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Accurate mapping of mitochondrial DNA deletions and duplications using deep sequencing.

ABSTRACT: Deletions and duplications in mitochondrial DNA (mtDNA) cause mitochondrial disease and accumulate in conditions such as cancer and age-related disorders, but validated high-throughput methodology that can readily detect and discriminate between these two types of events is lacking. Here we establish a computational method, MitoSAlt, for accurate identification, quantification and visualization of mtDNA deletions and duplications from genomic sequencing data. Our method was tested on simulated sequencing reads and human patient samples with single deletions and duplications to verify its accuracy. Application to mouse models of mtDNA maintenance disease demonstrated the ability to detect deletions and duplications even at low levels of heteroplasmy.

SUBMITTER: Basu S

PROVIDER: S-EPMC7769605 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Accurate mapping of mitochondrial DNA deletions and duplications using deep sequencing.

Basu Swaraj S Xie Xie X Uhler Jay P JP Hedberg-Oldfors Carola C Milenkovic Dusanka D Baris Olivier R OR Kimoloi Sammy S Matic Stanka S Stewart James B JB Larsson Nils-Göran NG Wiesner Rudolf J RJ Oldfors Anders A Gustafsson Claes M CM Falkenberg Maria M Larsson Erik E

PLoS genetics 20201214 12

Deletions and duplications in mitochondrial DNA (mtDNA) cause mitochondrial disease and accumulate in conditions such as cancer and age-related disorders, but validated high-throughput methodology that can readily detect and discriminate between these two types of events is lacking. Here we establish a computational method, MitoSAlt, for accurate identification, quantification and visualization of mtDNA deletions and duplications from genomic sequencing data. Our method was tested on simulated s ...[more]

PMID: 33315859

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Accurate mapping of mitochondrial DNA deletions and duplications using deep sequencing

Project description:Deletions and duplications in mitochondrial DNA (mtDNA) cause mitochondrial disease and accumulate in conditions such as cancer and age-related disorders, but validated high-throughput methodology that can readily detect and discriminate between these two types of events is lacking. Here we present MitoSAlt, a computational method for accurate identification, quantification and visualization of mtDNA deletions and duplications from whole genome, whole exome or transcriptome sequencing data. MitoSAlt was tested on simulated sequencing reads and human patient samples with single deletions and duplications to verify its accuracy. Application to mouse models of mtDNA maintenance disease further demonstrated the ability to detect deletions and duplications even at low levels of heteroplasmy. MitoSAlt paves the way for simple and reliable determination of mtDNA deletions and duplications across a wide range of relevant conditions and available sequencing datasets. Ã¢Â€Âƒ

| EGAS00001004380 | EGA

DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.

Project description:BACKGROUND:Calling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data? RESULTS:In this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project. CONCLUSIONS:Our work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs.

| S-EPMC6909530 | biostudies-literature

DNA sequence features underlying large-scale duplications and deletions in human.

Project description:Copy number variants (CNVs) may cover up to 12% of the whole genome and have substantial impact on phenotypes. We used 5867 duplications and 33,181 deletions available from the 1000 Genomes Project to characterise genomic regions vulnerable to CNV formation and to identify sequence features characteristic for those regions. The GC content for deletions was lower and for duplications was higher than for randomly selected regions. In regions flanking deletions and downstream of duplications, content was higher than in the random sequences, but upstream of duplication content was lower. In duplications and downstream of deletion regions, the percentage of low-complexity sequences was not different from the randomised data. In deletions and upstream of CNVs, it was higher, while for downstream of duplications, it was lower as compared to random sequences. The majority of CNVs intersected with genic regions - mainly with introns. GC content may be associated with CNV formation and CNVs, especially duplications are initiated in low-complexity regions. Moreover, CNVs located or overlapped with introns indicate their role in shaping intron variability. Genic CNV regions were enriched in many essential biological processes such as cell adhesion, synaptic transmission, transport, cytoskeleton organization, immune response and metabolic mechanisms, which indicates that these large-scaled variants play important biological roles.

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Accurate mitochondrial DNA sequencing using off-target reads provides a single test to identify pathogenic point mutations.

Project description:PurposeMitochondrial disorders are a common cause of inherited metabolic disease and can be due to mutations affecting mitochondrial DNA or nuclear DNA. The current diagnostic approach involves the targeted resequencing of mitochondrial DNA and candidate nuclear genes, usually proceeds step by step, and is time consuming and costly. Recent evidence suggests that variations in mitochondrial DNA sequence can be obtained from whole-exome sequence data, raising the possibility of a comprehensive single diagnostic test to detect pathogenic point mutations.MethodsWe compared the mitochondrial DNA sequence derived from off-target exome reads with conventional mitochondrial DNA Sanger sequencing in 46 subjects.ResultsMitochondrial DNA sequences can be reliably obtained using three different whole-exome sequence capture kits. Coverage correlates with the relative amount of mitochondrial DNA in the original genomic DNA sample, heteroplasmy levels can be determined using variant and total read depths, and-providing there is a minimum read depth of 20-fold-rare sequencing errors occur at a rate similar to that observed with conventional Sanger sequencing.ConclusionThis offers the prospect of using whole-exome sequence in a diagnostic setting to screen not only all protein coding nuclear genes but also all mitochondrial DNA genes for pathogenic mutations. Off-target mitochondrial DNA reads can also be used to assess quality control and maternal ancestry, inform on ethnic origin, and allow genetic disease association studies not previously anticipated with existing whole-exome data sets.

