CoSpliceNet: a framework for co-splicing network inference from transcriptomics data

Aghamirzaie, Delasa; Collakova, Eva; Li, Song; Grene, Ruth

doi:10.1186/s12864-016-3172-6

Cited by 11 publications

(7 citation statements)

References 67 publications

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“…In fact, a large fraction of the isoforms in both Arabidopsis and soybean do not change their protein coding sequences, the difference being found in the UTR regions of the transcripts being compared. This is consistent with published results in Arabidopsis and soybean [50,51]. We developed a Python script that can identify RBH genes from the above two species from BLAST results.…”

Section: Methodssupporting

confidence: 88%

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Lee

Heath

Grene

et al. 2019

Preprint

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28Comparative transcriptome analysis is the comparison of expression patterns between homologous genes 29 in different species. Since most molecular mechanistic studies in plants have been performed in model 30 species including Arabidopsis and rice, comparative transcriptome analysis is particularly important for 31 functional annotation of genes in other plant species. Many biological processes, such as embryo 32 development, are highly conserved between different plant species. The challenge is to establish one-to-33 one mapping of the developmental stages between two species. In this protocol, we solve this problem by 34 converting the gene expression patterns into a co-expression network and then apply network module-35 finding algorithms to the cross-species co-expression network. We describe how to perform such analysis 36 using bash scripts for preliminary data processing and R programming language, which implemented 37 simulated annealing method for module finding. We also provide instructions on how to visualize the 38 resulting co-expression networks across species. 39 40 Keywords 41 Comparative transcriptome analysis, Network, Sequence homology, Arabidopsis, Soybean, Emybro 42 development 43 44 65 seed embryo expression data from Arabidopsis [22] with data from the same tissue in soybean [23] as a 66 demonstration of how to apply computational tools to comparative transcriptome analysis. 67 68 In contrast with the time course data examined here, many other datasets have been reported from 69 "treatment-control" experiments (one time point only, two treatment conditions). For example, soybean 70 roots were treated with drought stress in one experiment [4]. To address the question of functional 71 conservation versus functional divergence within gene families, these soybean root data can be compared 72 with transcriptome data from Arabidopsis roots, under a similar stress [24]. This is a relatively simple 73 problem, because, in both experiments, we can identify lists of differentially expressed genes in response 74to the same or similar treatments. It is a simple two-step process to identify conserved co-expressed genes 75 for treatment-control experiments. First, one needs to identify a list of gene pairs that are homologous 76 between these two species. A simple BLAST search or other more sophisticated approaches such as OMA, 77 EggNog, or Plaza [9,10,12] can be used to identify homologous genes. Second, the two lists of 78 4 differentially expressed genes can be compared to find whether any pairs of these homologous genes 79 appear in both lists. 81In this article, we are focusing on a more complex scenario: two time-series experiments were 82 performed for the same developmental process in two different species [25]. Time course data provide 83 more data points than simple treatment-control experiments and, thus, can reveal relationships based on 84 development between homologous genes in two organisms. However, this is also challenging, because 85 the number of time points in the two experiments ...

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Section: Methodssupporting

confidence: 88%

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Lee

Heath

Grene

et al. 2019

Preprint

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“…Splicing regulation is another key step that determines the final sequence and concentration of many plant genes. In the same spirit of constructing TF-gene regulatory networks, computational modeling can help to identify sequence motifs and characterize splicing regulatory networks ( Aghamirzaie et al, 2016 ).…”

Section: Modeling Of Plant Abiotic Stress Responsesmentioning

confidence: 99%

Multilevel Regulation of Abiotic Stress Responses in Plants

et al. 2017

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The sessile lifestyle of plants requires them to cope with stresses in situ. Plants overcome abiotic stresses by altering structure/morphology, and in some extreme conditions, by compressing the life cycle to survive the stresses in the form of seeds. Genetic and molecular studies have uncovered complex regulatory processes that coordinate stress adaptation and tolerance in plants, which are integrated at various levels. Investigating natural variation in stress responses has provided important insights into the evolutionary processes that shape the integrated regulation of adaptation and tolerance. This review primarily focuses on the current understanding of how transcriptional, post-transcriptional, post-translational, and epigenetic processes along with genetic variation orchestrate stress responses in plants. We also discuss the current and future development of computational tools to identify biologically meaningful factors from high dimensional, genome-scale data and construct the signaling networks consisting of these components.

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“…Alternative Splicing (AS) is known to be a tightly-regulated process in which splicing factors interact to create cell type-specific isoform expression patterns 78 . The transcriptome-level consequences of AS regulation have been studied in different ways, including, but not limited to, the detection of within-isoform coordination of alternative sites 40,41 , the generation of gene-isoform networks to uncover novel regulatory relationships [79][80][81][82] and the application of single-cell data to unravel cell type-specific expression patterns for same-gene isoforms 27,83 . However, the extent to which AS regulation creates co-expression patterns among alternative isoforms from different genes has not yet been fully addressed.…”

Section: Discussionmentioning

confidence: 99%

Acorde: unraveling functionally-interpretable networks of isoform co-usage from single cell data

Arzalluz-Luque

Salguero

Tarazona

et al. 2021

Preprint

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Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we developed and validated percentile correlations, a novel approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across neural cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs and domains potentially controlled by the coordination of post-transcriptional regulation.

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CoSpliceNet: a framework for co-splicing network inference from transcriptomics data

Cited by 11 publications

References 67 publications

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Multilevel Regulation of Abiotic Stress Responses in Plants

Acorde: unraveling functionally-interpretable networks of isoform co-usage from single cell data

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