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Article ; Online: FastBMD: an online tool for rapid benchmark dose-response analysis of transcriptomics data.

Ewald, Jessica / Soufan, Othman / Xia, Jianguo / Basu, Niladri

2020 Volume 37, Issue 7, Page(s) 1035–1036

Abstract: Motivation: Transcriptomics dose-response analysis is a promising new approach method for toxicity testing. While international regulatory agencies have spent substantial effort establishing a standardized statistical approach, existing software that ... ...

Abstract	Motivation: Transcriptomics dose-response analysis is a promising new approach method for toxicity testing. While international regulatory agencies have spent substantial effort establishing a standardized statistical approach, existing software that follows this approach is computationally inefficient and must be locally installed. Results: FastBMD is a web-based tool that implements standardized methods for transcriptomics benchmark dose-response analysis in R. It is >60 times faster than the current leading software, supports transcriptomics data from 13 species, and offers a comprehensive analytical pipeline that goes from processing and normalization of raw gene expression values to interactive exploration of pathway-level benchmark dose results. Availability and implementation: FastBMD is freely available at www.fastbmd.ca. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Benchmarking ; Computational Biology ; Software ; Transcriptome
Language	English
Publishing date	2020-08-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa700
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Article ; Online: miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology.

Chang, Le / Zhou, Guangyan / Soufan, Othman / Xia, Jianguo

Nucleic acids research

2020 Volume 48, Issue W1, Page(s) W244–W251

Abstract: miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 ...

Abstract	miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primarily on miRNA-target gene interactions, it has become clear that in order to obtain a global view of miRNA functions, it is necessary to bring other important players into the context during analysis. Driven by this concept, in miRNet version 2.0, we have (i) added support for transcription factors (TFs) and single nucleotide polymorphisms (SNPs) that affect miRNAs, miRNA-binding sites or target genes, whilst also greatly increased (>5-fold) the underlying knowledgebases of miRNAs, ncRNAs and disease associations; (ii) implemented new functions to allow creation and visual exploration of multipartite networks, with enhanced support for in situ functional analysis and (iii) revamped the web interface, optimized the workflow, and introduced microservices and web application programming interface (API) to sustain high-performance, real-time data analysis. The underlying R package is also released in tandem with version 2.0 to allow more flexible data analysis for R programmers. The miRNet 2.0 website is freely available at https://www.mirnet.ca.
MeSH term(s)	Computer Graphics ; Gene Expression Regulation ; Gene Regulatory Networks ; Humans ; Knowledge Bases ; MicroRNAs/metabolism ; Multiple Sclerosis/genetics ; Multiple Sclerosis/metabolism ; Polymorphism, Single Nucleotide ; Software ; Systems Biology ; Transcription Factors/metabolism
Chemical Substances	MicroRNAs ; Transcription Factors
Language	English
Publishing date	2020-06-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkaa467
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Article ; Online: A novel graph mining approach to predict and evaluate food-drug interactions.

Rahman, Md Mostafizur / Vadrev, Srinivas Mukund / Magana-Mora, Arturo / Levman, Jacob / Soufan, Othman

Scientific reports

2022 Volume 12, Issue 1, Page(s) 1061

Abstract: Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset ...

Abstract	Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.
MeSH term(s)	Algorithms ; Databases, Factual ; Databases, Pharmaceutical ; Drug Interactions ; Food/classification ; Food Analysis ; Food-Drug Interactions ; Humans ; Pharmaceutical Preparations/chemistry ; Pharmacokinetics
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2022-01-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-05132-y
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Article ; Online: A novel graph mining approach to predict and evaluate food-drug interactions

Md. Mostafizur Rahman / Srinivas Mukund Vadrev / Arturo Magana-Mora / Jacob Levman / Othman Soufan

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 16

Abstract: Abstract Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. ... ...

Abstract	Abstract Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.
Keywords	Medicine ; R ; Science ; Q
Subject code	590
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Hepatic Transcriptomic Responses to Ethinylestradiol in Two Life Stages of Japanese Quail.

Jeon, Yeon-Seon / Crump, Doug / Boulanger, Emily / Soufan, Othman / Park, Bradley / Basu, Niladri / Hecker, Markus / Xia, Jianguo / Head, Jessica A

Environmental toxicology and chemistry

2022 Volume 41, Issue 11, Page(s) 2769–2781

Abstract: Chemical risk assessment for avian species typically depends on information from toxicity tests performed in adult birds. Early-life stage (ELS) toxicity tests have been proposed as an alternative, but incorporation of these data into existing frameworks ...

