Targeted decontamination of sequencing data with CLEAN.

Area of Science:

• Genomics
• Transcriptomics
• Bioinformatics

Background:

• Biological and medical research relies heavily on sequence data analysis.
• Sequence data collections often contain contaminants such as artificial spike-ins, overrepresented ribosomal RNA (rRNA), and human host DNA.
• Existing methods for removing these contaminants are often not fully reproducible or traceable.

Purpose of the Study:

• To develop a robust and reproducible pipeline for removing unwanted sequences from sequencing data.
• To address the challenge of overlooked contaminants, including technology-specific spike-ins and human DNA.
• To improve the accuracy and efficiency of downstream genomic and transcriptomic analyses.

Main Methods:

• Developed the CLEAN pipeline for processing both long- and short-read sequencing data.
• The pipeline is designed to remove technology-specific control sequences (e.g., Illumina, Nanopore), human host DNA, and rRNA.
• CLEAN generates purified sequences and provides statistics on identified contaminants in a summary report.

Main Results:

• CLEAN effectively removes unwanted sequences, including spike-ins and host DNA, from various sequencing datasets.
• The pipeline supports platform-independent data analysis for both genomics and transcriptomics.
• Output data is ready for immediate use in subsequent analyses, leading to faster computations and improved results.

Conclusions:

• CLEAN offers a reproducible and traceable solution for sequence data decontamination.
• The pipeline enhances the reliability of biological and medical research by ensuring data integrity.
• CLEAN is freely available, promoting standardized and efficient data processing in the scientific community.