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Structural genetic variants, from detection to phenotype
Submission status
Open
Submission deadline
Structural genetic variants include large (>50 base pairs) duplications, inversion, translocations or deletions. Recent advances in long-read sequencing and optical genome mapping, combined with more classic techniques, are making detection of structural variants more easily achievable, while identifying the consequences of such variants can still be challenging.
This cross-journal Collection with Communications Biology, Communications Medicine, Nature Communications and Scientific Reports invites submissions focusing on structural variants (SVs) in organismal biology and disease. Relevant topics include insights into the molecular and phenotypic consequences of SVs, and developments and methods to detect, interpret and model SVs or to predict their impact. While we may consider Reviews, Perspectives, and Comments, primary research and methods development will be the main priority in this Collection.
The role of germline variation in cancer remains underexplored. Here, the authors investigate the landscape of germline structural variants on tumour DNA methylation across pediatric brain and central nervous system tumour patients and suggest disease implications.
Genetic studies of Chinese individuals have been performed, but mostly with short read sequencing, limiting the types of variants that can be identified. Here, the authors perform long read sequencing of 945 han Chinese individuals, finding structural variants under natural selection and those associated with human traits and evolutionary history.
Multi-omics profiling of matched normal colon, primary tumor, and liver metastasis from colorectal carcinoma patients based on Hi-C, ATAC-seq and RNA-seq technologies sheds light on the changes and regulatory mechanisms of 3D genomics during the tumorigenesis and metastasis of colorectal carcinoma.
A methods application study demonstrates that the Splice-Break2 pipeline can detect large mitochondrial DNA deletions in RNA-Seq data. Analyses includes GTEx and GEO datasets and demonstrate differences across tissues, brain regions, and age.