Mapsembler

Mapsembler assembles targeted regions from next-generation sequencing (NGS) reads to reconstruct local sequences and graphs for detection of repeats, single nucleotide polymorphisms (SNPs), exon skipping, gene fusions, and other structural variations.

Key Features:

• Targeted Assembly: Targets specific genomic or transcriptomic regions such as repeats, SNPs, exon skipping, gene fusions, and other structural variations instead of assembling entire genomes or transcriptomes.
• Iterative Micro Assembly: Performs iterative micro-assembly of short reads around predefined regions and can produce plain assembled sequences or extension graphs representing local context.
• Efficient Algorithms: Implements algorithms to retrieve approximate occurrences of a query sequence from reads and to construct extension graphs.
• Localized Indexing: Uses localized indexing to minimize memory footprint during retrieval and assembly.
• De Novo Discovery: Enables de novo discovery of novel genetic events directly from raw sequencing reads, including candidate fusion genes.

Scientific Applications:

• Gene Fusion Detection: Detection and analysis of candidate gene fusions in human breast cancer from NGS reads.
• Structural Variation and Variant Characterization: Identification and characterization of repeats, SNPs, exon skipping events, and other structural variations within targeted regions.
• Targeted Analyses in Large Genomes: Facilitates focused investigation of loci of interest in large or complex genomes without requiring whole-genome assembly.

Methodology:

Iteratively assembles short sequences around targeted regions from sequencing reads, retrieves approximate occurrences of query sequences in reads, constructs extension graphs to represent local genomic context, and employs localized indexing to reduce memory usage.