Abyzov Lab

News archive

Publication from the lab in Nature Neuroscience
Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis
Idiopathic autism spectrum disorder (ASD) is highly heterogeneous, and it remains unclear how convergent biological processes in affected individuals may give rise to symptoms. Here, using cortical organoids and single-cell transcriptomics, we modeled alterations in the forebrain development between boys with idiopathic ASD and their unaffected fathers in 13 families. Transcriptomic changes suggest that ASD pathogenesis in macrocephalic and normocephalic probands involves an opposite disruption of the balance between excitatory neurons of the dorsal cortical plate and other lineages such as early-generated neurons from the putative preplate. The imbalance stemmed from divergent expression of transcription factors driving cell fate during early cortical development. While we did not find genomic variants in probands that explained the observed transcriptomic alterations, a significant overlap between altered transcripts and reported ASD risk genes affected by rare variants suggests a degree of gene convergence between rare forms of ASD and the developmental transcriptome in idiopathic ASD.
More: https://www.nature.com/articles/s41593-023-01399-0
posted August 19, 2023 by Alexej Abyzov
Publication from the lab in The CRISPR Journal
Clonally Selected Lines After CRISPR-Cas Editing Are Not Isogenic
The CRISPR-Cas9 system has enabled researchers to precisely modify/edit the sequence of a genome. A typical editing experiment consists of two steps: (1) editing cultured cells, (2) cell cloning and selection of clones with and without intended edit, presumed to be isogenic. The application of CRISPR-Cas9 system may result in off-target edits, whereas cloning will reveal culture-acquired mutations. We analyzed the extent of the former and the latter by whole genome sequencing in three experiments involving separate genomic loci and conducted by three independent laboratories. In all experiments we hardly found any off-target edits, whereas detecting hundreds to thousands of single nucleotide mutations unique to each clone after relatively short culture of 10-20 passages. Notably, clones also differed in copy number alterations (CNAs) that were several kb to several mb in size and represented the largest source of genomic divergence among clones. We suggest that screening of clones for mutations and CNAs acquired in culture is a necessary step to allow correct interpretation of DNA editing experiments. Furthermore, since culture associated mutations are inevitable, we propose that experiments involving derivation of clonal lines should compare a mix of multiple unedited lines and a mix of multiple edited lines.
More: https://www.liebertpub.com/doi/10.1089/crispr.2022.0050
posted June 14, 2023 by Alexej Abyzov
We are part of SMaHT network established by NIH Common Fund Program
NIH launches $140 million effort to investigate genetic variation in normal human cells and tissues. Common Fund Program will accelerate research on human development, aging, and disease.

