Mapping of Mitochondrial RNA-Protein Interactions by Digital RNase Footprinting

Liu, Ganqiang; Mercer, Tim R.; Shearwood, Anne-Marie J.; Siira, Stefan J.; Hibbs, Moira; Mattick, John S.; Rackham, Oliver; Filipovska, Aleksandra

doi:10.1016/j.celrep.2013.09.036

Cited by 38 publications

(29 citation statements)

References 37 publications

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“…Matthews to assess the performance of protein secondary structure prediction [12]. Then, it becomes a widely used performance measure in biomedical research [13–17]. MCC and Area Under ROC Curve (AUC) have been chosen as the elective metric in the US FDA-led initiative MAQC-II that aims to reach a consensus on the best practices for development and validation of predictive models for personalized medicine [16].…”

Section: MCC Metric For Imbalanced Datamentioning

confidence: 99%

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

2017

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Data imbalance is frequently encountered in biomedical applications. Resampling techniques can be used in binary classification to tackle this issue. However such solutions are not desired when the number of samples in the small class is limited. Moreover the use of inadequate performance metrics, such as accuracy, lead to poor generalization results because the classifiers tend to predict the largest size class. One of the good approaches to deal with this issue is to optimize performance metrics that are designed to handle data imbalance. Matthews Correlation Coefficient (MCC) is widely used in Bioinformatics as a performance metric. We are interested in developing a new classifier based on the MCC metric to handle imbalanced data. We derive an optimal Bayes classifier for the MCC metric using an approach based on Frechet derivative. We show that the proposed algorithm has the nice theoretical property of consistency. Using simulated data, we verify the correctness of our optimality result by searching in the space of all possible binary classifiers. The proposed classifier is evaluated on 64 datasets from a wide range data imbalance. We compare both classification performance and CPU efficiency for three classifiers: 1) the proposed algorithm (MCC-classifier), the Bayes classifier with a default threshold (MCC-base) and imbalanced SVM (SVM-imba). The experimental evaluation shows that MCC-classifier has a close performance to SVM-imba while being simpler and more efficient.

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Section: MCC Metric For Imbalanced Datamentioning

confidence: 99%

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

2017

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“…Another aspect of mito-nuclear RNA-protein co-evolution is reflected in the need for compatibility between the mtDNA-encoded tRNA Tyr and the nDNA-encoded tRNA Tyr-synthase to maintain normal development and mitochondrial function among Drosophila taxa ( Hoekstra et al, 2013 ; Meiklejohn et al, 2013 ). The recent identification of mitochondrial mRNA-binding by proteins and miRNAs ( Mercer et al, 2011 ; Liu et al, 2013 ; Wolf and Mootha, 2014 ; Zhang et al, 2014 ) may assist in isolating such binding factors and investigating their co-evolution with the bound mtDNA-encoded mRNAs. Such approaches may, in turn, assist in the identification of proteins involved in modes of mtDNA transcript modification, such as the recently discovered human mitochondrial RNA editing ( Bar-Yaacov et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Mito-nuclear co-evolution: the positive and negative sides of functional ancient mutations

et al. 2014

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Most cell functions are carried out by interacting factors, thus underlying the functional importance of genetic interactions between genes, termed epistasis. Epistasis could be under strong selective pressures especially in conditions where the mutation rate of one of the interacting partners notably differs from the other. Accordingly, the order of magnitude higher mitochondrial DNA (mtDNA) mutation rate as compared to the nuclear DNA (nDNA) of all tested animals, should influence systems involving mitochondrial-nuclear (mito-nuclear) interactions. Such is the case of the energy producing oxidative phosphorylation (OXPHOS) and mitochondrial translational machineries which are comprised of factors encoded by both the mtDNA and the nDNA. Additionally, the mitochondrial RNA transcription and mtDNA replication systems are operated by nDNA-encoded proteins that bind mtDNA regulatory elements. As these systems are central to cell life there is strong selection toward mito-nuclear co-evolution to maintain their function. However, it is unclear whether (A) mito-nuclear co-evolution befalls only to retain mitochondrial functions during evolution or, also, (B) serves as an adaptive tool to adjust for the evolving energetic demands as species’ complexity increases. As the first step to answer these questions we discuss evidence of both negative and adaptive (positive) selection acting on the mtDNA and nDNA-encoded genes and the effect of both types of selection on mito-nuclear interacting factors. Emphasis is given to the crucial role of recurrent ancient (nodal) mutations in such selective events. We apply this point-of-view to the three available types of mito-nuclear co-evolution: protein–protein (within the OXPHOS system), protein-RNA (mainly within the mitochondrial ribosome), and protein-DNA (at the mitochondrial replication and transcription machineries).

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“…The modular 35-amino-acid sequence repeated in tandem in canonical P motifs is characterized by high affinity for interactions with RNA ( 8 , 9 , 15 ). The binding to RNA is determined by amino acids located in specific positions of each P motif, defining a molecular code which establishes the modality of RNA recognition by PPR proteins ( 17 – 21 ). Recent structural work has shown that each P motif forms a hairpin of two antiparallel α-helices connected by a short turn of two amino acids ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

Drosophila melanogaster LRPPRC2 is involved in coordination of mitochondrial translation

Baggio

Bratić

Mourier

et al. 2014

Nucleic Acids Research

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Members of the pentatricopeptide repeat domain (PPR) protein family bind RNA and are important for post-transcriptional control of organelle gene expression in unicellular eukaryotes, metazoans and plants. They also have a role in human pathology, as mutations in the leucine-rich PPR-containing (LRPPRC) gene cause severe neurodegeneration. We have previously shown that the mammalian LRPPRC protein and its Drosophila melanogaster homolog DmLRPPRC1 (also known as bicoid stability factor) are necessary for mitochondrial translation by controlling stability and polyadenylation of mRNAs. We here report characterization of DmLRPPRC2, a second fruit fly homolog of LRPPRC, and show that it has a predominant mitochondrial localization and interacts with a stem-loop interacting RNA binding protein (DmSLIRP2). Ubiquitous downregulation of DmLrpprc2 expression causes respiratory chain dysfunction, developmental delay and shortened lifespan. Unexpectedly, decreased DmLRPPRC2 expression does not globally affect steady-state levels or polyadenylation of mitochondrial transcripts. However, some mitochondrial transcripts abnormally associate with the mitochondrial ribosomes and some products are dramatically overproduced and other ones decreased, which, in turn, results in severe deficiency of respiratory chain complexes. The function of DmLRPPRC2 thus seems to be to ensure that mitochondrial transcripts are presented to the mitochondrial ribosomes in an orderly fashion to avoid poorly coordinated translation.

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Mapping of Mitochondrial RNA-Protein Interactions by Digital RNase Footprinting

Cited by 38 publications

References 37 publications

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

Mito-nuclear co-evolution: the positive and negative sides of functional ancient mutations

Drosophila melanogaster LRPPRC2 is involved in coordination of mitochondrial translation

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