KibioR - Biological examples

In kibior: A Simple Data Management and Sharing Tool

library(knitr)
knitr::opts_chunk$set(include = TRUE, 
                      echo = TRUE, 
                      warning = FALSE, 
                      message = FALSE, 
                      results = "markup", 
                      collapse = TRUE,
                      cache = FALSE,
                      comment = "##")
library(magrittr)
library(ggplot2)
library(dplyr)
library(stringr)
library(readr)
library(kibior)

Vignette

This vignette contains the use case presented in the KibioR paper.

Prepare R environment

An infinite number of use case can be built as KibioR is versatile. We will base ours on Bioconductor known libraries.

Bioconductor additional requirements

As defined on Bioconductor website, common classes and methods already exist. Depending on what our interest is, these packages are required.

As a short sum up, we summarized here the system dependecies for Bioconductor importation packages:

| File format | Packages | Required system libraries | |-------------------------------|-------------------------------|---------------------------------------------------------------| | FASTA | Biostrings | zlib | | BAM | Rsamtools, GenomicAlignments | zlib, libbz2, liblzma, libxml2 | | GTF, GFF, BED, BigWig, etc. | rtracklayer | zlib, libbz2, liblzma, libxml2 | | VCF | VariantAnnotation | zlib, libbz2, liblzma, libxml2 | | FASTQ | ShortRead | zlib, libbz2, liblzma, libxml2, libpng, libjpeg | | Mass Spectro data | MSnbase | pthreads (optional), ncdf |

For instance, with Ubuntu system (apt-based), libraries names are:

• curl : curl
• zlib : zlib1g-dev
• libbz2 : libbz2-dev
• liblzma : liblzma-dev
• libxml2 : libxml2-dev
• libpng : libpng-dev
• libjpeg : libjpeg-dev
• pthreads: libevent-pthreads-2.0-5
• ncdf : libnetcdf-dev

We need rtracklayer package for our example, thus we will be able to import feature formats (GTF, GFF, BED). Since it has Rsamtools package as one of its dependencies, we will also be able to import alignment formats (BAM, SAM).

To install linked dependencies, use this command:

# as root
apt update && apt install -y zlib1g-dev libbz2-dev liblzma-dev libxml2-dev libpng-dev libjpeg-dev

Then, install rtracklayer in R with:

# install requirements
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("rtracklayer")

If package dependency is not met, It will be prompt during runtime.

Return types

Returned types per package are as followed:

| File format | Packages | Methods | Returned type after import | |-------------------------------|-------------------|-----------------------------|-----------------------------------------| | FASTA | Biostrings | read{DNA/RNA/AA}StringSet() | {DNA/RNA/AA}StringSet | | BAM | Rsamtools | scanBam() | list (per BAM) of list (BAM attributes) | | SAM, BAM | GenomicAlignments | readGAlignment*() | GAlignments | | GTF, GFF, BED, BigWig, etc. | rtracklayer | import() | GRanges |

Biological data importation

The $import() method will try importation from known file extensions. If it does not guess the file format, try using the right method it depends on:

• $import_sequences(): import fasta format
• $import_features(): import gtf, gff, bed formats
• $import_alignments(): import bam format
• $import_tabular(): import csv, txt, tab formats
• $import_json(): import json format

For instance, we can import all data from extdata in one command.

# get extdata folder path
system.file("extdata", package = "kibior") %>% 
    # list all files inside the folder
    list.files(full.names = TRUE) %>% 
    # import all files in R session
    lapply(kc_local$import)

Some file with gff3.gz, bed, fa.gz and json extensions were just imported in one shot. For some reasons, the bai extension file was not. We can try to import it with the right method $import_alignement(), which will cut down some checks and try to call the right method.

# get extdata folder path
system.file("extdata", package = "kibior") %>% 
    # list all files inside the folder
    list.files(full.names = TRUE) %>% 
    # Select only bai files
    .[endsWith(.,".bai")] %>% 
    # import them
    kc$import_alignments()

Then, it works.