Abstract	Chemical risk assessment for avian species typically depends on information from toxicity tests performed in adult birds. Early-life stage (ELS) toxicity tests have been proposed as an alternative, but incorporation of these data into existing frameworks will require knowledge about the similarities/differences between ELS and adult responses. The present study uses transcriptomics to assess hepatic gene expression in ELS and adult Japanese quail following exposure to ethinylestradiol (EE2). Prior to incubation, ELS quail were dosed with measured EE2 concentrations of 0.54, 6.3, and 54.2 µg/g egg weight via air cell injection. Adult quail were fed a single dose of EE2 at nominal concentrations of 0, 0.5, and 5 mg/kg body weight by gavage. Liver tissue was collected from five to six individuals per dose group at mid-incubation for ELS quail and 4 days after dosing for adults. A total of 283 and 111 differentially expressed genes (DEGs) were detected in ELS and adult quail, respectively, 16 of which were shared across life stages. Shared DEGs included estrogenic biomarkers such as vitellogenin genes and apovitellenin-1. For the dose groups that resulted in the highest number of DEGs (ELS, 6.3 µg/g; adult, 5 mg/kg), 21 and 35 Kyoto Encyclopedia of Genes and Genomes pathways were enriched, respectively. Ten of these pathways were shared between life stages, including pathways involved with signaling molecules and interaction and the endocrine system. Taken together, our results suggest conserved mechanisms of action following estrogenic exposure across two life stages, with evidence from differential expression of key biomarker genes and enriched pathways. The present study contributes to the development and evaluation of ELS tests and toxicogenomic approaches and highlights their combined potential for screening estrogenic chemicals. Environ Toxicol Chem 2022;41:2769-2781. © 2022 SETAC.
MeSH term(s)	Humans ; Animals ; Ethinyl Estradiol/toxicity ; Coturnix/genetics ; Coturnix/metabolism ; Vitellogenins/metabolism ; Transcriptome ; Liver/metabolism ; Quail/metabolism
Chemical Substances	Ethinyl Estradiol (423D2T571U) ; Vitellogenins
Language	English
Publishing date	2022-09-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	46234-2
ISSN	1552-8618 ; 0730-7268
ISSN (online)	1552-8618
ISSN	0730-7268
DOI	10.1002/etc.5464
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Article ; Online: EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species.

Ewald, Jessica D / Soufan, Othman / Crump, Doug / Hecker, Markus / Xia, Jianguo / Basu, Niladri

Environmental science & technology

2020 Volume 54, Issue 7, Page(s) 4376–4387

Abstract: Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, ... ...

Abstract	Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow (
MeSH term(s)	Animals ; Cyprinidae ; Toxicogenetics ; Water Pollutants, Chemical
Chemical Substances	Water Pollutants, Chemical
Language	English
Publishing date	2020-03-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1520-5851
ISSN (online)	1520-5851
DOI	10.1021/acs.est.9b06607
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Article ; Online: Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions.

Alazmi, Meshari / Kuwahara, Hiroyuki / Soufan, Othman / Ding, Lizhong / Gao, Xin

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 15, Page(s) 2634–2643

Abstract: Motivation: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems.: Results: Here, we introduce a machine learning method with ... ...

Abstract	Motivation: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. Results: Here, we introduce a machine learning method with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. Since a manual selection of 2D structure-based features can be a tedious and time-consuming task, requiring expert knowledge about the structure-activity relationship of chemical compounds, the systematic feature selection step in our method offers a convenient means to identify relevant 2D fingerprint-based features. By comparing our method with state-of-the-art linear regression-based methods for the standard Gibbs free energy prediction, we demonstrated that its prediction accuracy and prediction coverage are most favorable. Our results show direct evidence that a number of 2D fingerprints collectively provide useful information about the Gibbs free energy of biochemical reactions and that our systematic feature selection procedure provides a convenient way to identify them. Availability and implementation: Our software is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Machine Learning ; Software ; Structure-Activity Relationship
Language	English
Publishing date	2019-01-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty1035
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Article ; Online: Characterizing toxicity pathways of fluoxetine to predict adverse outcomes in adult fathead minnows (Pimephales promelas).

Colville, Carly / Alcaraz, Alper James / Green, Derek / Park, Bradley / Xia, Jianguo / Soufan, Othman / Hruṧka, Pavel / Potěšil, David / Zdráhal, Zbyněk / Crump, Doug / Basu, Niladri / Hogan, Natacha / Hecker, Markus

The Science of the total environment

2022 Volume 817, Page(s) 152747

Abstract: Current ecotoxicity testing programs are impeded as they predominantly rely on slow and expensive animal tests measuring adverse outcomes. Therefore, new approach methodologies (NAMs) increasingly involve short-term mechanistic assays that employ ... ...

Abstract	Current ecotoxicity testing programs are impeded as they predominantly rely on slow and expensive animal tests measuring adverse outcomes. Therefore, new approach methodologies (NAMs) increasingly involve short-term mechanistic assays that employ molecular endpoints to predict adverse outcomes of regulatory relevance. This study aimed to elucidate the application of NAMs in adult fathead minnows using fluoxetine (FLX) as a model compound. Fish were exposed to three FLX concentrations (measured: 2.42, 10.7, and 56.7 μgL
MeSH term(s)	Animals ; Cyprinidae/physiology ; Fertility ; Fluoxetine/toxicity ; Water Pollutants, Chemical/toxicity
Chemical Substances	Water Pollutants, Chemical ; Fluoxetine (01K63SUP8D)
Language	English
Publishing date	2022-01-11
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2021.152747
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Article: T1000: a reduced gene set prioritized for toxicogenomic studies.

Soufan, Othman / Ewald, Jessica / Viau, Charles / Crump, Doug / Hecker, Markus / Basu, Niladri / Xia, Jianguo

PeerJ

2019 Volume 7, Page(s) e7975

Abstract: There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of ... ...

Abstract	There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g.,
Language	English
Publishing date	2019-10-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.7975
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Article ; Online: NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis.

Zhou, Guangyan / Soufan, Othman / Ewald, Jessica / Hancock, Robert E W / Basu, Niladri / Xia, Jianguo

Nucleic acids research

2019 Volume 47, Issue W1, Page(s) W234–W241

Abstract: The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression ... ...

Abstract	The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.
MeSH term(s)	Computational Biology/methods ; Gene Expression/genetics ; Gene Expression Profiling/methods ; Gene Regulatory Networks/genetics ; Protein Interaction Mapping/methods ; Protein Interaction Maps ; Software
Language	English
Publishing date	2019-03-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkz240
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