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Advances in statistical bioinformatics : models and integrative inference for high-throughput data / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX.

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Language:
English.
Publication date:
2013
Publication:
New York : Cambridge University Press, 2013.
Format:
  • Book
  • xv, 481 pages, 16 unnumbered pages of plates : illustrations (some color) ; 24 cm
Bibliography:
Includes bibliographical references and index.
Summary:
"When Sanger and Coulson first described a reliable, efficient method for DNA sequencing in 1975 (Sanger and Coulson, 1975), they made possible the full sequencing of both genes and entire genomes. Although the method was resource-intensive, many institutions invested in the necessary equipment, and Sanger sequencing remained the standard for the next 30 years. Refinement of the process increased read lengths from around 25 to 2 Mohlere, Wang, and Manyam almost 750 base pairs (Schadt et al., 2010, fig. 1). While this greatly increased efficiency and reliability, the Sanger method still required not only large equipment but significant human investment, as the process requires the work of several people. This prompted researchers and companies such as Applied Biosystems to seek improved sequencing techniques and instruments. Starting in the late 2000s, new instruments came on the market that, although they actually decreased read length, lessened run time and could be operated more easily with fewer human resources (Schadt et al., 2010). Despite discoveries that have illuminated new therapeutic targets, clarified the role of specific mutations in clinical response, and yielded new methods for diagnosis and predicting prognosis (Chin et al., 2011), the initial promise of genomic data has largely remained so far unfulfilled. The difficulties are numerous"-- Provided by publisher.
Contributor:
Do, Kim-Anh, 1960-
Qin, Steven, 1972-
Vannucci, Marina, 1966-
Subjects:
ISBN:
9781107027527
1107027527

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