Biology subjects

Abyzov, A.

Publications and source records attributed to Abyzov, A..

23 records · Page 2Linked to original sources

bioRxiv2020

Abdominal single-step quantitative susceptibility mapping with spherical mean value filter and structure prior-based regularization

Abdominal quantitative susceptibility mapping (QSM), especially in small animals, is challenging because of respiratory motion and blood flow that, in addition to noise, deteriorate the quality of the input data. Efficient artefact suppression in QSM reconstruction is crucial in these conditions. Single-step QSM algorithms combine background field removal and magnetic field-to-susceptibility inverse problem regularization in a single optimization equation. Here, we propose a single-step QSM algorithm that uses spherical mean value kernels of different radii for background field removal and structure prior (consistency with magnitude image) with L1 norm for regularization. The optimization problem is solved using the split-Bregman method on the graphic processor unit. The method was compared with previously reported singlestep methods: a method using discrete Laplacian instead of spherical mean value kernels, a method using total variational penalty instead of structure prior, and a method using L2 norm for structure prior. With the proposed method relative to the previous ones, a numerical susceptibility phantom was reconstructed more precisely. In living mice, susceptibility maps with more homogeneous liver, higher contrast between liver and blood vessels, and well-preserved structural details were obtained. In patients, susceptibility maps with more homogeneous subcutaneous fat and higher contrast between subcutaneous fat and liver were obtained. These results show the potential of the proposed single-step method for abdominal QSM in small animals and humans.

pathology↗

bioRxiv2019

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

Active retrotransposons in the human genome (L1, Alu and SVA elements) can create genomic mobile element insertions (MEIs) in both germline and somatic tissue1. Specific somatic MEIs have been detected at high levels in human cancers2, and at lower to medium levels in human brains3. Dysregulation of somatic retrotransposition in the human brain has been hypothesized to contribute to neuropsychiatric diseases4, 5. However, individual somatic MEIs are present in small proportions of cells at a given anatomical location, and thus standard whole-genome sequencing (WGS) presents a difficult signal-to-noise problem, while single-cell approaches suffer from limited scalability and experimental artifacts introduced by enzymatic whole-genome amplification6. Previous studies produced widely differing estimates for the somatic retrotransposition rates in human brain3, 6-8. Here, we present a highly precise machine learning method (RetroSom) to directly identify somatic L1 and Alu insertions in <1% cells from 200x deep WGS, which allows circumventing the restrictions of whole-genome amplification. Using RetroSom we confirmed a lower rate of retrotransposition for individual somatic L1 insertions in human neurons. We discovered that anatomical distribution of somatic L1 insertion is as widespread in glia as in neurons, and across both hemispheres of the brain, indicating retrotransposition occurs during early embryogenesis. We characterized two of the detected brain-specific L1 insertions in great detail in neurons and glia from a donor with schizophrenia. Both insertions are within introns of genes active in brain (CNNM2, FRMD4A) in regions with multiple genetic associations with neuropsychiatric disorders9-11. Gene expression was significantly reduced by both somatic insertions in a reporter assay. Our results provide novel insights into the potential for pathological effects of somatic retrotransposition in the human brain, now including the large glial fraction. RetroSom has broad applicability in all disease states where somatic retrotransposition is expected to play a role, such as autoimmune disorders and cancer.

bioinformatics↗

bioRxiv2018

CompStor Novos: a low cost yet fast assembly-based variant calling for personal genomes

Application of assembly methods for personal genome analysis from next generation sequencing data has been limited by the requirement for an expensive supercomputer hardware or long computation times when using ordinary resources. We describe CompStor Novos, achieving supercomputer-class performance in de novo assembly computation time on standard server hardware, based on a tiered-memory implementation. Run on commercial off-the-shelf servers, Novos assembly is more precise and 10-20 times faster than that of existing assembly algorithms. Furthermore, we integrated Novos into a variant calling pipeline and demonstrate that both compute times and precision of calling point variants and indels compare well with standard alignment-based pipelines. Additionally, assembly eliminates bias in the estimation of allele frequency for indels and naturally enables discovery of breakpoints for structural variants with base pair resolution. Thus, Novos bridges the gap between alignment-based and assembly-based genome analyses. Extension and adaption of its underlying algorithm will help quickly and fully harvest information in sequencing reads for personal genome reconstruction.

genomics↗

bioRxiv2018

Haplotype-resolved and integrated genome analysis of ENCODE cell line HepG2

The HepG2 cancer cell line is one of the most widely-used biomedical research and one of the main cell lines of ENCODE. Vast numbers of functional genomics and epigenomics datasets have been produced to characterize its biology. However, the correct interpretation such data requires an understanding of the cell lines genome sequence and genome structure. Using a variety of sequencing and analysis methods, we identified a wide spectrum of HepG2 genome characteristics: copy numbers of chromosomal segments, SNVs and Indels (corrected for aneuploidy), phased haplotypes extending to entire chromosome arms, loss of heterozygosity, retrotransposon insertions, structural variants (SVs) including complex and somatic genomic rearrangements. We also identified allele-specific expression and DNA methylation genome-wide and assembled an allele-specific CRISPR/Cas9 targeting map.\n\nSIGNIFICANCEHaplotype-resolved and comprehensive whole-genome analysis of a widely-used cell line for cancer research and ENCODE, HepG2, serves as an essential resource for unlocking complex cancer gene regulation using a genome-integrated framework and also provides genomic context for the analysis of ~1,000 functional datasets to date on ENCODE for biological discovery. We also demonstrate how deeper insights into genomic regulatory complexity are gained by adopting a genome-integrated framework.

genomics↗

bioRxiv2017

Comprehensive, Integrated, and Phased Whole-Genome Analysis of the Primary ENCODE Cell Line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and Indels (both corrected for CN in aneuploid regions), loss of heterozygosity, mega-base-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs) including small and large-scale complex SVs and non-reference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we re-analyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

genomics↗