MOTIVATION: Microbial communities have a profound impact on both human health and various environments. Viruses infecting bacteria, known as bacteriophages or phages, play a key role in modulating bacterial communities within environments. High-quality phage genome sequences are essential for advancing our understanding of phage biology, enabling comparative genomics studies and developing phage-based diagnostic tools. Most available viral identification tools consider individual sequences to determine whether they are of viral origin. As a result of challenges in viral assembly, fragmentation of genomes can occur, and existing tools may recover incomplete genome fragments. Therefore, the identification and characterization of novel phage genomes remain a challenge, leading to the need of improved approaches for phage genome recovery. RESULTS: We introduce Phables, a new computational method to resolve phage genomes from fragmented viral metagenome assemblies. Phables identifies phage-like components in the assembly graph, models each component as a flow network, and uses graph algorithms and flow decomposition techniques to identify genomic paths. Experimental results of viral metagenomic samples obtained from different environments show that Phables recovers on average over 49% more high-quality phage genomes compared to existing viral identification tools. Furthermore, Phables can resolve variant phage genomes with over 99% average nucleotide identity, a distinction that existing tools are unable to make. AVAILABILITY AND IMPLEMENTATION: Phables is available on GitHub at https://github.com/Vini2/phables.

As bioinformatics analyses increase in size and complexity, workflow managers are becoming more popular for building pipelines [1][2][3].Workflow managers, such as Snakemake [4], Nextflow [5], and Cromwell [6] with WDL or CWL [7], empower researchers to build robust pipelines that call a series of tools and scripts to perform a bespoke analysis.Workflow managers enable non-bioinformaticians to run published pipelines with confidence, and workflow managers with graphical user interfaces such as Galaxy [8] and BioWorkflow [9] have helped nonbioinformaticians create their own simple pipelines.Earlier tools for workflow management have been around for a while, including GNU Make, ruffus [10], doit [11], rake for ruby [12], and Makeflow [13].However, the integration of cluster and cloud computing support in Snakemake, Nextflow, and Cromwell helped drive their current popularity.The use of workflow managers facilitates following the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for open scientific research [14].Interestingly, many existing bioinformatics command line tools are wrappers for a series of other software, but since that is the goal of workflow managers, they can be used instead.Examples of command line tools built on a workflow manager include Hecatomb [15,16], ATLAS [17], VirSorter2 [18], spacegraphcats [19], BlobToolKit [20], and PGAP [21].These tools all consist of two key components: a convenience launcher, which provides the command line interface for the tool and compiles the configuration from user command line arguments, and the workflow pipeline and associated files, which performs the actual analysis.Developing bioinformatics software is much quicker and easier when not reinventing the wheel.For instance, if a tool needs to parse a GenBank file, is it better to code that process manually, or simply load a library that is designed to robustly parse and validate these files?The same concept applies to how bioinformatics software runs.It is possible to write functions to compare file timestamps to add reentrancy to the software, catch error codes, and throw meaningful messages when system calls fail, validate and cleanup intermediate files, allow interaction with a job scheduling system, and perform steps in isolated containers, etc.When workflow managers were still in their infancy, the authors of several popular genome

BACKGROUND: Recent developments in third-gen long read sequencing and diploid-aware assemblers have resulted in the rapid release of numerous reference-quality assemblies for diploid genomes. However, assembly of highly heterozygous genomes is still problematic when regional heterogeneity is so high that haplotype homology is not recognised during assembly. This results in regional duplication rather than consolidation into allelic variants and can cause issues with downstream analysis, for example variant discovery, or haplotype reconstruction using the diploid assembly with unpaired allelic contigs. RESULTS: A new pipeline-Purge Haplotigs-was developed specifically for third-gen sequencing-based assemblies to automate the reassignment of allelic contigs, and to assist in the manual curation of genome assemblies. The pipeline uses a draft haplotype-fused assembly or a diploid assembly, read alignments, and repeat annotations to identify allelic variants in the primary assembly. The pipeline was tested on a simulated dataset and on four recent diploid (phased) de novo assemblies from third-generation long-read sequencing, and compared with a similar tool. After processing with Purge Haplotigs, haploid assemblies were less duplicated with minimal impact on genome completeness, and diploid assemblies had more pairings of allelic contigs. CONCLUSIONS: Purge Haplotigs improves the haploid and diploid representations of third-gen sequencing based genome assemblies by identifying and reassigning allelic contigs. The implementation is fast and scales well with large genomes, and it is less likely to over-purge repetitive or paralogous elements compared to alignment-only based methods. The software is available at https://bitbucket.org/mroachawri/purge_haplotigs under a permissive MIT licence.