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Revised

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

[version 4; peer review: 2 approved]
Previously titled: Biobtree: A tool to search, map and visualize bioinformatics identifiers and special keywords
PUBLISHED 20 Jan 2020
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This article is included in the Bioinformatics gateway.

This article is included in the EMBL-EBI collection.

Abstract

Biobtree is a bioinformatics tool to search and map bioinformatics datasets via identifiers or special keywords such as species name. It processes large bioinformatics datasets using a specialized MapReduce-based solution with optimum computational and storage resource usage. It provides uniform and B+ tree-based database output, a web interface, web services and allows performing chain mapping queries between datasets. It can be used via a single executable file or alternatively it can be used via the R or Python-based wrapper packages which are additionally provided for easier integration into existing pipelines. Biobtree is open source and available at GitHub.

Keywords

bioinformatics, identifiers, search, mapping, visualization

Revised Amendments from Version 3

Article has been revised to fix English language issues.

See the author's detailed response to the review by Samuel Lampa
See the author's detailed response to the review by Maxim N. Shokhirev

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 mappings 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 services 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.

Table 1. List of datasets.

DatasetDescriptionLocationFormat
ChEBIChEBI reference accession dataftp.ebi.ac.uk/chebi/Flat_file_tab_delimited/TSV
HGNCHuman gene nomenclatureftp.ebi.ac.uk/genenames/new/json/JSON
HMDBHuman metabolome databasehttp://www.hmdb.ca/system/downloads/current/XML
InterProProtein Familiesftp://ftp.ebi.ac.uk/pub/databases/interpro/currentXML
Literature mappingsLiterature pmid, pmcid and doi mappingsftp://ftp.ebi.ac.uk/pub/databases/pmc/DOI/CSV
TaxonomyNCBI Taxonomyftp://ftp.ebi.ac.uk/pub/databases/taxonomy/XML
UniparcUniProt Sequence Archiveftp.ebi.ac.uk/pub/databases/uniprot/current_release/uniparc/XML
UniProt reviewedUniProt Knowledgebase reviewedftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/complete/XML
UniProt unreviewedUniProt Knowledgebase unreviewedftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/complete/XML
Uniref50UniProt sequence clustersftp.ebi.ac.uk/pub/databases/uniprot/current_release/uniref/uniref50/ XML
Uniref90UniProt sequence clustersftp.ebi.ac.uk/pub/databases/uniprot/current_release/uniref/uniref90/XML
Uniref100UniProt sequence clustersftp.ebi.ac.uk/pub/databases/uniprot/current_release/uniref/uniref100/XML
GOGene Ontologyhttp://purl.obolibrary.org/obo/go.owlRDF/XML
ECOThe Evidence & Conclusion Ontologyhttp://purl.obolibrary.org/obo/eco.owlRDF/XML
EFOExperimental Factor Ontologyhttp://www.ebi.ac.uk/efo/efo.owlRDF/XML
ChEMBLChemical database of bioactive moleculesftp.ebi.ac.uk/pub/databases/chembl/ChEMBL-RDF/latest/RDF/XML
EnsemblEnsemblftp.ensembl.org/pub/current_json/
ftp.ensembl.org/pub/current_mysql/
ftp.ensembl.org/pub/current_gff3/
JSON,CSV,
GFF3
Ensembl Genomes
Metazoa
Ensembl Genomes Metazoaftp://ftp.ensemblgenomes.org/pub/current/metazoa/json/
ftp://ftp.ensemblgenomes.org/pub/current/metazoa/mysql/
ftp://ftp.ensemblgenomes.org/pub/current/metazoa/gff3/
JSON,CSV,
GFF3
Ensembl Genomes PlantsEnsembl Genomes Plantsftp://ftp.ensemblgenomes.org/pub/current/plants/json/
ftp://ftp.ensemblgenomes.org/pub/current/plants/mysql/
ftp://ftp.ensemblgenomes.org/pub/current/plants/gff3/
JSON,CSV,
GFF3
Ensembl Genomes FungiEnsembl Genomes Fungiftp://ftp.ensemblgenomes.org/pub/current/fungi/json/
ftp://ftp.ensemblgenomes.org/pub/current/fungi/mysql/
ftp://ftp.ensemblgenomes.org/pub/current/fungi/gff3/
JSON,CSV,
GFF3
Ensembl Genomes ProtistsEnsembl Genomes Protistsftp://ftp.ensemblgenomes.org/pub/current/protists/json/
ftp://ftp.ensemblgenomes.org/pub/current/protists/mysql/
ftp://ftp.ensemblgenomes.org/pub/current/protists/gff3/
JSON,CSV,
GFF3
Ensembl Genomes
Bacteria
Ensembl Genomes Bacteriaftp://ftp.ensemblgenomes.org/pub/current/bacteria/json/
ftp://ftp.ensemblgenomes.org/pub/current/bacteria/gff3/
JSON,GFF3

