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1. Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

   https://doi.org/10.1186/s12864-021-07791-z  

   Koonchanok, Ratanond ; Daulatabad, Swapna Vidhur ; Mir, Quoseena ; Reda, Khairi ; Janga, Sarath Chandra ( July 2021 , BMC Genomics)

   Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data.

   Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization.

   Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available athttps://github.com/dnonatar/Sequoia.

    

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2. FLARE: a fast and flexible workflow for identifying RNA editing foci

   https://doi.org/10.1186/s12859-023-05452-4  

   Kofman, Eric ; Yee, Brian ; Medina-Munoz, Hugo C. ; Yeo, Gene W. ( October 2023 , BMC Bioinformatics)

   Fusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors.

   We present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing.

   FLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available athttps://github.com/YeoLab/FLARE.

    

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3. Utility of the Python package Geoweaver_cwl for improving workflow reusability: an illustration with multidisciplinary use cases

   https://doi.org/10.1007/s12145-023-01045-0  

   Kale, Amruta ; Sun, Ziheng ; Ma, Xiaogang ( July 2023 , Earth Science Informatics)

   Computational workflows are widely used in data analysis, enabling automated tracking of steps and storage of provenance information, leading to innovation and decision-making in the scientific community. However, the growing popularity of workflows has raised concerns about reproducibility and reusability which can hinder collaboration between institutions and users. In order to address these concerns, it is important to standardize workflows or provide tools that offer a framework for describing workflows and enabling computational reusability. One such set of standards that has recently emerged is the Common Workflow Language (CWL), which offers a robust and flexible framework for data analysis tools and workflows. To promote portability, reproducibility, and interoperability of AI/ML workflows, we developedgeoweaver_cwl, a Python package that automatically describes AI/ML workflows from a workflow management system (WfMS) named Geoweaver into CWL. In this paper, we test our Python package on multiple use cases from different domains. Our objective is to demonstrate and verify the utility of this package. We make all the code and dataset open online and briefly describe the experimental implementation of the package in this paper, confirming thatgeoweaver_cwlcan lead to a well-versed AI process while disclosing opportunities for further extensions. Thegeoweaver_cwlpackage is publicly released online athttps://pypi.org/project/geoweaver-cwl/0.0.1/and exemplar results are accessible at:https://github.com/amrutakale08/geoweaver_cwl-usecases.

    

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4. Rapid growth and defence evolution following multiple introductions

   https://doi.org/10.1002/ece3.5275  

   van Boheemen, Lotte A. ; Bou‐Assi, Sarah ; Uesugi, Akane ; Hodgins, Kathryn A. ( June 2019 , Ecology and Evolution)

   Rapid adaptation can aid invasive populations in their competitive success. Resource allocation trade‐off hypotheses predict higher resource availability or the lack of natural enemies in introduced ranges allow for increased growth and reproduction, thus contributing to invasive success. Evidence for such hypotheses is however equivocal and tests among multiple ranges over productivity gradients are required to provide a better understanding of the general applicability of these theories.

   Using common gardens, we investigated the adaptive divergence of various constitutive and inducible defence‐related traits between the native North American and introduced European and Australian ranges, while controlling for divergence due to latitudinal trait clines, individual resource budgets, and population differentiation, using >11,000 SNPs.

   Rapid, repeated clinal adaptation in defence‐related traits was apparent despite distinct demographic histories. We also identified divergence among ranges in some defence‐related traits, although differences in energy budgets among ranges may explain some, but not all, defence‐related trait divergence. We do not identify a general reduction in defence in concert with an increase in growth among the multiple introduced ranges as predicted trade‐off hypotheses.

   Synthesis: The rapid spread of invasive species is affected by a multitude of factors, likely including adaptation to climate and escape from natural enemies. Unravelling the mechanisms underlying invasives' success enhances understanding of eco‐evolutionary theory and is essential to inform management strategies in the face of ongoing climate change.

   This article has been awarded Open Materials, Open Data, Preregistered Research Designs Badges. All materials and data are publicly accessible via the Open Science Framework athttps://doi.org/10.6084/m9.figshare.8028875.v1,https://github.com/lotteanna/defence_adaptation,https://doi.org/10.1101/435271.

    

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5. Transposable elements contribute to dynamic genome content in maize

   https://doi.org/10.1111/tpj.14489  

   Anderson, Sarah N. ; Stitzer, Michelle C. ; Brohammer, Alex B. ; Zhou, Peng ; Noshay, Jaclyn M. ; O'Connor, Christine H. ; Hirsch, Cory D. ; Ross‐Ibarra, Jeffrey ; Hirsch, Candice N. ; Springer, Nathan M. ( September 2019 , The Plant Journal)

   Transposable elements (TEs) are ubiquitous components of eukaryotic genomes and can create variation in genome organization and content. Most maize genomes are composed ofTEs. We developed an approach to define shared and variableTEinsertions across genome assemblies and applied this method to four maize genomes (B73, W22, Mo17 andPH207) with uniform structural annotations ofTEs. Among these genomes we identified approximately 400 000TEs that are polymorphic, encompassing 1.6 Gb of variableTEsequence. These polymorphicTEs include a combination of recent transposition events as well as deletions of olderTEs. There are examples of polymorphicTEs within each of the superfamilies ofTEs and they are found distributed across the genome, including in regions of recent shared ancestry among individuals. There are many examples of polymorphicTEs within or near maize genes. In addition, there are 2380 gene annotations in the B73 genome that are located within variableTEs, providing evidence for the role ofTEs in contributing to the substantial differences in annotated gene content among these genotypes.TEs are highly variable in our survey of four temperate maize genomes, highlighting the major contribution ofTEs in driving variation in genome organization and gene content.

   This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available athttps://github.com/SNAnderson/maizeTE_variation;https://mcstitzer.github.io/maize_TEs.

    

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