BESST

BESST improves genome assembly scaffolding by linking and orienting contigs and estimating gap sizes from contig files and BAM files produced by mapping paired-end and/or mate-pair reads from high-throughput sequencing.

Key Features:

• Inputs and outputs: Accepts contig files and BAM files from mapped paired-end and/or mate-pair reads and outputs linked and oriented scaffolds.
• Accurate gap estimation: Implements a statistical model that estimates gap-size distributions from reads spanning gaps to provide less biased distance estimates between contigs.
• Use of multiple information sources: Employs a novel algorithm that goes beyond relying solely on the number of read pairs supporting contig links by incorporating additional information about link evidence.
• Treatment of paired-end contamination: Identifies links in mate-pair libraries that represent paired-end contamination and treats them as paired-end links to improve scaffolding accuracy.
• Performance with wide insert-size distributions: Performs well on datasets characterized by wide library insert-size distributions, outperforming methods that assume narrow distributions.
• Handling complex genomes: Demonstrated capability on large and complex genomes, including the 20 Gbp Picea abies (Norway spruce) dataset.
• Robust evaluation across datasets: Validated across a wide variety of datasets rather than only small or specific test sets.

Scientific Applications:

• Genome assembly scaffolding: Produces improved scaffolds for short-read high-throughput sequencing assemblies, especially for complex genomes.
• Structural variation detection: Provides more reliable scaffolded assemblies that facilitate detection of structural variants.
• Library insert-size estimation: Enables more accurate estimation of library insert-size distributions through improved gap and link modeling.
• Downstream annotation and comparative genomics: Generates scaffolds that support gene prediction and comparative genomics analyses.

Methodology:

Accepts contig files and BAMs from mapped paired-end and/or mate-pair reads; applies a novel algorithm that incorporates additional information beyond read-pair counts; uses a statistical model of the distribution of reads spanning gaps for gap-size estimation; and detects paired-end contamination within mate-pair libraries to reclassify links accordingly.