Mutations in DNA that accumulate during the lifespan of each individual result in mosaic bodies, in which each cell has unique variants in the genome. That phenomenon is called somatic mosaicism. Despite the prevalence of somatic mosaicism, studying it has been limited by the lack of means to detect such variants at the level of single cells. Recent advances in single-cell genomics, however, make such research possible. Our group develops computational methods for precisely detecting somatic mosaic variants by harnessing new experimental approaches, including clonal expansion and whole genome amplification. By applying those methods to human samples, we aim to answer questions about the origin, spread, and consequence of mosaic mutations, which involves determining mutation rates, differences in the number and pattern of mutations between tissues and ages, relevance of the mutation to diseases and aging. Additionally, we are developing scalable approaches for tracing cell lineages using mutations as lineage markers.
Single-cell sequencing is the ultimate way to study somatic mosaicism in healthy tissues and in cancer. However, due to the scarcity of DNA in a single cell, an amplification process is required. Such amplifications can be achieved via clonal expansion, in which a single cell is cultured to produce a colony, and via in vitro whole genome amplification (WGA), in which DNA is amplified by using polymerases. We are currently formulating strategies for the quality control of WGA and to distinguish signal from noise that may be introduced during cell culture or DNA amplification, as well as developing approaches to estimate the contributions of signal and noise when they cannot be distinguished unambiguously.
During the past decade, high-throughput next-generation technologies coupled with computational algorithms have enabled us to better understand the biology of cancer as well as the molecular underpinnings of its development and progression. Numerous functionally significant point mutations as well as structural alterations have been identified in several types and subtypes of cancers that illustrate the diverse landscape of the cancer genome. In our laboratory, we focus on the discovery and analysis of somatic point mutations and structural alterations, including deletions, duplications, and copy number changes, in colon cancer and glioma. We are especially interested in understanding the relationship between patterns of genetic alterations and modes of evolution of cancer, as well as molecular differences between cancer-free and cancer-adjacent polyps.
Copy number variation (CNV) in the genome is a complex phenomenon that remains incompletely understood. Frequent in cancers, somatic copy number alterations (CNA) have been related to cancer susceptibility, cancer progression and invasiveness, individual response to the treatment, and patients’ quality of life after treatment. The detection of CNVs and CNAs is important to address a wide spectrum of clinical and scientific questions. Research in our laboratory is focused on the discovery and analysis of CNVs and CNAs along with their relevance to diseases. We have developed and continually improved a method, CNVnator/CNVpytor, for CNV discovery and genotyping from a read-depth analysis of personal genome or cancer sequencing that currently ranks among the best, most widely used methods for CNV analysis.
Simultaneous advances in genomics (i.e., in variant discovery), epigenomics, and functional genomics (i.e., emergence of ChiP-seq, ATAC-seq, Hi-C, and RNA-seq techniques) provide opportunities to study both the origins and consequences of genomic variants. We are interested in understanding various epigenomic properties that predispose mutational processes generating single nucleotide variation (SNV) and structural variation (SV). Inversely, germline and somatic variants affect genome function. However, because many of those variants occur in non-coding regions of the genome, their effects remain poorly understood. In response, our laboratory is actively working to elucidate such effects with a particular focus on variants contributing to neuro-developmental disorders such as autism spectrum disorders and Tourette syndrome.
The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.
For this issue of neuroDEVELOPMENTS we focus on the startling reality of the mosaic nature of genomes in the human brain. Since the meeting of Craig Venter, Francis Collins, and Bill Clinton at the White House on June 6, 2000 to announce the first draft of the human genome, the idea that we all carry our own version of the human genetic code is commonplace. It is now clear that this is a simplified view of reality because every cell in our body does not have precisely the same genome ...
