Biobtree: A tool to search and map bioinformatics identifiers and special keywords

Introduction

Mapping bioinformatics datasets through a web interface or programmatically via identifiers or special keywords and attributes such as gene name, gene location, protein accessions and species name is a common need during genomics research. These mappings play an essential role in molecular data integration (Huang et al., 2011) and allow the gathering of maximum biological insight (Mudunuri et al., 2009) for these diverse bioinformatics datasets.

There are several existing tools for these mapping needs; these tools are gene-centric, protein-centric or can provide both gene- and protein-centric solutions. One of the common gene-centric tools is BioMart (Zhang et al., 2011)-based tools such as Ensembl BioMarts (Kinsella et al., 2011) which covers Ensembl (Zerbino et al., 2018) and Ensembl Genomes (Kersey et al., 2018) datasets. The R programming language package biomarRt (Durinck et al., 2009) is also widely used via performing queries with BioMart-based tools. Other common gene-centric tools are MyGene.info (Xin et al., 2016), DAVID (Huang da et al., 2009) and g:Profiler (Raudvere et al., 2019). Uniprot ID mapping service (Huang et al., 2011) provides a protein-centric solution. bioDBnet (Mudunuri et al., 2009) and BridgeDb (van Iersel et al., 2010) provide services for both gene- and protein-centric solutions.

On the other hand, genomics data size is increasing continuously (Langmead & Nellore, 2018) especially via high throughput sequencing, so performing these mapping on these expanding data sizes in local computers, cloud computing or existing computing environments in a rapid and effective way via tools with easy installation and requiring minimum maintenance is a challenge (Marx, 2013).

The referenced existing gene-centric tools currently do not support large Ensembl Bacteria genomes. Existing tools either provide only online service or require specific technical knowledge such as a particular database or specific programming language to install, use and adapt to different computational environments such as a local computer. Another limitation of the referenced tools is that they provide one-dimensional filtering capability in a single mapping query.

Biobtree addresses these problems of existing tools, First, it can be used via a single executable file without requiring re-compilation or extra maintenance such as database administration. Alternatively, it can be used via the R or Python-based wrapper packages which have been provided to allow for easier integration into existing pipelines. To process large datasets, it uses a specialized MapReduce-based solution which is discussed in the next section. MapReduce is an effective way to deal with large datasets (Langmead & Nellore, 2018). After processing data, Biobtree provides a web interface, web services and chain mapping and filtering query capability in a single query with its intuitive query syntax which is demonstrated in the use cases section. Biobtree covers a range of bioinformatics datasets including Ensembl Bacteria genomes. The data resources currently used are ChEBI (Hastings et al., 2016), HGNC (Braschi et al., 2019), HMDB (Wishart et al., 2018), InterPro (Mitchell et al., 2019), Europe PMC (Europe PMC Consortium, 2015), UniProt (UniProt Consortium, 2019), Chembl (Gaulton et al., 2017), Gene Ontology (The Gene Ontology Consortium, 2019), EFO (Malone et al., 2010), ECO (Giglio et al., 2019), Ensembl (Zerbino et al., 2018) and Ensembl Genomes (Kersey et al., 2018). Table 1 shows details of these datasets.

Methods

Use cases

The Biobtree web interface can be used primarily for exploration purposes and web services related to integrating genomic analysis pipelines via the Biobtree executable file which is available at GitHub. However, use of the Biobtree executable requires some learning of Biobtree usage and also, in relation to integrating into pipelines written in the various different languages, requires some extra coding effort.

In order to address the above situations, R and Python based wrapper packages BiobtreeR and BiobtreePy have been provided. R and Python are commonly used in genomic analysis (Russell et al., 2018). Both the BiobtreeR and BiobtreePy packages provide very similar functionalities and allow Biobtree to be used seamlessly within pipelines written in these languages.

The BiobtreeR package is provided via Bioconductor (Huber et al., 2015) and meets the build, test and quality standards stipulated for a Bioconductor package.

Another usability feature aimed at easier integration with existing pipelines, which was suggested in the course of the BiobtreeR package review process and has been implemented, is that of providing built-in Biobtree databases for commonly studied datasets and organism genomes. This feature is intended to speed up the data build and update processes related to common datasets and organism genomes and includes example use cases via the web interface. These latter serve the purpose of familiarizing users with the Biobtree and its query syntax.

The following three use cases are demonstrated using BiobtreeR and involve its installation instructions. The first two use cases employ built-in databases, but in the last use case data is built from genomes belonging to specific taxonomy identifiers which are not included in the built-in databases.

BiobtreeR installation

# install package                                                                       
if (!requireNamespace("BiocManager", quietly = TRUE))                                   
install.packages("BiocManager")                                                         
BiocManager::install("tamerh/biobtreeR")                                                
                                                                                        
# import package                                                                        
library(biobtreeR)                                                                      
                                                                                        
# set a output directory for biobtree files                                             
bbUseOutDir('set directory path')                                                       
                                                                                        
# retrive built-in biobtree database for commonly studied dataset and organism genomes  
bbBuiltInDB()                                                                           
                                                                                        
# start biobtree server                                                                 
bbStart()                                                                               

usecase-1 Map Affymetrix identifiers to Ensembl human genome identifiers and then map these to the molecular function type GO terms

# Given comma seperated Affymetrix identifiers maps to GO identifiers                                                                                          
bbMapping("202763_at,209310_s_at",'map(transcript).map(ensembl).map(go).filter(go.type=="molecular_function")',source = "affy_hg_u133_plus_2",attrs = "name")  
                                                                                                                                                               
# query results                                                                                                                                                
##          input       input_dataset mapping_id                                                                                                               
## 1    202763_AT affy_hg_u133_plus_2 GO:0002020                                                                                                               
## 2            -                   - GO:0004190                                                                                                               
## 3            -                   - GO:0004197                                                                                                               
## 4            -                   - GO:0004861                                                                                                               
## 5            -                   - GO:0005123                                                                                                               
## 6            -                   - GO:0005515                                                                                                               
## 7            -                   - GO:0008233                                                                                                               
## 8            -                   - GO:0008234                                                                                                               
## 9            -                   - GO:0016005                                                                                                               
## 10           -                   - GO:0016787                                                                                                               
## 11           -                   - GO:0044877                                                                                                               
## 12           -                   - GO:0097153                                                                                                               
## 13           -                   - GO:0097199                                                                                                               
## 14           -                   - GO:0097200                                                                                                               
## 15 209310_S_AT affy_hg_u133_plus_2 GO:0004197                                                                                                               
## 16           -                   - GO:0005515                                                                                                               
## 17           -                   - GO:0008233                                                                                                               
## 18           -                   - GO:0008234                                                                                                               
## 19           -                   - GO:0016787                                                                                                               
## 20           -                   - GO:0050700                                                                                                               
## 21           -                   - GO:0097199                                                                                                               
##                                                                             name                                                                            
## 1                                                               protease binding                                                                            
## 2                                           aspartic-type endopeptidase activity                                                                            
## 3                                           cysteine-type endopeptidase activity                                                                            
## 4            cyclin-dependent protein serine/threonine kinase inhibitor activity                                                                            
## 5                                                         death receptor binding                                                                            
## 6                                                                protein binding                                                                            
## 7                                                             peptidase activity                                                                            
## 8                                               cysteine-type peptidase activity                                                                            
## 9                                            phospholipase A2 activator activity                                                                            
## 10                                                            hydrolase activity                                                                            
## 11                                            protein-containing complex binding                                                                            
## 12            cysteine-type endopeptidase activity involved in apoptotic process                                                                            
## 13  cysteine-type endopeptidase activity involved in apoptotic signaling pathway                                                                            
## 14 cysteine-type endopeptidase activity involved in execution phase of apoptosis                                                                            
## 15                                          cysteine-type endopeptidase activity                                                                            
## 16                                                               protein binding                                                                            
## 17                                                            peptidase activity                                                                            
## 18                                              cysteine-type peptidase activity                                                                            
## 19                                                            hydrolase activity                                                                            
## 20                                                           CARD domain binding                                                                            
## 21  cysteine-type endopeptidase activity involved in apoptotic signaling pathway                                                                            

usecase-2 Map human Ensembl identifiers with given genome location to the reviewed Uniprot identifiers

