High-resolution mapping of copy-number alterations with massively parallel sequencing

Nature Methods 6:99-103. Published: 2008.12.31

Derek Y. Chiang, Gad Getz, David B. Jaffe, Michael J.T. O'Kelly, Xiaojun Zhao, Scott L. Carter, Carsten Russ, Chad Nusbaum, Matthew Meyerson, Eric S. Lander

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Abstract

Cancer results from somatic alterations in key genes, including point mutations, copy number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here, we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to identify chromosomal breakpoints using massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of ~14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over two-fold better precision for localizing breakpoints (typically, to within ~1 kb).

Keywords: copy-number alterations next-generation sequencing segmentation algorithm tumor genome characterization

Supplemental Data

Description Link/Filename
Alignment positions of sequence reads (hg18) arachne_qltout_marks.tar.gz
Matlab files with alignable coordinates hg18_alignable_N36_D2.tar.gz
Matlab source code, SegSeq version 1.0.1 SegSeq_1.0.1.tar.gz