More: https://www.nih.gov/news-events/news-releases/nih-launches-140-million-effort-investigate-genetic-variation-normal-human-cells-tissues
posted May 11, 2023 by Alexej Abyzov
Publication from the lab in Nucleic Acid Research
Efficient reconstruction of cell lineage trees for cell ancestry and cancer
Mosaic mutations can be used to track cell ancestries and reconstruct high-resolution lineage trees during cancer progression and during development, starting from the first cell divisions of the zygote. However, this approach requires sampling and analyzing the genomes of multiple cells, which can be redundant in lineage representation, limiting the scalability of the approach. We describe a strategy for cost- and time-efficient lineage reconstruction using clonal induced pluripotent stem cell lines from human skin fibroblasts. The approach leverages shallow sequencing coverage to assess the clonality of the lines, clusters redundant lines and sums their coverage to accurately discover mutations in the corresponding lineages. Only a fraction of lines needs to be sequenced to high coverage. We demonstrate the effectiveness of this approach for reconstructing lineage trees during development and in hematologic malignancies. We discuss and propose an optimal experimental design for reconstructing lineage trees.
More: https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkad254/7110756
posted April 11, 2023 by Alexej Abyzov
Publication from the lab in Science
Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability
We analyzed 131 human brains (44 neurotypical, 19 with Tourette syndrome, 9 with schizophrenia, and 59 with autism) for somatic mutations after whole genome sequencing to a depth of more than 200×. Typically, brains had 20 to 60 detectable single-nucleotide mutations, but ~6% of brains harbored hundreds of somatic mutations. Hypermutability was associated with age and damaging mutations in genes implicated in cancers and, in some brains, reflected in vivo clonal expansions. Somatic duplications, likely arising during development, were found in ~5% of normal and diseased brains, reflecting background mutagenesis. Brains with autism were associated with mutations creating putative transcription factor binding motifs in enhancer-like regions in the developing brain. The top-ranked affected motifs corresponded to MEIS (myeloid ectopic viral integration site) transcription factors, suggesting a potential link between their involvement in gene regulation and autism.
More: https://www.science.org/doi/10.1126/science.abm6222
posted August 1, 2022 by TJ and Suki
Publication from the lab in PLoS Compututional Biology
All2: A tool for selecting mosaic mutations from comprehensive multi-cell comparisons
Accurate discovery of somatic mutations in a cell is a challenge that partially lays in immaturity of dedicated analytical approaches. Approaches comparing a cell's genome to a control bulk sample miss common mutations, while approaches to find such mutations from bulk suffer from low sensitivity. We developed a tool, All2, which enables accurate filtering of mutations in a cell without the need for data from bulk(s). It is based on pair-wise comparisons of all cells to each other where every call for base pair substitution and indel is classified as either a germline variant, mosaic mutation, or false positive. As All2 allows for considering dropped-out regions, it is applicable to whole genome and exome analysis of cloned and amplified cells. By applying the approach to a variety of available data, we showed that its application reduces false positives, enables sensitive discovery of high frequency mutations, and is indispensable for conducting high resolution cell lineage tracing.
More: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009487
posterd April 23, 2022 by Alexej Abyzov
Publication from the lab in GigaScience
CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing
Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Herein, we present an extension of CNVnator developed in Python -- CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number neutral losses of heterozygosity. CNVpytor is significantly faster than CNVnator -- particularly for parsing alignment files (2-20 times faster) -- and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.
More: https://academic.oup.com/gigascience/article/10/11/giab074/6431715
posted November 29, 2021 by Alexej Abyzov
We are highlighted in Nature Reviews Genetics
Human cell-lineage imbalances
Various barcoding and labelling strategies have been developed for cell-lineage tracing ...
More: https://www.nature.com/articles/s41576-021-00358-4
posted April 1, 2021 by Alexej Abyzov
Publication from the lab in Genome Biology
Comprehensive identification of somatic nucleotide variants in human brain tissue
Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from 0.005 to 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.
More: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02285-3
posted March 29, 2021 by Alexej Abyzov
Publication from the lab in Science
Early developmental asymmetries in cell lineage trees in living individuals
Mosaic mutations can be used to track cell lineages in humans. We used cell cloning to analyze embryonic cell lineages in two living individuals and a postmortem human specimen. Of 10 reconstructed postzygotic divisions, none resulted in balanced contributions of daughter lineages to tissues. In both living individuals, one of two lineages from the first cleavage was dominant across tissues, with 90% frequency in blood. We propose that the efficiency of DNA repair contributes to lineage imbalance. Allocation of lineages in postmortem brain correlated with anterior-posterior axis, associating lineage history with cell fate choices in embryos. We establish a minimally invasive framework for defining cell lineages in any living individual, which paves the way for studying their relevance in health and disease.
More: https://science.sciencemag.org/content/371/6535/1245
posted March 18, 2021 by Alexej Abyzov
Publication from the lab is featured by Genome Research
Complex mosaic structural variations in human fetal brains
Somatic mosaicism, manifesting as single nucleotide variants (SNVs), mobile element insertions, and structural changes in the DNA, is a common phenomenon in human brain cells, with potential functional consequences. Using a clonal approach, we previously detected 200-400 mosaic SNVs per cell in three human fetal brains (15-21 wk postconception). However, structural variation in the human fetal brain has not yet been investigated. Here, we discover and validate four mosaic structural variants (SVs) in the same brains and resolve their precise breakpoints. The SVs were of kilobase scale and complex, consisting of deletion(s) and rearranged genomic fragments, which sometimes originated from different chromosomes. Sequences at the breakpoints of these rearrangements had microhomologies, suggesting their origin from replication errors. One SV was found in two clones, and we timed its origin to ~14 wk postconception. No large scale mosaic copy number variants (CNVs) were detectable in normal fetal human brains, suggesting that previously reported megabase-scale CNVs in neurons arise at later stages of development. By reanalysis of public single nuclei data from adult brain neurons, we detected an extrachromosomal circular DNA event. Our study reveals the existence of mosaic SVs in the developing human brain, likely arising from cell proliferation during mid-neurogenesis. Although relatively rare compared to SNVs and present in ~10% of neurons, SVs in developing human brain affect a comparable number of bases in the genome (~6200 vs. ~4000 bp), implying that they may have similar functional consequences.
More: https://genome.cshlp.org/content/30/12/1695
posted Dec 3, 2020 by Alexej Abyzov
Publication from the lab in BMC Bioinformatics
SCELLECTOR: ranking amplification bias in single cells using shallow sequencing
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.
More: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03858-y
posted Nov 13, 2020 by Alexej Abyzov
We are featured in neuroDEVELOPMENTS
Mosaics in mind
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 ...
More: https://mailchi.mp/libd/neurodevelopments-1361622
posted Oct 27, 2020 by Alexej Abyzov
Publication from the lab in Annual Review of Genomics and Human Genetics
Cell Lineage Tracing and Cellular Diversity in Humans
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.
More: https://doi.org/10.1146/annurev-genom-083118-015241
posted Aug 12, 2020 by Alexej Abyzov
Publication from the lab in Bioinformatics
LongAGE: defining breakpoints of genomic structural variants through optimal and memory efficient alignments of long reads
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.
More: https://doi.org/10.1093/bioinformatics/btaa703
posted Aug 12, 2020 by Alexej Abyzov
New lab member!!!

