CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice

Cheng, Jianghua; Çelik, Muhammed Hasan; Nguyen, Thi Yen Duong; Avsec, Žiga

doi:10.1002/humu.23788

Cited by 11 publications

(13 citation statements)

References 32 publications

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“…Previously, CAGI has had just one small splicing challenge [https://genomeinterpretation.org/content/Splicing-2012]. CAGI5 included two full‐scale splicing challenges (Mount et al, ) and these have resulted in five papers from participants (Chen, Lu, Zhao, & Yang, ; Cheng, Çelik, Nguyen, Avsec, & Gagneur, ; Gotea, Margolin, & Elnitski, ; Naito, ; Wang, Wang, & Hu, ). The issue also contains an overview paper from one of the splicing data providers (Rhine et al, ).…”

Section: Introductionmentioning

confidence: 99%

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

et al. 2019

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Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, and increasingly also in complex trait disease, with applications ranging from basic research to clinical decisions. Many computational impact prediction methods have been developed, yet the field lacks a clear consensus on their appropriate use and interpretation. The Critical Assessment of Genome Interpretation (CAGI, /'kā‐jē/) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing with experimental and clinical data. CAGI has completed five editions with the goals of establishing the state of art in genome interpretation and of encouraging new methodological developments. This special issue (https://onlinelibrary.wiley.com/toc/10981004/2019/40/9) comprises reports from CAGI, focusing on the fifth edition that culminated in a conference that took place 5 to 7 July 2018. CAGI5 was comprised of 14 challenges and engaged hundreds of participants from a dozen countries. This edition had a notable increase in splicing and expression regulatory variant challenges, while also continuing challenges on clinical genomics, as well as complex disease datasets and missense variants in diseases ranging from cancer to Pompe disease to schizophrenia. Full information about CAGI is at https://genomeinterpretation.org.

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Section: Introductionmentioning

confidence: 99%

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

et al. 2019

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“…SpliceAI is a splice junction prediction model shown to outperform other models such as GeneSplicer [ 25 ] and NNSplice [ 26 ]. MMSplice is a model of exon skipping ranked first at the recent fifth Critical Assessment of Genome Interpretation group [ 27 , 28 ], shown to outperform state-of-the-art models such as COSSMO [ 20 ], HAL [ 19 ], SPANR [ 18 ], and MaxEntScan [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning based CRISPR gRNA design for therapeutic exon skipping

Louie

Shen²,

Tahiry³

et al. 2021

PLoS Comput Biol

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Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.

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“…Many computational tools have been developed to predict splice sites or splicing strength from sequence [22][23][24][25][26][27][28][29][30][31][32]. However, tools are lacking for predicting tissuespecific effects of human genetic variants on splicing.…”

Section: Introductionmentioning

confidence: 99%

“…We previously developed MMSplice, a neural network with a modular design that predicts the effect of variants on splicing [29,30]. Unlike SPANR, which has been trained on natural endogenous genomic sequence, MMSplice leverages perturbation data from a recently published massively parallel reporter assay [27].…”

Section: Introductionmentioning

confidence: 99%

MTSplice predicts effects of genetic variants on tissue-specific splicing

Cheng

Çelik

Kundaje

2020

Preprint

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Tissue-specific splicing of exons plays an important role in determining tissue identity. However, computational tools predicting tissue-specific effects of variants on splicing are lacking. To address this issue, we developed MTSplice (Multi-tissue Splicing), a neural network which quantitatively predicts effects of human genetic variants on splicing of cassette exons in 56 tissues. MTSplice combines the state-of-the-art predictor MMSplice, which models constitutive regulatory sequences, with a new neural network which models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting effects associated with naturally occurring genetic variants in most tissues of the GTEx dataset. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. MTSplice is provided free of use and open source at the model repository Kipoi. We foresee MTSplice to be useful for functional prediction and prioritization of variants associated with tissue-specific disorders.

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CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice

Cited by 11 publications

References 32 publications

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

Machine learning based CRISPR gRNA design for therapeutic exon skipping

MTSplice predicts effects of genetic variants on tissue-specific splicing

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