What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants
Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) inv...
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doaj-6ce6997482b54f3bab6aae42eeecedd82021-09-26T00:47:35ZengMDPI AGMethods and Protocols2409-92792021-09-014626210.3390/mps4030062What’s Wrong in a Jump? Prediction and Validation of Splice Site VariantsGiulia Riolo0Silvia Cantara1Claudia Ricci2Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, ItalyDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, ItalyDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, ItalyAlternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.https://www.mdpi.com/2409-9279/4/3/62alternative splicingsplicing sitessplice variantprediction toolsmachine learningexperimental validation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Giulia Riolo Silvia Cantara Claudia Ricci |
spellingShingle |
Giulia Riolo Silvia Cantara Claudia Ricci What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants Methods and Protocols alternative splicing splicing sites splice variant prediction tools machine learning experimental validation |
author_facet |
Giulia Riolo Silvia Cantara Claudia Ricci |
author_sort |
Giulia Riolo |
title |
What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants |
title_short |
What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants |
title_full |
What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants |
title_fullStr |
What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants |
title_full_unstemmed |
What’s Wrong in a Jump? Prediction and Validation of Splice Site Variants |
title_sort |
what’s wrong in a jump? prediction and validation of splice site variants |
publisher |
MDPI AG |
series |
Methods and Protocols |
issn |
2409-9279 |
publishDate |
2021-09-01 |
description |
Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research. |
topic |
alternative splicing splicing sites splice variant prediction tools machine learning experimental validation |
url |
https://www.mdpi.com/2409-9279/4/3/62 |
work_keys_str_mv |
AT giuliariolo whatswronginajumppredictionandvalidationofsplicesitevariants AT silviacantara whatswronginajumppredictionandvalidationofsplicesitevariants AT claudiaricci whatswronginajumppredictionandvalidationofsplicesitevariants |
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1716869785651773440 |