Comprehensive and scalable quantification of splicing differences with MntJULiP

Yang, Guangyu Robert; Sabunciyan, Sarven; Florea, Liliana

doi:10.1186/s13059-022-02767-y

Cited by 3 publications

(7 citation statements)

References 40 publications

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“…Of note, similar DSA analyses of the frontal cortex data show a clear separation of samples by 'biological sex', with or without covariate adjustment, and reveal distinguishing events including at the non-coding X Inactive Specific Transcript (XIST) gene involved in X chromosome inactivation in female early development processes (Supplementary Figure S6). Therefore, as we previously noted [2], DSR and DSA reflect different and complementary views and effects of alternative splicing on the transcriptional and functional outcomes.…”

Section: Deconvoluting the Effects Of Covariates On Human Frontal Cor...mentioning

confidence: 63%

“…MntJULiP [2] detects differences in splicing at the intron level, which greatly improves performance when transcript reconstructions are inaccurate or incomplete. It identifies events directly from the alignment data, without relying on a reference gene annotation, and therefore can find and report novel events.…”

Section: Differential Splicing (Ds) Detection With Mntjulipmentioning

confidence: 99%

“…Starting from an empirical transcript expression matrix trained on an RNA-seq data set from lung fibroblasts (GenBank A# SRR493366) and using GENCODE v.41 as reference, we generated 11.5 million 100 bp long paired-end reads per sample from 2,000 genes with two or more expressed isoforms. Changes in splicing were 4 simulated as described in [2], separately for the DSR and the DSA models; detailed descriptions are provided in the Supplementary Method M2.…”

Section: Covariate Augmented Bayesian Modelsmentioning

confidence: 99%

“…Increasingly large and complex RNA-seq datasets that include multiple confounders such as sex, age, ethnicity and clinical attributes, are emerging from disease study cohorts and population-level projects, which demand highly specialized analysis tools. While multiple methods exist to detect differences in splicing, including LeafCutter, MntJULiP, rMATS, SUPPA2 and DARTS [1][2][3][4][5], few are equipped to handle the complexities of the data. In particular, there is a scarcity of programs that can rigorously account for the effect of confounding attributes on the observed data.…”

Section: Introductionmentioning

confidence: 99%

“…S1 ). Additionally, MntJULiP has the rare ability to perform multiple comparisons simultaneously, which we showed was more accurate at capturing global differences in a time series or complex experiments [2].…”

Section: Introductionmentioning

confidence: 99%

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MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

Lui,

Yang,

Florea

2024

Preprint

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Differences in alternative splicing patterns can reveal important markers of phenotypic differentiation, including biomarkers of disease. Emerging large and complex RNA-seq datasets from disease and population studies include multiple confounders such as sex, age, ethnicity and clinical attributes, which demand highly specialized data analysis tools. However, few methods are equipped to handle the new challenges. We describe an implementation of our programs MntJULiP and Jutils for differential splicing detection and visualization from RNA-seq data that takes into account covariates. MntJULiP detects intron-level differences in alternative splicing from RNA-seq data using a Bayesian mixture model. Jutils visualizes alternative splicing variation with heatmaps, PCA and sashimi plots, and Venn diagrams. Our tools are scalable and can process thousands of samples within hours. We applied our methods to the collection of GTEx brain RNA-seq samples to deconvolute the effects of sex and age at death on the splicing patterns. In particular, clustering of covariate adjusted data identifies a subgroup of individuals undergoing a distinct splicing program during aging. MntJULiP and Jutils are implemented in Python and are available fromhttps://github.com/splicebox/.

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Section: Deconvoluting the Effects Of Covariates On Human Frontal Cor...mentioning

confidence: 63%

Section: Differential Splicing (Ds) Detection With Mntjulipmentioning

confidence: 99%

Section: Covariate Augmented Bayesian Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

Lui,

Yang,

Florea

2024

Preprint

Self Cite

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Bioinformatic Analysis of Alternative Splicing

Acharya,

Medini,

Thakur

et al. 2024

Reference Module in Life Sciences

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Multimodal genome-wide survey of progressing and non-progressing breast ductal carcinoma in-situ

Debeljak,

Cho,

Downs

et al. 2024

Breast Cancer Res

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Background Ductal carcinoma in-situ (DCIS) is a pre-invasive form of invasive breast cancer (IBC). Due to improved breast cancer screening, it now accounts for ~ 25% of all breast cancers. While the treatment success rates are over 90%, this comes at the cost of considerable morbidity, considering that the majority of DCIS never become invasive and our understanding of the molecular changes occurring in DCIS that predispose to invasive disease is limited. The aim of this study is to characterize molecular changes that occur in DCIS, with the goal of improving DCIS risk stratification. Methods We identified and obtained a total of 197 breast tissue samples from 5 institutions (93 DCIS progressors, 93 DCIS non-progressors, and 11 adjacent normal breast tissues) that had at least 10-year follow-up. We isolated DNA and RNA from archival tissue blocks and characterized genome-wide mRNA expression, DNA methylation, DNA copy number variation, and RNA splicing variation. Results We obtained all four genomic data sets in 122 of the 197 samples. Our intrinsic expression subtype-stratified analyses identified multiple molecular differences both between DCIS subtypes and between DCIS and IBC. While there was heterogeneity in molecular signatures and outcomes within intrinsic subtypes, several gene sets that differed significantly between progressing and non-progressing DCIS were identified by Gene Set Enrichment Analysis. Conclusion DCIS is a molecularly highly heterogenous disease with variable outcomes, and the molecular events determining DCIS disease progression remain poorly defined. Our genome-wide multi-omic survey documents DCIS-associated alterations and reveals molecular heterogeneity within the intrinsic DCIS subtypes. Further studies investigating intrinsic subtype-stratified characteristics and molecular signatures are needed to determine if these may be exploitable for risk assessment and mitigation of DCIS progression. The highly significant associations of specific gene sets with IBC progression revealed by our Gene Set Enrichment Analysis may lend themselves to the development of a prognostic molecular score, to be validated on independent DCIS cohorts.

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Comprehensive and scalable quantification of splicing differences with MntJULiP

Cited by 3 publications

References 40 publications

MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

Bioinformatic Analysis of Alternative Splicing

Multimodal genome-wide survey of progressing and non-progressing breast ductal carcinoma in-situ

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