Background Reference genome assemblies are essential for high-throughput sequencing analysis projects. Typically, genome assemblies are stored on disk alongside related resources; e.g., many sequence aligners require the assembly to be indexed. The resulting indexes are broadly applicable for downstream analysis, so it makes sense to share them. However, there is no simple tool to do this. Results Here, we introduce refgenie, a reference genome assembly asset manager. Refgenie makes it easier to organize, retrieve, and share genome analysis resources. In addition to genome indexes, refgenie can manage any files related to reference genomes, including sequences and annotation files. Refgenie includes a command line interface and a server application that provides a RESTful API, so it is useful for both tool development and analysis. Conclusions Refgenie streamlines sharing genome analysis resources among groups and across computing environments. Refgenie is available at https://refgenie.databio.org.
Supplementary data are available at Bioinformatics online.
Reference genome assemblies are essential for high-throughput sequencing analysis projects. Typically, genome assemblies are stored on disk alongside related resources; for example, many sequence aligners require the assembly to be indexed. The resulting indexes are broadly applicable for downstream analysis, so it makes sense to share them. However, there is no simple tool to do this. To this end, we introduce refgenie, a reference genome assembly asset manager. Refgenie makes it easier to organize, retrieve, and share genome analysis resources. In addition to genome indexes, refgenie can manage any files related to reference genomes, including sequences and annotation files. Refgenie includes a command-line interface and a server application that provides a RESTful API, so it is useful for both tool development and analysis.Availabilityhttps://refgenie.databio.org
BackgroundHere, we present an R package for entropy/variability analysis that facilitates prompt and convenient data extraction, manipulation and visualization of protein features from multiple sequence alignments. BALCONY can work with residues dispersed across a protein sequence and map them on the corresponding alignment of homologous protein sequences. Additionally, it provides several entropy and variability scores that indicate the conservation of each residue.ResultsOur package allows the user to visualize evolutionary variability by locating the positions most likely to vary and to assess mutation candidates in protein engineering.ConclusionIn comparison to other R packages BALCONY allows conservation/variability analysis in context of protein structure with linkage of the appropriate metrics with physicochemical features of user choice.Availability: CRAN project page: https://cran.r-project.org/package=BALCONY and our website: http://www.tunnelinggroup.pl/software/ for major platforms: Linux/Unix, Windows and Mac OS X.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2294-z) contains supplementary material, which is available to authorized users.
Background Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software. Results To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata. Conclusions The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/.
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