Progress and challenges in the computational prediction of gene function using networks

Pavlidis, Paul; Gillis, Jesse

doi:10.12688/f1000research.1-14.v1

Cited by 21 publications

(14 citation statements)

References 16 publications

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“…Despite the accumulation of vast amounts of genomics data or of differential expression data, computational attempts to predict gene function so far have a rather limited degree of success (Pavlidis & Gillis, ; Piro & Di Cunto, ; Pavlidis & Gillis, ; Lehtinen et al, ). Conventional cell biology approaches regularly identify new functions for proteins and, indeed, the predictive computational methods are largely anchored by conventional cell biology results.…”

Section: Discussionmentioning

confidence: 99%

Spatial perspectives in the redox code—Mass spectrometric proteomics studies of moonlighting proteins

Pinto

Radulović

Godovac‐Zimmermann

2016

Mass Spectrometry Reviews

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The Redox Code involves specific, reversible oxidative changes in proteins that modulate protein tertiary structure, interactions, trafficking, and activity, and hence couple the proteome to the metabolic/oxidative state of cells. It is currently a major focus of study in cell biology. Recent studies of dynamic cellular spatial reorganization with MS-based subcellular-spatial-razor proteomics reveal that protein constituents of many subcellular structures, including mitochondria, the endoplasmic reticulum, the plasma membrane, and the extracellular matrix, undergo changes in their subcellular abundance/distribution in response to oxidative stress. These proteins are components of a diverse variety of functional processes spatially distributed across cells. Many of the same proteins are involved in response to suppression of DNA replication indicate that oxidative stress is strongly intertwined with DNA replication/proliferation. Both are replete with networks of moonlighting proteins that show coordinated changes in subcellular location and that include primary protein actuators of the redox code involved in the processing of NAD /NADH, NADP /NADPH, Cys/CySS, and GSH/GSSG redox couples. Small groups of key proteins such as {KPNA2, KPNB1, PCNA, PTMA, SET} constitute "spatial switches" that modulate many nuclear processes. Much of the functional response involves subcellular protein trafficking, including nuclear import/export processes, vesicle-mediated trafficking, the endoplasmic reticulum/Golgi pathway, chaperone-assisted processes, and other transport systems. This is not visible to measurements of total protein abundance by transcriptomics or proteomics. Comprehensive pictures of cellular function will require collection of data on the subcellular transport and local functions of many moonlighting proteins, especially of those with critical roles in spatial coordination across cells. The proteome-wide analysis of coordinated changes in abundance and trafficking of proteins offered by MS-based proteomics has a unique, crucial role to play in deciphering the complex adaptive systems that underlie cellular function. © 2016 Wiley Periodicals, Inc. Mass Spec Rev.

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Section: Discussionmentioning

confidence: 99%

Spatial perspectives in the redox code—Mass spectrometric proteomics studies of moonlighting proteins

Pinto

Radulović

Godovac‐Zimmermann

2016

Mass Spectrometry Reviews

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“…In the same way that research focus affects the topological structure of PINs ( von Mering et al , 2002 ; Rual et al , 2005 ; Schaefer et al , 2015 ), it also affects which proteins amass functional annotations ( Pesquita et al , 2008 ). This phenomenon and its effects are well-described in the field of gene function prediction ( Greene and Troyanskaya, 2012 ; Myers et al , 2006 ; Pavlidis and Gillis, 2012 ; Schnoes et al , 2013 ). Yet, how annotation bias manifests itself in module detection remains to be investigated.…”

Section: Introductionmentioning

confidence: 80%

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias

et al. 2017

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MotivationDetecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins.ResultsWe propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology.Availability and implementationThe CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/.Supplementary information Supplementary data are available at Bioinformatics online.

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“…This is especially true for gene and protein interactions extracted from literature using text mining with simple co-occurrence or co-citation statistics. Separating context specific evidence may lend better insights to improve the robustness of the results 122. Thus taking finer semantic information into text mining consideration is likely to improve the accuracy of the mined relations.…”

Section: Challenges Of Text Mining In Gene Prioritizationmentioning

confidence: 99%

Text Mining in Cancer Gene and Pathway Prioritization

2014

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Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

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Progress and challenges in the computational prediction of gene function using networks

Cited by 21 publications

References 16 publications

Spatial perspectives in the redox code—Mass spectrometric proteomics studies of moonlighting proteins

Spatial perspectives in the redox code—Mass spectrometric proteomics studies of moonlighting proteins

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias

Text Mining in Cancer Gene and Pathway Prioritization

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