CALANGO

Comparative AnaLysis with ANnotation-based Genomic cOmponentes
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CALANGO logo. Drawn by Brazilian artist Berze - https://www.facebook.com/berzearte

DESCRIPTION

CALANGO is a first-principles, phylogeny-aware comparative genomics package to search for annotation terms (e.g Pfam IDs, GO terms or superfamilies), formally described in a dictionary-like structure and used to annotate genomic components, associated with a quantitative/rank variable (e.g. number of cell types, genome size or density of specific genomic elements).

Our software has been freely inspired by (and explicitly modeled to take into account information from) ideas and tools as diverse as comparative phylogenetics, genome annotation, gene enrichment analysis, and data visualization and interactivity.

HOW TO INSTALL

The latest version of CALANGO can be installed directly from the repository using devtools::install_github():

devtools::install_github("LABpackages/CALANGO")

Alternatively, you should soon be able to install the last release version from CRAN by simply doing:

install.packages("CALANGO")

In either case, please make sure that you have the latest R version (at least 3.6.1) as well as updated versions of all installed packages.

INSTALLING AND UPDATING DEPENDENCIES

CALANGO depends on some packages from Bioconductor. This requires an extra installation step before CALANGO can be used. Just run:

library(CALANGO)
install_bioc_dependencies(which = "all")

to install / update all dependencies (packages listed as CALANGO’s imports and suggests) to their latest versions.

HOW TO USE - OVERVIEW

To run CALANGO you need two things:

  1. a set of data
  2. an input list defining the path to that data and what is to be done.

Retrieving data

A set of example data folders and files can accessed directly from the package using function retrieve_data_files(). For instance, a call:

CALANGO::retrieve_data_files("./data")

will create a folder called data in the current working directory path, and download the sample files into subfolders within it. These subfolders will contain:

  • genome annotation data files (under ./data/Pfam/ or ./data/gene2GO/)
  • metadata files (under ./data/metadata/)
  • phylogenetic tree files (under ./data/trees/)
  • dictionary files (under ./data/dics/)

The downloaded data will also contain files that describe the input list mentioned above, under ./data/parameters/. These full examples represent all input files required to locally reproduce several types of analyses.

Once these two pieces are in place, the CALANGO pipeline can be run by invoking the main function of the package. For instance:

output <- run_CALANGO(defs = "./data/parameters/parameters_domain2GO_freq.txt", cores = 2)

This call will generate an enriched CALANGO list as the output, and generate a dynamic HTML5 output (plus several tab-separated value (tsv) files in the directory provided as output.dir.

PREPARING YOUR INPUT FILES

CALANGO requires the following files (please check the examples in in ./data/parameters/ if in doubt about file specifications):

genome annotation file

A text file for each species describing their set of biologically meaningful genomic elements and their respective annotation IDs (e.g. non redundant proteomes annotated to GO terms, or non-redundant protein domains annotated to protein domain IDs). An example of such file, where gene products are annotated using Gene Ontology (GO) terms and Kegg Orthology (KO) identifiers would be as follows:

Entry   GO_IDs   KEGG_Orthology_ID
Q7L8J4  GO:0017124;GO:0005737;GO:0035556;GO:1904030;GO:0061099;GO:0004860
Q8WW27  GO:0016814;GO:0006397;GO:0008270  K18773
Q96P50  GO:0005096;GO:0046872   K12489

And is specified as:

  • Fixed number of columns per file (minimum 2) separated by tabs, or “.

  • First line as the header, each column having a unique column name.

  • Each following line having an instance of a genomic feature represented by an unique ID (a specific coding-gene locus found in a genome, for instance) identifier.

  • Multiple terms of the same entry (e.g. a gene annotated to multiple GO terms) should be in the same column and separated by “;”, with any number of spaces before and after it. It’s use after the last term is optional.

  • A column can have no terms in any given row.

In a more abstract representation, files representing genome annotations for a single annotation schema would have two columns and the following general structure:

genomic_element_name/ID_1     annotation_ID_1;(...);annotation_ID_N
genomic_element_name/ID_2     annotation_ID_12

phylogenetic tree file

newick or PHYLIP format, containing at least:

  • all species to be analyzed (species IDs in the tree must be the same name of text files from the previous step)

  • branch lengths proportional to divergence times (a chronogram)

  • no polytomies (if there are such cases, CALANGO will resolve star branches using multi2di method as implemented in ape package.