Methods

Implementation

The Biobtree implementation process starts by retrieving selected datasets as shown in Table 1 and retrieving data entries belonging to these datasets with their attributes and mapping information from their public locations, which are also shown in Table 1. During this data retrieval, the whole of the data do not get stored and uncompressed on the disk, instead data are retrieved and uncompressed in a streaming manner in the memory, which allows avoiding excessive disk space usage. Necessary data, which are these mapping and attributes, are compactly stored as chunks on the disk. During these data retrievals, all the idle CPU cores have been utilized to merge and sort these chunks recursively with each other. It is essential that the produced files are sorted to make fast batch inserts to the LMDB database which Biobtree uses as a database to store its result data. Once the data retrieval process is completed, result chunk files are globally merged using the patience sort technique and inserted into the LMDB database as keys and values. Keys consist of identifiers and special keywords such as gene names or species name, and values are attributes such as genomic coordinates and mapped identifiers. In these processes, data retrieval and creation of sorted chunks represent the map phase, global merge of the chunks and database creation represent the reduce phase of the MapReduce solution. Once the database is created, the Biobtree web module provides a web interface and web services to perform both searching for identifiers and mapping queries. Mapping queries has been done with a query syntax which allows chains of mapping and filtering between datasets. An example use case with this syntax is demonstrated in the next section. Biobtree uses a B+ tree data structure-based LMDB key-value store. LMDB provides fast batch inserts and reads which fits the bioinformatics datasets update cycle well where datasets are often updated periodically, and then only intensive read operations are performed. LMDB is embedded into Biobtree’s executable binary code so it does not require a separate installation or special maintenance.

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.

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Gur T. Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.12688/f1000research.17927.4)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 4
VERSION 4
PUBLISHED 20 Jan 2020
Revised
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Reviewer Report 21 Jan 2020
Samuel Lampa, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden;  Savantic AB, Stockholm, Sweden 
Approved
VIEWS 9
I hereby again confirm ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Lampa S. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.24382.r58843)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 3
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PUBLISHED 07 Jan 2020
Revised
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Reviewer Report 15 Jan 2020
Maxim N. Shokhirev, Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, USA 
Approved
VIEWS 14
The author has made additional efforts to improve the installation and use of the tool. I am ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Shokhirev MN. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.24176.r58289)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 16 Jan 2020
    Tamer Gur, EMBL European Bioinformatics Institute, UK, UK
    16 Jan 2020
    Author Response
    Thank you for your review and suggestions to make the article accepted. I have submitted a new version of the article for the English language issues. And yes I will maintain and ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 16 Jan 2020
    Tamer Gur, EMBL European Bioinformatics Institute, UK, UK
    16 Jan 2020
    Author Response
    Thank you for your review and suggestions to make the article accepted. I have submitted a new version of the article for the English language issues. And yes I will maintain and ... Continue reading
Views
15
Cite
Reviewer Report 13 Jan 2020
Samuel Lampa, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden;  Savantic AB, Stockholm, Sweden 
Approved
VIEWS 15
I hereby confirm my approval status of this article, while suggesting to fix the following language issues:

Introduction:
  • "performing these mapping" --> "performing these mappings".
     