# 'homo sapiens' refers to identifier of 9606 in taxonomy                                                                                                                
# built-in 'within' genomic range function in the query which                                                                                                            
# equivalents to ensembl.start>100000000 && ensembl.end< 101000000                                                                                                       
bbMapping('homo sapiens','map(ensembl).filter(ensembl.within(100000000,101000000) && ensembl.seq_region=="X").map(uniprot).filter(uniprot.reviewed)',attrs = "names[1]" )
                                                                                                                                                                         
# query results                                                                                                                                                          
##   mapping_id                                 names[1]                                                                                                                 
## 1     O43657                            Tetraspanin-6                                                                                                                 
## 2     Q9H2S6                              Tenomodulin                                                                                                                 
## 3     Q9Y5S8                          NADPH oxidase 1                                                                                                                 
## 4     P33240    Cleavage stimulation factor subunit 2                                                                                                                 
## 5     O60687    Sushi repeat-containing protein SRPX2                                                                                                                 
## 6     Q96C24             Synaptotagmin-like protein 4                                                                                                                 
## 7     Q8TAB3                         Protocadherin-19                                                                                                                 
## 8     Q5H913 ADP-ribosylation factor-like protein 13A                                                                                                                 
## 9     Q6PP77                     XK-related protein 2                                                                                                                 

usecase-3 Map all taxonomic children of given bacteria and then map these children to Ensembl with given genome location and contains a given word

# this use case requires new data build                                                                                                     
# stop running server                                                                                                                       
# clean output directory or set new one to keep both data                                                                                   
bbStop()                                                                                                                                    
                                                                                                                                            
# build data with specific bacteria genomes                                                                                                 
bbBuildCustomDB(taxonomyIDs = "595,984254,465517,1249525")                                                                                  
                                                                                                                                            
# start server with new data                                                                                                                
bbStart()                                                                                                                                   
                                                                                                                                            
# taxonomy identifier 59201 is used instead of full name 'Salmonella enterica subsp. enterica'                                              
bbMapping("59201",'map(taxchild).map(ensembl).filter(ensembl.start<10000&&ensembl.description.contains("SopD"))',attrs="strand,start,end")  
                                                                                                                                            
# query results                                                                                                                             
##      mapping_id strand start   end                                                                                                       
## 1   ACH54_23895      +  2525  3484                                                                                                       
## 2   ACH56_04205      -    27   986                                                                                                       
## 3   AEW14_05145      -  3410  4369                                                                                                       
## 4   AEW14_15935      -     1    89                                                                                                       
## 5    DE27_21250      +  8885  9967                                                                                                       
## 6    DE87_06330      +  7839  8921                                                                                                       
## 7 LPMST02_21800      +  8983 10065                                                                                                       

Discussion

A mapping between bioinformatics datasets via identifiers or special keywords such as species names is often performed during genomic analyses and plays an essential role in molecular data integration and getting maximum biological insight from these datasets. There are several gene-centric, protein-centric and both protein- and gene-centric tools for addressing these mapping needs. These tools currently do not support the large Ensembl Genomes Bacteria dataset. In addition, these tools provide either only online services or require specific technical knowledge to install and adapt to new computing environments. Existing tools also provide one-dimensional filtering in a single mapping query. Biobtree addresses these problems by managing a tool with a single executable file or alternatively additionally provided R or Python based wrapper packages and processing large datasets with its specialized MapReduce-based solution. Based on processed data, it creates a uniform database and allows searching identifiers and chain mappings and filtering queries.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.

Software availability

All source codes and binaries available at: https://www.github.com/tamerh/biobtree.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.2547047

License: BSD 3-Clause “New” or “Revised” license.