MaLTA

MaLTA reconstructs and quantifies transcriptomes from Ion Torrent RNA-Seq data using a maximum likelihood framework to resolve transcript structure and expression.

Key Features:

• Maximum likelihood integration: Integrates a maximum likelihood model into both assembly and quantification to improve inference of transcript structures.
• Enhanced IsoEM (Iterative Expectation-Maximization): Uses an adapted version of the IsoEM algorithm tailored to handle Ion Torrent RNA-Seq reads.
• Expression estimation: Provides precise estimation of transcript expression levels and addresses ambiguities in mapping reads to transcripts.
• Novel transcript reconstruction: Reconstructs novel transcripts from deep sequencing data.
• Error and coverage handling: Accounts for sequencing errors and uneven coverage of expressed transcripts during analysis.
• Alternative splicing resolution: Distinguishes between highly similar transcripts resulting from alternative splicing.
• Validation on datasets: Has been experimentally validated on synthetic and real datasets using Ion Torrent RNA-Seq data.

Scientific Applications:

• Transcriptome assembly: Reconstruction of full-length transcript models from Ion Torrent RNA-Seq reads.
• Expression quantification: Estimation of transcript-level expression for gene expression profiling.
• Novel transcript discovery: Detection and characterization of previously unannotated transcripts.
• Alternative splicing analysis: Analysis and discrimination of isoforms arising from alternative splicing.
• Comprehensive transcriptome studies: Use in diverse biological contexts for comprehensive transcriptome analyses with Ion Torrent data.

Methodology:

Integration of a maximum likelihood model into assembly and quantification combined with an enhanced IsoEM (Iterative Expectation-Maximization) algorithm tailored for Ion Torrent RNA-Seq reads to estimate transcript expression and resolve mapping ambiguities.