Postdoctoral fellow Yifan Wang, Ph.D. joined the lab on February 17, 2020. Welcome!

posted Feb 17, 2020 by Alexej Abyzov
New lab member!!!

Postdoctoral fellow Yeongjun Jang, Ph.D. joined the lab on September 2, 2019. Welcome!

posted Sep 2, 2019 by Alexej Abyzov
New lab member!!!

Postdoctoral fellow Arijit Panda, Ph.D. joined the lab on June 10, 2019. Welcome!

posted June 10, 2019 by Alexej Abyzov
New lab member!!!

Postdoctoral fellow Shobana Sekar, Ph.D. joined the lab on October 8, 2018. Welcome!

posted Oct 16, 2018 by Alexej Abyzov
New lab member!!!

Postdoctoral fellow Milovan Suvakov, Ph.D. joined the lab on September 4, 2018. Welcome!

posted Oct 7, 2018 by Alexej Abyzov
Publication from the lab in Nucleic Acids Research
Chromatin organization modulates the origin of heritable structural variations in human genome
Structural variations (SVs) in the human genome originate from different mechanisms related to DNA repair, replication errors, and retrotransposition. Our analyses of 26 927 SVs from the 1000 Genomes Project revealed differential distributions and consequences of SVs of different origin, e.g. deletions from non-allelic homologous recombination (NAHR) are more prone to disrupt chromatin organization while processed pseudogenes can create accessible chromatin. Spontaneous double stranded breaks (DSBs) are the best predictor of enrichment of NAHR deletions in open chromatin. This evidence, along with strong physical interaction of NAHR breakpoints belonging to the same deletion suggests that majority of NAHR deletions are non-meiotic i.e. originate from errors during homology directed repair (HDR) of spontaneous DSBs. In turn, the origin of the spontaneous DSBs is associated with transcription factor binding in accessible chromatin revealing the vulnerability of functional, open chromatin. The chromatin itself is enriched with repeats, particularly fixed Alu elements that provide the homology required to maintain stability via HDR. Through co-localization of fixed Alus and NAHR deletions in open chromatin we hypothesize that old Alu expansion had a stabilizing role on the human genome.
More: https://doi.org/10.1093/nar/gkz103
posted Feb 18, 2019 by Alexej Abyzov
Publication from the lab in Science
Transcriptome and epigenome landscape of human cortical development modeled in organoids
Genes implicated in neuropsychiatric disorders are active in human fetal brain, yet difficult to study in a longitudinal fashion. We demonstrate that organoids from human pluripotent cells model cerebral cortical development on the molecular level before 16 weeks postconception. A multiomics analysis revealed differentially active genes and enhancers, with the greatest changes occurring at the transition from stem cells to progenitors. Networks of converging gene and enhancer modules were as sembled into six and four global patterns of expression and activity across time. A pattern with progressive down-regulation was enriched with human-gained enhancers, suggesting their importance in early human brain development. A few convergent gene and enhancer modules were enriched in autism-associated genes and genomic variants in autistic children. The organoid model helps identify functional elements that may drive disease onset.
More: http://doi.org/10.1126/science.aat6720
posted Dec 13, 2018 by Alexej Abyzov
Publication from the lab in Science
Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis
Somatic mosaicism in the human brain may alter function of individual neurons. We analyzed genomes of single cells from the forebrains of three human fetuses (15 to 21 weeks post-conception) using clonal cell populations. We detected 200-400 single nucleotide variations (SNVs) per cell. SNV patterns resembled those found in cancer cell genomes, indicating a role of background mutagenesis in cancer. SNVs with a frequency of >2% in brain were shared with the spleen, revealing a pregastrulation origin. We reconstructed cell lineages for the first five post-zygotic cleavages and calculated a mutation rate of ~1.3 per division per cell. Later in development, during neurogenesis, the mutation spectrum shifted toward oxidative damage and the mutation rate increased. Both neurogenesis and early embryogenesis exhibit drastically more mutagenesis than adulthood.
More: http://doi.org/10.1126/science.aan8690
posted Dec 7, 2017 by Alexej Abyzov
Publication from the lab in Science
Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network
