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Standards for a new genomic era
October 22, 2009
A team of geneticists at Los Alamos National Laboratory, together with a consortium of international researchers, has recently proposed a set of standards designed to elucidate the quality of publicly available genetic sequencing information. The new standards could eventually allow genetic researchers to develop vaccines more efficiently or help public health or security personnel more quickly respond to potential public-health emergencies. In a recent issue of Science, Los Alamos geneticist Patrick Chain and colleagues presented six labels for genome sequence data that are, or will become, available in public databases rather than the two labels used today. The six labels would roughly characterize the completeness and accuracy-and consequently, the potential reliability-of genetic sequencing data. This is of great importance since researchers use such data on a daily basis for cross-referencing unknown genetic material with the genetic material of known organisms.
Every living organism with DNA has chromosomes containing the four molecular building blocks, or base pairs, represented by letters A, T, G, and C. One chromosome can contain millions of base pairs arranged like rungs on a ladder of DNA. The base pairs are arranged in sets of specific sequences that make up genes. These gene sequences can contain genetic instructions that help or harm an organism-for example by encoding enzymes that digest certain foods, or inducing cellular aberrations that give rise to certain diseases.
Genome researchers have catalogued genetic data from thousands of organisms and placed them in publicly available libraries. Researchers can use these libraries to crosscheck genetic data, for example when attempting to isolate an unknown public health threat, or to determine where a potentially helpful or harmful gene may be located on an organism's chromosome. For scientific fields such as biofuels research or environmental remediation, genetic data could help researchers determine whether microorganisms can efficiently break down plant matter to aid in ethanol production, or digest environmental contaminants like hydrocarbons.
However, because of the complexity of genetic data, genetic information in public libraries can range from very rough to very refined. In the past, genetic data has been classified either as "draft" or "finished," leaving a wide range of uncertainty about the potential accuracy of genetic data.
"In the past few years we've seen major advances in genetic sequencing technology, so we've seen an explosion in the amount of publicly available data," said Chain, who is lead author of the Science paper. "The amount of base-pair sequencing data generated each day is in the billions-orders of magnitude larger than what was generated a few years ago. Different sequencing technologies have different levels of accuracy. High degrees of uncertainty in a sequence can potentially lead a researcher down a wrong path that they could follow for a year or more. We now have a need for standards that will provide researchers with an unambiguous estimation of the quality of genetic sequence data."
Working with researchers from genome sequencing centers big and small-including the U.S. Department of Energy's Joint Genome Institute, the Sanger Institute, the Human Microbiome Project Jumpstart Consortium sequencing centers, Michigan State University, and the Ontario Institute for Cancer Research, among others-Chain and colleagues have proposed that sequence data be placed into one of six categories that augment the existing two categories. The six standards range from "standard draft sequence," representing minimum requirements for public submission, to a "finished sequence," the highest standard, which can be verified to contain only one sequencing error per 100,000 base pairs.
"My hope is all the major genome centers and advanced genomics groups use the gradations that fit their needs," said Chris Detter, LANL Genome Science Group Leader and Joint Genome Institute-LANL Center director. "Some centers may want all six, while some may only want three, but as long as they keep them intact, we are in good shape. Then, my hope is that the smaller genomics groups adopt the classes as written to help the rest of the scientific community know what they are generating and submitting."
DOE/Los Alamos National Laboratory
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Genetic Data Analysis 2: Methods for Discrete Population Genetic Data
by Bruce S. Weir (Author)
Genetic Data Analysis, first published in 1990, became the standard reference for ways to interpret discrete population genetic data. Genetic Data Analysis II retains the strengths of the original book and, based upon the suggestions of users, includes many new features, notably the revision of Chapter 10 (Phylogeny Reconstruction) to incorporate newer methods, and new chapters on Linkage and Individual Identification. Genetic Data Analysis II features an expanded set of Exercises, with solutions, and an expanded list of references. In addition, a suite of Windows-based programs written by Paul O. Lewis and Dmitri Zaykin is available without charge from the Web site maintained by the program in Statistical Genetics at North Carolina State University.
