Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

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Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of machine learning validation in biology. Adopting a structured methods description for machine learning based on DOME (data, optimization, model, evaluation) will allow both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are complemented by a machine learning summary table which can be easily included in the supplementary material of published papers. Redundancy between data splitsMaximum pairwise identity within and between training and test set is 25% enforced with UniqueProt tool. Availability of dataYes, URL: http://protein.bio.unipd.it/espritz/ Optimization Algorithm BRNN (Bi-directional recurrent neural network) with ensemble averaging. Meta-predictionsNo. Data encodingSliding window of length 23 residues on input sequence with "one hot" encoding (i.e. 20 inputs per residue).

Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it's sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They're often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.

Transplant-associated thrombotic microangiopathy (TA-TMA) is a life-threatening complication of allogeneic hematopoietic cell transplantation (HCT). We hypothesized that pretransplant genetic susceptibility is evident in adult TA-TMA and further investigated the association of TMA-associated variants with clinical outcomes. We studied 40 patients with TA-TMA, donors of 18 patients and 40 control non-TMA HCT recipients, without significant differences in transplant characteristics. Genomic DNA from pretransplant peripheral blood was sequenced for TMA-associated genes. Donors presented significantly lower frequency of rare variants and variants in exonic/splicing/untranslated region (UTR) regions, compared with TA-TMA patients. Controls also showed a significantly lower frequency of rare variants in ADAMTS13, CD46, CFH, and CFI. The majority of TA-TMA patients (31/40) presented with pathogenic or likely pathogenic variants. Patients refractory to conventional treatment (62%) and patients that succumbed to transplant-related mortality (65%) were significantly enriched for variants in exonic/splicing/UTR regions. In conclusion, increased incidence of pathogenic, rare and variants in exonic/splicing/UTR regions of TA-TMA patients suggests genetic susceptibility not evident in controls or donors. Notably, variants in exonic/splicing/UTR regions were associated with poor response and survival. Therefore, pretransplant genomic screening may be useful to intensify monitoring and early intervention in patients at high risk for TA-TMA.