The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

Improving benefit-harm assessment of glucocorticoid therapy incorporating the patient perspective: The OMERACT glucocorticoid core domain set.

OBJECTIVE: Our primary objective was to develop an Outcome Measures in Rheumatology (OMERACT) core domain set to capture the impact of glucocorticoids (GC), both positive and negative, on patients with Rheumatic conditions. METHODS: The OMERACT Filter 2.1 was used to guide core domain selection. Systematic literature reviews, qualitative studies and quantitative surveys were conducted by the OMERACT GC Impact working group to identify candidate domains for a core domain set. A summary of prior work and Delphi exercise were presented at the OMERACT 2020 virtual GC workshop. A proposed GC Impact core domain set derived from this work was presented for discussion in facilitated breakout groups. Participants voted on the proposed GC Impact core domain set. RESULTS: 113 people, including 23 patient research partners, participated in two virtual workshops conducted at different times on the same day. The proposed mandatory domains to be evaluated in clinical trials involving GCs were: infection, bone fragility, hypertension, diabetes, weight, fatigue, mood disturbance and death. In addition, collection of disease specific outcomes was included in the core domain set as "mandatory in specific circumstances". The proposed core domain set was endorsed by 100% (23/23) of the patient research partners and 92% (83/90) of the remaining participants, including clinicians, researchers and industry stakeholders. CONCLUSION: A GC Impact core domain set was endorsed at the OMERACT 2020 virtual workshop. The OMERACT GC Impact working group will now progress to identify, develop and validate measurement tools to best address these domains in clinical trials.

Racial, ethnic, and healthcare disparities in rheumatoid arthritis.

PURPOSE OF REVIEW: This review highlights the available data describing racial and ethnic health disparities among patients with rheumatoid arthritis in the United States from an epidemiological, disease activity, and wider socioeconomic standpoint. RECENT FINDINGS: Despite centralized government initiatives to include more underrepresentative minority populations into research, many of the studies that examined rheumatoid arthritis still fail to include sizeable cohorts of races or ethnic groups other than whites. Evidence is slowly mounting that individual, provider, and system-level barriers exist and contribute to unequal care that leads to poorer outcomes amongst patients with rheumatoid arthritis. As rheumatoid arthritis is a progressive disease, early treatment is crucial to delay functional decline - a narrow window for many minority patients who are disproportionality affected by disability. SUMMARY: To combat the inequality that exists amongst rheumatoid arthritis patients we must focus on why discrepancies exist on every level, system, physician, patient, and illness. Further research is needed to tease the complex interplay between race, social economic status, medical access, and outcomes to explain the disparities found in rheumatoid arthritis.

Are special read alignment strategies necessary and cost-effective when handling sequencing reads from patient-derived tumor xenografts?

BACKGROUND: Patient-derived tumor xenografts in mice are widely used in cancer research and have become important in developing personalized therapies. When these xenografts are subject to DNA sequencing, the samples could contain various amounts of mouse DNA. It has been unclear how the mouse reads would affect data analyses. We conducted comprehensive simulations to compare three alignment strategies at different mutation rates, read lengths, sequencing error rates, human-mouse mixing ratios and sequenced regions. We also sequenced a nasopharyngeal carcinoma xenograft and a cell line to test how the strategies work on real data. RESULTS: We found the "filtering" and "combined reference" strategies performed better than aligning reads directly to human reference in terms of alignment and variant calling accuracies. The combined reference strategy was particularly good at reducing false negative variants calls without significantly increasing the false positive rate. In some scenarios the performance gain of these two special handling strategies was too small for special handling to be cost-effective, but it was found crucial when false non-synonymous SNVs should be minimized, especially in exome sequencing. CONCLUSIONS: Our study systematically analyzes the effects of mouse contamination in the sequencing data of human-in-mouse xenografts. Our findings provide information for designing data analysis pipelines for these data.

HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0.

We describe the potential of current Web 2.0 technologies to achieve data mashup in the health care and life sciences (HCLS) domains, and compare that potential to the nascent trend of performing semantic mashup. After providing an overview of Web 2.0, we demonstrate two scenarios of data mashup, facilitated by the following Web 2.0 tools and sites: Yahoo! Pipes, Dapper, Google Maps and GeoCommons. In the first scenario, we exploited Dapper and Yahoo! Pipes to implement a challenging data integration task in the context of DNA microarray research. In the second scenario, we exploited Yahoo! Pipes, Google Maps, and GeoCommons to create a geographic information system (GIS) interface that allows visualization and integration of diverse categories of public health data, including cancer incidence and pollution prevalence data. Based on these two scenarios, we discuss the strengths and weaknesses of these Web 2.0 mashup technologies. We then describe Semantic Web, the mainstream Web 3.0 technology that enables more powerful data integration over the Web. We discuss the areas of intersection of Web 2.0 and Semantic Web, and describe the potential benefits that can be brought to HCLS research by combining these two sets of technologies.

Assessment of endodontically treated teeth adjacent to proposed implant sites.

Many partially dentate dentitions are now being restored using dental implants; but assessment of endodontically treated teeth adjacent to the proposed implant sites has seldom been reported. Assessment criteria are given for sound- and endodontically compromised teeth to ensure adequate preimplant preventive and restorative treatment and to minimize the chances of failure of dental implants adjacent to these teeth. Cases are used to illustrate pitfalls in the treatment planning process.