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1. dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra

   https://doi.org/10.1101/2023.06.15.545182  

   Huang, Xin; Struck, Travis J; Davey, Sean W; Gutenkunst, Ryan N (June 2023, bioRxiv)

   Abstract Summarydadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing. Availability and Implementationdadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/. 

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2. 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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3. TSSr: an R package for comprehensive analyses of TSS sequencing data

   https://doi.org/10.1093/nargab/lqab108  

   Lu, Zhaolian; Berry, Keenan; Hu, Zhenbin; Zhan, Yu; Ahn, Tae-Hyuk; Lin, Zhenguo (November 2021, NAR Genomics and Bioinformatics)

   Abstract Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5′end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr. 

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4. Individual‐based landscape genomics for conservation: An analysis pipeline

   https://doi.org/10.1111/1755-0998.13884  

   Chambers, E Anne; Bishop, Anusha P; Wang, Ian J (October 2023, Molecular Ecology Resources)

   Grueber, Catherine E (Ed.)

   Abstract Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non‐model systems has also enabled a shift away from population‐based sampling to individual‐based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual‐based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population‐based sampling to individual‐based sampling schemes. Here, we discuss the benefits of individual‐based sampling for conservation and describe how landscape genomic methods, paired with individual‐based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user‐friendly, open‐source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). Thealgatrpackage includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology. 

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5. An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example

   https://doi.org/10.3390/ijgi11010013  

   Malarvizhi, Anusha Srirenganathan; Liu, Qian; Sha, Dexuan; Lan, Hai; Yang, Chaowei (January 2022, ISPRS International Journal of Geo-Information)

   Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository. 

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