Neuropsychiatric disorders have a complex genetic architecture. Human genetic population-based studies have identified numerous heritable sequence and structural genomic variants associated with susceptibility to neuropsychiatric disease. However, these germline variants do not fully account for disease risk. During brain development, progenitor cells undergo billions of cell divisions to generate the ~80 billion neurons in the brain. The failure to accurately repair DNA damage arising during replication, transcription, and cellular metabolism amid this dramatic cellular expansion can lead to somatic mutations. Somatic mutations that alter subsets of neuronal transcriptomes and proteomes can, in turn, affect cell proliferation and survival and lead to neurodevelopmental disorders. The long life span of individual neurons and the direct relationship between neural circuits and behavior suggest that somatic mutations in small populations of neurons can significantly affect individual neurodevelopment. The Brain Somatic Mosaicism Network has been founded to study somatic mosaicism both in neurotypical human brains and in the context of complex neuropsychiatric disorders.
More: http://science.sciencemag.org/content/356/6336/eaal1641.full
posted Apr 27, 2017 by Alexej Abyzov
Publication from the lab in Genome Research
One thousand somatic SNVs per skin fibroblast cell set baseline of mosaic mutational load with patterns that suggest proliferative origin
Few studies have been conducted to understand post-zygotic accumulation of mutations in cells of the healthy human body. We reprogrammed 32 skin fibroblast cells from families of donors into human induced pluripotent stem cell (hiPSC) lines. The clonal nature of hiPSC lines allows a high-resolution analysis of the genomes of the founder fibroblast cells without being confounded by the artifacts of single cell whole genome amplification. We estimate that on average a fibroblast cell in children has 1,035 mostly benign mosaic SNVs. On average, 235 SNVs could be directly confirmed in the original fibroblast population by ultra-deep sequencing, down to an allele frequency (AF) of 0.1%. More sensitive droplet digital PCR experiments confirmed more SNVs as mosaic with AF as low as 0.01%, suggesting that 1,035 mosaic SNVs per fibroblast cell is the true average. Similar analyses in adults revealed no significant increase in the number of SNVs per cell, suggesting that a major fraction of mosaic SNVs in fibroblasts arises during development. Mosaic SNVs were distributed uniformly across the genome and were enriched in a mutational signature previously observed in cancers and in de novo variants and which, we hypothesize, is a hallmark of normal cell proliferation. Finally, AF distribution of mosaic SNVs had distinct narrow peaks, which could be a characteristic of clonal cell selection, clonal expansion, or both. These findings reveal a large degree of somatic mosaicism in healthy human tissues, link de novo and cancer mutations to somatic mosaicism and couple somatic mosaicism with cell proliferation.
More: http://genome.cshlp.org/content/early/2017/02/24/gr.215517.116.abstract
posted Mar 7, 2017 by Alexej Abyzov
Publication from the lab in Genome Research
Elevated variant density around SVs breakpoints in germline lineage lends support to error prone replication hypothesis
Copy number variants (CNVs) are a class of structural variants that may involve complex genomic rearrangements (CGRs) and that are hypothesized to have additional mutations around their breakpoints. Understanding the mechanisms underlying CNV formation is fundamental for understanding the repair and mutation mechanisms in cells, thereby shedding light on evolution, genomic disorders, cancer, and complex human traits. In this study, we employ data from the 1000 Genomes Project, to analyze hundreds of loci harboring heterozygous germline deletions in the subjects NA12878 and NA19240. By utilizing synthetic long-read data (longer than 2 kbp) in combination with high coverage short-read data and, in parallel, by comparing with parental genomes, we interrogated the phasing of these deletions with the flanking tens of thousands of heterozygous SNPs and indels. We found, that the density of SNPs/indels flanking the breakpoints of deletions (in-phase variants) is approximately twice as high as the corresponding density for the variants on the haplotype without deletion (out-of-phase variants). This fold-change was even larger, for the subset of deletions with signatures of replication-based mechanism of formation. The allele frequency (AF) spectrum for deletions is enriched for rare events
More: http://genome.cshlp.org/content/early/2016/05/23/gr.205484.116.abstract
posted May 29, 2016 by Taejeong Bae
New lab member!!!

