Automatic Extraction of ICD-O-3 Primary Sites from Cancer Pathology Reports

Abstract Although registry specific requirements exist, cancer registries primarily identify reportable cases using a combination of particular ICD-O-3 topography and morphology codes assigned to cancer case abstracts of which free text pathology reports form a main component. The codes are generally extracted from pathology reports by trained human coders, sometimes with the help of software programs. Here we present results that improve on the state-ofthe-art in automatic extraction of 57 generic sites from pathology reports using three representative machine learning algorithms in text classification. We use a dataset of 56,426 reports arising from 35 labs that report to the Kentucky Cancer Registry. Employing unigrams, bigrams, and named entities as features, our methods achieve a class-based micro F-score of 0.9 and macro F-score of 0.72. To our knowledge, this is the best result on extracting ICD-O-3 codes from pathology reports using a large number of possible codes. Given the large dataset we use (compared to other similar efforts) with reports from 35 different labs, we also expect our final models to generalize better when extracting primary sites from previously unseen reports

Viral coinfection analysis using a MinHash toolkit

Abstract: Background: Human papillomavirus (HPV) is a common sexually transmitted infection associated with cervical cancer that frequently occurs as a coinfection of types and subtypes. Highly similar sublineages that show over 100-fold differences in cancer risk are not distinguishable in coinfections with current typing methods. Results: We describe an efficient set of computational tools, rkmh, for analyzing complex mixed infections of related viruses based on sequence data. rkmh makes extensive use of MinHash similarity measures, and includes utilities for removing host DNA and classifying reads by type, lineage, and sublineage. We show that rkmh is capable of assigning reads to their HPV type as well as HPV16 lineage and sublineages. Conclusions: Accurate read classification enables estimates of percent composition when there are multiple infecting lineages or sublineages. While we demonstrate rkmh for HPV with multiple sequencing technologies, it is also applicable to other mixtures of related sequences

The dopamine D2 receptor gene and depressive and anxious symptoms in childhood: Associations and evidence for gene-environment correlation and gene-environment interaction

ObjectiveS: Research implicates the A1 allele of the dopamine D2 receptor gene (DRD2) Taq1A polymorphism in the development of depression and anxiety. Furthermore, recent papers suggest that children with A1 allele of this gene may receive less positive parenting, and that the effects of this gene on child symptoms may be moderated by parenting. We sought to replicate and extend these findings using behavioral measures in a nonclinical sample of young children. Methods: In a sample of 473 preschool-aged children and their mothers, structured clinical interview measures and maternal reports of child symptoms were collected, and standardized observations of parent-child interactions were conducted. Results: An association was detected between the DRD2 A1 allele and symptoms of depression and anxiety indexed using interview and parent report methods. As found in previous reports, children with the DRD2 A1 allele received less supportive parenting and displayed higher levels of negative emotionality during parent-child interactions. Tests of mediation and moderation were conducted. Conclusion: We found associations between the DRD2 A1 allele and early-emerging anxious and depressive symptoms in a community sample of preschool-aged children, and evidence of a gene-environment correlation and moderation of the main effect of child genotype on child symptoms by parenting. © 2010 Wolters Kluwer Health | Lippincott Williams & Wilkins

WormBase: better software, richer content

WormBase (), the public database for genomics and biology of Caenorhabditis elegans, has been restructured for stronger performance and expanded for richer biological content. Performance was improved by accelerating the loading of central data pages such as the omnibus Gene page, by rationalizing internal data structures and software for greater portability, and by making the Genome Browser highly customizable in how it views and exports genomic subsequences. Arbitrarily complex, user-specified queries are now possible through Textpresso (for all available literature) and through WormMart (for most genomic data). Biological content was enriched by reconciling all available cDNA and expressed sequence tag data with gene predictions, clarifying single nucleotide polymorphism and RNAi sites, and summarizing known functions for most genes studied in this organism

WormBase: new content and better access

WormBase (), a model organism database for Caenorhabditis elegans and other related nematodes, continues to evolve and expand. Over the past year WormBase has added new data on C.elegans, including data on classical genetics, cell biology and functional genomics; expanded the annotation of closely related nematodes with a new genome browser for Caenorhabditis remanei; and deployed new hardware for stronger performance. Several existing datasets including phenotype descriptions and RNAi experiments have seen a large increase in new content. New datasets such as the C.remanei draft assembly and annotations, the Vancouver Fosmid library and TEC-RED 5′ end sites are now available as well. Access to and searching WormBase has become more dependable and flexible via multiple mirror sites and indexing through Google

Demonstration of Protein-Based Human Identification Using the Hair Shaft Proteome

YesHuman identification from biological material is largely dependent on the ability to characterize genetic polymorphisms in DNA. Unfortunately, DNA can degrade in the environment, sometimes below the level at which it can be amplified by PCR. Protein however is chemically more robust than DNA and can persist for longer periods. Protein also contains genetic variation in the form of single amino acid polymorphisms. These can be used to infer the status of non-synonymous single nucleotide polymorphism alleles. To demonstrate this, we used mass spectrometry-based shotgun proteomics to characterize hair shaft proteins in 66 European-American subjects. A total of 596 single nucleotide polymorphism alleles were correctly imputed in 32 loci from 22 genes of subjects’ DNA and directly validated using Sanger sequencing. Estimates of the probability of resulting individual non-synonymous single nucleotide polymorphism allelic profiles in the European population, using the product rule, resulted in a maximum power of discrimination of 1 in 12,500. Imputed non-synonymous single nucleotide polymorphism profiles from European–American subjects were considerably less frequent in the African population (maximum likelihood ratio = 11,000). The converse was true for hair shafts collected from an additional 10 subjects with African ancestry, where some profiles were more frequent in the African population. Genetically variant peptides were also identified in hair shaft datasets from six archaeological skeletal remains (up to 260 years old). This study demonstrates that quantifiable measures of identity discrimination and biogeographic background can be obtained from detecting genetically variant peptides in hair shaft protein, including hair from bioarchaeological contexts.The Technology Commercialization Innovation Program (Contracts #121668, #132043) of the Utah Governors Office of Commercial Development, the Scholarship Activitie

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead