Long-read sequencing in human genetics

Haack T, Eggermann T. Quality assurance within the context of genome diagnostics (a german perspective)

The rapid and dynamic implementation of Next-Generation Sequencing (NGS)-based assays has revolutionized genetic testing, and in the near future, nearly all molecular alterations of the human genome will be diagnosable via massive parallel sequencing. While this progress will further corroborate the central role of human genetics in the multidisciplinary management of patients with genetic disorders, it must be accompanied by quality assurance measures in order to allow the safe and optimal use of knowledge ascertained from genome diagnostics. To achieve this, several valuable tools and guidelines have been developed to support the quality of genome diagnostics. In this paper, authors with experience in diverse aspects of genomic analysis summarize the current status of quality assurance in genome diagnostics, with the aim of facilitating further standardization and quality improvement in one of the core competencies of the field.

Defective COX1 expression in aging mice liver

Mitochondrial defects are associated with aging processes and age-related diseases, including cardiovascular diseases, neurodegenerative diseases and cancer. In addition, some recent studies suggest mild mitochondrial dysfunctions appear to be associated with longer lifespans. In this context, liver tissue is considered to be largely resilient to aging and mitochondrial dysfunction. Yet, in recent years studies report dysregulation of mitochondrial function and nutrient sensing pathways in ageing livers. Therefore, we analyzed the effects of the aging process on mitochondrial gene expression in liver using wildtype C57BL/6N mice. In our analyses, we observed alteration in mitochondrial energy metabolism with age. To assess if defects in mitochondrial gene expression are linked to this decline, we applied a Nanopore sequencing based approach for mitochondrial transcriptomics. Our analyses show that a decrease of the Cox1 transcript correlates with reduced respiratory complex IV activity in older mice livers.

3GOLD: optimized Levenshtein distance for clustering third-generation sequencing data

BACKGROUND

Third-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence has rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data.

RESULTS

We successfully introduced a bidirectional frameshift allowance with end-user determined accommodation caps combined with weighted error discrimination. Furthermore, our modifications dramatically improved the computational speed of Levenstein distance. For simulated ONT MinION and PacBio Sequel datasets, the average clustering sensitivity for 3GOLD was 41.45% (S.D. 10.39) higher than Sequence-Levenstein distance, 52.14% (S.D. 9.43) higher than Levenshtein distance, 55.93% (S.D. 8.67) higher than Starcode, 42.68% (S.D. 8.09) higher than CD-HIT-EST and 61.49% (S.D. 7.81) higher than DNACLUST. For biological ONT MinION data, 3GOLD clustering sensitivity was 27.99% higher than Sequence-Levenstein distance, 52.76% higher than Levenshtein distance, 56.39% higher than Starcode, 48% higher than CD-HIT-EST and 70.4% higher than DNACLUST.

CONCLUSION

Our modifications to Levenshtein distance have improved its speed and accuracy compared to the classic Levenshtein distance, Sequence-Levenshtein distance and other commonly used clustering approaches on simulated and biological third-generation sequenced datasets. Our clustering approach is appropriate for datasets of unknown cluster centroids, such as those generated with unique molecular identifiers as well as known centroids such as barcoded datasets. A strength of our approach is high accuracy in resolving small clusters and mitigating the number of singletons.

Nutrition and Rheumatoid Arthritis in the 'Omics' Era

Modern high-throughput 'omics' science tools (including genomics, transcriptomics, proteomics, metabolomics and microbiomics) are currently being applied to nutritional sciences to unravel the fundamental processes of health effects ascribed to particular nutrients in humans and to contribute to more precise nutritional advice. Diet and food components are key environmental factors that interact with the genome, transcriptome, proteome, metabolome and the microbiota, and this life-long interplay defines health and diseases state of the individual. Rheumatoid arthritis (RA) is a chronic autoimmune disease featured by a systemic immune-inflammatory response, in genetically susceptible individuals exposed to environmental triggers, including diet. In recent years increasing evidences suggested that nutritional factors and gut microbiome have a central role in RA risk and progression. The aim of this review is to summarize the main and most recent applications of 'omics' technologies in human nutrition and in RA research, examining the possible influences of some nutrients and nutritional patterns on RA pathogenesis, following a nutrigenomics approach. The opportunities and challenges of novel 'omics technologies' in the exploration of new avenues in RA and nutritional research to prevent and manage RA will be also discussed.

A comprehensive review of scaffolding methods in genome assembly

In the field of genome assembly, scaffolding methods make it possible to obtain a more complete and contiguous reference genome, which is the cornerstone of genomic research. Scaffolding methods typically utilize the alignments between contigs and sequencing data (reads) to determine the orientation and order among contigs and to produce longer scaffolds, which are helpful for genomic downstream analysis. With the rapid development of high-throughput sequencing technologies, diverse types of reads have emerged over the past decade, especially in long-range sequencing, which have greatly enhanced the assembly quality of scaffolding methods. As the number of scaffolding methods increases, biology and bioinformatics researchers need to perform in-depth analyses of state-of-the-art scaffolding methods. In this article, we focus on the difficulties in scaffolding, the differences in characteristics among various kinds of reads, the methods by which current scaffolding methods address these difficulties, and future research opportunities. We hope this work will benefit the design of new scaffolding methods and the selection of appropriate scaffolding methods for specific biological studies.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

Large scale in silico characterization of repeat expansion variation in human genomes

Significant progress has been made in elucidating single nucleotide polymorphism diversity in the human population. However, the majority of the variation space in the genome is structural and remains partially elusive. One form of structural variation is tandem repeats (TRs). Expansion of TRs are responsible for over 40 diseases, but we hypothesize these represent only a fraction of the pathogenic repeat expansions that exist. Here we characterize long or expanded TR variation in 1,115 human genomes as well as a replication cohort of 2,504 genomes, identified using ExpansionHunter Denovo. We found that individual genomes typically harbor several rare, large TRs, generally in non-coding regions of the genome. We noticed that these large TRs are enriched in their proximity to Alu elements. The vast majority of these large TRs seem to be expansions of smaller TRs that are already present in the reference genome. We are providing this TR profile as a resource for comparison to undiagnosed rare disease genomes in order to detect novel disease-causing repeat expansions.

Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.