SegSeq

SegSeq identifies chromosomal breakpoints and copy-number alterations from massively parallel sequencing data to enable detection of somatic genomic alterations in cancer.

Key Features:

• Detection of Copy-Number Alterations: Identifies genomic regions with recurrent copy-number alterations (CNAs) in tumor genomes.
• Massively Parallel Sequencing Integration: Leverages massively parallel sequencing as an alternative to DNA microarrays for CNA detection.
• Statistical Analysis Capability: Implements a statistical framework that assesses the power to detect CNAs of varying sizes.
• Segmentation Algorithm: Segments regions of equal copy number from sequence read data.
• Precision in Breakpoint Localization: Achieves over twofold better precision than DNA microarrays for localizing breakpoints, typically to approximately 1 kilobase.

Scientific Applications:

• Cancer Genomics: Identification of recurrent CNAs to discover genes contributing to oncogenesis.
• Breakpoint Mapping: Precise localization of chromosomal breakpoints and structural rearrangements that may disrupt gene function or regulation.
• Tumor–Normal Comparative Analysis: Detection of somatic genomic events through analysis of matched tumor and normal samples.

Methodology:

Analyzes aligned sequence reads from human cell lines, segments sequence data to define regions of equal copy number, and applies a statistical framework to assess detection power; evaluated on three matched tumor–normal pairs comprising approximately 14 million reads.