Structural variant calling

Accumulating evidence suggests alternative splicing (AS) is a co-transcriptional splicing process not only controlled by RNA-binding splicing factors, but also mediated by epigenetic regulators, such as chromatin structure, nucleosome density, and histone modification. Aberrant AS plays an important role in regulating various diseases, including cancers.

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Alzheimer’s disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the specific effects that pathological mutations and coding variants associated with AD have on the cellular composition of the brain are often ignored.

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The last two human genome assemblies have extended the previous linear golden-path paradigm of the human genome to a graph-like model to better represent regions with a high degree of structural variability. The new model offers opportunities to improve the technical validity of variant calling in whole-genome sequencing (WGS).

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Here, we describe a novel approach for rapid discovery of a set of tumor-specific genomic structural variants (SVs), based on a combination of low coverage cancer genome sequencing using Oxford Nanopore with an SV calling and filtering pipeline. We applied the method to tumor samples of high-grade ovarian and prostate cancer patients and validated on average ten somatic SVs per patient with breakpoint-spanning PCR mini-amplicons.

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The vast quantities of short-read sequencing data being generated are often exchanged and stored as aligned reads. However, aligned data becomes outdated as new reference genomes and alignment methods become available.

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Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution—giving rise to the differences within populations and among species

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Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations.

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Genomic differences range from single nucleotide differences to complex structural variations. Current methods typically annotate sequence differences ranging from SNPs to large indels accurately but do not unravel the full complexity of structural rearrangements, including inversions, translocations, and duplications, where highly similar sequence changes in location, orientation, or copy number.

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Genome graphs allow very general representations of genetic variation; depending on the model and implementation, variation at different length-scales (single nucleotide polymorphisms (SNPs), structural variants) and on different sequence backgrounds can be incorporated with different levels of transparency.

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Long-read sequencing has been shown to have advantages in structural variation (SV) detection and methylation calling. Many studies focus either on SV, methylation, or phasing of SNV; however, only the combination of variants provides a comprehensive insight into the sample and thus enables novel findings in biology or medicine.

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Structural variations (SVs) have been reported to play an important role in genetic diversity and trait regulation. Many computer algorithms detecting SVs have recently been developed, but the use of multiple algorithms to detect high-confidence SVs has not been studied. The most suitable sequencing depth for detecting SVs in pear is also not known.

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The trading of individual animal genotype information often involves only the exchange of the called genotypes and not necessarily the additional information required to effectively call structural variants.

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The advent of long read technologies has completely changed the situation. In 2019, two independent cross technologies studies have published the most complete variant callsets with sequence resolved insertions in human individuals. Among the reported insertions, only 17 to 28% could be discovered with short-read based tools.

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Whole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection.

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The cryptographic algorithms Kuznyechik and Magma since 2015 are block cipher standardized in the Russian Federation, formally called GOST R 34.12-2015. Both use fixed functions as a priori selected and differ on the structure, the block length and the bit-level of the processed blocks. In the present paper, we provide a dynamic variant of Kuznyechik and Magma where some of their functions are randomly generated and dependent on pseudorandom sequences.

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The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Now, other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies.

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RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (A → I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family.

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Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data.

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Next-generation sequencing (NGS) is a high throughput sequencing technology, which has revolutionized both basic and clinical research of the human genetic disorders. This technology is also called massively parallel sequencing (MPS) due to its ability to generate a huge amount of output data in a cost- and time-effective manner. NGS is widely utilized for different sequencing applications such as targeted sequencing (a group of candidate genes), exome sequencing (all coding regions), and whole genome sequencing (the entire human genome).

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The human MUC4 gene encodes a large membrane-associated mucin, characterized by a mucin tandem repeat domain and a growth factor-like transmembrane domain. In addition to the originally published sequence (sv0-MUC4), several MUC4 cDNA sequences (called sv1-MUC4 to sv21MUC4, MUC4/X, MUC4/Y) from various tissues and cell lines have been recently described. They differ from sv0MUC4 by deletions and/or insertions located in the 3¢ region or, for two of them, by deletion of the central repetitive domain.

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