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Reversing ecology reveals ancient environments
February 26, 2009
From hair color to the ancestral line of parasitic bacteria, scientists can glean a lot from genes. But imagine if genes also revealed where you lived or who you spent time with. It turns out they do, if you know where and how to look. Stanford researchers with collaborators at Tel-Aviv University have now laid the foundation for opening such a window to the past using a technique called "reverse ecology." The technique uses genomic data to examine metabolic networks and pulls out proxies for reconstructing bacterial environments millions of years in the past. The work, published in the February issue of the Journal of Computational Biology, offers clues to the complex evolutionary interplay between organisms such as parasites and hosts.
"Based on reverse ecology, you can start with an organism-say, a certain bacterial species that you know nothing about ecologically. But by looking at its genome and metabolic network, you can recreate that past environment the organism lived in," said Elhanan Borenstein, lead author of the paper and a postdoctoral researcher in the Biology Department at Stanford. "And we've done this with hundreds of different species."
Researchers have used genomic data to study metabolic networks-the chemical reactions in metabolism that determine the physiological and biochemical properties of cells-in great detail. But Borenstein, with co-author Marcus Feldman, a professor of biology at Stanford, took this understanding a step further.
Through the metabolic network, organisms accumulate biochemical compounds from their interactions with the surrounding environment (e.g., oxygen, glutamine or sulfate). These molecules also correlate with other environmental properties like temperature and salinity. "This gives us a way to predict the biochemical environment of organisms and learn ecology from the genomic data on a large scale," Borenstein said.
The researchers collected clues about not only the organism's environment but also its relationship to other species. For example, they detected a specific signature for adaptation between a parasite and host. What's more, they could tell what kind of host the parasite was living in based on the alignment between the environment a parasite requires and that required by the host. "We can see a signal to distinguish between a mammal parasite and an insect parasite," Borenstein said. "And how the interaction evolved over time."
Now that they have a data set that reveals the current and ancient environment of hundreds of bacterial species, Borenstein and colleagues hope to use their data to discern major environmental events of the past, including key events in the history of life on Earth.
The next step is to move from looking at individual bacteria species to entire communities. In particular, Borenstein hopes to explore large collections of host-dwelling bacteria like those living in the human gut or mouth or the communities found in soils. The complicated and intricate ecology of these systems should now be accessible given the genomic data that researchers continue to unveil.
"The important thing," Borenstein said, "is that we now know there is a way to learn ecology from genomic data, and we can do this on a very large scale."
This work builds on research published last fall in the Proceedings of the National Academy of Sciences.
Borenstein is also a postdoctoral researcher at the Santa Fe Institute. The work was funded by the Morrison Institute for Population and Resource Studies, the James S. McDonnell Foundation and the National Institutes of Health.
Cassandra Brooks is a science-writing intern at the Stanford News Service.
Stanford University
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Bioinformatics and the Cell: Modern Computational Approaches in Genomics, Proteomics and Transcriptomics
by Xuhua Xia (Author)
The many books that have been written on bioinformatics tend to fall on two extremes: books that feature computational details with a great deal of mathematics, for computational scientists and mathematicians, or books that treat bioinformatics mostly as a giant black box, for biologists. Previous books written on bioinformatics often have limited contribution to creating interdisciplinary scientists needed in modern biological and biomedical sciences. This book aims to render both mathematical equations and biology to numbers, to help truly interdisciplinary scientists. Although the book covers bioinformatics methods at a level more advanced than most other bioinformatics books, the extensive numerical illustration of these methods should make it accessible to most senior undergraduate...
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Introduction to Computational Genomics: A Case Studies Approach
by Nello Cristianini (Author), Matthew W. Hahn (Author)
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden...
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Computational Text Analysis: For Functional Genomics and Bioinformatics
by Soumya Raychaudhuri (Author)
This book brings together the two disparate worlds of computational text analysis and biology and presents some of the latest methods and applications to proteomics, sequence analysis and gene expression data. Modern genomics generates large and comprehensive data sets but their interpretation requires an understanding of a vast number of genes, their complex functions, and interactions. Keeping up with the literature on a single gene is a challenge itself-for thousands of genes it is simply impossible. Here, Soumya Raychaudhuri presents the techniques and algorithms needed to access and utilize the vast scientific text, i.e. methods that automatically "read" the literature on all the genes. Including background chapters on the necessary biology, statistics and genomics, in...
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Sequence Data Mining (Advances in Database Systems)
by Guozhu Dong (Author), Jian Pei (Author)
Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and...
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Data Mining and Applications in Genomics (Lecture Notes in Electrical Engineering)
by Sio-Iong Ao (Author)
"Data Mining and Applications in Genomics" contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. "Data Mining and Applications in Genomics" offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.
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Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data (Wiley Series on Methods and Applications in Data Mining)
by D. M. Dziuda (Author)
Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.
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Biological and Medical Data Analysis: 5th International Symposium, ISBMDA 2004, Barcelona, Spain, November 18-19, 2004, Proceedings (Lecture Notes in Computer Science)
by José María Barreiro (Editor), Fernando Martin-Sanchez (Editor), Víctor Maojo (Editor), Ferran Sanz (Editor)
This book constitutes the refereed proceedings of the 5th International Symposium on Biological and Medical Data Analysis, ISBMDA 2004, held in Barcelona, Spain in November 2004. The 50 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data analysis for image processing, data visualization, decision support systems, information retrieval, knowledge discovery and data mining, statistical methods and tools, time series analysis, data management and analysis in bioinformatics, integration of biological and medical data, metabolic data and pathways, and microarray data analysis and visualization.
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Fundamentals of Data Mining in Genomics and Proteomics
by Werner Dubitzky (Editor), Martin Granzow (Editor), Daniel P. Berrar (Editor)
This book aims to present state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. Research and development in genomics and proteomics depend on the analysis and interpretation of large amounts of data generated by high-throughput techniques. To exploit data obtained from experimental and observational studies, life scientists need to understand the analytical techniques and methods from statistics and data mining. These techniques are not easily accessible to life scientists working on genomics and proteomics problems, as the available material is presented from a highly mathematical perspective, favoring formal rigor over conceptual clarity and assessment of practical relevance. This book addresses these...
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Data Analysis and Visualization in Genomics and Proteomics
by Francisco Azuaje (Editor), Joaquin Dopazo (Editor)
Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems ...
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Computer Scientists Mine Genomic Data.: An article from: Analytic Separations News
by Business Communications Company, Inc. (Publisher)
This digital document is an article from Analytic Separations News, published by Business Communications Company, Inc. on March 1, 2004. The length of the article is 737 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.
Citation Details Title: Computer Scientists Mine Genomic Data. Publication: Analytic Separations News (Newsletter) Date: March 1, 2004 Publisher: Business Communications Company, Inc. Volume: 1 Issue: 10
Distributed by Thomson Gale
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