Using published data, we can import Hetionet (ref)

# downlaod dataset from one compressed json file
url <- "https://github.com/hetio/hetionet/raw/master/hetnet/json/hetionet-v1.0.json.bz2"
t <- tempfile(fileext=".json.bz2")
download.file(url, t)
hetionet <- jsonlite::fromJSON(t)

# get nodes and edges
hetionet$nodes %>% kc_local$push("hetionet_nodes")
hetionet$edges %>% kc_local$push("hetionet_edges")

Use cases

These use cases come from the Kibio and KibioR publication.

Use case 1: Characterize the effects of drugs using tissue-specific interactions

The objective of this analysis is to investigate targets for the drug metformin within the framework of tissue-specific gene expression patterns and Protein-Protein Interactions (PPIs) of that target protein. The resources used to reach this objective are DrugBank[^wishart_2018], HUGO Gene Nomenclature Committee (HGNC[^gray_2015]) and the Database of Human Tissue Protein-Protein Interactions (TissueNet v.2[^basha_2017]). In this example, we perform consecutive search/pull commands with KibioR to emulate what one do when exploring datasets following a classical iterative manner.

A litterature search had identified metformin[^rojas_2013] as a drug that helps control blood sugar and is used to treat pre-diabetes, type 2 diabetes and gestational diabetes. We searched for metformin in the DrugBank to obtained its 3 target genes (proteins): protein kinase AMP-activated non-catalytic subunit beta 1 (PRKAB1), glycerol-3-phosphate dehydrogenase 1 (GPD1) and electron transfer flavoprotein dehydrogenase (ETFDH). The gene symbols in HGNC gave us the associated Ensembl Gene IDs. We then injected these Ensembl IDs in another search with TissueNet2 database to find tissue-specific interactions for these target genes. This resulted in all the subcutaneous adipose interactions (170) and all whole blood interactions (167). With these parameters, we obtained the non-redondant 3 interactions between subcutaneous adipose and whole blood tissues: solute carrier family 25 member 10 (SLC25A10, ENSG00000183048), cell death inducing DFFA like effec-tor A (CIDEA, ENSG00000176194) and coagulation factor X (F10, ENSG00000126218).

# get the metformin drugbank id (DB00331)
kc$search("pub_drugbank", 
          query = "metformin", 
          columns = c("name", "drugbank_id"))[[1]]

# query targeted proteins associaated with metformin id (PRKAB1, ETFDH, GPD1)
kc$pull("pub_drugbank_proteins", 
        query = "drug_ids:DB00331 && protein_type:target",
        columns = c("name", "gene_name", "protein_type"))[[1]]

# hgnc id to get the associated Ensembl Gene ID             
#   GPD1   = ENSG00000167588     
#   ETFDH  = ENSG00000171503      
#   PRKAB1 = ENSG00000111725 
res <- kc$search("pub_hgnc", 
        query = "symbol:(PRKAB1 || ETFDH || GPD1)", 
        columns = c("symbol", "ensembl_gene_id"))[[1]]

# list all unique tissues
kc$keys("pub_tissuenet_v2", "tissue")

# get all interactions for both tissues: adipose subcutaneous and whole blood.
net_blood <- kc$pull("pub_tissuenet_v2", query = "(ENSG00000167588 || ENSG00000171503 || ENSG00000111725) && tissue:whole_blood")[[1]]

net_adipo <- kc$pull("pub_tissuenet_v2", query = "(ENSG00000167588 || ENSG00000171503 || ENSG00000111725) && tissue:adipose_subcutaneous")[[1]]

# compare blood and adipose tissue with unique ENS IDs
setdiff(unique(c(net_blood$protein_one, net_blood$protein_two)),
        unique(c(net_adipo$protein_one, net_adipo$protein_two)))

# ENSG00000183048 = SLC25A10
# ENSG00000176194 = CIDEA
# ENSG00000126218 = F10

Findings in mice suggest SLC25A10 as a possible target for anti-obesity treatments[^kulyte_2017] and mice without a functional CIDEA are resistant to diet-induced obesity and diabetes[^puri_2008]. These tissue-specific interactions seem to be interesting targets for diabetes, and could explain potential mechanisms of action in subcutaneous adipose. These results emphasize the need to consider prior knowledge of tissue-specific drug-protein interactions for drug design, as it could potentially improve the prediction of drug effects and/or adverse effects.

Use case 2: Identification of miRNAs linked to inflammation in the prostate

It has been demonstrated that miRNAs regulate antitumor immunity[^yang_2018]. Aberrant expression of miRNAs was observed in prostate cancer[^sharma_2019]. The objective of this case study was to identify miRNAs that could regulate genes linked to immunity mainly expressed in the prostate, and that could be involved in cancer. This analysis required 4 resources: 1/ InnateDB a database of genes involved in innate immune response[^breuer_2013], 2/ the Human Protein Atlas, a database of all the human proteins in cells, tissues and organs[^uhlen_2015], 3/ miRTarBase, a curated database of miRNA-target interactions[^chou_2018] and 4/ miRBase, a microRNA sequences and annotations database[^kozomara_2019]. Here, we simply used 3 joins with filters between 4 databases to get our answer.

We started the exploration from a list of human genes involved in innate immune response from InnateDB and selected those expressed in "glandular cell" type and "prostate" tissues using the Human Protein Atlas dataset. This resulted in a list of 768 unique genes. We then found the miRNA targets for these genes using the miRTarBase. This search was restricted to human miRNA-mRNA associations with strong validation evidence[^kuhn_2008], namely those with at least "qRT-PCR", "Luciferase reporter assay" and "Western blot" as methods of validation. This resulted in a list of 1180 miRNA-mRNA interactions. Finally, the miRNA identifiers of these interactions were linked to the miRBase where the “experimental” filter was applied to obtain a reduced list of 48 unique miRNAs.

# Get all gene names from prostate glandular cells in the Human Protein Atlas
# that have a reference in InnateDB
res <- kc$inner_join("pub_protein_atlas_v19_3", "pub_innatedb", 
            left_query = 'cell_type:"glandular cells" && tissue:prostate', 
            left_columns = "gene_name", 
            by = c("gene_name"="gene_symbol"))

# unique gene names 
res$gene_name %>% unique() %>% length()

# Join the previous result to 3 types of experimental human data from miRTarBase
# by binding the target gene
res2 <- kc$inner_join(res, "pub_mirtarbase_v8", 
            right_query = 'species_mirna:"Homo sapiens" && experiments:("qRT-PCR" && "Western blot" && "Luciferase reporter assay")',
            by = c("gene_name" = "target_gene"))

# unique miRNA-mRNA interactions
res2$mirtarbase_unique_id %>% unique() %>% length()

# Join again the previous result to miRBase, only on experimental data
# to find miRNA names
res3 <- kc$inner_join(res2, "pub_mirbase_v2", 
            right_query = "features_evidence:experimental", 
            right_columns = c("id", "name", "description"), 
            by = c("mirna" = "name"))

# List unique miRNA identifiers
res3$id %>% unique()

The expression profiles of two of those miRNAs have evidence of being involved in prostate cancer[^vanacore_2017]. In fact, miR-375 (RF00700) is known to be important for early diagnosis[^wen_2014] and miR-650 (RF00952) can suppress the cellular stress response 1 (CSR1) expression and promote tumor growth[^zuo_2015]. Finding new targets could help to develop miRNA-based strategies for more effective immunotherapeutic interventions in cancer. This finding confirms the utility of our approach and motivates further investigation of other miRNAs that were identified for their potential roles in prostate cancer pathophysiology and/or treatment.

Use case 3: Identification of pathways for metabolites produced by the microbiome

The aim of this exploration was to find drug metabolism pathways linked to metabolites of human gut microbiome origin[^wilson_2017]. The resources used to reach this objective are HMDB[^wishart_2018] and the Small Molecule Pathway Database (SMPDB[^jewison_2014]). We used a more complex set of commands to show the ease of KibioR imbrication into common R command steps. We also did not limit our search to only one column for the first query, making it truely searching in all textual fields and bringing results from potential free-text comments containing the word "microbial".

