hafeZ: Active prophage identification through read mapping
Turkington, C. J. R.; Abadi, N. N.; Edwards, R. A.; Grasis, J. A.
Show abstract
SummaryBacteriophages that have integrated their genomes into bacterial chromosomes, termed prophages, are widespread across bacteria. Prophages are key components of bacterial genomes, with their integration often contributing novel, beneficial, characteristics to the infected host. Likewise, their induction--through the production and release of progeny virions into the surrounding environment--can have considerable ramifications on bacterial communities. Yet, not all prophages can excise following integration, due to genetic degradation by their host bacterium. Here, we present hafeZ, a tool able to identify active prophages (i.e. those undergoing induction) within bacterial genomes through genomic read mapping. We demonstrate its use by applying hafeZ to publicly available sequencing data from bacterial genomes known to contain active prophages and show that hafeZ can accurately identify their presence and location in the host chromosomes. Availability and ImplementationhafeZ is implemented in Python 3.7 and freely available under an open-source GPL-3.0 license from https://github.com/Chrisjrt/hafeZ. Bugs and issues may be reported by submitting them via the hafeZ github issues page. Contactcturkington@ucmerced.edu or chrisjrt1@gmail.com
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Accurate and fast graph-based pangenome annotation and clustering with ggCaller 95%
- Taxor: Fast and space-efficient taxonomic classification of long reads with hierarchicalinterleaved XOR filters 95%
- HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.