Oscar Westesson

Visualizing next-generation sequencing data with JBrowse

JBrowse is a web-based genome browser, allowing many sources of data to be visualized, interpreted and navigated in a coherent visual framework. JBrowse uses efficient data structures, pre-generation of image tiles and client-side rendering to provide a fast, interactive browsing experience. Many of JBrowses design features make it well suited for visualizing high-volume data, such as aligned next-generation sequencing reads.

Accurate Reconstruction of Insertion-Deletion Histories by Statistical Phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoffs probability matrices and Felsensteins pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.

AAV ANCESTRAL RECONSTRUCTION LIBRARY ENABLES SELECTION OF BROADLY INFECTIOUS VIRAL VARIANTS

Adeno-associated virus (AAV) vectors have achieved clinical efficacy in treating several diseases. However, enhanced vectors are required to extend these landmark successes to other indications and protein engineering approaches may provide the necessary vector improvements to address such unmet medical needs. To generate new capsid variants with potentially enhanced infectious properties and to gain insights into AAV’s evolutionary history, we computationally designed and experimentally constructed a putative ancestral AAV library. Combinatorial variations at 32 amino acid sites were introduced to account for uncertainty in their identities. We then analyzed the evolutionary flexibility of these residues, the majority of which have not been previously studied, by subjecting the library to iterative selection on a representative cell line panel. The resulting variants exhibited transduction efficiencies comparable to the most efficient extant serotypes and, in general, ancestral libraries were broadly infectious across the cell line panel, indicating that they favored promiscuity over specificity. Interestingly, putative ancestral AAVs were more thermostable than modern serotypes and did not use sialic acids, galactose or heparan sulfate proteoglycans for cellular entry. Finally, variants mediated 19- to 31-fold higher gene expression in the muscle compared with AAV1, a clinically used serotype for muscle delivery, highlighting their promise for gene therapy.

Developing and Applying Heterogeneous Phylogenetic Models with XRate

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the “grammar” of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate s new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate s flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART.

Abstract 2173: Robust estimation of mutation burden

Developing a more robust approach to measure mutational burden is of central importance to improving the characterization of the molecular profile of tumor and may improve our ability to predict tumor progression or response to therapy in patients. Mutational burden is typically calculated as a direct enumeration of called somatic mutations per megabase covered. However, there is a growing appreciation that tumor purity, variable sequencing coverage, and copy number alterations can substantially impact the accurate identification any specific somatic mutation. Furthermore, population genetic theory and empirical data indicate that in many cases the vast majority of somatic mutations appear in only a small subpopulation of tumor cells, a context in which there is a high likelihood that an individual subclonal mutation may not be identified by conventional analysis. This tendency to miss low frequency mutations is highly variable and dependent, in part, upon sample purity and results in a strong source of bias not addressed in existing methods to measure variant allele frequencies. We present a novel computational method that incorporates these sources of bias in a coherent probabilistic framework that enables maximum-likelihood inference of relevant population parameters such as mutation burden. We apply our method to simulated data as well as patient tumor samples diluted with varying known proportions of normal DNA. We show that our approach allows us to generate estimates of mutation burden that are robust to the substantial variations in purity and sequencing coverage that are frequently encountered in patient tumor analysis. Hence, our novel method may improve the accurate detection and quantification of variant alleles in patient tumors to better understand their genetic landscape and guide clinical management. Citation Format: Oscar Westesson, Rasmus Nielsen, John St John, Aleah Caulin, Nicholas Hahner, Stewart Stewart, Catherine Foo, Kimberly Lung, Jeff Catalano, Mandy Lee, Petros Giannikopoulos, Will Polkinghorn, Jonathan Wiessman, Aviv Regev, Trever Bivona. Robust estimation of mutation burden. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2173. doi:10.1158/1538-7445.AM2015-2173

Abstract 4707: Comprehensive integrated genomic analysis

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Despite recent advances in the understanding of the biology and genetics of lung cancer, and despite the introduction of multiplex somatic mutation testing in the clinic, the long-term survival for all lung cancer patients, particularly for those with advanced disease, remains low. Lung cancer is the leading cause of cancer death globally, resulting in 1.4 million deaths annually, including 165,000 patients in the United States per year. In order to address the critical need for comprehensive profiling of these patients, we developed a novel, CLIA-certified, whole exome and low-coverage whole genome sequencing assay that applies a disease-focused, integrated approach to identify therapeutically actionable drivers of disease. A panel (12) of surgically resected NSCLC specimens along with corresponding adjacent normal tissue underwent DNA extraction in a clinical (CLIA) environment. Tumor and normal genomic DNA was prepared for whole exome sequencing using the using the Agilent SureSelectXT Human All Exon V5 kit according to the manufacturers instructions, and libraries were sequenced on the Illumina HiSeq2500 at an average depth of 500X. Genomic DNA was then prepared for whole genome sequencing using Illuminas Nextera system and run on the Illumina HiSeq2500 platform at an average depth of 1-2X. Somatic variants were detected using Strelka and somatic copy number alterations (SCNAs) were identified using a novel algorithm comparing normalized read counts within genomic segments as well as genes in the tumor to a panel of normal tissues. In parallel, the same tumor/normal specimens were analyzed by two separate CLIA laboratories via 1) a clinically validated Ion Torrent AmpliSeq Cancer Panel assay, and 2) a clinically validated cancer-focused, high-resolution comparative genome hybridization (CGH) array. In addition, a well-characterized panel of 10 germline samples obtained from the 1000 Genomes Project were pooled to simulate a broad spectrum of somatic single nucleotide variant and indel allele frequencies. Sequencing, data analysis and clinical reporting were completed for all 12 cases with an average turnaround time of less than 3 weeks. Single nucleotide variants and indels were identified with an accuracy of greater than 99%, with a limit of detection of 5-10% mutant allele frequency. Somatic copy number alterations were observed with an overall accuracy of greater than 95%. Actionable variants were identified by cross-referencing individual results with our internally developed, lung-cancer focused therapeutic association database. Citation Format: Catherine K. Foo, John St. John, Nicholas Hahner, Oscar Westesson, Mitchell E. Skinner, Urvish Parikh, Kimberly Lung, Aleah F. Cauhlin, Jeffrey P. Catalano, Anne S. Wellde, Jonathan K. Barry, George W. Wellde, Patrick Ma, Rafael Rosell, Andres Felipe Cardona Zorilla, William R. Polkinghorn, Trever G. Bivona, Jonathan S. Weissman, Petros Giannikopoulos. Comprehensive integrated genomic analysis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4707. doi:10.1158/1538-7445.AM2014-4707