IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling
Prediction of the effect of a single-nucleotide variant (SNV) in an intronic region on aberrant pre-mRNA splicing is challenging except for an SNV affecting the canonical GU/AG splice sites (ss). To predict pathogenicity of SNVs at intronic positions −50 (Int-50) to −3 (Int-3) close to the 3’ ss, we...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-07-01
|
Series: | Frontiers in Genetics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.701076/full |
id |
doaj-14aff9fb6e614f06b2297d27d3a5ff7a |
---|---|
record_format |
Article |
spelling |
doaj-14aff9fb6e614f06b2297d27d3a5ff7a2021-07-19T11:53:47ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-07-011210.3389/fgene.2021.701076701076IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM ModelingJun-ichi Takeda0Sae Fukami1Akira Tamura2Akihide Shibata3Akihide Shibata4Kinji Ohno5Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, JapanDivision of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, JapanDivision of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, JapanDivision of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, JapanDepartment of Anesthesiology, Toranomon Hospital, Tokyo, JapanDivision of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, JapanPrediction of the effect of a single-nucleotide variant (SNV) in an intronic region on aberrant pre-mRNA splicing is challenging except for an SNV affecting the canonical GU/AG splice sites (ss). To predict pathogenicity of SNVs at intronic positions −50 (Int-50) to −3 (Int-3) close to the 3’ ss, we developed light gradient boosting machine (LightGBM)-based IntSplice2 models using pathogenic SNVs in the human gene mutation database (HGMD) and ClinVar and common SNVs in dbSNP with 0.01 ≤ minor allelic frequency (MAF) < 0.50. The LightGBM models were generated using features representing splicing cis-elements. The average recall/sensitivity and specificity of IntSplice2 by fivefold cross-validation (CV) of the training dataset were 0.764 and 0.884, respectively. The recall/sensitivity of IntSplice2 was lower than the average recall/sensitivity of 0.800 of IntSplice that we previously made with support vector machine (SVM) modeling for the same intronic positions. In contrast, the specificity of IntSplice2 was higher than the average specificity of 0.849 of IntSplice. For benchmarking (BM) of IntSplice2 with IntSplice, we made a test dataset that was not used to train IntSplice. After excluding the test dataset from the training dataset, we generated IntSplice2-BM and compared it with IntSplice using the test dataset. IntSplice2-BM was superior to IntSplice in all of the seven statistical measures of accuracy, precision, recall/sensitivity, specificity, F1 score, negative predictive value (NPV), and matthews correlation coefficient (MCC). We made the IntSplice2 web service at https://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice2.https://www.frontiersin.org/articles/10.3389/fgene.2021.701076/fullsplice acceptor siteaberrant splicingsingle nucleotide variationsintronic mutationsLightGBM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun-ichi Takeda Sae Fukami Akira Tamura Akihide Shibata Akihide Shibata Kinji Ohno |
spellingShingle |
Jun-ichi Takeda Sae Fukami Akira Tamura Akihide Shibata Akihide Shibata Kinji Ohno IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling Frontiers in Genetics splice acceptor site aberrant splicing single nucleotide variations intronic mutations LightGBM |
author_facet |
Jun-ichi Takeda Sae Fukami Akira Tamura Akihide Shibata Akihide Shibata Kinji Ohno |
author_sort |
Jun-ichi Takeda |
title |
IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling |
title_short |
IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling |
title_full |
IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling |
title_fullStr |
IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling |
title_full_unstemmed |
IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling |
title_sort |
intsplice2: prediction of the splicing effects of intronic single-nucleotide variants using lightgbm modeling |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2021-07-01 |
description |
Prediction of the effect of a single-nucleotide variant (SNV) in an intronic region on aberrant pre-mRNA splicing is challenging except for an SNV affecting the canonical GU/AG splice sites (ss). To predict pathogenicity of SNVs at intronic positions −50 (Int-50) to −3 (Int-3) close to the 3’ ss, we developed light gradient boosting machine (LightGBM)-based IntSplice2 models using pathogenic SNVs in the human gene mutation database (HGMD) and ClinVar and common SNVs in dbSNP with 0.01 ≤ minor allelic frequency (MAF) < 0.50. The LightGBM models were generated using features representing splicing cis-elements. The average recall/sensitivity and specificity of IntSplice2 by fivefold cross-validation (CV) of the training dataset were 0.764 and 0.884, respectively. The recall/sensitivity of IntSplice2 was lower than the average recall/sensitivity of 0.800 of IntSplice that we previously made with support vector machine (SVM) modeling for the same intronic positions. In contrast, the specificity of IntSplice2 was higher than the average specificity of 0.849 of IntSplice. For benchmarking (BM) of IntSplice2 with IntSplice, we made a test dataset that was not used to train IntSplice. After excluding the test dataset from the training dataset, we generated IntSplice2-BM and compared it with IntSplice using the test dataset. IntSplice2-BM was superior to IntSplice in all of the seven statistical measures of accuracy, precision, recall/sensitivity, specificity, F1 score, negative predictive value (NPV), and matthews correlation coefficient (MCC). We made the IntSplice2 web service at https://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice2. |
topic |
splice acceptor site aberrant splicing single nucleotide variations intronic mutations LightGBM |
url |
https://www.frontiersin.org/articles/10.3389/fgene.2021.701076/full |
work_keys_str_mv |
AT junichitakeda intsplice2predictionofthesplicingeffectsofintronicsinglenucleotidevariantsusinglightgbmmodeling AT saefukami intsplice2predictionofthesplicingeffectsofintronicsinglenucleotidevariantsusinglightgbmmodeling AT akiratamura intsplice2predictionofthesplicingeffectsofintronicsinglenucleotidevariantsusinglightgbmmodeling AT akihideshibata intsplice2predictionofthesplicingeffectsofintronicsinglenucleotidevariantsusinglightgbmmodeling AT akihideshibata intsplice2predictionofthesplicingeffectsofintronicsinglenucleotidevariantsusinglightgbmmodeling AT kinjiohno intsplice2predictionofthesplicingeffectsofintronicsinglenucleotidevariantsusinglightgbmmodeling |
_version_ |
1721294947540271104 |