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  • National Science Foundation

  • Authors: Helpdesk, UniProt;

    The central hub for the collection of functional information on proteins with accurate, consistent and rich annotation. It consists of: UniProtKB/Swiss-Prot (manually-annotated records and curator-evaluated computational analysis) and UniProtKB/TrEMBL (computationally analyzed records awaiting manual annotation).

    bio.toolsarrow_drop_down
    bio.tools
    Software . 2015
    License: CC-BY-ND-3.0
    Data sources: bio.tools
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      bio.tools
      Software . 2015
      License: CC-BY-ND-3.0
      Data sources: bio.tools
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Team, Galaxy Support;

    Convert between various FASTQ quality formats for a list of inputs.

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    bio.tools
    Software . 2016
    Data sources: bio.tools
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      bio.tools
      Software . 2016
      Data sources: bio.tools
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: O’Brien, Edward P.;

    RiboA (Ribo-Seq A-site Identifier) is a web application to identify ribosome A-site locations in ribosome profiling data.

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    bio.tools
    Software . 2021
    License: MIT
    Data sources: bio.tools
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ bio.toolsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      bio.tools
      Software . 2021
      License: MIT
      Data sources: bio.tools
  • Authors: Choppali Sudarshan, Chetan; Matkar, Nikhil; Vrudhula, Sarma; Sapatnekar, Sachin; +1 Authors

    The paper introduces ECO-CHIP, a framework for measuring the carbon footprint (CFP) of a heterogeneous system across its lifespan. This artifact is released on Zenodo and contains two parts. The first is ECO-CHIP submodule from GitHub, and the second is a folder that consists of the experiments performed in this paper using ECO-CHIP. The minimal hardware requirements are any single-core CPU, and the software requirements are Python 3.9 with pip 22.04.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Boettiger, Carl; Mangel, Marc; Munch, Stephan;

    Software package, data, and dynamic document source (.Rmd) for Avoiding tipping points in fisheries management through Gaussian process dynamic programming PRSB, doi:10.1098/rspb.2014.1631

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2014
    License: CC 0
    Data sources: ZENODO
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      ZENODO
      Software . 2014
      License: CC 0
      Data sources: ZENODO
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    First release The input and output files used to model the spectra of Supernova 2014dt using the radiative transfer code TARDIS in Camacho-Neves et al. 2023. Each input file has a corresponding output file with a nearly identical name. The input files (TARDIS input file) are in the folder input_yml_files and are named as "UT-date_phase_telescope_model.yml". The output files (synthetic spectra) are in the folder output_synthetic_spectra and are named as "UT-date_phase_telescope_model_spec.dat". Each output file is a two-column text file with wavelength (Angstrom) and luminosity (erg/s/Angstrom).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2023
    Data sources: Datacite
    ZENODO
    Software . 2023
    Data sources: ZENODO
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      ZENODO
      Software . 2023
      Data sources: Datacite
      ZENODO
      Software . 2023
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zhiye Guo; Tianqi Wu; Jian Liu; Jie Hou; +1 Authors

    Motivation: Accurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance pre-diction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results: Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps.

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  • Authors: Cummings, Rachel; Kaptchuk, Gabriel; Redmiles, Elissa;

    Code and data for JPC article Originally released on Aug 31, 2023. Only update made to README and paper citation.

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  • Added PDFs of read depth graphs for all 12 libraries (plotted with R code, not included in v1 record)

    ZENODOarrow_drop_down
    ZENODO
    Software . 2020
    Data sources: ZENODO
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      ZENODO
      Software . 2020
      Data sources: ZENODO
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    Authors: Chourasia, Amit; Nadeau, David; Norman, Michael;

    FolderShare enables file sharing and data organization capability on Drupal websites. It manages shared files and folders like those for Windows, Mac, or Linux systems. Files are stored on the web server and tracked in the website database. Access is controlled using website user accounts, permissions, and shared access selections made by users. Official url: www.drupal.org/project/foldershare Project url: https://seedmelab.org Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No. 1443083. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. {"references": ["Chourasia, Amit; Nadeau, David; Wong, Mona; Norman, Michael (2018): SeedMe2: Extensible data sharing websites for teams. figshare. Paper. https://doi.org/10.6084/m9.figshare.7070291.v1", "Chourasia, Amit; Nadeau, David R.; Norman, Michael L. (2017): SeedMe: Data sharing building blocks. figshare. https://doi.org/10.6084/m9.figshare.5479588.v1"]}

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    ZENODO
    Software . 2019
    Data sources: ZENODO
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      ZENODO
      Software . 2019
      Data sources: ZENODO
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727 Research products
  • Authors: Helpdesk, UniProt;

    The central hub for the collection of functional information on proteins with accurate, consistent and rich annotation. It consists of: UniProtKB/Swiss-Prot (manually-annotated records and curator-evaluated computational analysis) and UniProtKB/TrEMBL (computationally analyzed records awaiting manual annotation).

    bio.toolsarrow_drop_down
    bio.tools
    Software . 2015
    License: CC-BY-ND-3.0
    Data sources: bio.tools
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      bio.toolsarrow_drop_down
      bio.tools
      Software . 2015
      License: CC-BY-ND-3.0
      Data sources: bio.tools
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    Authors: Team, Galaxy Support;

    Convert between various FASTQ quality formats for a list of inputs.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ bio.toolsarrow_drop_down
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    bio.tools
    Software . 2016
    Data sources: bio.tools
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      bio.tools
      Software . 2016
      Data sources: bio.tools
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    Authors: O’Brien, Edward P.;

    RiboA (Ribo-Seq A-site Identifier) is a web application to identify ribosome A-site locations in ribosome profiling data.

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    bio.tools
    Software . 2021
    License: MIT
    Data sources: bio.tools
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      bio.tools
      Software . 2021
      License: MIT
      Data sources: bio.tools
  • Authors: Choppali Sudarshan, Chetan; Matkar, Nikhil; Vrudhula, Sarma; Sapatnekar, Sachin; +1 Authors

    The paper introduces ECO-CHIP, a framework for measuring the carbon footprint (CFP) of a heterogeneous system across its lifespan. This artifact is released on Zenodo and contains two parts. The first is ECO-CHIP submodule from GitHub, and the second is a folder that consists of the experiments performed in this paper using ECO-CHIP. The minimal hardware requirements are any single-core CPU, and the software requirements are Python 3.9 with pip 22.04.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Boettiger, Carl; Mangel, Marc; Munch, Stephan;

    Software package, data, and dynamic document source (.Rmd) for Avoiding tipping points in fisheries management through Gaussian process dynamic programming PRSB, doi:10.1098/rspb.2014.1631

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    First release The input and output files used to model the spectra of Supernova 2014dt using the radiative transfer code TARDIS in Camacho-Neves et al. 2023. Each input file has a corresponding output file with a nearly identical name. The input files (TARDIS input file) are in the folder input_yml_files and are named as "UT-date_phase_telescope_model.yml". The output files (synthetic spectra) are in the folder output_synthetic_spectra and are named as "UT-date_phase_telescope_model_spec.dat". Each output file is a two-column text file with wavelength (Angstrom) and luminosity (erg/s/Angstrom).

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    Authors: Zhiye Guo; Tianqi Wu; Jian Liu; Jie Hou; +1 Authors

    Motivation: Accurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance pre-diction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results: Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps.

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  • Authors: Cummings, Rachel; Kaptchuk, Gabriel; Redmiles, Elissa;

    Code and data for JPC article Originally released on Aug 31, 2023. Only update made to README and paper citation.

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  • Added PDFs of read depth graphs for all 12 libraries (plotted with R code, not included in v1 record)

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