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1. Advanced searches of the Protein Data Bank in Jupyter notebooks

   Rack, Anna; Pick, Murphy; Greever, Victoria; Craig, Paul A (March 2025, American Chemical Society)

   The Protein Data Bank (PDB) holds an extensive amount of information, and can be a vital tool when performing background research for biochemical work. In an attempt to make the information in the PDB more accessible, the RCSB Search API was employed within Jupyter Notebooks to create more customizable and user-friendly tools with simple Python code. Areas of focus include structure motif searches used to predict the function of proteins based on the 3-dimensional shape of their active sites, searches for FDA Approved Drugs, as well as searches targeting ligands with specific characteristics. This code has been built into Jupyter Notebook templates that include both examples of these searches as well as annotated code that users can customize to more efficiently run advanced searches on the PDB and download structure and small molecule files returned by the search. Future plans include increasing the amount and type of information available from a search, as well as expanding the scope of our notebooks to cover more types of searches. 

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2. Facilitating Dependency Exploration in Computational Notebooks

   https://doi.org/10.1145/3597465.3605222  

   Brown, Colin; Alhoori, Hamed; Koop, David (June 2023, HILDA '23: Proceedings of the Workshop on Human-In-the-Loop Data Analytics)

   Computational notebooks promote exploration by structuring code, output, and explanatory text, into cells. The input code and rich outputs help users iteratively investigate ideas as they explore or analyze data. The links between these cells–how the cells depend on each other–are important in understanding how analyses have been developed and how the results can be reproduced. Specifically, a code cell that uses a particular identifier depends on the cell where that identifier is defined or mutated. Because notebooks promote fluid editing where cells can be moved and run in any order, cell dependencies are not always clear or easy to follow. We examine different tools that seek to address this problem by extending Jupyter notebooks and evaluate how well they support users in accomplishing tasks that require understanding dependencies. We also evaluate visualization techniques that provide views of the dependencies to help users navigate cell dependencies. 

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3. JBrowse Jupyter: a Python interface to JBrowse 2

   https://doi.org/10.1093/bioinformatics/btad032  

   De Jesus Martinez, Teresa; Hershberg, Elliot A; Guo, Emma; Stevens, Garrett J; Diesh, Colin; Xie, Peter; Bridge, Caroline; Cain, Scott; Haw, Robin; Buels, Robert M; et al (January 2023, Bioinformatics)

   Marschall, Tobias (Ed.)

   Abstract MotivationJBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization. ResultsWe have developed a package that provides a Python interface to JBrowse 2’s suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google’s Colab notebooks, providing reproducible interactive views. Availability and implementationJBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter. 

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4. A Case for Integrating Experimental Containers with Notebooks

   https://doi.org/10.1109/CloudCom.2019.00032  

   Anderson, J.; and Keahey, K. (December 2019, Proceedings of the 11th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2019))

   Computational notebooks have gained much pop- ularity as a way of documenting research processes; they allow users to express research narrative by integrating ideas expressed as text, process expressed as code, and results in one executable document. However, the environments in which the code can run are currently limited, often containing only a fraction of the resources of one node, posing a barrier to many computations. In this paper, we make the case that integrating complex experimental environments, such as virtual clusters or complex networking environments that can be provisioned via infrastructure clouds, into computational notebooks will significantly broaden their reach and at the same time help realize the potential of clouds as a platform for repeatable research. To support our argument, we describe the integration of Jupyter notebooks into the Chameleon cloud testbed, which allows the user to define complex experimental environments and then assign processes to elements of this environment similarly to the way a laptop user may switch between different desktops. We evaluate our approach on an actual experiment from both the development and replication perspective. 

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5. Detecting and correcting misclassified sequences in the large-scale public databases

   https://doi.org/10.1093/bioinformatics/btaa586  

   Bagheri, Hamid; Severin, Andrew; Rajan, Hridesh; Elofsson, Arne (June 2020, Bioinformatics)

   Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the non-redundant (NR) database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than 2 million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability Source code, dataset, documentation, Jupyter notebooks, and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online. 

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