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25 pages, 1207 KiB

Open AccessArticle

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

by Valentina Cipriani, Nikolas Pontikos, Gavin Arno, Panagiotis I. Sergouniotis, Eva Lenassi, Penpitcha Thawong, Daniel Danis, Michel Michaelides, Andrew R. Webster, Anthony T. Moore, Peter N. Robinson, Julius O.B. Jacobsen and Damian Smedley

Genes 2020, 11(4), 460; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11040460 - 23 Apr 2020

Cited by 35 | Viewed by 8235

Abstract

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data [...] Read more.

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein–protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients’ HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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5 pages, 608 KiB

Open AccessCommunication

TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform

by Danze Chen, Qianqian Zhao, Leiming Jiang, Shuaiyuan Liao, Zhigang Meng and Jianzhen Xu

Genes 2019, 10(7), 519; https://0-doi-org.brum.beds.ac.uk/10.3390/genes10070519 - 10 Jul 2019

Cited by 1 | Viewed by 2722

Abstract

Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding [...] Read more.

Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncoding RNAs (lncRNAs) identification in transcriptome analysis. It also prioritizes the transcripts with a visualization framework that automatically integrates rich annotation with known genomic features. TGStools is a Python package freely available at Github. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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16 pages, 1603 KiB

Open AccessArticle

Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics

by Faith Dunbar, Hongyan Xu, Duchwan Ryu, Santu Ghosh, Huidong Shi and Varghese George

Genes 2019, 10(4), 298; https://0-doi-org.brum.beds.ac.uk/10.3390/genes10040298 - 12 Apr 2019

Cited by 2 | Viewed by 2548

Abstract

Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose [...] Read more.

Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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17 pages, 4489 KiB

Open AccessArticle

Inferring Drug-Protein–Side Effect Relationships from Biomedical Text

by Min Song, Seung Han Baek, Go Eun Heo and Jeong-Hoon Lee

Genes 2019, 10(2), 159; https://0-doi-org.brum.beds.ac.uk/10.3390/genes10020159 - 19 Feb 2019

Cited by 7 | Viewed by 3125

Abstract

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study [...] Read more.

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side effect—from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein–side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. Results: We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by P@5-P@20. In addition, we confirmed that the paths are novel hypotheses that are worth investigating further. Discussion: The risk of side effects has been an important issue for the US Food and Drug Administration (FDA). However, the causes and mechanisms of such side effects have not been fully elucidated. This study extends previous research on understanding drug side effects by using various techniques such as Named Entity Recognition (NER), Relation Extraction (RE), and semantic similarity. Conclusion: It is not easy to reveal the biomedical mechanisms of side effects due to a huge number of possible paths. However, we automatically generated predictable paths using the proposed approach, which could provide meaningful information to biomedical researchers to generate plausible hypotheses for the understanding of such mechanisms. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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Review

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19 pages, 1767 KiB

Open AccessEditor’s ChoiceReview

Genetic Modifiers and Rare Mendelian Disease

by K. M. Tahsin Hassan Rahit and Maja Tarailo-Graovac

Genes 2020, 11(3), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11030239 - 25 Feb 2020

Cited by 85 | Viewed by 10727

Abstract

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it [...] Read more.

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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24 pages, 375 KiB

Open AccessReview

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

by Sandra Brasil, Carlota Pascoal, Rita Francisco, Vanessa dos Reis Ferreira, Paula A. Videira and Gonçalo Valadão

Genes 2019, 10(12), 978; https://0-doi-org.brum.beds.ac.uk/10.3390/genes10120978 - 27 Nov 2019

Cited by 62 | Viewed by 9674

Abstract

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and [...] Read more.

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

15 pages, 662 KiB

Open AccessReview

Biological Network Approaches and Applications in Rare Disease Studies

by Peng Zhang and Yuval Itan

Genes 2019, 10(10), 797; https://0-doi-org.brum.beds.ac.uk/10.3390/genes10100797 - 12 Oct 2019

Cited by 23 | Viewed by 4468

Abstract

Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that [...] Read more.

Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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18 pages, 1475 KiB

Open AccessReview

Uncovering Missing Heritability in Rare Diseases

by Tatiana Maroilley and Maja Tarailo-Graovac

Genes 2019, 10(4), 275; https://0-doi-org.brum.beds.ac.uk/10.3390/genes10040275 - 04 Apr 2019

Cited by 35 | Viewed by 6449

Abstract

The problem of ‘missing heritability’ affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The ‘missing heritability’ concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to [...] Read more.

The problem of ‘missing heritability’ affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The ‘missing heritability’ concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases—the majority of RDs are also facing the ‘missing heritability’ problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources. Full article

(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)

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