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

Introduction

Bioinformatics datasets often consist of entries, where each entry is represented by unique identifier. Depending on the dataset, each entry contains various types of information such as sequence data, biological function, chemical structure or literature reference etc. In addition, entries often contain cross-referencing information to other dataset entries via identifiers. Let’s take as an example entry the proto-oncogene vav protein in humans, which is encoded by the VAV1 gene. If we display this protein on the UniProt website we see cross references to many other datasets. These cross references represent relations of datasets with each other. Various tools exist to deal with such data; however, the growing volume of bioinformatics datasets, emerging new software tools and analysis approaches motivates new solutions. Biobtree¹ presented herein is capable of improved and rapid processing of large numbers of unique identifiers of entries and related identifiers that are specified via cross-reference data.

In some datasets, in addition to unique identifiers there is information that is strongly related to entries but not necessarily unique for each entry. Species names or UniProt secondary accessions are example of this type. Information that is strongly related to the entries but not necessarily unique is a second data source for Biobtree. In Biobtree, these types are called special keywords and each of these can be related to multiple entries among the datasets.

Biobtree retrieves all these identifiers, related identifiers and special keywords from various bioinformatics resources and stores it in a single database. The data resources currently used are ChEBI², HGNC³, HMDB⁴, InterPro⁵, Europe PMC⁶ and UniProt⁷. Table 1 shows details of these datasets.

Based on stored data, Biobtree provides search, map and visualization functionalities via provided web services or a web interface. For instance, all the UniProt proteins entries belonging to a gene name, or, all Ensembl⁸ genome transcripts identifiers and ENA⁹ sequence identifiers that map to a protein identifier can be accessed. These relations, determined via identifiers, are stored bidirectionally so all actions can also be done in the opposite way.

Biobtree is managed from a single executable file for each major operating system without requiring any installation or compilation. As a database, Biobtree uses a B+ tree data structure based LMDB key value store. LMDB provides fast batch inserts and reads and allows effective operation on a large number of records. LMDB is embedded into Biobtree’s executable binary code so it does not require a separate installation.

Methods

Update phase

The purpose of the update phase is to retrieve dataset identifiers and special keywords from remote servers or a local disk and produce files that contains identifiers and special keywords with their referred identifiers as keys and values in a sorted order. It is essential that the produced files are sorted to make fast batch inserts to LMDB database in the next phase. The updating phase is started via the Biobtree CLI with the update command. For example, the following command starts the update phase for the hgnc dataset.

$ biobtree --d hgnc update

Updating reads selected datasets as a stream and saves Biobtree-related data in a series of files. An advantage of reading dataset as streams is that it does not require fully downloading the dataset to the local disk. Datasets can have different formats like XML, JSON, TSV or CSV. Biobtree has specialized parsers for each dataset and parses them to produce its output files. When Biobtree runs the first time it retrieves its configuration, license and web interface files from the source code repository. Configuration files contain Biobtree runtime settings and dataset definitions.

$ biobtree --d my_data update

$ biobtree --d uniparc –idx 1 update
$ biobtree --d uniref50 –idx 2 update

Generate phase

The purpose of this phase is to merge all the files produced in the update phase by keeping the sorted order and generate the final key and values in the generated LMDB database. Keys consist of identifiers and special keywords and values are identifiers that are referred to by these keys with their dataset information. If the values size for each key are above a certain threshold they are saved in pages. The following command starts the generate phase.

$ biobtree generate

The generated database output is used in next web phase but it can be also used directly. Example source codes for using the database directly can be found in the project github page.

Web phase

The purpose of this phase is to provide web services and a web interface via the produced output of the generate phase. The following command starts web phase

$ biobtree web

With this command the Biobtree web server is started instantly, serving the REST, gRPC and web interface services.

service BiobtreeService {

rpc Get     (BiobtreeGetRequest)     returns (BiobtreeGetResponse);
rpc GetPage (BiobtreeGetPageRequest) returns (BiobtreeGetPageResponse);
rpc Filter  (BiobtreeFilterRequest)  returns (BiobtreeFilterResponse);
rpc Meta    (BiobtreeMetaRequest)    returns (BiobtreeMetaResponse);

}

Web interface

The Web interface allow user to visualize the produced database via the RESTful service. Once the web phase is started it is accessed via the browser from the following address:

                    http://localhost:8888/ui

The Web interface provides searching of multiple identifiers and special keywords, visualizing and filtering results, and executing bulk queries. On the result page for each result, it provides a url to access the main website where the data are originally produced. Figure 1 and Figure 2 show the main and result page of the web interface.

Figure 1. Web interface main page.

Figure 2. Web interface result page.

Operation

Biobtree executable file is available from GitHub page for Windows, MacOS and Linux operating systems. For default datasets and configuration Biobtree uses up to 4 GB of memory. For large datasets it is advised to use computer which has large RAM space such as 16 GB to speed up finishing update and generate phases. RAM usage for these phases can be managed from configuration file via kvgenChunkSize and batchSize variables. For CPU Biobtree uses all available CPU powers when it needed. This default behaviour can be restricted via CLI with maxcpu argument.

Benchmarks

Software benchmarks should often be taken with a grain of salt, especially where the input data is large, because benchmarks results can be affected by many factors like the processor, operating system, storage, network speed, input data, application parameters etc. Considering these, the purpose of Biobtree benchmarks is mainly to show overall capabilities and resource usage of Biobtree. Table 2 shows the benchmark details and results. Benchmarks were primarily computed at DigitalOcean London datacentres using their CPU optimized droplets with block storage volumes. Hundred thousand sample query which used in the benchmarks can be found on the project GitHub page.

Discussion

The benchmarks show that Biobtree¹ has produced LMDB database output in relatively acceptable times. Let’s discuss how Biobtree behaves if UniProt provides tens of times larger data. Clearly, more disk space would have been needed.

If enough disk space is provided, the next obstacle would have happened during the update phase, because currently UniProt provides a single gzip compressed file for each dataset and Biobtree reads each file as a stream from the beginning to end. Gzip does not allow a random access to a file unless there are checkpoints defined. This characteristic of gzip prevents the processing of a single large file in a split manner and utilizes more computing resources if available. Two solutions can address the issue. The first could be for UniProt to allow its datasets to be capable of parallel processing, like splitting and compressing files inside a tar archive. The second solution would be to implement a new functionality in Biobtree and save and decompress these files to local disk and make parallel access to decompressed files.

A further obstacle would have happened during the generate phase, since we would have obtained more files from update phase. The generate phase struggles to merge all these files and could cause much longer output generation times. To address this obstacle, a new phase could be implemented that runs before the generate phase and merges files coming from the update phase.

Limitations

Although duplicate values for each key are discarded during the update phase for each dataset, the generate phase could rarely produce duplicate values. The user needs to discard these duplicate records manually if these are created. Another limitation is when querying the special keywords, they need to be fully specified including all space characters.

Future work

The limitations can be addressed and new functionalities added in the future. For instance, different bioinformatics datasets like Ensembl⁸ or ENA⁹ can be integrated. Another feature would be sorting result values based on a certain criterion.

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.2547047¹.

License: BSD 3-Clause "New" or "Revised" license.