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Drucker, Travis

Mayo Clinic

Mayo Clinic

Citations 5

Copy number variant analysis using genome‐wide mate‐pair sequencing
Smadbeck, James B. , Johnson, Sarah H. , Smoley, Stephanie A., Gaitatzes, Athanasios , Drucker, Travis, Zenka, Roman M. , Kosari, Farhad , Hoppman, Nicole L., Sukov, William R. , Jenkins, Robert B. , Kearney, Hutton M. , Vasmatzis, George , Drucker, Travis M, Hoppman, Nicole, Aypar, Umut , Murphy, Stephen J. , Feldman, Andrew L.

Genes, Chromosomes and Cancer | May 4, 2018 | Volume: 57 | Issue: 9 | Pages: 459-470

10.1002/gcc.5 Get It

AbstractCopy number variation (CNV) is a common form of structural variation detected in human genomes, occurring as both constitutional and somatic events. Cytogenetic techniques like chromosomal microarray (CMA) are widely used in analyzing CNVs. However, CMA techniques cannot resolve the full nature of these structural variations (i.e. the orientation and location of associated breakpoint junctions) and must be combined with other cytogenetic techniques, such as karyotyping or FISH, to do so. This makes the development of a next‐generation sequencing (NGS) approach capable of resolving both CNVs and breakpoint junctions desirable. Mate‐pair sequencing (MPseq) is a NGS technology designed to find large structural rearrangements across the entire genome. Here we present an algorithm capable of performing copy number analysis from mate‐pair sequencing data. The algorithm uses a step‐wise procedure involving normalization, segmentation, and classification of the sequencing data. The segmentation technique combines both read depth and discordant mate‐pair reads to increase the sensitivity and resolution of CNV calls. The method is particularly suited to MPseq, which is designed to detect breakpoint junctions at high resolution. This allows for the classification step to accurately calculate copy number levels at the relatively low read depth of MPseq. Here we compare results for a series of hematological cancer samples that were tested with CMA and MPseq. We demonstrate comparable sensitivity to the state‐of‐the‐art CMA technology, with the benefit of improved breakpoint resolution. The algorithm provides a powerful analytical tool for the analysis of MPseq results in cancer.
Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks
Cunningham, Scott A. , Jeraldo, Patricio , Quest, Daniel, Boxrud, Dave, Jenkins, Gregory D. , Drucker, Travis, Nelson, Heidi , Patel, Robin , Chia, Nicholas , Jeraldo, Patricio R , Quest, Daniel J, Drucker, Travis M, Chia, Nicholas , Johnson, Julie A. , Taylor, Angela , Chen, Jun , Patel, Robin , Cunningham, Scott A

Journal of Clinical Microbiology | April 13, 2017 | Volume: 55 | Issue: 6 | Pages: 1946-1953

10.1128/jcm.00029-17 Get It

ABSTRACT Whole-genome sequencing (WGS) can provide excellent resolution in global and local epidemiological investigations of Staphylococcus aureus outbreaks. A variety of sequencing approaches and analytical tools have been used; it is not clear which is ideal. We compared two WGS strategies and two analytical approaches to the standard method of SmaI restriction digestion pulsed-field gel electrophoresis (PFGE) for typing S. aureus . Forty-two S. aureus isolates from three outbreaks and 12 reference isolates were studied. Near-complete genomes, assembled de novo with paired-end and long-mate-pair (8 kb) libraries were first assembled and analyzed utilizing an in-house assembly and analytical informatics pipeline. In addition, paired-end data were assembled and analyzed using a commercial software package. Single nucleotide variant (SNP) analysis was performed using the in-house pipeline. Two assembly strategies were used to generate core genome multilocus sequence typing (cgMLST) data. First, the near-complete genome data generated with the in-house pipeline were imported into the commercial software and used to perform cgMLST analysis. Second, the commercial software was used to assemble paired-end data, and resolved assemblies were used to perform cgMLST. Similar isolate clustering was observed using SNP calling and cgMLST, regardless of data assembly strategy. All methods provided more discrimination between outbreaks than did PFGE. Overall, all of the evaluated WGS strategies yielded statistically similar results for S. aureus typing.
Integrated Genomic Analysis of Pancreatic Ductal Adenocarcinomas Reveals Genomic Rearrangement Events as Significant Drivers of Disease
Hart, Steven N. , Halling, Geoffrey C. , Johnson, Sarah H. , Smadbeck, James B. , Drucker, Travis, Lima, Joema Felipe , Rohakhtar, Fariborz Rakhshan, Harris, Faye R. , Kosari, Farhad , Subramanian, Subbaya , Petersen, Gloria M. , Wiltshire, Timothy D. , Kipp, Benjamin R. , Truty, Mark J. , Vasmatzis, George , McWilliams, Robert R. , Murphy, Stephen J. , Couch, Fergus J. , Hart, Steven N

