Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software.

BACKGROUND: Long-read sequencing is revolutionizing genome assembly: as PacBio and Nanopore technologies become more accessible in technicity and in cost, long-read assemblers flourish and are starting to deliver chromosome-level assemblies. However, these long reads are usually error-prone, making the generation of a haploid reference out of a diploid genome a difficult enterprise. Failure to properly collapse haplotypes results in fragmented and structurally incorrect assemblies and wreaks havoc on orthology inference pipelines, yet this serious issue is rarely acknowledged and dealt with in genomic projects, and an independent, comparative benchmark of the capacity of assemblers and post-processing tools to properly collapse or purge haplotypes is still lacking. RESULTS: We tested different assembly strategies on the genome of the rotifer Adineta vaga, a non-model organism for which high coverages of both PacBio and Nanopore reads were available. The assemblers we tested (Canu, Flye, NextDenovo, Ra, Raven, Shasta and wtdbg2) exhibited strikingly different behaviors when dealing with highly heterozygous regions, resulting in variable amounts of uncollapsed haplotypes. Filtering reads generally improved haploid assemblies, and we also benchmarked three post-processing tools aimed at detecting and purging uncollapsed haplotypes in long-read assemblies: HaploMerger2, purge_haplotigs and purge_dups. CONCLUSIONS: We provide a thorough evaluation of popular assemblers on a non-model eukaryote genome with variable levels of heterozygosity. Our study highlights several strategies using pre and post-processing approaches to generate haploid assemblies with high continuity and completeness. This benchmark will help users to improve haploid assemblies of non-model organisms, and evaluate the quality of their own assemblies.

Bioinformatics (2019) doi: 10.1093/bioinformatics/btz102 Several corrections have been made to the above article. In the article abstract, the word ‘read’ has been replaced by the word ‘sequence.’ The caption for figure 1 has been replaced with the following text: ‘Four issues with k-mer-spectrum methods, and how Bcool handles them (blue half-arrows represent the paths of the graph on which given reads map). (1) Genomic k-mers may be appear weak because of their low abundance: by using a very low k-mer abundance threshold coupled with a unitig abundance threshold, Bcool retains low-abundance k-mers and manages to correct the reads that contain them. (2) Erroneous k-mers may appear solid because of their high abundance: Bcool detects the tip pattern produced by such solid erroneous k-mers and is therefore able to discard them (other erroneous k-mers are detected at the unitig filtering step). (3) Sequencing errors may be validated by genomic k-mers originating from other parts of the genome: by considering mappings globally, Bcool chooses the best path for each read, i.e. the one on which it maps with the smallest number of mismatches. (4) Multiple errors may occur on a k-mer, resulting in a large weak region: by using unitigs instead of k-mers to correct reads, Bwise is able to correct properly reads that contain several nearby errors’ On page 3 of the PDF, ‘k-mers’ has been replaced with ‘k-values.’ On page 5 of the PDF the ‘correction ratio’ has been corrected from ‘= (TP + FN) / (FN + TP)’ to ‘= (TP + FN) / (FN + FP)’ The following sentence has been added to the Figure 6 caption: ‘BFC and Musket ran out of memory an all full human datasets’ The reference given for ‘Limasset, A. et al. (2017)’ has been corrected to the following: Limasset, A. et al. (2017) Fast and Scalable Minimal Perfect Hashing for Massive Key Sets. In: Iliopolis, C.S. et al. (eds) Proceedings of the 16th International symposium on Experimental Algorithms (SEA 2017), London, UK, June 21-23, 2017, Leibniz International Proceedings in informatics Volume 75, Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing, Saarbrücken/Wadern, Germany, pp. 25: 1–25:16. The publisher apologises for these errors which have now been corrected.

MOTIVATION: Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large datasets or consider reads as mere suites of k-mers, without taking into account their full-length sequence information. RESULTS: We propose a new method to correct short reads using de Bruijn graphs and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond. AVAILABILITY AND IMPLEMENTATION: The implementation is open source, available at http://github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.