SEQU-INTO: Early detection of impurities, contamination and off-targets (ICOs) in long read/MinION sequencing

Joppich, Markus; Olenchuk, Margaryta; Mayer, Julia M.; Emslander, Quirin; Jimenez-Soto, Luisa F.; Zimmer, Ralf (2020): SEQU-INTO: Early detection of impurities, contamination and off-targets (ICOs) in long read/MinION sequencing. In: Computational and Structural Biotechnology Journal, Vol. 18: pp. 1342-1351

Abstract

The MinION sequencer by Oxford Nanopore Technologies turns DNA and RNA sequencing into a routine task in biology laboratories or in field research. For downstream analysis it is required to have a sufficient amount of target reads. Especially prokaryotic or bacteriophagic sequencing samples can contain a significant amount of off-target sequences in the processed sample, stemming from human DNA/RNA contamination, insufficient rRNA depletion, or remaining DNA/RNA from other organisms (e.g. host organism from bacteriophage cultivation). Such impurity, contamination and off-targets (ICOs) block read capacity, requiring to sequence deeper. In comparison to second-generation sequencing, MinION sequencing allows to reuse its chip after a (partial) run. This allows further usage of the same chip with more sample, even after adjusting the library preparation to reduce ICOs. The earlier a sample's ICOs are detected, the better the sequencing chip can be conserved for future use. Here we present sequ into, a low-resource and user-friendly cross-platform tool to detect ICO sequences from a predefined ICO database in samples early during a MinION sequencing run. The data provided by sequ-into empowers the user to quickly take action to preserve sample material and chip capacity. sequ-into is available from https://github.com/mjoppich/sequ-into .

• Item Type: Journal article
• Faculties: Medicine; Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 600 Technology > 610 Medicine and health; 000 Computer science, information and general works > 004 Data processing computer science
• ISSN: 2001-0370
• Language: English