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1. XHap: haplotype assembly using long-distance read correlations learned by transformers

   https://doi.org/10.1093/bioadv/vbad169  

   Consul, Shorya; Ke, Ziqi; Vikalo, Haris; Forslund, ed. Sofia (November 2023, Bioinformatics Advances)

     SummaryReconstructing haplotypes of an organism from a set of sequencing reads is a computationally challenging (NP-hard) problem. In reference-guided settings, at the core of haplotype assembly is the task of clustering reads according to their origin, i.e. grouping together reads that sample the same haplotype. Read length limitations and sequencing errors render this problem difficult even for diploids; the complexity of the problem grows with the ploidy of the organism. We present XHap, a novel method for haplotype assembly that aims to learn correlations between pairs of sequencing reads, including those that do not overlap but may be separated by large genomic distances, and utilize the learned correlations to assemble the haplotypes. This is accomplished by leveraging transformers, a powerful deep-learning technique that relies on the attention mechanism to discover dependencies between non-overlapping reads. Experiments on semi-experimental and real data demonstrate that the proposed method significantly outperforms state-of-the-art techniques in diploid and polyploid haplotype assembly tasks on both short and long sequencing reads. Availability and implementationThe code for XHap and the included experiments is available at https://github.com/shoryaconsul/XHap. 

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2. Aether: leveraging linear programming for optimal cloud computing in genomics

   https://doi.org/10.1093/bioinformatics/btx787  

   Luber, Jacob M.; Tierney, Braden T.; Cofer, Evan M.; Patel, Chirag J.; Kostic, Aleksandar D.; Berger, ed., Bonnie (December 2017, Bioinformatics)

   Abstract MotivationAcross biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. ResultsHere, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementationData utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. JBrowseR: an R interface to the JBrowse 2 genome browser

   https://doi.org/10.1093/bioinformatics/btab459  

   Hershberg, Elliot A; Stevens, Garrett; Diesh, Colin; Xie, Peter; De Jesus Martinez, Teresa; Buels, Robert; Stein, Lincoln; Holmes, Ian (November 2021, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract MotivationGenome browsers are an essential tool in genome analysis. Modern genome browsers enable complex and interactive visualization of a wide variety of genomic data modalities. While such browsers are very powerful, they can be challenging to configure and program for bioinformaticians lacking expertise in web development. ResultsWe have developed an R package that provides an interface to the JBrowse 2 genome browser. The package can be used to configure and customize the browser entirely with R code. The browser can be deployed from the R console, or embedded in Shiny applications or R Markdown documents. Availability and implementationJBrowseR is available for download from CRAN, and the source code is openly available from the Github repository at https://github.com/GMOD/JBrowseR/. 

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4. Galaxy Helm chart: a standardized method for deploying production Galaxy servers

   https://doi.org/10.1093/bioinformatics/btae486  

   Goonasekera, Nuwan; Mahmoud, Alexandru; Suderman, Keith; Afgan, Enis; Martelli, ed., Pier_Luigi (August 2024, Bioinformatics)

   Abstract MotivationThe Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. ResultsWe describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one’s own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. Availability and implementationThe source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s. 

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5. Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction

   https://doi.org/10.1093/bioadv/vbad184  

   Song, Jiangning; Kurgan, Lukasz; Arighi, ed., Cecilia (December 2023, Bioinformatics Advances)

   Abstract MotivationDevelopment of bioinformatics methods is a long, complex and resource-hungry process. Hundreds of these tools were released. While some methods are highly cited and used, many suffer relatively low citation rates. We empirically analyze a large collection of recently released methods in three diverse protein function and disorder prediction areas to identify key factors that contribute to increased citations. ResultsWe show that provision of a working web server significantly boosts citation rates. On average, methods with working web servers generate three times as many citations compared to tools that are available as only source code, have no code and no server, or are no longer available. This observation holds consistently across different research areas and publication years. We also find that differences in predictive performance are unlikely to impact citation rates. Overall, our empirical results suggest that a relatively low-cost investment into the provision and long-term support of web servers would substantially increase the impact of bioinformatics tools. 

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