Cancer Research | December 16, 2015 | Volume: 76 | Issue: 3 | Pages: 749-761

10.1158/0008-5472.can-15-2198 Get It

Abstract Many somatic mutations have been detected in pancreatic ductal adenocarcinoma (PDAC), leading to the identification of some key drivers of disease progression, but the involvement of large genomic rearrangements has often been overlooked. In this study, we performed mate pair sequencing (MPseq) on genomic DNA from 24 PDAC tumors, including 15 laser-captured microdissected PDAC and 9 patient-derived xenografts, to identify genome-wide rearrangements. Large genomic rearrangements with intragenic breakpoints altering key regulatory genes involved in PDAC progression were detected in all tumors. SMAD4, ZNF521, and FHIT were among the most frequently hit genes. Conversely, commonly reported genes with copy number gains, including MYC and GATA6, were frequently observed in the absence of direct intragenic breakpoints, suggesting a requirement for sustaining oncogenic function during PDAC progression. Integration of data from MPseq, exome sequencing, and transcriptome analysis of primary PDAC cases identified limited overlap in genes affected by both rearrangements and point mutations. However, significant overlap was observed in major PDAC-associated signaling pathways, with all PDAC exhibiting reduced SMAD4 expression, reduced SMAD-dependent TGFβ signaling, and increased WNT and Hedgehog signaling. The frequent loss of SMAD4 and FHIT due to genomic rearrangements strongly implicates these genes as key drivers of PDAC, thus highlighting the strengths of an integrated genomic and transcriptomic approach for identifying mechanisms underlying disease initiation and progression. Cancer Res; 76(3); 749–61. ©2015 AACR.
Identification of Independent Primary Tumors and Intrapulmonary Metastases Using DNA Rearrangements in Non–Small-Cell Lung Cancer
Aubry, Marie-Christine, Harris, Faye R. , Halling, Geoffrey C. , Johnson, Sarah H. , Terra, Simone , Drucker, Travis, Kipp, Benjamin R. , Yi, Eunhee S. , Peikert, Tobias, Vasmatzis, George , Wigle, Dennis A. , Drucker, Travis M, Asiedu, Michael K. , Murphy, Stephen J. , Yang, Ping

Journal of Clinical Oncology | November 11, 2014 | Volume: 32 | Issue: 36 | Pages: 4050-4058

10.1200/jco.2014.56.7644 Get It

Purpose Distinguishing independent primary tumors from intrapulmonary metastases in non–small-cell carcinoma remains a clinical dilemma with significant clinical implications. Using next-generation DNA sequencing, we developed a chromosomal rearrangement–based approach to differentiate multiple primary tumors from metastasis. Methods Tumor specimens from patients with known independent primary tumors and metastatic lesions were used for lineage test development, which was then applied to multifocal tumors. Laser capture microdissection was performed separately for each tumor. Genomic DNA was isolated using direct in situ whole-genome amplification methodology, and next-generation sequencing was performed using an Illumina mate-pair library protocol. Sequence reads were mapped to the human genome, and primers spanning the fusion junctions were used for validation polymerase chain reaction. Results A total of 41 tumor samples were sequenced (33 adenocarcinomas [ADs] and eight squamous cell carcinomas [SQCCs]), with a range of three to 276 breakpoints per tumor identified. Lung tumors predicted to be independent primary tumors based on different histologic subtype did not share any genomic rearrangements. In patients with lung primary tumors and paired distant metastases, shared rearrangements were identified in all tumor pairs, emphasizing the patient specificity of identified breakpoints. Multifocal AD and SQCC samples were reviewed independently by two pulmonary pathologists. Concordance between histology and genomic data occurred in the majority of samples. Discrepant tumor samples were resolved by genome sequencing. Conclusion A diagnostic lineage test based on genomic rearrangements from mate-pair sequencing demonstrates promise for distinguishing independent primary from metastatic disease in lung cancer.
BIMA V3: an aligner customized for mate pair library sequencing
Drucker, Travis, Johnson, Sarah H. , Cradic, Kendall W, Therneau, Terry M. , Vasmatzis, George , Drucker, Travis M, Murphy, Stephen J.

Bioinformatics | February 14, 2014 | Volume: 30 | Issue: 11 | Pages: 1627-1629

10.1093/bioinformatics/btu078 Get It

Abstract Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate-pair read pairs to a reference genome is a challenging and time-consuming process for most next-generation sequencing alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.) and presence of structural variant breakpoints within reads increase mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular next-generation sequencing alignment programs when processing mate pair sequencing. Availability: http://bioinformaticstools.mayo.edu/research/bima/ Contact: vasm@mayo.edu or vasmatzis.george@mayo.edu Supplementary Information: Supplementary data are available at Bioinformatics online.