BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology

Moreno, Pablo; Beisken, Stephan; Harsha, Bhavana; Muthukrishnan, Venkatesh; Tudose, Ilinca; Dekker, Adriano; Dornfeldt, Stefanie; Taruttis, Franziska; Große, Ivo; Hastings, Janna; Neumann, Steffen; Steinbeck, Christoph

doi:10.1186/s12859-015-0486-3

Cited by 38 publications

(30 citation statements)

References 13 publications

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“…Ontology-based enrichment analysis aids in the interpretation of large-scale biological data (Moreno et al, 2015). ChEBI (Chemical Entities of Biological Interest), Model TA ES10 TA ES20 TA ES40 TA ES80 2 SD 2 SD 3 SD 3 SD a manually curated database and ontology that organizes small molecule knowledge, which offers an ontology of chemical structural classes.…”

Section: Discussionmentioning

confidence: 99%

Metabolomics study of renal fibrosis and intervention effects of total aglycone extracts of Scutellaria baicalensis in unilateral ureteral obstruction rats

Fang

Wang

Sun

et al. 2016

Journal of Ethnopharmacology

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Section: Discussionmentioning

confidence: 99%

Metabolomics study of renal fibrosis and intervention effects of total aglycone extracts of Scutellaria baicalensis in unilateral ureteral obstruction rats

Fang

Wang

Sun

et al. 2016

Journal of Ethnopharmacology

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“…With PubChem fully classified, the indexing of PubMed documents will now be much easier. Combining structure-based annotations with biological data could also assist scientists in various projects, such as ontology-based chemical enrichment analysis [17]. Moreover, through ClassyFire, it is now possible to perform a variety of fast data searches and retrievals of PubChem data, as outlined below.…”

Section: Use Casesmentioning

confidence: 99%

“…Some examples of structure-based chemical classification or ontological schemes include the ChEBI ontology [15], the Medical Subject Heading (MeSH) thesaurus [16], and the LIPID MAPS classification scheme [13]. These databases and ontologies/thesauri are excellent and have been used in various studies including chemical enrichment analysis [17], and knowledge-based metabolic model reconstruction [18], among others. However, they are all produced manually, thus making the classification/annotation process somewhat tedious, error-prone and inconsistent (Fig.…”

Section: Introductionmentioning

confidence: 99%

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

Eisner²,

et al. 2016

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BackgroundScientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible.ResultsWe have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper.ConclusionClassyFire, in combination with ChemOnt (ClassyFire’s comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million “ClassyFire” classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0174-y) contains supplementary material, which is available to authorized users.

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“…BiNChE is an open‐source enrichment analysis tool for small molecules based on their Chemical Entities of Biological Interest (ChEBI) role or similar structural ontologies that displays interactive graphs that are exportable in high resolution image and network formats . This may aid in the exploration of large sets of metabolites identified in metabolomics or other systems biology research contexts.…”

Section: Pathway Analysis Network Construction Visualization and Bmentioning

confidence: 99%

Updates in metabolomics tools and resources: 2014–2015

2015

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acquisition. Improved machinery in metabolomics generate increasingly complex data sets which 6 create the need for more and better processing and analysis software and in-silico approaches to 7 understand the resulting data. However, a comprehensive source of information describing the 8 utility of the most recently developed and released metabolomics resources --in the form of 9 tools, software, and databases -is currently lacking. Thus, here we provide an overview of 10 freely-available, open-source, tools, algorithms and frameworks to make both upcoming and 11 established metabolomics researchers aware of the recent developments in an attempt to advance 12 and facilitate data processing workflows in their metabolomics research. The major topics 13 include tools and researches for data processing, data annotation, and data visualization in MS 14 and NMR based metabolomics. Most in this review described tools are dedicated to untargeted 15 metabolomics workflows; however, some more specialist tools are described as well. All tools 16 and resources described including their analytical and computational platform dependencies are 17 summarized in an overview Table. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 In metabolomics, data processing and interpretation represent some of the most 2 challenging and time-consuming steps in the high throughput process, regardless of the 3 analytical platform used for data generation. The exponentially growing volume of generated 4 data has triggered the research and development of tools, software, programs, databases, and 5 applications to facilitate the robust understanding of metabolic processes of biological systems. 6For instance, natural products discovery has greatly benefited from the development of open-7 access spectral and chemical databases [1]. In addition, a large number of chemoinformatics 8 tools are finding direct application in handling metabolomics data or helping in annotation. 9Targeted metabolomics investigations obtain quantitative data on a predefined set of compounds, 10while untargeted metabolomics studies provide a broader exploration of metabolites with the 11 goal of identifying new compounds [2]. Untargeted metabolomic studies are characterized by 12simultaneous qualitative and quantitative analysis of a large number of metabolites in samples. 13Currently, untargeted metabolomics is being increasingly applied in diverse areas of research however, all these applications share the same bottlenecks: i.e., the harmonization and coverage 17of existing analytical methods, the lack of automation of spectral data processing, and the data 18 interpretation [3]. These limitations are the major driving force providing impetus for the 19 development of a huge plethora of metabolomics tools, software, programs, and databases. 20Although excellent compilations are available for M...

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BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology

Cited by 38 publications

References 13 publications

Metabolomics study of renal fibrosis and intervention effects of total aglycone extracts of Scutellaria baicalensis in unilateral ureteral obstruction rats

Metabolomics study of renal fibrosis and intervention effects of total aglycone extracts of Scutellaria baicalensis in unilateral ureteral obstruction rats

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

Updates in metabolomics tools and resources: 2014–2015

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