  • "provide only online
... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Lampa S. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.24176.r58288)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 16 Jan 2020
    Tamer Gur, EMBL European Bioinformatics Institute, UK
    16 Jan 2020
    Author Response
    Thank you again for your review and suggestions to make the article accepted.
    I have submitted the new version for the English language issues.
    Competing Interests: No competing interests were disclosed.
COMMENTS ON THIS REPORT
  • Author Response 16 Jan 2020
    Tamer Gur, EMBL European Bioinformatics Institute, UK
    16 Jan 2020
    Author Response
    Thank you again for your review and suggestions to make the article accepted.
    I have submitted the new version for the English language issues.
    Competing Interests: No competing interests were disclosed.
Version 2
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PUBLISHED 16 Sep 2019
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Reviewer Report 21 Oct 2019
Samuel Lampa, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden;  Savantic AB, Stockholm, Sweden 
Approved
VIEWS 10
The revised article is now much improved, with a solid introduction explaining the problem area and previous research, use cases, and with the title amended to better reflect the functionality of the tool. I think it can now be approved, ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Lampa S. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.22620.r53983)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 22 Oct 2019
    Tamer Gur, EMBL European Bioinformatics Institute, UK, UK
    22 Oct 2019
    Author Response
    Thank you very much for taking time to review the article and for all the comments and suggestions. I will address your new minor suggestions in the next version together ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 22 Oct 2019
    Tamer Gur, EMBL European Bioinformatics Institute, UK, UK
    22 Oct 2019
    Author Response
    Thank you very much for taking time to review the article and for all the comments and suggestions. I will address your new minor suggestions in the next version together ... Continue reading
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11
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Reviewer Report 27 Sep 2019
Maxim N. Shokhirev, Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, USA 
Approved with Reservations
VIEWS 11
The author now includes references to previously published annotation tools and services such as BioMart and MyGene.info and points out several limitations of the extant tools that are addressed with Biobtree. In addition, the author now includes an online portal ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Shokhirev MN. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.22620.r53984)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 1
VERSION 1
PUBLISHED 04 Feb 2019
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Cite
Reviewer Report 15 Apr 2019
Samuel Lampa, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden;  Savantic AB, Stockholm, Sweden 
Approved with Reservations
VIEWS 19
The article describes a commandline tool, Biobtree, that is claimed to allow to process relations between bioinformatics datasets based on various characteristics such as identifiers and keywords.

The manuscript describes the tool in a clear way technically, ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Lampa S. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.19605.r46335)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Views
49
Cite
Reviewer Report 07 Mar 2019
Maxim N. Shokhirev, Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, USA 
Not Approved
VIEWS 49
While it is important to create a consistent and queryable database of biological identifiers, it is unclear what advances this tool brings to the field. For example, how does this tool compare to other queryable database tools such as mygene.info, ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Shokhirev MN. Reviewer Report For: Biobtree: A tool to search and map bioinformatics identifiers and special keywords [version 4; peer review: 2 approved]. F1000Research 2020, 8:145 (https://doi.org/10.5256/f1000research.19605.r45074)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 10 Mar 2019
    Tamer Gur, EMBL European Bioinformatics Institute, UK
    10 Mar 2019
    Author Response
    Thank you for reviewing the article. I agree that there are several similar tools exist with different dataset and functionalities such as Biomart and mygene.info. However, this tool can still ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 10 Mar 2019
    Tamer Gur, EMBL European Bioinformatics Institute, UK
    10 Mar 2019
    Author Response
    Thank you for reviewing the article. I agree that there are several similar tools exist with different dataset and functionalities such as Biomart and mygene.info. However, this tool can still ... Continue reading

Comments on this article Comments (0)

Version 4
VERSION 4 PUBLISHED 04 Feb 2019
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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