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Results: 3

1.
Fig. 1.

Fig. 1. From: Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome.

Association of breakpoints and SDs. Genomic locations of SDs are indicated by BlastZ (32) self-chain matches to the human reference sequence (black vertical bars). SDs coinciding with deletion/duplication breakpoints are highlighted by a red dashed line. The association of breakpoints and SDs [consistent with earlier observations (1, 2, 9, 16, 22)] indicates that nucleotide sequence signatures can facilitate breakpoint mapping.

Jan O. Korbel, et al. Proc Natl Acad Sci U S A. 2007 June 12;104(24):10110-10115.
2.
Fig. 2.

Fig. 2. From: Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome.

Overview of BreakPtr and its parameter optimization procedure. (A) Data from HighRes-CGH experiments are statistically integrated with nucleotide sequence signatures. Finder fine-maps CNV breakpoints. The subsequently implemented Annotator provides information in terms of copy number ratios, and Flagger identifies putative cross-hybridization for regions for which Finder has predicted CNVs (i.e., regions colored in light gray are disregarded). (HighRes-CGH signals shown in the figure do not correspond to original data but were generated for visualization purposes.) (B) Parameter optimization. Training data and gold standards are used to estimate initial parameters. Parameters are then optimized by using an EM-based algorithm (25). Finally, CNV breakpoints are predicted, and sequenced. A new round of parameter estimation is initiated subsequently by using further knowledge from validated breakpoints.

Jan O. Korbel, et al. Proc Natl Acad Sci U S A. 2007 June 12;104(24):10110-10115.
3.
Fig. 3.

Fig. 3. From: Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome.

Hidden Markov models (HMMs): architecture and parameters. (A) HMMs: arrows indicate transitions used by the dbHMM (gray and black arrows) and by the univariate HMM (black arrows only), e.g., for the core parameterization. (B) Emission distributions for the dbHMM shown as heat maps, here exemplified by a 5 × 25-bin-model (x and y axes refer to each individual heat map). (C) Scheme illustrating the incorporation of discretized signals into bins: (1) scores quantifying DNA sequence characteristics, i.e., SD-like repeats (horizontal axis; schematically depicted distributions (in gray) are drawn for visualization purposes only); (2) normalized microarray fluorescent intensity log2-ratios (vertical axis).

Jan O. Korbel, et al. Proc Natl Acad Sci U S A. 2007 June 12;104(24):10110-10115.

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