Published in ACM Digital Library: SeqScreen-Nano: a computational platform for streaming, in-field characterization of microbial pathogens


The COVID-19 pandemic forever underscored the need for bio-surveillance platforms capable of rapidly detecting emerging pathogens. Oxford Nanopore Technology (ONT) couples long-read sequencing with in-field capability, opening the door to real-time, in-field biosurveillance. Though a promising technology, streaming assignment of accurate functional and taxonomic labels with nanopore reads remains challenging given: (i) individual reads can span multiple genes, (ii) individual reads may contain truncated genes and pseudogenes, (iii) the error rate of the ONT platform that may introduce frameshifts and missense errors, and (iv) the computational costs of read-by-read analysis may exceed that of in-field computational equipment. Altogether, these challenges highlight a need for novel computational approaches. To this end, we describe Seqscreen-Nano, a novel and portable computational platform focused on detecting microbial pathogens. Based on results from simulated and synthetic microbial communities, SeqScreen-Nano can identify Open Reading Frames (ORFs) across the length of raw ONT reads and then use the predicted ORFs for accurate functional characterization and taxonomic classification. SeqScreen-Nano also runs efficiently in a memory-constrained environment (less than 32GB of RAM), allowing it to be utilized in resource-limited settings. Furthermore, SeqScreen-Nano can process reads directly from the ONT MinION sequencing device, enabling streaming and in-field characterization of microbial pathogens. SeqScreen-Nano (v4.2) is available on GitLab at:

Read the full paper here.

In recognition of the importance and quality of this work, SeqScreen-Nano received a Paper Award at the 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB). Congratulations to the authors!

Advait Balajia, Yunxi Liua, Michael G. Nutea Bingbing HuaAnthony D. Kappellb Danielle S. LeSassierb, Gene D. Godboldb Krista L. Ternusb, Todd J. Treangena

aDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX,USA
bSignature Science LLC, 8329 North Mopac Expressway, Austin, TX, USA