Genomics Virtual Laboratory: a practical bioinformatics workbench for the cloud

Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets ; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces ; highly available, scalable computational resources ; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise

The association of cancer survival with four socioeconomic indicators: a longitudinal study of the older population of England and Wales 1981–2000

BACKGROUND: Many studies have found socioeconomic differentials in cancer survival. Previous studies have generally demonstrated poorer cancer survival with decreasing socioeconomic status but mostly used only ecological measures of status and analytical methods estimating simple survival. This study investigate socio-economic differentials in cancer survival using four indicators of socioeconomic status; three individual and one ecological. It uses a relative survival method which gives a measure of excess mortality due to cancer. METHODS: This study uses prospective record linkage data from The Office for National Statistics Longitudinal Study for England and Wales. The participants are Longitudinal Study members, recorded at census in 1971 and 1981 and with a primary malignant cancer diagnosed at age 45 or above, between 1981 and 1997, with follow-up until end 2000. The outcome measure is relative survival/excess mortality, compared with age and sex adjusted survival of the general population. Relative survival and Poisson regression analyses are presented, giving models of relative excess mortality, adjusted for covariates. RESULTS: Different socioeconomic indicators detect survival differentials of varying magnitude and definition. For all cancers combined, the four indicators show similar effects. For individual cancers there are differences between indicators. Where there is an association, all indicators show poorer survival with lower socioeconomic status. CONCLUSION: Cancer survival differs markedly by socio-economic status. The commonly used ecological measure, the Carstairs Index, is adequate at demonstrating socioeconomic differentials in survival for combined cancers and some individual cancers. A combination of car access and housing tenure is more sensitive than the ecological Carstairs measure at detecting socioeconomic effects on survival – confirming Carstairs effects where they occur but additionally identifying effects for other cancers. Social class is a relatively weak indicator of survival differentials

Determinants of progression to AIDS or death after HIV diagnosis, United States, 1996 to 2001.

PURPOSE: The aim of the study is to determine factors associated with disease progression after human immunodeficiency virus (HIV) infection diagnosis. METHODS: We applied generalized linear models with Poisson errors to obtain adjusted relative excess risk for death for persons diagnosed with acquired immunodeficiency syndrome (AIDS) or HIV infection (with or without concurrent AIDS) during 1996 to 2001. We examined differences in time between HIV diagnosis and AIDS by using standardized Kaplan-Meier survival methods. RESULTS: Relative excess risk for death within 3 years after AIDS diagnosis was significantly greater for non-Hispanic blacks (1.15; 95% confidence interval [CI], 1.12-1.18), American Indians (1.33; 95% CI, 1.16-1.52), and Hispanics (1.16; 95% CI, 1.13-1.20) compared with whites. Risk for death also was greater among injection drug users (men, 1.50; 95% CI, 1.46-1.54; women, 1.57; 95% CI, 1.51-1.62) compared with men who have sex with men and among those diagnosed at older ages compared with younger persons. Similar disparities between groups in risk for death were observed from HIV diagnosis. Risk for progression from HIV to AIDS was greater for nonwhites, men, and older persons compared with whites, women, and younger persons, respectively. CONCLUSIONS: Interventions should target those at excess risk for death or morbidity to ensure access to quality care and adherence to treatment to slow disease progression

Cancer survival in Kentucky and health insurance coverage.

BACKGROUND: Access to health insurance influences the amount and quality of health care received, which in turn is likely to be related to survival. Few studies have systematically examined cancer survival by individual level health insurance data from a state population-based cancer registry for 4 anatomic sites. METHODS: Men and women aged 18 to 99 years who were registered from 1995 to 1998 with the Kentucky Cancer Registry, Lexington, with colorectal, lung, breast, or prostate cancer were followed up through 1999. Three-year crude and relative survival proportion by 7 health insurance categories and by sex for all 4 sites were calculated. Poisson regression was used to model the risk of death (controlling for age group at diagnosis, sex, race, stage at diagnosis, and treatment) relative to expected deaths in the general population from all 4 cancers by health insurance category. RESULTS: Among patients with prostate cancer, 3-year relative survival proportion was 98% for the privately insured and 83% for the uninsured; comparable figures were 91% and 78% for patients with breast cancer; 71% and 53% for patients with colorectal cancer; and 23% and 13% for patients with lung cancer. For all 4 cancers the uninsured ranked fifth or sixth on survival, above patients with unknown insurance type or Medicaid/welfare. CONCLUSION: These findings confirm purported disparities in cancer care and point toward the need to make quality care accessible to all segments of the population

Bionitio:Demonstrating and facilitating best practices for bioinformatics command-line software

BACKGROUND: Bioinformatics software tools are often created ad hoc, frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices. This results in the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability, and interoperability; and erroneous or inaccurate results. FINDINGS: We have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command-line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardized building and packaging, user documentation, code documentation, a standard open source software license, software revision control, and containerization. CONCLUSIONS: Bionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users because tools are more easily installed and consistent in their usage. Bionitio is released as open source software under the MIT License and is available at https://github.com/bionitio-team/bionitio

Upward trends in symptom reporting in the UK Armed Forces.

Several reports have shown increases in the prevalence of non-specific symptoms in the general population. Research in the military tends to focus on comparisons between deployed and non-deployed personnel and does not examine trends over time. 4,257 and 4,295 male participants of the Gulf war and Iraq war studies not deployed to either of these wars were randomly sampled and surveyed in 1997/1998 and 2004/2006 in two independent cross-sectional studies. Information was collected on 50 symptoms and the General Health Questionnaire (GHQ-12). Factor analysis was performed to identify an underlying pattern of symptom dimensions, and multivariate regressions were carried out to examine changes in symptom dimensions between the two surveys and the possible role of psychological morbidity. Factor analysis identified a robust pattern of eight symptom dimensions. An increase in the prevalence of symptoms was evident across all symptom dimensions. Adjustment for demographic and service characteristics revealed increases in the odds of scoring highly on symptom dimensions, varying from odds ratios 1.57, 95% CI 1.36-1.81 (cardio-respiratory dimension) to 2.24, 95% CI 1.93-2.60 (fatigue dimension). Unexpectedly, increases were even greater when adjusting for psychological morbidity. There is clear evidence of an increase in the reporting of non-specific symptoms over a 7 year period in the UK Armed Forces. It suggests that the threshold for reporting symptoms has decreased and cannot be explained by psychological distress. The possible implication of this trend for medical practice in the wider population deserves close scrutiny

A summary of the criteria that would define a general genomics workbench environment, and suggested implications on technical requirements.

<p>A summary of the criteria that would define a general genomics workbench environment, and suggested implications on technical requirements.</p