Tracing cell lineages is fundamental for understanding the rules governing development in multicellular organisms and delineating complex biological processes involving the differentiation of multiple cell types with distinct lineage hierarchies. In humans, experimental lineage tracing is unethical, and one has to rely on natural-mutation markers that are created within cells as they proliferate and age. Recent studies have demonstrated that it is now possible to trace lineages in normal, noncancerous cells with a variety of data types using natural variations in the nuclear and mitochondrial DNA as well as variations in DNA methylation status. It is also apparent that the scientific community is on the verge of being able to make a comprehensive and detailed cell lineage map of human embryonic and fetal development. In this review, we discuss the advantages and disadvantages of different approaches and markers for lineage tracing. We also describe the general conceptual design for how to derive a lineage map for humans.
Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is a challenging problem due to large gaps in alignment. Previously, Alignment with Gap Excision (AGE) enabled us to define breakpoints of SVs at single-nucleotide resolution, however, AGE requires a vast amount of memory when aligning a pair of long sequences. To address this, we developed a memory-efficient implementation - LongAGE - based on the classical Hirschberg algorithm. We demonstrate an application of LongAGE for resolving breakpoints of SVs embedded into segmental duplications on Pacific Biosciences (PacBio) reads that can be longer than 10Kbp. Furthermore, we observed different breakpoints for a deletion and a duplication in the same locus, providing direct evidence that such multi-allelic copy number variants (mCNVs) arise from two or more independent ancestral mutations.
application accepted year-round
Applicants are invited to apply for a post-doctoral (i.e., postdoc) position in Abyzov lab at Mayo Clinic. The choice of project will depend on the applicant's interests and skills, however, the research must be purely computational and focus on one of the following main fields of computational biology: population/personal human omics, cancer omics, single cell and somatic omics, and the analysis of next-generation sequencing data. Specific sub-areas of interest are discovery, annotation, and the functional annotation of human genomic variants, cancer genomics, cancer evolution, somatic mosaicism in normal human cells.
The ideal applicant will have a Ph.D. in computational biology or bioinformatics, experience in one of the aforementioned research areas, demonstrate a record of peer-reviewed publications, and possess motivation for independent research. He or she should have a very strong understanding of biology and be skilled in programming and using computers to solve problems (e.g., experience with C/C++, Java, Python/Perl, R/ROOT, etc.). Oral and written proficiency in English is also a big plus.
To apply, please email your CV, including a list of publications and details for three references, to abyzov dot alexej at mayo dot edu. Please include the phrase “PostDoc application” and your full name in the subject of the email.
application accepted year-round
Applications are invited for an internship at the Mayo Clinic. Anticipated projects will be related to the analysis of whole genome sequencing data, with the aims of studying germline and somatic variants (SNPs, CNVs, etc.). The analysis will involve applications of commonly used, and in-house developed, software tools, and making biological hypothesis from statistical data analysis. Intern applicants with strong programming skills will have opportunities to participate in developing new tools and improving our existing software.
To apply, please email your CV, including a list of publications to abyzov dot alexej at mayo dot edu. Please include the phrase “Internship application” and your full name in the subject of the email.
Graduate students (M.S or Ph.D.) wishing to conduct research in the Abyzov lab at Mayo Clinic are invited to contact Dr. Abyzov (abyzov dot alexej at mayo dot edu). The choice of the project will depend on the applicant's interests and skills. However, the research must be purely computational and focus on one of the following main fields of computational biology: population/personal human omics, cancer omics, single cell and somatic omics, and the analysis of next-generation sequencing data. Specific sub-areas of interest are discovery, annotation, and the functional annotation of human genomic variants such as SNPs, SNVs, indels, structural variations, retrotransposition, etc.
We are looking for candidates that possess motivation for independent research, have experience in computational biology or bioinformatics, and are familiar with one of the aforementioned research areas. They should have a very strong understanding of biology and be skilled in programming and using computers to solve problems (e.g., experience with C/C++ Java, Python/Perl, R/ROOT, etc.). Record of peer-reviewed publications and oral and written proficiency in English is also a big plus.
Please express your interest by emailing your CV, including a list of publications to abyzov dot alexej at mayo dot edu. Please include the phrase “PhD/MS interest” and your full name in the subject of the email.
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