Postdoctoral fellow Tanmoy Roychowdhury, Ph.D. joined the lab on February 8, 2016. Welcome!

posted Feb 17, 2016 by Alexej Abyzov
Publication from the lab in Nature
An integrated map of structural variation in 2,504 human genomes
Our collaborative work within the framework of the 1000 Genomes Project on discovery and analysis of genome structural variants has been published in Nature.

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.
More: http://www.nature.com/nature/journal/v526/n7571/full/nature15394.html
posted Oct 1, 2015 by Alexej Abyzov

PAPERS

2023

86.	Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis. Jourdon A, Wu F, Mariani J, Capauto D, Norton S, Tomasini L, Amiri A, Suvakov M, Schreiner JD, Jang Y, Panda A, Nguyen CK, Cummings EM, Han G, Powell K, Szekely A, McPartland JC, Pelphrey K, Chawarska K, Ventola P, Abyzov A, Vaccarino FM Nat Neurosci 2023; [Epub ahead of print]
85.	Efficient reconstruction of cell lineage trees for cell ancestry and cancer. Jang Y, Fasching L, Bae T, Tomasini L, Schreiner J, Szekely A, Fernandez TV, Leckman JF, Vaccarino FM, Abyzov A Nucleic Acids Res 2023; 51(10):e57
84.	Control-independent mosaic single nucleotide variant detection with DeepMosaic. Yang X, Xu X, Breuss MW, Antaki D, Ball LL, Chung C, Shen J, Li C, George RD, Wang Y, Bae T, Cheng Y, Abyzov A, Wei L, Alexandrov LB, Sebat JL, Gleeson JG Nat Biotechnol 2023; 41(6):870-877
83.	Clonally Selected Lines After CRISPR-Cas Editing Are Not Isogenic. Panda A, Suvakov M, Mariani J, Drucker KL, Park Y, Jang Y, Kollmeyer TM, Sarkar G, Bae T, Kim JJ, Yoon WH, Jenkins RB, Vaccarino FM, Abyzov A CRISPR J 2023; 6(2):176-182
82.	Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development. Chung C, Yang X, Bae T, Vong KI, Mittal S, Donkels C, Westley Phillips H, Li Z, Marsh APL, Breuss MW, Ball LL, Garcia CAB, George RD, Gu J, Xu M, Barrows C, James KN, Stanley V, Nidhiry AS, Khoury S, Howe G, Riley E, Xu X, Copeland B, Wang Y, Kim SH, Kang HC, Schulze-Bonhage A, Haas CA, Urbach H, Prinz M, Limbrick DD Jr, Gurnett CA, Smyth MD, Sattar S, Nespeca M, Gonda DD, Imai K, Takahashi Y, Chen HH, Tsai JW, Conti V, Guerrini R, Devinsky O, Silva WA Jr, Machado HR, Mathern GW, Abyzov A, Baldassari S, Baulac S, Gleeson JG Nat Genet 2023; 55(2):209-220

