MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts

Basha, Omer; Flom, Dvir; Barshir, Ruth; Smoly, Ilan; Tirman, Shoval; Yeger-Lotem, Esti

doi:10.1093/nar/gkv515

Cited by 51 publications

(44 citation statements)

References 49 publications

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“…There are other resources integrating PPIs and gene functional interactions (40,41), and some of them make it possible to generate tissue-specific (42,43) or more general context-specific networks (44). For example, the GIANT web server (45) predicts functional protein maps in 144 tissues by integrating thousands of expression and PPI experiments.…”

Section: Resultsmentioning

confidence: 99%

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

2016

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The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein–protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein–Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.

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Section: Resultsmentioning

confidence: 99%

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

2016

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“…Human PPIs were downloaded from BioGrid (4), DIP (24), MINT (23) and IntAct (22), using the MyProteinNet web-server (25). The usage of MyProteinNet ensured that only PPIs detected by established methods for detection of physical interactions were considered, and resulted in a global interactome that contained a subset of the PPIs recorded in the different databases.…”

Section: Methodsmentioning

confidence: 99%

“…To this end, we gathered PPIs from four major PPI databases, BioGrid (4), IntAct (22), MINT (23) and DIP (24), and consolidated them by using the MyProteinNet web-server (25). This resulted in a global human interactome that contained 243 706 PPIs between 17 283 human proteins.…”

Section: Enhancements and New Features In Tissuenet V2mentioning

confidence: 99%

The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues

et al. 2016

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Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein–protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues. TissueNet v.2 is available at http://netbio.bgu.ac.il/tissuenet.

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“…Physical interactions between human proteins and their reliability scores were retrieved via MyProteinNet (Basha et al, 2015) from BIOGRID, MINT, INTACT, and DIP, and scored using a uniform weight scheme. Data of upregulated genes following influenza infection were gathered from Shapira et al (2009) by using thresholds therein.…”

Section: Methodsmentioning

confidence: 99%

Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human–viral Infection Patterns

Smoly

Carmel

Shemer‐Avni

et al. 2016

Journal of Computational Biology

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Network querying is a powerful approach to mine molecular interaction networks. Most state-of-the-art network querying tools either confine the search to a prespecified topology in the form of some template subnetwork, or do not specify any topological constraints at all. Another approach is grammar-based queries, which are more flexible and expressive as they allow for expressing the topology of the sought pattern according to some grammar-based logic. Previous grammar-based network querying tools were confined to the identification of paths. In this article, we extend the patterns identified by grammar-based query approaches from paths to trees. For this, we adopt a higher order query descriptor in the form of a regular tree grammar (RTG). We introduce a novel problem and propose an algorithm to search a given graph for the k highest scoring subgraphs matching a tree accepted by an RTG. Our algorithm is based on the combination of dynamic programming with color coding, and includes an extension of previous k-best parsing optimization approaches to avoid isomorphic trees in the output. We implement the new algorithm and exemplify its application to mining viral infection patterns within molecular interaction networks. Our code is available online.

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MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts

Cited by 51 publications

References 49 publications

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues

Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human–viral Infection Patterns

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