BubbleGun: enumerating bubbles and superbubbles in genome graphs

Abstract

Motivation With the fast development of sequencing technology, accurate de novo genome assembly is now possible even for larger genomes. Graph-based representations of genomes arise both as part of the assembly process, but also in the context of pangenomes representing a population. In both cases, polymorphic loci lead to bubble structures in such graphs. Detecting bubbles is hence an important task when working with genomic variants in the context of genome graphs. Results Here, we present a fast general-purpose tool, called BubbleGun, for detecting bubbles and superbubbles in genome graphs. Furthermore, BubbleGun detects and outputs runs of linearly connected bubbles and superbubbles, which we call bubble chains. We showcase its utility on de Bruijn graphs and compare our results to vg’s snarl detection. We show that BubbleGun is considerably faster than vg especially in bigger graphs, where it reports all bubbles in less than 30 minutes on a human sample de Bruijn graph of around 2 million nodes. Availability BubbleGun is available and documented as a Python3 package at https://github.com/fawaz-dabbaghieh/bubble_gun under MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

Exploring gene content with pangene graphs

“…4f ) does not have a superbubble when and are taken as different vertices. Dabbaghie et al (2022) implemented the algorithm by Onodera et al (2013) for sequence graphs, but how the issues above are handled is not apparent. The closest concept to weak superbubble in bidirected graphs is snarl ( Paten et al 2018 ), which leads to a bidirected subgraph that can be separated from the rest of the graph.…”

New insights into functional divergence and adaptive evolution of uncultured bacteria in anammox community by complete genome-centric analysis

“…The HiFi cMAGs were filtered from initial assemblies using an adapted workflow published before (Kim et al, 2022). The workflow is briefly described as follows: (1) the circular contigs without assembly bubbles (assessed by BubbleGun v1.1.6 (Dabbaghie et al, 2022) as required) and repeats were filtered from the three assemblies; (2) contigs with sequence lengths shorter than 100 kb were removed; (3) the completeness and taxonomic classification of remaining contigs were assessed using GTDB-Tk v2.1.1 (Parks et al, 2022), and contigs with bacterial marker genes (bac120) less than 80 were removed (at this step, no archaea genome was found); (4) the SSU rRNA of the remaining contigs was predicted using barrnap v0.9 (https://github.com/tseemann/barrnap), and contigs missing 5S, 16S or 23S rRNA were removed; (5) the tRNA of remaining contigs was identified using tRNAscan-SE v2.0.11 (Chan et al, 2021), and contigs with tRNA types fewer than 18 were filtered out; and (6) ANI values of contigs were calculated using FastANI v1.33 (Jain et al, 2018). Redundant contigs were those with ANI values ≥ 99% and alignment percentages ≥ 95%, and the contigs with the maximal sum of ANI values were selected as the representative sequences.…”

New insights into functional divergence and adaptive evolution of uncultured bacteria in anammox community by complete genome-centric analysis

“…The HiFi cMAGs were filtered from initial assemblies using an adapted workflow published before (Kim et al, 2022). The workflow is briefly described as follows: (1) the circular contigs without assembly bubbles (assessed by BubbleGun v1.1.6 (Dabbaghie et al, 2022) as required) and repeats were filtered from the three assemblies;…”