Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350380PMC
http://dx.doi.org/10.1093/bib/bbae367DOI Listing

Publication Analysis

Top Keywords

source code
12
haxe
8
bioinformatic applications
8
case story
8
haxe swiss
4
swiss knife
4
knife bioinformatic
4
seqphase
4
applications seqphase
4
seqphase case
4

Similar Publications

SPACE: STRING proteins as complementary embeddings.

Bioinformatics

September 2025

Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.

Motivation: Representation learning has revolutionized sequence-based prediction of protein function and subcellular localization. Protein networks are an important source of information complementary to sequences, but the use of protein networks has proven to be challenging in the context of machine learning, especially in a cross-species setting.

Results: We leveraged the STRING database of protein networks and orthology relations for 1,322 eukaryotes to generate network-based cross-species protein embeddings.

View Article and Find Full Text PDF

Summary: In the era of large data, the cloud is increasingly used as a computing environment, necessitating the development of cloud-compatible pipelines that can provide uniform analysis across disparate biological datasets. The Warp Analysis Research Pipelines (WARP) repository is a GitHub repository of open-source, cloud-optimized workflows for biological data processing that are semantically versioned, tested, and documented. A companion repository, WARP-Tools, hosts Docker containers and custom tools used in WARP workflows.

View Article and Find Full Text PDF

Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinicians are time-intensive and subjective.

View Article and Find Full Text PDF

Background: Powerlifting is a strength sport featuring some of the world's strongest athletes. Recent decades have seen an exponential increase in research into the applied sport science and medicine of powerlifting and its Paralympic counterpart, para powerlifting. A scoping review of the area would provide athletes, coaches, policymakers, and researchers with an overview of the existing evidence to support performance, reduce injury, and foster further growth of these sports.

View Article and Find Full Text PDF

Introduction: Electronic health records can be used to create high-quality databases if data are structured and well-registered, which is the case for most perioperative data in the Capital and Zealand Regions of Denmark. We present the purpose and development of the AI and Automation in Anaesthesia (TRIPLE-A) database-a platform designed for epidemiology, prediction, quality control, and automated research data collection.

Methods: Data collection from the electronic medical record (EPIC Systems Corporation, WI, USA) was approved by the Capital Region, Denmark, and ethical approval was waived.

View Article and Find Full Text PDF