Bridger

Bridger assembles de novo transcriptomes from RNA-Seq reads to reconstruct full-length transcripts for gene expression and alternative splicing analysis.

Key Features:

• Input/Output Formats: Accepts RNA-Seq input in FASTA or FASTQ formats and produces assembled transcripts in FASTA format.
• Partitioning into Splicing Graphs: Partitions reads into individual splicing graphs that capture the transcriptional complexity of a gene or small gene clusters.
• Minimum Path Cover Optimization: Employs a minimum path cover mathematical model to identify an optimal set of paths within splicing graphs for transcript reconstruction.
• Algorithmic Similarity to Cufflinks: Leverages techniques similar to those employed in Cufflinks, achieving comparable sensitivity and accuracy.
• Performance Efficiency: Runs faster than most existing de novo transcriptome assemblers and requires less memory space.
• Accuracy and Sensitivity on Benchmark Datasets: In tests on dog, human, and mouse RNA-Seq datasets, assembled more full-length reference transcripts with fewer candidate transcripts than state-of-the-art assemblers, reducing false positives.

Scientific Applications:

• De novo transcriptome assembly: Reconstruction of transcriptomes from RNA-Seq data when no reference genome is available.
• Novel transcript discovery: Identification of new transcripts and genes from RNA-Seq experiments.
• Gene expression and alternative splicing analysis: Reconstruction of full-length transcripts to support studies of expression levels and splicing isoforms.

Methodology:

Input RNA-Seq reads are partitioned into individual splicing graphs representing transcriptional complexity, and a minimum path cover model is applied to identify optimal paths within those graphs to assemble transcripts.