gmap_iit_store

gmap_iit_store constructs a map store for known genes and single nucleotide polymorphisms (SNPs) and performs cDNA-to-genome mapping and alignment leveraging GMAP algorithms for genomic mapping and alignment tasks.

Key Features:

• Map store creation: Constructs a map store for known genes and single nucleotide polymorphisms (SNPs).
• cDNA-to-genome mapping and alignment: Maps and aligns cDNA sequences to genomes using GMAP algorithms.
• Low startup time and memory requirements: Operates with minimal startup time and reduced memory usage for individual and batch processing.
• Accurate gene structure identification: Generates precise gene structures in the presence of substantial polymorphisms and sequence errors without relying on probabilistic splice site models.
• Minimal sampling strategy: Uses a minimal sampling strategy for efficient genomic mapping.
• Oligomer chaining: Employs oligomer chaining to enable approximate alignment of sequences.
• Sandwich Dynamic Programming: Applies sandwich dynamic programming for robust splice site detection.
• Microexon identification with statistical significance testing: Identifies microexons and assesses their statistical significance.
• High splice-site recovery on mutated mRNAs: Identified all splice sites in over 99.3% of human mRNA sequences with 1% and 3% random mutations.
• Comparative alignment quality: Produced higher-quality alignments more frequently than BLAT on human expressed sequence tags and than GeneSeqer on Arabidopsis cDNAs.
• Increased speed: Achieves a several-fold increase in speed compared with existing genomic mapping programs.

Scientific Applications:

• Gene Expression Analysis: Facilitates study of gene expression by mapping cDNA sequences to genomes.
• Polymorphism Studies: Enables examination of genetic variation such as single nucleotide polymorphisms (SNPs).
• Transcriptome Research: Supports transcriptome analyses by aligning expressed sequence tags and cDNAs for transcript reconstruction.
• Large-scale sequencing data processing: Applies to next-generation DNA sequencing datasets requiring accurate and rapid alignment at scale.

Methodology:

Leverages GMAP algorithms to map and align cDNA to genomes and construct a map store for genes and SNPs using a minimal sampling strategy, oligomer chaining, sandwich dynamic programming for splice-site detection, and statistical significance testing for microexon identification.