Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.

Availability: http://www.broad.mit.edu/mpg/haploview/

Contact: jcbarret@broad.mit.edu

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Haploview: analysis and visualization of LD and haplotype maps

Motivation: DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Availability: Freely available to academic users from: http://www.ub.edu/dnasp

Contact: [email protected]

DnaSP v5

Basics of qualitative research: Grounded theory procedures and techniques

The CONSORT statement is used worldwide to improve the reporting of randomised controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience. To encourage dissemination of the CONSORT 2010 Statement, this article is freely accessible on bmj.com and will also be published in the Lancet, Obstetrics and Gynecology, PLoS Medicine, Annals of Internal Medicine, Open Medicine, Journal of Clinical Epidemiology, BMC Medicine, and Trials.

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CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials

Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.

Ethical issues in using ambient intelligence in health-care settings

The use of racial categories in biomedicine has had a long history in the United States. However, social hierarchy and discrimination, justified by purported scientific differences, has also plagued the history of racial categories. Because "race" has some correlation with biological and genetic characteristics, there has been a call not to "throw the baby out with the bathwater" by eliminating race as a research or clinical category. I argue that race is too undefined and fluid to be useful as a proxy for biology or genetics.

Racial and ethnic categories in biomedical research: there is no baby in the bathwater.

The recent release of new, non-invasive prenatal tests for fetal aneuploidy using cell-free fetal DNA (cffDNA) has been hailed as a revolution in prenatal testing and has triggered significant commercial interest in the field. Ongoing research portends the arrival of a wide range of cffDNA tests. However, it is not yet clear how these tests will be integrated into well-established prenatal testing strategies in the USA, as the timing of such testing and the degree to which new non-invasive tests will supplement or replace existing screening and diagnostic tools remain uncertain. We argue that there is an urgent need for policy-makers, regulators and professional societies to provide guidance on the most efficient and ethical manner for such tests to be introduced into clinical practice in the USA.

Cell-free fetal DNA testing for fetal aneuploidy and beyond: clinical integration challenges in the US context

Objective: To examine the role of the practitioner, informed consent, and genetic counseling in genetic testing decisions and to assess their relative influence on women’s decision

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How Do Women Decide? Accepting or Declining BRCA1/2 Testing in a Nationwide Clinical Sample in the United States

The increasing complexity of human subjects research and its oversight has prompted researchers, as well as institutional review boards (IRBs), to have a forum in which to discuss challenging or novel ethical issues not fully addressed by regulations. Research ethics consultation (REC) services provide such a forum. In this article, we rely on the experiences of a national Research Ethics Consultation Collaborative that collected more than 350 research ethics consultations in a repository and published 18 challenging cases with accompanying ethical commentaries to highlight four contexts in which REC can be a valuable resource. REC assists: 1) investigators before and after the regulatory review; 2) investigators, IRBs, and other research administrators facing challenging and novel ethical issues; 3) IRBs and investigators with the increasing challenges of informed consent and risk/benefit analysis; and 4) in providing flexible and collaborative assistance to overcome study hurdles, mediate conflicts with...

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Mildred K. Cho

Papers

Ethical issues in using ambient intelligence in health-care settings

Racial and ethnic categories in biomedical research: there is no baby in the bathwater.

Cell-free fetal DNA testing for fetal aneuploidy and beyond: clinical integration challenges in the US context

How Do Women Decide? Accepting or Declining BRCA1/2 Testing in a Nationwide Clinical Sample in the United States

The Emergence of Clinical Research Ethics Consultation: Insights From a National Collaborative