Advances in statistical bioinformatics : models and integrative inference for high-throughput data / [edited by] Kim-Anh Do, Zhaohui Steve Qin and Marina Vannucci.
Material type:
- 9781107027527 (hardback)
- 000SB:572.80285 23 D631
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Books | ISI Library, Kolkata | 000SB:572.80285 D631 (Browse shelf(Opens below)) | Available | 135131 |
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000SB:572.636 Am485 Exploration and analysis of DNA microarray and other high-dimensional data / | 000SB:572.8 M234 Some statistical issues pertaining to genome-wide association studies / | 000SB:572.8 Sa158 Introduction to evolutionary genomics / | 000SB:572.80285 D631 Advances in statistical bioinformatics : | 000SB:572.8633 D953 Biological sequence analysis : | 000SB:572.8633 C518 Statistical methods for QTL mapping / | 000SB:573.21 B327 Some statistical contributions to the analysis of human genome diversity and evolution |
Includes bibliographical references and index.
"Chapter 1 An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang, and Ganiraju Manyam The University of Texas. MD Anderson Cancer Center 1.1 Introduction 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"--
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