euler diagrams for computational biologists, and indeed many other fields. Its ability to support real-time graphics changes that are linked to downloadable code that can be integrated into automated pipelines will greatly facilitate the improved visualization of complex datasets. For application support please contact Paul.Boutros@oicr.on.ca.

Published
2016
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7. BAMQL: a query language for extracting reads from BAM files.

Author

Masella AP, Lalansingh CM, Sivasundaram P, Fraser M, Bristow RG, and Boutros PC

Subjects
Base Sequence, Chromosomes genetics, Internet, Mitochondria genetics, User-Computer Interface, Software
Abstract

Background: It is extremely common to need to select a subset of reads from a BAM file based on their specific properties. Typically, a user unpacks the BAM file to a text stream using SAMtools, parses and filters the lines using AWK, then repacks them using SAMtools. This process is tedious and error-prone. In particular, when working with many columns of data, mix-ups are common and the bit field containing the flags is unintuitive. There are several libraries for reading BAM files, such as Bio-SamTools for Perl and pysam for Python. Both allow access to the BAM's read information and can filter reads, but require substantial boilerplate code; this is high overhead for mostly ad hoc filtering., Results: We have created a query language that gathers reads using a collection of predicates and common logical connectives. Queries run faster than equivalents and can be compiled to native code for embedding in larger programs., Conclusions: BAMQL provides a user-friendly, powerful and performant way to extract subsets of BAM files for ad hoc analyses or integration into applications. The query language provides a collection of predicates beyond those in SAMtools, and more flexible connectives.

Published
2016
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