ORMAN

ORMAN assigns multimapping RNA-Seq reads to specific transcripts to resolve mapping ambiguity and improve transcript quantification and detection of novel transcriptomic events.

Key Features:

• Resolution of Mapping Ambiguity: Assigns each multimapping read to a specific transcript using an estimated distribution of the genomic region covering the read so reads contribute accurately to transcript quantification.
• Combinatorial Optimization Approach: Formulates a combinatorial optimization problem to compute the minimum number of potential transcript products per gene and solves it using approximation algorithms, integer linear programs, and heuristics.
• Enhancement of Transcriptomic Analysis: Improves performance of transcript discovery and quantification methods such as Cufflinks, IsoLasso, and CLIIQ by providing more accurate read assignments for novel transcripts.
• Validation on Simulated and Real Data: Validated on simulated RNA-Seq datasets (including random subsets of transcripts from the UCSC database) and on real RNA-Seq reads, producing coverage values that closely resemble original distributions, including genes with highly non-uniform coverage.

Scientific Applications:

• Alternative Splicing Analysis: Enables detection and quantification of novel splicing events and indels by resolving ambiguous read mappings in RNA-Seq data.
• Gene Fusion and Transcript Variation Detection: Supports identification of gene fusions and other transcript variations by improving assignment of multimapping reads.
• Transcript Discovery and Quantification: Enhances algorithms for transcript reconstruction and expression quantification from RNA-Seq input.
• Functional Genomics: Provides more precise transcript-level coverage estimates to support basic biological discovery and functional genomics studies.

Methodology:

Assigns multimapping reads based on an estimated distribution of the region covering each read and formulates a combinatorial optimization problem to compute a minimum set of transcript products per gene, solved using approximation algorithms, integer linear programming, and heuristics.