A tree in newick format (however, with no branch lengths), would be:

(genome_ID_1,(genome_ID_2,genome_ID_3))

A metadata file

Containing species-specific information:

  • genome ID column (same name of text files and of species in phylogenetic trees, must be the first column);

  • The quantitative/rank variable used to sort/rank genomes;

  • A normalizing factor (e.g. proteome size or length; number of annotation terms) to correct for potential biases in datasets (e.g. organisms with different annotation levels or with highly discrepant proteome sizes) If users are using GO as annotation schema, CALANGO can compute a normalizing factor that takes into account all GO counts, including internal nodes, and therefore may not provide any normalizing factor for this case.

The tabular format for the correlation analysis where column 1 contains the genome IDs, column 2 contains the variable to rank genomes and column 3 contains the normalizing factor could be as follows:

../projects/my_project/genome_ID_1  1.7  2537
../projects/my_project/genome_ID_2  1.2  10212
../projects/my_project/genome_ID_3  0.9  1534

Metadada files are specified as follows:

  • Fixed number of columns (minimum 2) separated by tabs.

  • No header.

  • Each line having a unique identifier (first column) with the path of the genome it is referring to or to genome ID. Referred as “genome ID column”.

  • One mandatory numeric value in every other column, referred as “variable columns”.

A dictionary

Tab delimited, linking annotation IDs to their biologically meaningful descriptions. Our software currently supports two dictionary types:

  • Gene Ontology (GO) - in this case, CALANGO will automatically recover annotation description and compute values for internal GOs not explicitly used to describe data annotation.

  • other - in this case, users need to describe a new dictionary linking annotation term IDs (e.g. “46456”) to their descriptions (e.g. “All alpha proteins”), separated by tabs. An example of such file would be:

Annotation_ID     Annotation_definition
annotation_ID_1   All alpha proteins
annotation_ID_2   Globin-like
annotation_ID_3   Globin-like
annotation_ID_4   Truncated hemoglobin
(...)
annotation_ID_N   annotation_ID_description

CALANGO can treat each identifier as its own description, preventing the need to prepare an ontology that isn’t natively supported. For that, do not specify any ontology file and set the ontology = "other".

SETTING UP CALANGO PARAMETERS

CALANGO’s parameters are listed in the documentation of run_CALANGO(), as well as in the examples file provided (./data/parameters/). They are, for the current version:

  • annotation.files.dir (required, string) - Folder where annotation files are located.

  • output.dir (required, string) - output folder for results

  • dataset.info (required, string) - genome metadata file, it should contain at least:

    1. path for annotation data (if “annotation.files.dir” not provided OR file names (if “annotation.files.dir) is provided. Please notice this information should be the first column in metadata file;
    2. phenotype data (numeric, this is the value CALANGO uses to rank species when searching for associations)
    3. normalization data (numeric, this is the value CALANGO uses as a denominator to compute annotation term frequencies to remove potential biases caused by, for instance, over annotation of model organisms or large differences in the counts of genomic elements). Please notice CALANGO does not require normalization data for GO, as it computes the total number of GO terms per species and uses it as a normalizing factor.

  • x.column (required, numeric) - which column in “dataset.info” contains the phenotype data?

  • ontology (required, string) - which dictionary data type to use? Possible values are “GO” and “other”. For GO, CALANGO can compute normalization data.

  • dict.path (required, string) - file for dictionary file (two-column file containing annotation IDs and their descriptions, not needed for GO

  • column (required, string) - which column in annotation files should be used (column name)

  • denominator.column (optional, numeric) - which column contains normalization data?

  • tree.path (required, string) - path for tree file in either newick or nexus format

  • tree.type (required, string) - tree file type (either “nexus” or “newick”, case-sensitive)

  • cores (optional, numeric) - how many cores to use? If not provided the function defaults to 1.

  • linear.model.cutoff = 0.5 (required, numeric) - Parameter that regulates how much graphical output is produced. We configure it to generate plots only for annotation terms with corrected q-values for phylogenetically independent contrasts smaller than 0.5.

  • MHT.method = “BH” (optional, string) - type of multiple hypothesis correction to be used. Accepts all methods listed by stats::p.adjust.methods(). If not provided the function defaults to “BH”.

  • type = “correlation” (optional, string) type of analysis to perform. Currently only “correlation” is supported.

CALANGO OUTPUT

Live examples of CALANGO output HTML5 pages can be found here.

CALANGO produces as its main output a dynamic HMTL5 website containing:

  • A description of the input values and a button to download the input and results list.
  • Interactive heatmaps and scatterplots
  • A dynamic table of results.

Please check our examples page at https://labpackages.github.io/CALANGO/ to explore the full output of CALANGO for a variety of examples. The required data to fully reproduce these examples can be obtained by using CALANGO::retrieve_data_files().

CALANGO MASTERMIND

OTHER DEVELOPERS

NON-CODING COLLABORATORS

  • Gabriel M. F. Almeida - scientific adviser (analysis of phage data)