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"Jones, Steven"
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CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer
Tumors from individuals with cancer are frequently genetically profiled for information about the driving forces behind the disease. We present the CancerMine resource, a text-mined and routinely updated database of drivers, oncogenes and tumor suppressors in different types of cancer. All data are available online (http://bionlp.bcgsc.ca/cancermine) and downloadable under a Creative Commons Zero license for ease of use.CancerMine, a resource based on literature mining, offers a database of drivers, oncogenes and tumor suppressors for gene–cancer associations, updated monthly.
Journal Article
Whole-Genome Sequencing and Social-Network Analysis of a Tuberculosis Outbreak
An outbreak of tuberculosis occurred over a 3-year period in a medium-size community in British Columbia, Canada. The results of mycobacterial interspersed repetitive unit–variable-number tandem-repeat (MIRU-VNTR) genotyping suggested the outbreak was clonal. Traditional contact tracing did not identify a source. We used whole-genome sequencing and social-network analysis in an effort to describe the outbreak dynamics at a higher resolution.
Mycobacterium tuberculosis
is an important infectious disease even in developed countries with extensive control programs. This is the case in British Columbia, Canada, where the 2007 incidence rate of 6.4 cases per 100,000 population exceeded the national average of 4.7 cases per 100,000 population.
1
In May 2006, a case of smear-negative pleural tuberculosis was diagnosed in an adult in a medium-size community in British Columbia. A second case, manifested as disseminated tuberculosis, was reported in an infant in July 2006. Reverse contact tracing identified nine additional cases between August and October 2006, when the British Columbia Centre for Disease Control . . .
Journal Article
Aberrant patterns of H3K4 and H3K27 histone lysine methylation occur across subgroups in medulloblastoma
Recent sequencing efforts have described the mutational landscape of the pediatric brain tumor medulloblastoma. Although
MLL2
is among the most frequent somatic single nucleotide variants (SNV), the clinical and biological significance of these mutations remains uncharacterized. Through targeted re-sequencing, we identified mutations of
MLL2
in 8 % (14/175) of MBs, the majority of which were loss of function. Notably, we also report mutations affecting the MLL2-binding partner
KDM6A
, in 4 % (7/175) of tumors. While
MLL2
mutations were independent of age, gender, histological subtype, M-stage or molecular subgroup,
KDM6A
mutations were most commonly identified in Group 4 MBs, and were mutually exclusive with
MLL2
mutations. Immunohistochemical staining for H3K4me3 and H3K27me3, the chromatin effectors of MLL2 and KDM6A activity, respectively, demonstrated alterations of the histone code in 24 % (53/220) of MBs across all subgroups. Correlating these MLL2- and KDM6A-driven histone marks with prognosis, we identified populations of MB with improved (K4+/K27−) and dismal (K4−/K27−) outcomes, observed primarily within Group 3 and 4 MBs. Group 3 and 4 MBs demonstrate somatic copy number aberrations, and transcriptional profiles that converge on modifiers of H3K27-methylation (
EZH2
,
KDM6A
,
KDM6B
), leading to silencing of PRC2-target genes. As PRC2-mediated aberrant methylation of H3K27 has recently been targeted for therapy in other diseases, it represents an actionable target for a substantial percentage of medulloblastoma patients with aggressive forms of the disease.
Journal Article
Tigmint: correcting assembly errors using linked reads from large molecules
Background
Genome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity. As a result, assembly errors are common. In the absence of a reference genome, these misassemblies may be identified by comparing the sequencing data to the assembly and looking for discrepancies between the two. Once identified, these misassemblies may be corrected, improving the quality of the assembled sequence. Although tools exist to identify and correct misassemblies using Illumina paired-end and mate-pair sequencing, no such tool yet exists that makes use of the long distance information of the large molecules provided by linked reads, such as those offered by the 10x Genomics Chromium platform. We have developed the tool Tigmint to address this gap.
Results
To demonstrate the effectiveness of Tigmint, we applied it to assemblies of a human genome using short reads assembled with ABySS 2.0 and other assemblers. Tigmint reduced the number of misassemblies identified by QUAST in the ABySS assembly by 216 (27%). While scaffolding with ARCS alone more than doubled the scaffold NGA50 of the assembly from 3 to 8 Mbp, the combination of Tigmint and ARCS improved the scaffold NGA50 of the assembly over five-fold to 16.4 Mbp. This notable improvement in contiguity highlights the utility of assembly correction in refining assemblies. We demonstrate the utility of Tigmint in correcting the assemblies of multiple tools, as well as in using Chromium reads to correct and scaffold assemblies of long single-molecule sequencing.
Conclusions
Scaffolding an assembly that has been corrected with Tigmint yields a final assembly that is both more correct and substantially more contiguous than an assembly that has not been corrected. Using single-molecule sequencing in combination with linked reads enables a genome sequence assembly that achieves both a high sequence contiguity as well as high scaffold contiguity, a feat not currently achievable with either technology alone.
Journal Article
Goldenlocks and the three pirates
While pirates Papa, Mama, and Baby are away from their seaworthy sloop, Goldilocks comes aboard and makes herself useful.
Book
Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images
Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54–0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.
Journal Article