metaprotr

NEWS code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} metaprotr 1.2.2 Addition of four datasets to test the different functions. Verification that graphical setting are restored upon exit of the functions. The statements T/F were changed to TRUE/FALSE in all the functions. The examples of the functions write data on tempdir(). To compile with the CRAN policies, we explicitly ask the user whether a file (csv or pdf) should be created on the working directory when a given function is called. metaprotr 1.2.1 Addition of NEWS.md file to track changes to the package.

README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} metaprotr: R package for post-processing metaproteomics data Description Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis. Application case Pipeline to analyse the metaproteomes of gut microbiota A curated R script is available with the detailed instructions to analyse intestinal microbiota. Data inputs The required files to use the package are : Peptide abundances expressed as spectral counts. This file is generated from X!Tandempipeline using an adapted iterative approach described by Bassignani, 2019 . Contact PAPPSO for more details. This file should have the first seven columns named: Group : protein group number, proteins are grouped together if they share at least one peptide Peptide : a unique reference of the identified peptide Sequence : peptide sequence Modifs : textual informations of peptide modifications MhTheo : theoretical MH+ of the peptide Charge : list of all possible peptides charges Subgroup : protein subgroup number, proteins inside a group sharing exactly the same set of peptides (indistinguishable) The next columns should contain the peptide abundances as spectral counts. The name of the columns should be identical to the content of the column msrunfile from the metadata information. List of protein names associated to the identified peptides. This file should have eight columns named: Group : protein group number, proteins are grouped together if they share at least one peptide Subgroup : protein subgroup number, proteins inside a group sharing exactly the same set of peptides (indistinguishable) Protein : protein number, a single reference to the protein inside the subgroup Description : protein information obtained from the fasta database at the stage of identification Total : total number of spectra per protein Specific : total number of spectra that are specific to a subgroup of proteins. It is only available if there are more than one subgroup within a group Specific Unique : number of unique peptide sequence specific to this subgroup of proteins. It is only available if there are more than one subgroup within a group. SubGroup count : number of subgroups (also known as metaproteins) per group Metadata information. At least three columns must be present and named as: SC_name : sample names assigned by the user msrunfile : name of samples as indicated in the corresponding columns of peptide abundances SampleID : the content should indicate the experimental groups Additional columns containing complementary information can be added by the user (ex. replicates, order of injection, etc.). The separation between columns should be indicated by tabulation Catalog of genes with taxonomic annotations with the following format: The first column named gene must contain the same identifiers of those present in the column Description from the list of proteins Another column named organism containing the name of the strain assigned to a given protein A column named species.genus.family.order.class.phylum.superkingdom . The taxonomic classification can be obtained from a tool of sequences aligment and must be ordered by species, genus, family, order, class, phylum and superkingdom. The characters inside must be concatenated by a comma (ex.”Streptococcus anginosus,Streptococcus,Streptococcaceae,Lactobacillales,Bacilli,Firmicutes,Bacteria”). For the application case you can download the Integrated non-redundant Gene Catalog (IGC) 9.9 database. Functional annotations of genes (optional). The functional annotations from the Kyoto Encyclopedia of Genes and Genomes (KEGG) were added to the IGC 9.9 database. . This file should include two columns named: gene_name : indicating the same protein names to those present in the gene column from the file with taxonomic annotations ko : indicating the KEGG Orthology code assigned to a given protein Alt text Documentation Checkout the documentation and the cheatsheet that displays the available functions on metaprotr . Contribute to the project Everybody is welcome to contribute to the metaprotr . Indicate errors :warning: :bangbang: If you found an error please describe it in the issues section and address it to the package mantainer. Please provide the following information : * Summarize the bug encountered concisely. * What is the current bug behavior? * What is the expected correct behavior? * Describe the steps to reproduce it. * Paste logs and/or screenshots. * Add possible fixes. Add modifications :star: :thumbsup: To improve, modify or add a new feature/function to the project please follow this procedure: Create a new branch from “stable” and name it with the feature/function that you will work on. Make changes and commits to this branch while developing. When making commits it is recommended to use the following graphical identifiers: Identifier Code Description :lollipop: : lollipop : Minor change (ex. comment, renaming) :pencil2: : pencil2 : New code :wrench: : wrench : Code refactoring :checkered_flag: : checkered_flag : code test, check or verification :bug: : bug : bug detected Example: git commit -m ':pencil2: writing core logic of an awesome function' Make a pull request to the branch “stable” .

