SNIKT

SNIKT identifies and removes adapter contamination from long-read shotgun sequencing data for DNA and RNA whole-genome and metagenomic analyses.

Key Features:

• Sequence-independent identification: Identifies adapter contamination without prior knowledge of adapter sequences by operating independently of predefined sequence motifs.
• Input-assisted removal: Leverages input data characteristics to guide removal of putative adapter contaminants from reads.
• Long-read shotgun focus: Targets adapter detection and removal specifically for long-read shotgun sequencing datasets used in whole-genome and metagenomic studies.
• R implementation: Implemented in R to enable statistical handling and integration with other R-based bioinformatics workflows.

Scientific Applications:

• Genome assembly and annotation: Improves quality of long-read assemblies and downstream genome annotation by removing adapter contamination.
• Metagenomic community analysis: Cleans long-read metagenomic datasets to support accurate taxonomic profiling and comparative analyses.
• Comparative genomics: Enhances reliability of comparative analyses by reducing adapter-derived artifacts in long-read data.

Methodology:

Sequence-independent processing of long-read shotgun sequencing data using algorithmic strategies that adaptively identify and remove adapter contaminants based on input data characteristics, implemented in R for statistical handling and workflow integration.