Richard K. Wilson

Bio: Richard K. Wilson is an academic researcher from Nationwide Children's Hospital. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 173, co-authored 463 publications receiving 260000 citations. Previous affiliations of Richard K. Wilson include University of Washington & St. Jude Children's Research Hospital.

Palindromic GOLGA8 core duplicons promote chromosome 15q13.3 microdeletion and evolutionary instability

Abstract: Recurrent deletions of chromosome 15q13.3 associate with intellectual disability, schizophrenia, autism and epilepsy. To gain insight into the instability of this region, we sequenced it in affected individuals, normal individuals and nonhuman primates. We discovered five structural configurations of the human chromosome 15q13.3 region ranging in size from 2 to 3 Mb. These configurations arose recently (∼0.5-0.9 million years ago) as a result of human-specific expansions of segmental duplications and two independent inversion events. All inversion breakpoints map near GOLGA8 core duplicons-a ∼14-kb primate-specific chromosome 15 repeat that became organized into larger palindromic structures. GOLGA8-flanked palindromes also demarcate the breakpoints of recurrent 15q13.3 microdeletions, the expansion of chromosome 15 segmental duplications in the human lineage and independent structural changes in apes. The significant clustering (P = 0.002) of breakpoints provides mechanistic evidence for the role of this core duplicon and its palindromic architecture in promoting the evolutionary and disease-related instability of chromosome 15.

PathScan: a tool for discerning mutational significance in groups of putative cancer genes

Abstract: Motivation The expansion of cancer genome sequencing continues to stimulate development of analytical tools for inferring relationships between somatic changes and tumor development. Pathway associations are especially consequential, but existing algorithms are demonstrably inadequate. Methods Here, we propose the PathScan significance test for the scenario where pathway mutations collectively contribute to tumor development. Its design addresses two aspects that established methods neglect. First, we account for variations in gene length and the consequent differences in their mutation probabilities under the standard null hypothesis of random mutation. The associated spike in computational effort is mitigated by accurate convolution-based approximation. Second, we combine individual probabilities into a multiple-sample value using Fisher-Lancaster theory, thereby improving differentiation between a few highly mutated genes and many genes having only a few mutations apiece. We investigate accuracy, computational effort and power, reporting acceptable performance for each. Results As an example calculation, we re-analyze KEGG-based lung adenocarcinoma pathway mutations from the Tumor Sequencing Project. Our test recapitulates the most significant pathways and finds that others for which the original test battery was inconclusive are not actually significant. It also identifies the focal adhesion pathway as being significantly mutated, a finding consistent with earlier studies. We also expand this analysis to other databases: Reactome, BioCarta, Pfam, PID and SMART, finding additional hits in ErbB and EPHA signaling pathways and regulation of telomerase. All have implications and plausible mechanistic roles in cancer. Finally, we discuss aspects of extending the method to integrate gene-specific background rates and other types of genetic anomalies. Availability PathScan is implemented in Perl and is available from the Genome Institute at: http://genome.wustl.edu/software/pathscan.

Mutational Analysis of EGFR and Related Signaling Pathway Genes in Lung Adenocarcinomas Identifies a Novel Somatic Kinase Domain Mutation in FGFR4

Abstract: Background Fifty percent of lung adenocarcinomas harbor somatic mutations in six genes that encode proteins in the EGFR signaling pathway, i.e., EGFR, HER2/ERBB2, HER4/ERBB4, PIK3CA, BRAF, and KRAS. We performed mutational profiling of a large cohort of lung adenocarcinomas to uncover other potential somatic mutations in genes of this signaling pathway that could contribute to lung tumorigenesis. Methodology/Principal Findings We analyzed genomic DNA from a total of 261 resected, clinically annotated non-small cell lung cancer (NSCLC) specimens. The coding sequences of 39 genes were screened for somatic mutations via high-throughput dideoxynucleotide sequencing of PCR-amplified gene products. Mutations were considered to be somatic only if they were found in an independent tumor-derived PCR product but not in matched normal tissue. Sequencing of 9MB of tumor sequence identified 239 putative genetic variants. We further examined 22 variants found in RAS family genes and 135 variants localized to exons encoding the kinase domain of respective proteins. We identified a total of 37 non-synonymous somatic mutations; 36 were found collectively in EGFR, KRAS, BRAF, and PIK3CA. One somatic mutation was a previously unreported mutation in the kinase domain (exon 16) of FGFR4 (Glu681Lys), identified in 1 of 158 tumors. The FGFR4 mutation is analogous to a reported tumor-specific somatic mutation in ERBB2 and is located in the same exon as a previously reported kinase domain mutation in FGFR4 (Pro712Thr) in a lung adenocarcinoma cell line. Conclusions/Significance This study is one of the first comprehensive mutational analyses of major genes in a specific signaling pathway in a sizeable cohort of lung adenocarcinomas. Our results suggest the majority of gain-of-function mutations within kinase genes in the EGFR signaling pathway have already been identified. Our findings also implicate FGFR4 in the pathogenesis of a subset of lung adenocarcinomas.

Epigenomic analysis of the HOX gene loci reveals mechanisms that may control canonical expression patterns in AML and normal hematopoietic cells.

Abstract: HOX genes are highly expressed in many acute myeloid leukemia (AML) samples, but the patterns of expression and associated regulatory mechanisms are not clearly understood. We analyzed RNA sequencing data from 179 primary AML samples and normal hematopoietic cells to understand the range of expression patterns in normal versus leukemic cells. HOX expression in AML was restricted to specific genes in the HOXA or HOXB loci, and was highly correlated with recurrent cytogenetic abnormalities. However, the majority of samples expressed a canonical set of HOXA and HOXB genes that was nearly identical to the expression signature of normal hematopoietic stem/progenitor cells. Transcriptional profiles at the HOX loci were similar between normal cells and AML samples, and involved bidirectional transcription at the center of each gene cluster. Epigenetic analysis of a subset of AML samples also identified common regions of chromatin accessibility in AML samples and normal CD34(+) cells that displayed differences in methylation depending on HOX expression patterns. These data provide an integrated epigenetic view of the HOX gene loci in primary AML samples, and suggest that HOX expression in most AML samples represents a normal stem cell program that is controlled by epigenetic mechanisms at specific regulatory elements.

Optimizing cancer genome sequencing and analysis

Abstract: Tumors are typically sequenced to depths of 75-100× (exome) or 30-50× (whole genome). We demonstrate that current sequencing paradigms are inadequate for tumors that are impure, aneuploid or clonally heterogeneous. To reassess optimal sequencing strategies, we performed ultra-deep (up to ~312×) whole genome sequencing (WGS) and exome capture (up to ~433×) of a primary acute myeloid leukemia, its subsequent relapse, and a matched normal skin sample. We tested multiple alignment and variant calling algorithms and validated ~200,000 putative SNVs by sequencing them to depths of ~1,000×. Additional targeted sequencing provided over 10,000× coverage and ddPCR assays provided up to ~250,000× sampling of selected sites. We evaluated the effects of different library generation approaches, depth of sequencing, and analysis strategies on the ability to effectively characterize a complex tumor. This dataset, representing the most comprehensively sequenced tumor described to date, will serve as an invaluable community resource (dbGaP accession id phs000159).

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Abstract: The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

Initial sequencing and analysis of the human genome.

Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。