Collective prediction of protein functions from protein-protein interaction networks

Wu, Qingyao; Ye, Yunming; Ng, Michael K.; Ho, Shen-Shyang; Shi, Ruichao

doi:10.1186/1471-2105-15-s2-s9

Cited by 18 publications

(8 citation statements)

References 27 publications

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“…However, the functional annotation of CDSs is particularly difficult to automate [57]. Current state-of-theart functional annotation methods integrate multiple types of evidences [11,4,37], but unfortunately the quality of functional annotations remains generally poor [2,56,49] and is highly dependent on resourceintensive manual curation [40,42].…”

Section: Erroneous Annotationsmentioning

confidence: 99%

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

Bouadjenek

Verspoor

Zobel

2017

Preprint

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We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as "confident" or "suspicious".Our experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available.Overall, the obtained results show promise for the development of a new kind of approach to detecting lowquality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature.

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Section: Erroneous Annotationsmentioning

confidence: 99%

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

Bouadjenek

Verspoor

Zobel

2017

Preprint

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“…• Protein structure: the function prediction using protein structure, by using some approaches to analyze the secondary [14,15,16] and tertiary structures [17,6] of proteins. • Protein-protein interactions (PPIs): protein function prediction using proteinprotein interactions [18,19,20,21,22,23,24,25] can be deduced from the interaction of the neighborhood. Chua et al [26] underscored the useful strategies using the PPIs as a complementary approach to sequence homology by specifying the maximum additional coverage for the PPIs.…”

Section: Introductionmentioning

confidence: 99%

TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

Djeddi

Yahia

NGUIFO

2020

Preprint

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Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPI networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on a similar sequence, structure 2D/3D, amino acid patterns of phylogenetic profiles. Results: In this work, we introduce a new approach, called TANA, for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. In doing so, we are able to discover the functions of proteins that could not be easily described by sequence homology. We assess the performance of our approach using the standard metrics established by the CAFA challenge and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions and cellular components. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e. pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known beforehand

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“…• Protein structure: the function prediction using protein structure, by using some approaches to analyze the secondary [14,15,16] and tertiary structures [17,6] of proteins. • Protein-protein interactions (PPIs): protein function prediction using proteinprotein interactions [18,19,20,21,22,23,24,25] can be deduced from the interaction of the neighborhood. Chua et al [26] demonstrated the useful strategies using the PPIs as a complementary approach to sequence homology by specifying the maximum additional coverage for the protein-protein interactions.…”

Section: Introductionmentioning

confidence: 99%

TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

Djeddi

Yahia

NGUIFO

2020

Preprint

View full text Add to dashboard Cite

Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactions networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on similar sequence, structure 2D/3D, amino acid motifs or phylogenetic profiles. Results: In this work, we introduce a new approach called TANA for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. Doing so, we are able to discover the functions of proteins that could not to be easily described by sequence homology. We assess the performance of our method using the standard metrics established by the CAFA and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known in advance.

show abstract

Collective prediction of protein functions from protein-protein interaction networks

Cited by 18 publications

References 27 publications

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

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