C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c a Software ENgineeringA generator of efficient strongly typed abstract syntax trees in Java A generator of efficient strongly typed abstract syntax trees in Java ABSTRACT Abstract syntax trees are a very common data-structure in language related tools. For example compilers, interpreters, documentation generators, and syntax-directed editors use them extensively to extract, transform, store and produce information that is key to their functionality. We present a Java back-end for ApiGen, a tool that generates implementations of abstract syntax trees. The generated code is characterized by strong typing combined with a generic interface and maximal sub-term sharing for memory efficiency and fast equality checking. The goal of this tool is to obtain safe and efficient programming interfaces for abstract syntax trees. The contribution of this work is the combination of generating a strongly typed data-structure with maximal sub-term sharing in Java. Practical experience shows that this approach can not only be used for tools that are otherwise manually constructed, but also for internal datastructures in generated tools. Abstract. Abstract syntax trees are a very common data-structure in language related tools. For example compilers, interpreters, documentation generators, and syntax-directed editors use them extensively to extract, transform, store and produce information that is key to their functionality. We present a Java back-end for ApiGen, a tool that generates implementations of abstract syntax trees. The generated code is characterized by strong typing combined with a generic interface and maximal sub-term sharing for memory efficiency and fast equality checking. The goal of this tool is to obtain safe and efficient programming interfaces for abstract syntax trees. The contribution of this work is the combination of generating a strongly typed data-structure with maximal sub-term sharing in Java. Practical experience shows that this approach can not only be used for tools that are otherwise manually constructed, but also for internal data-structures in generated tools. ACM Computing Classification
Conformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise conformation capture data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.
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