We started by searching the HMDB metabolites dataset for metabolites of “microbial” origin. We obtained 546 metabolite records listed as such on the 114,100 initial records. From these selected metabolites, we change the referential by joining on to the SMPDB protein database relation, where we retrieve the microbial metabolites associated pathways. Finally, to find all data linked to these pathways, the SMPDB pathways database was mined, pruning only for the “drug metabolism” pathways. This search resulted in 41 unique pathways.

```r=

One big command using magrittr pipe "%>%"

get HMDB protein accession from HMDB metabolites that contain the "microbial" word

it can be a type or a single word in a comment

res <- kc$pull("pub_hmdb_metabolites_v2", query = "Microbial",
columns = "protein_association_protein_accessions") %>% # extract all unique HMDB IDs unlist(use.names = FALSE) %>% unique() %>% # split them in bulks of 20 max HMDB IDs # this avoid limitations in sending potentially huge requests split(., ceiling(seq_along(.)/20)) %>% # only search in HMDB IDs column lapply(function(x) paste0(x, collapse = " || ")) %>% lapply(function(x) paste0('hmdbp_id:(', x ,')')) %>% (function(x){ message("Searching for subsets of HMDB IDs in SMPDB"); x }) %>% # Get assocaited Pathways names in HMDB proteins lapply(function(x) kc$pull("pub_smpdb_proteins_v2", query= x, columns="pathway_name")[[1]]) %>% # fusion into one table (one column = pathway_name) data.table::rbindlist() %>% # join all selected pathway names with those having # only a drug metabolism subject, and get all data # returned by SMPDB Pathways kc$inner_join("pub_smpdb_pathways_v2", right_query = 'subject:"drug metabolism"',
by = c("pathway_name" = "name"))

number of unique pathways

res$pw_id %>% unique() %>% length()

This investigation identified drug metabolism pathways that could be influenced by different microbiota composition and/or variations in microbial-derived metabolite precursor availability. It is noteworthy that data exploration can be tailored to diverse applications. For example, if the interest is understanding skin aging, one could navigate to the Digital Ageing Atlas (Ageing Map)[^craig_2015] database to highlight pathways and metabolites that could potentially act on aging. This could be an interesting lead for the development of new therapeutics.



<br/><br/><br/><br/>

# How to...

## Search elements from a vector of IDs

Basically, the general form is `kc$search("index", query = "column:(id1 || id2 || id3)")`.

To automate in a script, one can use:

```r
#> one vector of IDs
vect <- c("id1", "id2", "id3")
#> search everywhere for these IDs
vect %>% paste0(collapse = " || ") %>% kc$search("*", query = .)

Delete all indices

Be very cautious with this one. Deleting an index cannot be undone.

By default, kibior will not allow you to delete everything at once with $delete("*"). But, if you need to do it, you can forcefully apply it with kc$list() %>% kc$delete().

Push a list of datasets

#> Suppose we have the following list of datasets
ds <- list(
    "sw" = dplyr::starwars,
    "st" = dplyr::storms
)
#> One can easily push all of them in one go with their attributed list name.
purrr::imap(ds, kc$push)
kc$list()

Import data from a single big JSON file

You can find a lot of data already availabel on the web. We can take an example with Hetionet doi:10.7554/elife.26726 available on het.io. The file is big and contains graph data (nodes and edges). One way of integrating it in Kibio is to separate the nodes from the edges available inside the file with a simple code.

# download dataset from one compressed json file
url <- "https://github.com/hetio/hetionet/raw/master/hetnet/json/hetionet-v1.0.json.bz2"
t <- tempfile(fileext=".json.bz2")
download.file(url, t)
hetionet <- jsonlite::fromJSON(t)

# get nodes and edges
hetionet$nodes %>% kc_local$push("hetionet_nodes")
hetionet$edges %>% kc_local$push("hetionet_edges")

References

kibior documentation built on Jan. 28, 2021, 5:05 p.m.