Identification and visualization of differential isoform expression in RNA-seq time series

Nueda, María José; Martorell-Marugán, Jordi; Martí, Cristina; Tarazona, Sonia; Conesa, Ana

doi:10.1093/bioinformatics/btx578

Cited by 20 publications

(19 citation statements)

References 6 publications

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“…The application includes the analysis methods described above and implements existing tools when appropriate, including NOISeq 108 and maSigPro 106 for differential gene expression; DEXseq 6 and Iso-maSigPro 109 for differential isoform usage.…”

Section: Fi = Einc Einc + Eexcmentioning

confidence: 99%

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

Fuente

Arzalluz-Luque

Tardáguila

et al. 2019

Preprint

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Traditionally, the functional analysis of gene expression data has used pathway and network enrichment algorithms. These methods are usually gene rather than transcript centric and hence fall short to unravel functional roles associated to posttranscriptional regulatory mechanisms such as Alternative Splicing (AS) and Alternative PolyAdenylation (APA), jointly referred here as Alternative Transcript Processing (AltTP). Moreover, short-read RNA-seq has serious limitations to resolve full-length transcripts, further complicating the study of isoform expression. Recent advances in long-read sequencing open exciting opportunities for studying isoform biology and function. However, there are no established bioinformatics methods for the functional analysis of isoform-resolved transcriptomics data to fully leverage these technological advances. Here we present a novel framework for Functional Iso-Transcriptomics analysis (FIT). This framework uses a rich isoform-level annotation database of functional domains, motifs and sites -both coding and noncoding-and introduces novel analysis methods to interrogate different aspects of the functional relevance of isoform complexity. The Functional Diversity Analysis (FDA) evaluates the variability at the inclusion/exclusion of functional domains across annotated transcripts of the same gene. Parameters can be set to evaluate if AltTP partially or fully disrupts functional elements. FDA is a measure of the potential of a multiple isoform transcriptome to have a functional impact. By combining these functional labels with expression data, the Differential Analysis Module evaluates the relative contribution of transcriptional (i.e. gene level) and post-transcriptional (i.e. transcript/protein levels) regulation on the biology of the system. Measures of inclusion of NLS, transmembrane domains or DNA binding motifs, for example.Some of these findings were experimentally validated by others and us.In summary, we propose a novel framework for the functional analysis of transcriptomes at isoform resolution. We anticipate the tappAS tool will be an important resource for the adoption of the Functional Iso-Transcriptomics analysis by functional genomics community.

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Section: Fi = Einc Einc + Eexcmentioning

confidence: 99%

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

Fuente

Arzalluz-Luque

Tardáguila

et al. 2019

Preprint

Self Cite

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“…Differentially expressed genes (DEG) during dynamic changes in liver regeneration at different time points in mice were identified using maSigPro software [8,18,19]. Genes with significant changes in expression between the experimental and control groups were identified by dummy variables, which was defined by maSigPro as a two-regression step method.…”

Section: The Identification Of Differentially Expressed Genes During mentioning

confidence: 99%

Explorative analysis of the gene expression profile during liver regeneration of mouse: a microarray-based study

Wang

Lin

et al. 2019

Artificial Cells, Nanomedicine, and Biotechnology

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The liver is an amazing organ due to its powerful regenerative capacity. Although many studies on liver regeneration have been documented, the detailed mechanisms remain unclear. Two-third partial hepatectomy (PH) in rodents plays a crucial role in the study of liver regeneration. In this study, the time series data of gene expression during liver regeneration in mouse were analyzed using the gene set numbered GSE6998 in GEO. A variety of bioinformatics methods, including masigPro, Weighted Gene Co-expression Network Analysis (WGCNA), spatial analysis of functional enrichment (SAFE) and ingenuity canonical pathway analysis (IPA) were used to identify and compare the significantly changed pathways, potential upstream regulators and key genes during liver regeneration. Our study showed that liver regeneration in the mouse is a coordinated process, which cell-cycle-related progress are at the centre of the interaction network involved in liver regeneration. Several candidate upstream regulators including PPARA, NFE2L2, MAD1 and CNR1 and some key genes such as Cdk1, Plk1, Cdc20, Aurka, Racgap1, Cenpa, Rrm1, Rrm2 were identified. In conclusion, these findings could contribute to revealing the molecular mechanism of liver regeneration after PH, which could provide new ideas and treatment methods for regenerative medicine, oncological drug development and oncological treatment.

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“…This creates an obstacle for users with limited computational experience who want to analyze the gene expression results from their RNA-seq studies and has led to the increased need to interactivity in DGE analyses and integrated visualization generation of DGE results (Perkel, 2018). While there have been substantial efforts to provide platforms for DGE analysis and visualization of DGE results (Ge, 2017;Goff, et al, 2013;Harshbarger, et al, 2017;McDermaid, et al, 2018;Nelson, et al, 2017;Nueda, et al, 2017;Powell, 2015;Younesy, et al, 2015), numerous pitfalls and bottlenecks persist. Among these pitfalls are the difficulty of experimental design implementation, lack of comprehensive integrated preliminary analyses and DGE tools, and lack of functionalities and interactivity related to visualizing the analysis results.…”

Section: Introductionmentioning

confidence: 99%

IRIS-DGE: An integrated RNA-seq data analysis and interpretation system for differential gene expression

Monier

McDermaid

Zhao

et al. 2018

Preprint

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Motivation: Next-Generation Sequencing has made available much more large-scale genomic and transcriptomic data. Studies with RNA-sequencing (RNA-seq) data typically involve generation of gene expression profiles that can be further analyzed, many times involving differential gene expression (DGE). This process enables comparison across samples of two or more factor levels. A recurring issue with DGE analyses is the complicated nature of the comparisons to be made, in which a variety of factor combinations, pairwise comparisons, and main or blocked main effects need to be tested.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/283341 doi: bioRxiv preprint first posted online Mar. 16, 2018; 2 Results: Here we present a tool called IRIS-DGE, which is a server-based DGE analysis tool developed using Shiny. It provides a straightforward, user-friendly platform for performing comprehensive DGE analysis, and crucial analyses that help design hypotheses and to determine key genomic features. IRIS-DGE integrates the three most commonly used R-based DGE tools to determine differentially expressed genes (DEGs) and includes numerous methods for performing preliminary analysis on user-provided gene expression information. Additionally, this tool integrates a variety of visualizations, in a highly interactive manner, for improved interpretation of preliminary and DGE analyses.

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Identification and visualization of differential isoform expression in RNA-seq time series

Cited by 20 publications

References 6 publications

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

Explorative analysis of the gene expression profile during liver regeneration of mouse: a microarray-based study

IRIS-DGE: An integrated RNA-seq data analysis and interpretation system for differential gene expression

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