Doruk Beyter

Our study of structural variants (SVs) detected using long-read sequencing on 3,622 Icelanders is now published! Huge thanks to @bvhalldorsson @tungufoss @asmundurhreinn @hannespetur @hakon_jon and others! Shareable version: rdcu.be/ckjRD nature.com/articles/s4158…

Proud to announce that our study on actionable genotypes and their association with life span using WGS data from 57,933 Icelanders has been published in @NEJM. @GudnyAnna @RunFridriksd @hanleygubrick @patsule nej.md/3swUQCR 1/8

Our research lab is growing! We're recruiting enthusiastic and motivated students interested in pursuing a Computer Science PhD to develop new tools in computational metabolomics at UC Riverside.

Today we announce the first release of Severus: a new tool for somatic SV calling for long reads! It's designed for complex (and simple) rearrangements in cancer genomes, but also works well for germline. github.com/KolmogorovLab/…

A mind-blowing paper has come out today in @Nature In 2016, JC Venter Institute scientists trimmed a bacterial genome to its barest minimum required for life to synthesize what they called a "minimal genome" (science.org/doi/10.1126/sc…). Today, a group of scientists from Indiana University reports how that minimal genome evolved over 2000 generations in comparison to the non-minimal genome. The authors found that even when you reduce a bacterial genome to its absolute minimum where every nucleotide matters, the genome undergoes mutational events generation after generation as much as the non-minimal genome. One simply cannot stop the evolution. Just over 300 days of evolution (equivalent to 40,000 years in humans) the minimal cell has gained everything it lacked in fitness on day one in comparison to the non-minimal cell. When comparing the evolved traits between the minimal and non-minimal cells, the scientists found something striking. The evolutionary process increased the cell size of non-minimal cells but not that of the minimal cell. But that is not the striking part. The scientists were able to identify the key mutation that resulted in cell size evolution. And it turned out that the mutation that helped the non-minimal cells to grow bigger is the same that helped the minimal cells to stay smaller. Growing bigger had a survival advantage for non-minimal cells and not growing bigger had a survival advantage for minimal cells. So, the mutation had a context-dependent effect. This just demonstrates that the evolutionary effects on traits have no absolute direction. All that matter is what is beneficial for the organism's survival. The conclusion of the paper is metaphorically a quote from the Jurassic Park movie: “Listen, if there’s one thing the history of evolution has taught us is that life will not be contained. Life breaks free. It expands to new territories, and it crashes through barriers painfully, maybe even dangerously, but . . . life finds a way". (scienmag.com/artificial-cel…) nature.com/articles/s4158…

A Review provides an overview of computational methods recently developed for detecting and analyzing structural variants using long-read sequencing data. nature.com/articles/s4159…

Delighted to report that our paper on "Sequence variants affecting the genome-wide rate of germline microsatellite mutations" is now out in @NatureComms, nature.com/articles/s4146…. Thread below: 1/7

A remaining hurdle for long-read sequencing is scalability and cost. Here, we developed a single flowcell ONT protocol + push-button pipeline that generates state-of-the-art small and structural variant calls and scales to 1000s of genomes: biorxiv.org/content/10.110…

New exciting preprint from the gnomAD team describing a new mutational constraint map of human genome quantified using whole genome sequencing (WGS) data of ~76,000 individuals. @gnomad_project @konradjk @dgmacarthur @bmneale biorxiv.org/content/10.110…

Latest work by @TsungYuLu1 for VNTR analysis. Take home message is treating VNTRs as SV (gain and loss of sequence) leaves a lot on the table. biorxiv.org/content/10.110… /1

The sequences of 150,119 genomes in the UK biobank biorxiv.org/content/10.110… - Our analysis of the WGS of the UKB updated.

Improved structural variant discovery in hard-to-call regions using sample-specific string detection from accurate long reads biorxiv.org/cgi/content/sh… #biorxiv_bioinfo

Congratulations @ErikNBergstrom and Ludmil Alexandrov lab for leading this #ecDNA study! Like circular viral episomes, ecDNA can also be the target for APOBEC3 mutagenesis, driving greater mutational diversity and tumor heterogeneity. nature.com/articles/s4158…

Please RT: Happy to release Sniffles v2 with @smolkmo! We improved SV calling for accuracy+ speed. Solving population/family calling (~gVCF SV implementation)& calling mosaic SV over bulk LR data. Preprint soon @BCM_HGSC #Bioinformatics @nanopore @PacBio github.com/fritzsedlazeck…

Happy to present our paper on the discovery of a new intellectual disability syndrome gene, CPSF3, by using the deficit of carriers of homozygous missense variants in the Icelandic population nature.com/articles/s4146… @asmundurhreinn @bryojen @BjornssonL @patsule

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients cell.com/cell/fulltext/… 🤩

Dr. Sihan Wu (@SihanSean) and his lab focus on ecDNA and how it is maintained in cancer. “If we can understand these mechanisms, we can develop specific therapeutic strategies to eliminate ecDNA and target oncogenes that are currently undruggable. cri.utsw.edu/scientists/sih…

Influences of rare copy number variation on human complex traits biorxiv.org/cgi/content/sh… #biorxiv_genetic

⁦⁦@gydbjo⁩ published today a complete story. Variant/gene/function/disease. Proud to be part of it: Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology ⁦@NatureComms⁩ nature.com/articles/s4146…

Just released the first version of popVCF, a tool to improve lossless compression of multi sample VCF data. Reaches about up to 5.5x smaller files compared to standard bgzip. Feedback is welcome. Github URL: github.com/DecodeGenetics…