Insights of Non-canonical Splice-site Variants on RNA Splicing in Patients with Congenital Hypothyroidism

Abstract Context Congenital hypothyroidism (CH) is caused by mutations in the genes for thyroid hormone synthesis. In our previous investigation of CH patients, ~53% of patients had mutations in either coding exons or canonical splice-sites of causative genes. Non-canonical splice-sites variants in the intron were detected but their pathogenic significance was not known. Objective To evaluate non-canonical splice-site variants on pre-mRNA splicing of CH-causing genes. Methods Next-generation sequencing data of 55 CH cases in 47 families were analyzed to identify rare intron variants. The effects of variants on pre-mRNA splicing were investigated by minigene RNA-splicing assays. Results Four intron variants were found in 3 patients: SLC26A4 c.1544 + 9C>T and c.1707 + 94C>T in one patient, and SLC5A5 c.970-48G>C and c.1652-97A>C in two other patients. The c.1707 + 94C>T and c.970-48G>C caused exons 15 and 16 skipping, and exon 8 skipping, respectively. The remaining variants had no effect on RNA splicing. Furthermore, we analyzed 28 previously reported non-canonical splice-site variants (4 in TG and 24 in SLC26A4). Among them, 15 variants (~54%) resulted in aberrant splicing and 13 variants had no effect on RNA splicing. These data were compared with three variant-prediction programs (FATHMM-XF, FATHMM-MKL, and CADD). Among 32 variants, FATHMM-XF, FATHMM-MKL, and CADD correctly predicted 20 (63%), 17 (53%), and 26 (81%) variants, respectively. Conclusions Two novel deep intron mutations have been identified in SLC26A4 and SLC5A5, bringing the total number of solved families with disease-causing mutations to ~45% in our cohort. Approximately 46% (13/28) reported non-canonical splice-site mutations do not disrupt pre-mRNA splicing. CADD provides highest prediction accuracy of non-canonical splice-site variants.

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Faculty Opinions recommendation of VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718272765.793499663 ◽

2014 ◽

Author(s):

Gary Bader ◽

Mohamed Helmy

Keyword(s):

Next Generation Sequencing ◽

Network Analysis ◽

Next Generation Sequencing Data ◽

Cancer Genes ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

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Faculty Opinions recommendation of Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727775566.793536095 ◽

2017 ◽

Author(s):

Steve Pereira

Keyword(s):

Pancreatic Cancer ◽

Next Generation Sequencing ◽

Precision Medicine ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Assisted Analysis ◽

Generation Sequencing

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NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab174 ◽

2021 ◽

Author(s):

Anne Krogh Nøhr ◽

Kristian Hanghøj ◽

Genis Garcia Erill ◽

Zilong Li ◽

Ida Moltke ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genetic Research ◽

Likelihood Estimation ◽

Software Tool ◽

Estimation Methods ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Ngs Data ◽

Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

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recoup: flexible and versatile signal visualization from next generation sequencing

BMC Bioinformatics ◽

10.1186/s12859-020-03902-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Panagiotis Moulos

Keyword(s):

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Special Focus ◽

Next Generation ◽

Sequencing Data ◽

User Friendliness ◽

Computational Environment ◽

Level Data ◽

Data Signal ◽

Generation Sequencing

Abstract Background The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. Results recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. Conclusion While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

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