19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      PepSIRF + QIIME 2: software tools for automated, reproducible analysis of highly-multiplexed serology data

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          PepSIRF is a command-line, module-based open-source software package that facilitates the analysis of data from highly-multiplexed serology assays (e.g., PepSeq or PhIP-Seq). It has nine separate modules in its current release (v1.5.0): demux, info, subjoin, norm, bin, zscore, enrich, link, and deconv. These modules can be used together to conduct analyses ranging from demultiplexing raw high-throughput sequencing data to the identification of enriched peptides. QIIME 2 is an open-source, community-developed and plugin-based bioinformatics platform that focuses on data and analytical transparency. QIIME 2's features include integrated and automatic tracking of data provenance, a semantic type system, and built-in support for many types of user interfaces. Here, we describe three new QIIME 2 plugins that allow users to conduct PepSIRF analyses within the QIIME 2 environment and extend the core functionality of PepSIRF in two key ways: 1) enabling generation of interactive visualizations and 2) enabling automation of analysis pipelines that include multiple PepSIRF modules.

          Related collections

          Author and article information

          Journal
          23 July 2022
          Article
          2207.11509
          a2ca7939-ef67-4b89-9872-510d11e30b3b

          http://creativecommons.org/licenses/by/4.0/

          History
          Custom metadata
          9 pages, 6 figures, software announcement
          q-bio.QM

          Quantitative & Systems biology
          Quantitative & Systems biology

          Comments

          Comment on this article