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Genetic Algorithms + Data Structures = Evolution Programs
by Zbigniew Michalewicz (Author)
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover...
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Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data
by Michael R. Barnes (Editor)
Praise from the reviews: "Without reservation, I endorse this text as the best resource I've encountered that neatly introduces and summarizes many points I've learned through years of experience. The gems of truth found in this book will serve well those who wish to apply bioinformatics in their daily work, as well as help them advise others in this capacity." CIRCGENETICS "This book may really help to get geneticists and bioinformaticians on 'speaking-terms'... contains some essential reading for almost any person working in the field of molecular genetics." EUROPEAN JOURNAL OF HUMAN GENETICS "... an excellent resource... this book should ensure that any researcher's skill base is maintained." GENETICAL RESEARCH “… one of the best...
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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems)
by Earl Cox (Author)
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.
You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used...
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Applied Statistical Genetics with R: For Population-based Association Studies (Use R)
by Andrea S. Foulkes (Author)
The vast array of molecular level information now available presents exciting opportunities to characterize the genetic underpinnings of complex diseases while discovering novel biological pathways to disease progression. In this introductory graduate level text, Dr. Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations. Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field. Couched in the language of biostatistics, this...
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Statistical Inference from Genetic Data on Pedigrees (Nsf-Cbms Conference Series in Probability & Statistics Volume 6) (V 6)
by Elizabeth A. Thompson (Author)
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Genetic Data Analysis III
by Bruce S. Weir (Author)
"Genetic Data Analysis", first published in 1990, became the standard reference for ways to interpret discrete population genetic data. The third edition of this acclaimed text now includes new features and revisions whilst retaining the strengths of the original. It is suitable for all those studying and working in the fields of genetics and other biological sciences involving genetic analysis.
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American Indian mtDNA, Y Chromosome Genetic Data, and the Peopling of North America
by Peter N. Jones (Author)
The field of molecular anthropology has grown in recent years with the advent of new methodologies and theoretical assumptions. The field has been particularly insightful in helping understand the initial peopling of North America. The author discusses the field of molecular anthropology and its insights into the peopling of North America, examining in detail the mtDNA and Y chromosome genetic data. Written in a clear, readable fashion, the author gives an overview of the topic for researchers, graduate students, and other professionals who are interested in this exciting new area of inquiry and the possibilities it holds for such contentious issues as biological affiliation, the peopling of North America, and historic population movements.
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Handbook on Analyzing Human Genetic Data: Computational Approaches and Software
by Shili Lin (Editor), Hongyu Zhao (Editor)
The discipline of statistical genetics is highly computational. Be it exact computational methods, simulation based, or a hybrid of the two, computational packages are indispensable tools and constant companions of researchers in the field. This handbook is intended to provide human geneticists and other biomedical researchers with guidance on selections of appropriate computational methods and software packages for their specific genetic problems. It may also be used by students and other learners as a reference in conjunction with a more theoretical and/or methodologically oriented text book. This book tries to strike a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among competing methods and software are...
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Multivariate quantitative genetics of anthropometric traits from the Boas data.: An article from: Human Biology
by Lyle W. Konigsberg (Author), Stephen D. Ousley (Author)
This digital document is an article from Human Biology, published by Wayne State University Press on June 1, 1995. The length of the article is 5711 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: KEY WORDS: BIOLOGICAL DISTANCE, ALLOMETRY, NATIVE AMERICANS, EVOLUTIONARY THEORY
Citation Details Title: Multivariate quantitative genetics of anthropometric traits from the Boas data. Author: Lyle W. Konigsberg Publication: Human Biology (Refereed) Date: June 1, 1995 Publisher: Wayne State University Press Volume: v67 Issue: n3 Page: p481(18)
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