NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology

Qing, Wei; Khan, Ishita; Ding, Ziyun; Yerneni, Satwica

doi:10.1186/s12859-017-1600-5

Cited by 64 publications

(53 citation statements)

References 32 publications

(46 reference statements)

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“…Such analysis can be carried out from the GO project website [ 3 ], using other web applications (e.g. GOrilla [ 4 ], NaviGO [ 5 ], DAVID [ 2 ], AmiGO [ 6 ]) or if a programmatic approach is needed one can use available modules for Python (e.g. GOATools [ 7 ], goenrich [ 8 ]) and R (e.g.…”

Section: Introductionmentioning

confidence: 99%

GOnet: a tool for interactive Gene Ontology analysis

Pomaznoy¹,

Ha²,

Peters³

2018

BMC Bioinformatics

233

168

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BackgroundBiological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships.ResultsWe created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries.ConclusionsThe application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.

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Section: Introductionmentioning

confidence: 99%

GOnet: a tool for interactive Gene Ontology analysis

Pomaznoy¹,

Ha²,

Peters³

2018

BMC Bioinformatics

233

168

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“…The graph based methods use only the graph of Gene Ontology (GO). Wang is provided by several online tools (GoSemSim [ 28 ], G-SESAME [ 29 ] and NaviGo [ 30 ], etc. ).…”

Section: Resultsmentioning

confidence: 99%

SAlign–a structure aware method for global PPI network alignment

2020

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Background High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein–protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. Results This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3–63% semantically better than all existing techniques. Furthermore, it also aligns 5–14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce $$\hbox {SAlign}^{\mathrm{mc}}$$ SAlign mc , a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments. Conclusion The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.

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“…This observation triggers various interesting biological questions, for example, how proteins gain moonlighting functions during evolution and biophysical mechanisms that enable a protein to have multiple functions. Correct annotation to proteins with dual functions also affects to functional enrichment analysis ( Wei et al , 2017 ), which is commonly used in systems and network biology ( Dotan-Cohen et al , 2009 ; Hawkins et al , 2010 ; Rachlin et al , 2006 ). This work is also relevant to computational biologists, particularly those who are working on developing function prediction methods ( Hawkins and Kihara, 2007 ), genome annotation, function analysis on networks and curation of functional annotation in databases.…”

Section: Discussionmentioning

confidence: 99%

DextMP: deep dive into text for predicting moonlighting proteins

Khan

Bhuiyan

2017

Bioinformatics

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MotivationMoonlighting proteins (MPs) are an important class of proteins that perform more than one independent cellular function. MPs are gaining more attention in recent years as they are found to play important roles in various systems including disease developments. MPs also have a significant impact in computational function prediction and annotation in databases. Currently MPs are not labeled as such in biological databases even in cases where multiple distinct functions are known for the proteins. In this work, we propose a novel method named DextMP, which predicts whether a protein is a MP or not based on its textual features extracted from scientific literature and the UniProt database.ResultsDextMP extracts three categories of textual information for a protein: titles, abstracts from literature, and function description in UniProt. Three language models were applied and compared: a state-of-the-art deep unsupervised learning algorithm along with two other language models of different types, Term Frequency-Inverse Document Frequency in the bag-of-words and Latent Dirichlet Allocation in the topic modeling category. Cross-validation results on a dataset of known MPs and non-MPs showed that DextMP successfully predicted MPs with over 91% accuracy with significant improvement over existing MP prediction methods. Lastly, we ran DextMP with the best performing language models and text-based feature combinations on three genomes, human, yeast and Xenopus laevis, and found that about 2.5–35% of the proteomes are potential MPs.Availability and ImplementationCode available at http://kiharalab.org/DextMP.

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NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology

Cited by 64 publications

References 32 publications

GOnet: a tool for interactive Gene Ontology analysis

GOnet: a tool for interactive Gene Ontology analysis

SAlign–a structure aware method for global PPI network alignment

DextMP: deep dive into text for predicting moonlighting proteins

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