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Transcriptional Mutagenesis Prevents Ribosomal DNA Deterioration: The Role of Duplications and Deletions.

Project description:Clashes between transcription and replication complexes can cause point mutations and chromosome rearrangements on heavily transcribed genes. In eukaryotic ribosomal RNA genes, the system that prevents transcription-replication conflicts also causes frequent copy number variation. Such fast mutational dynamics do not alter growth rates in yeast and are thus selectively near neutral. It was recently found that yeast regulates these mutations by means of a signaling cascade that depends on the availability of nutrients. Here, we investigate the long-term evolutionary effect of the mutational dynamics observed in yeast. We developed an in silico model of single-cell organisms whose genomes mutate more frequently when transcriptional load is larger. We show that mutations induced by high transcriptional load are beneficial when biased toward gene duplications and deletions: they decrease mutational load even though they increase the overall mutation rates. In contrast, genome stability is compromised when mutations are not biased toward gene duplications and deletions, even when mutations occur much less frequently. Taken together, our results show that the mutational dynamics observed in yeast are beneficial for the long-term stability of the genome and pave the way for a theory of evolution where genetic operators are themselves cause and outcome of the evolutionary dynamics.

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Accurate diagnosis of carriers of deletions and duplications in Duchenne/Becker muscular dystrophy by fluorescent dosage analysis.

Project description:We have developed a semiautomated approach to amplify 25 exons of the dystrophin gene using two fluorescent multiplex PCR assays which detect over 98% of reported deletions and 90% of duplications causing Duchenne/Becker muscular dystrophy. The 5' multiplex detects 11 exons from the proximal deletion hotspot of the gene while the 3' multiplex detects 14 exons from the central deletion hotspot. The PCR products are accurately sized and quantified by a fluorescent DNA sequencer after only 18 cycles of amplification. The amount of product amplified from each exon in a multiplex is divided by that from each of the other exons, and this ratio is compared with those from control samples to obtain a series of dosage quotients (DQ), from which the copy number of each exon is determined. No overlap was observed between the DQ values obtained from single and double copy loci. The assays can be used to screen both affected males and at risk female relatives for a mutation. The method has been evaluated as a female carrier test by conducting a blind trial on 150 coded samples. Sixty-three deletion carriers, two duplication carriers, and 84 normal female controls were all correctly identified, showing that carrier diagnosis is possible even in families where the nature of the mutation is unknown. Additionally the analysis showed a non-pathogenic duplication involving the muscle specific promoter and exon 1. Together these two multiplex assays detect over 70% of all mutations in the dystrophin gene, greatly simplifying and partly automating molecular diagnosis in Duchenne and Becker muscular dystrophy.

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Resolving genomic disorder-associated breakpoints within segmental DNA duplications using massively parallel sequencing.

Project description:The most common recurrent copy-number variants associated with autism, developmental delay and epilepsy are flanked by segmental duplications. Complete genetic characterization of these events is challenging because their breakpoints often occur within high-identity, copy-number polymorphic paralogous sequences that cannot be specifically assayed using hybridization-based methods. Here we provide a protocol for breakpoint resolution with sequence-level precision. Massively parallel sequencing is performed on libraries generated from haplotype-resolved chromosomes, genomic DNA or molecular inversion probe (MIP)-captured breakpoint-informative regions harboring paralog-distinguishing variants. Quantification of sequencing depth over informative sites enables breakpoint localization, typically within several kilobases to tens of kilobases. Depending on the approach used, the sequencing platform, and the accuracy and completeness of the reference genome sequence, this protocol takes from a few days to several months to complete. Once established for a specific genomic disorder, it is possible to process thousands of DNA samples within as little as 3-4 weeks.

| S-EPMC4114152 | biostudies-literature

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Project description:Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE's effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.

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Project description:Precise identification of RNA-coding regions and transcriptomes of eukaryotes is a significant problem in biology. Currently, eukaryote transcriptomes are analyzed using deep short-read sequencing experiments of complementary DNAs. The resulting short-reads are then aligned against a genome and annotated junctions to infer biological meaning. Here we use long-read complementary DNA datasets for the analysis of a eukaryotic transcriptome and generate two large datasets in the human K562 and HeLa S3 cell lines. Both data sets comprised at least 4 million reads and had median read lengths greater than 500 bp. We show that annotation-independent alignments of these reads provide partial gene structures that are very much in-line with annotated gene structures, 15% of which have not been obtained in a previous de novo analysis of short reads. For long-noncoding RNAs (i.e., lncRNA) genes, however, we find an increased fraction of novel gene structures among our alignments. Other important aspects of transcriptome analysis, such as the description of cell type-specific splicing, can be performed in an accurate, reliable and completely annotation-free manner, making it ideal for the analysis of transcriptomes of newly sequenced genomes. Furthermore, we demonstrate that long read sequence can be assembled into full-length transcripts with considerable success. Our method is applicable to all long read sequencing technologies.

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| S-EPMC4568238 | biostudies-literature

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