Help for package metaprotr const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {metaprotr} Contents add_kegg add_taxonomy crumble_taxonomy export_ipath3 export_robject export_vennlists fecal_waters filter_shared filter_text filter_unshared getsc_specific identify_differences inspect_sample_elements load_protspeps plot_dendocluster plot_fulltaxo plot_intensities plot_intensities_ratio plot_pca plot_pietaxo plot_stackedtaxo plot_venn remove_element select_element species_annot_fw species_fw venn_methods Title: Metaproteomics Post-Processing Analysis Version: 1.2.2 Date: 2021-01-28 Author: Aaron Millan-Oropeza [aut, cre], Catherine Juste [aut, ctb], Ariane Bassignani [aut, ctb], Céline Henry [aut, ctb] Maintainer: Aaron Millan-Oropeza <aaron.ibt@gmail.com> Description: Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis. License: GPL-3 Encoding: UTF-8 URL: https://forgemia.inra.fr/pappso/metaprotr LazyData: true Depends: R (≥ 3.5.0) Imports: ade4, dendextend, dplyr, ggforce, ggrepel, reshape2, stringr, tidyverse RoxygenNote: 7.1.0 NeedsCompilation: no Packaged: 2021-01-28 20:20:48 UTC; amillan Repository: CRAN Date/Publication: 2021-02-05 08:10:02 UTC add_kegg Description Integrates a database containing the functional annotation of the identified metaproteins into a list defined as "spectral_count_object". The proteins from the “spectral_count_object” must contain taxonomic information. The functional annotation was obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology database. This database contains the molecular functions represented in terms of functional orthologs (KO terms). Check KEGG for more details. Usage add_kegg( spectral_count_object, annotation_db, taxonomic_db, metaproteome_origin, protein_file, peptide_file, text_to_filter = "HUMAN", taxonomic_levels_allowed = 1 ) Arguments spectral_count_object List defined as "spectral_count_object" containing the abundance of the elements (groups, subgroups or peptides) expressed as spectral counts and organized by taxonomic levels. The format of this object is similar to that generated from the function "crumble_taxonomy". annotation_db Dataframe containing the functional annotation of the proteins. This dataframe must contain two variables: i) "gene_name": indicating the same protein names to those present in the variable "Accession" from the "peptides_proteins", third dataframe in the list defined as "spectral_count_object"; and, ii) "ko": indicating the KEGG Orthology code assigned to a given protein. An example can be found in this repository . taxonomic_db Dataframe containing the taxonomic information for each protein. The first column must contain the same identifiers of those present in the column "Accession" from the dataframe "peptides_proteins" of the "metaproteome_object". Two additional columns have to be present: i) one named "organism" containing the name of the strain assigned to a given protein; and ii) the other named "species.genus.family.order.class.phylum.superkingdom". The taxonomic classification can be obtained from a tool of sequences aligment and must be ordered as follows: species, genus, family, order, class, phylum and superkingdom. The characters inside must be concatenated by a comma without spaces (ex. "Streptococcus anginosus,Streptococcus,Streptococcaceae,Lactobacillales,Bacilli,Firmicutes,Bacteria"). An example can be found in this repository . metaproteome_origin List defined as "metaproteome_object" generated from the function 'load_protspeps'. protein_file Character indicating the location of a txt file containing the list of proteins generated in X!TandemPipeline using an adapted iterative approach described by Bassignani, 2019 . Separation between columns should be indicated by tabulation. For more details regarding data input check format examples . peptide_file Character indicating the location of a txt file containing peptides abundances expressed as spectral counts. This file is generated from X!TandemPipeline using an adapted iterative approach described by Bassignani, 2019 . Separation between columns should be indicated by tabulation. For more details regarding data input check format examples . text_to_filter Character containig a part of text to be searched in the "Description" of the protein file. All the elements containing this character will be removed. The default value was set to "HUMAN". taxonomic_levels_allowed Numeric value indicating the maximal number of taxonomic levels allowed per spectral group or subgroup (in function of the type of spectral data). The default value is set to 1. Value A list defined as "spectral_count_object" with the functional annotation added to the identified proteins. A new column is added to the dataframe "peptides_proteins". Two quality control plot are also generated, one with the number of taxonomic entities per spectral level and another with the number of KO terms per spectral level. Examples ## Not run: # Download functional and taxonmical annotation db: https://zenodo.org/record/3997093#.X0UYI6Zb_mE meta99_full_taxo <- read.csv2("full_taxonomy_MetaHIT99.tsv", header= TRUE, sep="\t") kegg_db <- read.csv2("hs_9_9_igc_vs_kegg89.table", header = TRUE, sep = "\t") # Files with spectral abundance and proteins list from X!Tandempipeline protein_file <- "your/specific/location/protein_list.txt" peptide_file <- "your/specific/location/peptide_counting.txt" metadata_file <- "your/location/metadata.csv" metaproteome_origin <- load_protspeps(protein_file, peptide_file, metadata_file) SCsgp_species <- crumble_taxonomy(SC_subgroups, "species") SCsgp_species_annot <- add_kegg( SCsgp_species, kegg_db, meta99_full_taxo, metaproteome_origin, protein_file, peptide_file, text_to_filter = "HUMAN" ) ## End(Not run) add_taxonomy Description Integrates the database containing the taxonomic classification of the identified proteins in a "metaproteome_object". The taxonomic classification is previously obtained by aligment algorithms and must include seven taxonomic levels assigned to a given protein: species, genus, family, order, class, phylum and superkingdom Usage add_taxonomy(metaproteome_object, taxonomic_database) Arguments metaproteome_object List defined as "metaproteome_object" containing proteins and peptides abundances. The format of this object is similar to that generated from the function "load_protspeps". taxonomic_database Dataframe containing the taxonomic information for each protein. The first column must contain the same identifiers of those present in the column "Accession" from the dataframe "peptides_proteins" of the "metaproteome_object". Two additional columns have to be present: i) one named "organism" containing the name of the strain assigned to a given protein; and ii) the other named "species.genus.family.order.class.phylum.superkingdom". The taxonomic classification can be obtained from a tool of sequences aligment and must be ordered as follows: species, genus, family, order, class, phylum and superkingdom. The characters inside must be concatenated by a comma (ex."Streptococcus anginosus,Streptococcus,Streptococcaceae,Lactobacillales,Bacilli,Firmicutes,Bacteria"). An