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  "Title": "Handle Ambiguity of Protein Identifications from Shotgun\nProteomics",
  "Version": "0.1.0",
  "Authors@R": "c(person(given = \"Laura\",\nfamily = \"Fancello\",\nrole = c(\"aut\", \"cre\"),\nemail = \"laura.fancello@cea.fr\",\ncomment = c(ORCID = \"0000-0003-4708-4080\")),\nperson(given = \"Thomas\",\nfamily = \"Burger\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"thomas.burger@cea.fr\",\ncomment = c(ORCID = \"0000-0003-3539-3564\")))",
  "Maintainer": "Laura Fancello <laura.fancello@cea.fr>",
  "Description": "In shotgun proteomics, shared peptides (i.e., peptides\nthat might originate from different proteins sharing homology,\nfrom different proteoforms due to alternative mRNA splicing,\npost-translational modifications, proteolytic cleavages, and/or\nallelic variants) represent a major source of ambiguity in\nprotein identifications. The 'net4pg' package allows to assess\nand handle ambiguity of protein identifications. It implements\nmethods for two main applications. First, it allows to\nrepresent and quantify ambiguity of protein identifications by\nmeans of graph connected components (CCs). In graph theory, CCs\nare defined as the largest subgraphs in which any two vertices\nare connected to each other by a path and not connected to any\nother of the vertices in the supergraph. Here, proteins sharing\none or more peptides are thus gathered in the same CC\n(multi-protein CC), while unambiguous protein identifications\nconstitute CCs with a single protein vertex (single-protein\nCCs). Therefore, the proportion of single-protein CCs and the\nsize of multi-protein CCs can be used to measure the level of\nambiguity of protein identifications. The package implements a\nstrategy to efficiently calculate graph connected components on\nlarge datasets and allows to visually inspect them. Secondly,\nthe 'net4pg' package allows to exploit the increasing\navailability of matched transcriptomic and proteomic datasets\nto reduce ambiguity of protein identifications. More precisely,\nit implement a transcriptome-based filtering strategy\nfundamentally consisting in the removal of those proteins whose\ncorresponding transcript is not expressed in the sample-matched\ntranscriptome. The underlying assumption is that, according to\nthe central dogma of biology, there can be no proteins without\nthe corresponding transcript. Most importantly, the package\nallows to visually inspect the effect of the filtering on\nprotein identifications and quantify ambiguity before and after\nfiltering by means of graph connected components. As such, it\nconstitutes a reproducible and transparent method to exploit\ntranscriptome information to enhance protein identifications.\nAll methods implemented in the 'net4pg' package are fully\ndescribed in Fancello and Burger (2022)\n<doi:10.1186/s13059-022-02701-2>.",
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  "Date/Publication": "2022-06-29 13:30:56 UTC",
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  "Author": "Laura Fancello [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4708-4080>),\nThomas Burger [aut, ctb] (ORCID:\n<https://orcid.org/0000-0003-3539-3564>)",
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    "reduce_inc_matrix",
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    {
      "page": "cc_composition",
      "title": "Get peptides and peptide-to-protein mappings for each connected component",
      "topics": [
        "cc_composition"
      ]
    },
    {
      "page": "cc_stats",
      "title": "Provide statistics on the CCs size",
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        "cc_stats"
      ]
    },
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      "page": "get_adj_matrix",
      "title": "Generate adjacency matrix",
      "topics": [
        "get_adj_matrix"
      ]
    },
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      "page": "get_cc",
      "title": "Generate graph and calculate its connected components",
      "topics": [
        "get_cc"
      ]
    },
    {
      "page": "peptide_stats",
      "title": "Calculate percentage of shared vs specific peptides",
      "topics": [
        "peptide_stats"
      ]
    },
    {
      "page": "plot_cc",
      "title": "Plot peptide-to-protein mapping graph",
      "topics": [
        "plot_cc"
      ]
    },
    {
      "page": "read_inc_matrix",
      "title": "Read incidence matrix of proteomic identifications",
      "topics": [
        "read_inc_matrix"
      ]
    },
    {
      "page": "reduce_inc_matrix",
      "title": "Reduce size of incidence matrix for downstream analyses",
      "topics": [
        "reduce_inc_matrix"
      ]
    },
    {
      "page": "transcriptome_filter",
      "title": "Perform transcriptome-informed post-hoc filtering",
      "topics": [
        "transcriptome_filter"
      ]
    }
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      "title": "An introduction to net4pg",
      "author": "Laura Fancello, Thomas Burger",
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      "headings": [
        "Introduction",
        "Build a graph from shotgun proteomic identifications and calculate its connected components (CCs) to quantify ambiguity of protein identifications",
        "Read in input the incidence matrix describing peptide-to-protein mappings",
        "Reduce the size of the incidence matrix",
        "Calculate the adjacency matrix describing protein-to-protein connections via shared peptides",
        "Generate a graph of protein-to-protein connections and calculate its connected components (CCs)",
        "Assess the ambiguity of protein identifications based on the proportion of multi-protein CCs and shared peptides",
        "Visualize peptide-to-protein mappings for ambiguous protein identifications of interest",
        "Perform a transcriptome-informed filtering of shotgun proteomic identifications to reduce ambiguity of protein identifications",
        "Read in input the incidence matrix describing peptide-to-protein mappings",
        "Perform transcriptome-informed filtering",
        "Assess the impact of transcriptome-informed filtering on ambiguity of protein identifications",
        "Compare the proportion and size of multi-protein CCs obtained before and after filtering",
        "Compare the proportion of shared peptides obtained before and after filtering",
        "Visualize peptide-to-protein mappings for ambiguous protein identifications of interest"
      ],
      "created": "2021-09-16 16:45:03",
      "modified": "2021-09-20 07:40:25",
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