A Network of Splice Isoforms for the Mouse

Li, Hong Dong; Menon, Rajasree; Eksi, Ridvan; Guerler, Aysam; Zhang, Yang; Omenn, Gilbert S.; Guan, Yuanfang

doi:10.1038/srep24507

Cited by 20 publications

(39 citation statements)

References 60 publications

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“…RNA sequencing (RNA-seq) now allows quantification of isoformlevel expression, providing an opportunity to study regulation of splicing using a network analysis. However, current research estimating RNA isoform-level networks (Li et al , 2015(Li et al , 2016a has focused on total expression of each isoform, and the resulting network structures do not distinguish between regulation of transcription and regulation of splicing in an interpretable way. Initial work on clustering relative isoform abundances has also been explored (Dai et al 2012;Iancu et al 2015) but does not support discovery of fine-grained network structure or identification of regulatory genes.…”

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confidence: 99%

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

et al. 2017

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Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

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confidence: 99%

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

et al. 2017

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“…This has led to the improvement and refinement of genome annotations. Functional networks, at the mRNA isoform level are important for understanding gene function but are largely uninvestigated [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these cannot be directly applied to mRNA isoform function prediction because they ignore the distinct functions of alternatively spliced mRNA isoforms. However, important advancements have been made by recent studies towards mRNA isoform level understanding of gene functions [18,19,[21][22][23][24][25] such as the prediction of more immune related gene ontology terms for the mRNA isoform ADAM15B than isoform ADAM15A of ADAM15 gene, which is involved in B-cell-mediated immune mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…The predicted human isoform-isoform physical interaction network was restricted to the gene pairs already present in IntAct. The problem of mRNA isoform functional network prediction is formulated as a complex multiple instance learning (MIL) problem in [18]. In MIL, a gene is treated as a "bag" of mRNA isoforms ("instances").…”

Section: Introductionmentioning

confidence: 99%

“…The goal of MIL is to identify the specific instance pairs which are functional and maximize the difference between them and the instance pairs of non-functionally related bags. A Bayesian network based MIL algorithm was developed by [18] to predict a mouse mRNA isoform level functional network using RNA-Seq datasets, exon array, pseudo-amino acid composition and isoform-docking data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tissue-specific mouse mRNA isoform networks

Kandoi

Dickerson

2019

Preprint

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Alternative Splicing produces multiple mRNA isoforms of genes which have important diverse roles such as regulation of gene expression, human heritable diseases, and response to environmental stresses. However, little has been done to assign functions at the mRNA isoform level. Functional networks, where the interactions are quantified by their probability of being involved in the same biological process are typically generated at the gene level. We use a diverse array of tissue-specific RNA-seq datasets and sequence information to train random forest models that predict the functional networks. Since there is no mRNA isoform-level gold standard, we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein interactions as functionally related (positive pair). To generate the non-functional pairs (negative pair), we use the Gene Ontology annotations tagged with "NOT" qualifier. We describe 17 Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) following a leave-one-tissue-out strategy in addition to an organism level reference functional network for mouse. We validate our predictions by comparing its performance with previous methods, randomized positive and negative class labels, updated Gene Ontology annotations, and by literature evidence. We demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes.

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Imbalance deep multi‐instance learning for predicting isoform–isoform interactions

Zeng

Wang

et al. 2021

Int J Intell Syst

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Multi‐instance learning (MIL) can model complex bags (samples) that are further made of diverse instances (subsamples). In typical MIL, the labels of bags are known while those of individual instances are unknown and to be specified. In this paper we propose an imbalanced deep multi‐instance learning approach (IDMIL‐III) and apply it to predict genome‐wide isoform–isoform interactions (IIIs). This prediction task is crucial for precisely understanding the interactome between proteoforms and to reveal their functional diversity. The current solutions typically formulate the prediction of IIIs as a MIL problem by pairing two genes as a “bag” and any two isoforms spliced from these two genes as “instances.” The key instances (interacting isoform pairs) trigger the label of the positive (interacting) gene bags, which is important for identifying the IIIs. Furthermore, the prediction task was simplified as a balanced classification problem, which in practice is a rather imbalanced one. To address these issues, IDMIL‐III fuses RNA‐seq, nucleotide sequence, amino acid sequence and exon array data, and further introduces a novel loss function to separately model the loss of positive pairs and of negative pairs, and thus to avoid the expected loss dominated by majority negative pairs. In addition, it includes an attention strategy to identify positive isoform pairs from a positive gene bag. Extensive experimental results prove the effectiveness of IDMIL‐III on predicting IIIs. Particularly, IDMIL‐III achieves an F1 value as 95.4%, at least 3.8% higher than those of competitive methods at the gene‐level; and obtains an F1 as 29.8%, at least 2.4% higher than the state‐of‐the‐art methods at the isoform‐level. The code of IDMIL‐III is available at http://mlda.swu.edu.cn/codes.php?name=IDMIL-III.

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A Network of Splice Isoforms for the Mouse

Cited by 20 publications

References 60 publications

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Tissue-specific mouse mRNA isoform networks

Imbalance deep multi‐instance learning for predicting isoform–isoform interactions

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