The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

/pdf/initial-sequencing-and-analysis-of-the-human-genome-5dapk1rsx1.pdf

Initial sequencing and analysis of the human genome.

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.

/pdf/the-genome-analysis-toolkit-a-mapreduce-framework-for-4aygfa9b66.pdf

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

/pdf/haploview-analysis-and-visualization-of-ld-and-haplotype-1p9iyir38a.pdf

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

The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.

The International HapMap Project

The complexity of data available in human genetics continues to grow at an explosive rate. With that growth, the challenges to understanding the meaning of the underlying information also grow. A currently popular approach to dissecting such information falls under the broad category of data mining. This can apply to any approach that tries to extract relevant information from large amounts of data, but often refers to methods that deal, in a non-linear fashion, with very large numbers of variables that cannot be simultaneously handled by more conventional statistical methods. To explore the usefulness of some of these approaches, 13 groups applied a variety of strategies to the first dataset provided to GAW 15 participants. With the extensive microarray and SNP data provided for 14 CEPH families, these groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene-gene interaction, functional similarities, co-regulated gene expression and the mapping of gene expression determinants, among others. In general, the methods offered strategies to provide a better understanding of the complex pathways involved in gene expression and function. These are still "works in progress," often exploratory in nature, but they provide insights into ways in which the data might be interpreted. Despite the still preliminary nature of some of these methods and the diversity of the approaches, some common themes emerged. The collection of papers and methods offer a starting point for further exploration of complex interactions in human genetic data now readily available.

Data mining of RNA expression and DNA genotype data: presentation group 5 contributions to Genetic Analysis Workshop 15.

Latent class (LCA) and cluster analysis (CLA) were utilized to identify trait loci for the Genetic Analysis Workshop 12 simulated disease. These techniques create non-overlapping subsets of concordant and discordant affected relative pairs based upon identity-by-descent (IBD) allele sharing at sequences of markers. Subgroups with a large proportion of affected pairs are used to identify markers in proximity to disease susceptibility loci. Both methods are model-free and make use of information from affected and unaffected subjects. In analyses performed without knowledge of the true disease model, LCA and CLA identified regions containing five of the seven trait loci.

A clustering approach for localizing disease susceptibility loci.

There are errors in Table 1, \" Characteristics of participants in the Urban Health Study and the Women's Interagency HIV Study. \" Please see the corrected Table 1 here. access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Erratum: Novel genetic locus implicated for HIV-1 acquisition with putative regulatory links to HIV replication and infectivity: A genome-wide association study (PLoS ONE (2015) 10:3( e0118149) 10.1371/journal.pone.0118149)

Nancy L. Saccone

Papers

Data mining of RNA expression and DNA genotype data: presentation group 5 contributions to Genetic Analysis Workshop 15.

A clustering approach for localizing disease susceptibility loci.

Erratum: Novel genetic locus implicated for HIV-1 acquisition with putative regulatory links to HIV replication and infectivity: A genome-wide association study (PLoS ONE (2015) 10:3( e0118149) 10.1371/journal.pone.0118149)