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Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Summary The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.

Introduction: Search of MEDLINE using the PUBMED interface still remains a preferred approach for many scientists, even in the presence of alternatives. While PUBMED provides a rich interface for querying, it does not provide deep analysis of the query results to refine queries and build hypotheses. To provide an improved search experience in PubMed, we have developed a Firefox browser plugin called “TPX plus” that enables concept-assisted search and navigation of the MEDLINE database while remaining in the PubMed website. Some of the features of the plugin include identification and highlighting of biomedical concept types like genes, processes, diseases etc. in the PubMed results page; link-outs to dictionary sources for identified terms; a Concept Explorer to view ranked automatically identified concepts in the results as well as use them for filtering/refining the search; deriving related concepts using a statistical concept-associations network; bookmarking and managing articles, storing notes and sharing comments on articles.

Background: Text-processing of unstructured biomedical text has become crucial to pharma companies, both with regards to legacy as well as topical documentation. The Apache Unstructured Information Management Applications (UIMA) framework addresses general information extraction requirements. We present in this poster two use cases of using UIMA for specific unstructured biomedical information extraction tasks in pharma companies. The first use case requires extraction of values belonging to specific fields from legacy clinical study documents. These fields could be diverse, examples being study duration, study population, study arm, completion date and co-morbidity. The second use case deals with accurate propagation of drug label information to digital channels such as drug-specific websites. Due to the increased importance of such websites and mobile applications, pharma companies are looking at text-processing solutions to keep information in such channels accurate and up-to-date.

TPX is a web-based PubMed search enhancement tool that enables faster article searching using analysis and exploration features. These features include identification of relevant biomedical concepts from search results with linkouts to source databases, concept based article categorization, concept assisted search and filtering, query refinement. A distinguishing feature here is the ability to add user-defined concept names and/or concept types for named entity recognition. The tool allows contextual exploration of knowledge sources by providing concept association maps derived from the MEDLINE repository. It also has a full-text search mode that can be configured on request to access local text repositories, incorporating entity co-occurrence search at sentence/paragraph levels. Local text files can also be analyzed on-the-fly. Availability http://tpx.atc.tcs.com/

Background Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones. Method We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts. Results Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further. Conclusion A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.