BioServices: a common Python package to access biological Web Services programmatically

Cokelaer, Thomas; Pultz, Dennis; Harder, Lea Mørch; Serra-Musach, Jordi; Sáez-Rodríguez, Julio

doi:10.1093/bioinformatics/btt547

Cited by 110 publications

(85 citation statements)

References 5 publications

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“…Most of these applications use some protocols for communication between the client and the Web Service, like SOAP (Simple Object Access Protocol; www.w3.org/TR/soap). One advantage of Web Services is that client-side applications do not need any intimate knowledge of the database provided by the service itself [18]. Nowadays, scientists are frequently relying on these services for data analysis and several studies have been published using results from these services [19].…”

Section: Methodsologymentioning

confidence: 99%

Integrating Biological Databases in the Context of Transcriptional Regulatory Networks

Pereira¹,

Mendes²

2014

IJBBB

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Abstract-Several studies show that biological knowledge is growing at a continuous rate and distributed among different databases, making the process of data integration a hard task to perform, because they have different structures, different ways of storing data and also different approaches to export information, and are usually developed to provide information for a specific organism. Due to the large amount of biological data, the process of data integration has been one of the major challenges in the field of bioinformatics as well as discovering information about Transcriptional Regulatory Networks (TRN). When using a single source, this task is not easy to perform since the source often lacks enough information for the successful completion of the task. Therefore it is necessary to find information in several databases in order to create a useful body of knowledge. This work presents a new approach of integrating data related with TRNs for the Escherichia coli by creating a new integrated data repository gathering information from KEGG, EcoCyc, Regulon and NCBI databases.

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Section: Methodsologymentioning

confidence: 99%

Integrating Biological Databases in the Context of Transcriptional Regulatory Networks

Pereira¹,

Mendes²

2014

IJBBB

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“…A number of analysis packages that make use of PC data have been developed (42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52) . Here we briefly describe several tools developed by the PC team.…”

Section: Analytical Tools Using the Pathway Commons Data Sourcementioning

confidence: 99%

Pathway Commons: 2019 Update

Rodchenkov

Babur

Luna

et al. 2019

Preprint

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Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset.Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and~2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

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“…The methodology for the construction of the GSM of S. leeuwenhoekii C34 is summarized in Figure 1. Concisely, the information of genes/ proteins/reactions of other Streptomyces, especially that of S. coelicolor, enzyme commission (EC) numbers and pathways was downloaded from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database (Kanehisa & Goto, 2000;Kanehisa et al, 2014) using the bioservices module (Cokelaer, Pultz, Harder, Serra-Musach, & Saez-Rodriguez, 2013) with a script written in python programming language version 2.7 (python programme available at https://www.python. org/download/releases/2.7/).…”

Section: Construction Of the Genome Scale Modelmentioning

confidence: 99%

Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites

Razmilic

Castro

Andrews

et al. 2018

Biotech & Bioengineering

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The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains.

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BioServices: a common Python package to access biological Web Services programmatically

Cited by 110 publications

References 5 publications

Integrating Biological Databases in the Context of Transcriptional Regulatory Networks

Integrating Biological Databases in the Context of Transcriptional Regulatory Networks

Pathway Commons: 2019 Update

Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites

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