2022

81.	Somatic genomic mosaicism in the brain during aging: Scratching the surface. Bae T, Wang Y, Vaccarino FM, Abyzov A Clin Transl Med 2022; 12(12):e1138
80.	Postmortem Human Dura Mater Cells Exhibit Phenotypic, Transcriptomic and Genetic Abnormalities that Impact their Use for Disease Modeling. Argouarch AR, Schultz N, Yang AC, Jang Y, Garcia K, Cosme CG, Corrales CI, Nana AL, Karydas AM, Spina S, Grinberg LT, Miller B, Wyss-Coray T, Abyzov A, Goodarzi H, Seeley WW, Kao AW Stem Cell Rev Rep 2022; 18(8):3050-3065
79.	A noncoding single-nucleotide polymorphism at 8q24 drives IDH1-mutant glioma formation. Yanchus C, Drucker KL, Kollmeyer TM, Tsai R, Winick-Ng W, Liang M, Malik A, Pawling J, De Lorenzo SB, Ali A, Decker PA, Kosel ML, Panda A, Al-Zahrani KN, Jiang L, Browning JWL, Lowden C, Geuenich M, Hernandez JJ, Gosio JT, Ahmed M, Loganathan SK, Berman J, Trcka D, Michealraj KA, Fortin J, Carson B, Hollingsworth EW, Jacinto S, Mazrooei P, Zhou L, Elia A, Lupien M, He HH, Murphy DJ, Wang L, Abyzov A, Dennis JW, Maass PG, Campbell K, Wilson MD, Lachance DH, Wrensch M, Wiencke J, Mak T, Pennacchio LA, Dickel DE, Visel A, Wrana J, Taylor MD, Zadeh G, Dirks P, Eckel-Passow JE, Attisano L, Pombo A, Ida CM, Kvon EZ, Jenkins RB, Schramek D Science 2022; 378(6615):68-78
78.	Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability. Bae T, Fasching L, Wang Y, Shin JH, Suvakov M, Jang Y, Norton S, Dias C, Mariani J, Jourdon A, Wu F, Panda A, Pattni R, Chahine Y, Yeh R, Roberts RC, Huttner A, Kleinman JE, Hyde TM, Straub RE, Walsh CA, Urban AE, Leckman JF, Weinberger DR, Vaccarino FM, Abyzov A Science 2022; 377(6605):511-517
77.	All2: A tool for selecting mosaic mutations from comprehensive multi-cell comparisons. Sarangi V, Jang Y, Suvakov M, Bae T, Fasching L, Sekar S, Tomasini L, Mariani J, Vaccarino FM, Abyzov A PLoS Comput Biol 2022; 18(4):e1009487
76.	OpBerg: Discovering Causal Sentences Using Optimal Alignments. Wood J, Matiasz N, Silva A, Hsu W, Abyzov A, Wang W, Wrembel R, Gamper J, Kotsis G, Tjoa A, Khalil I Cham: Springer International Publishing 2022;

2021

75.	CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing. Suvakov M, Panda A, Diesh C, Holmes I, Abyzov A Gigascience 2021; 10(11):giab074
74.	LongAGE: defining breakpoints of genomic structural variants through optimal and memory efficient alignments of long reads. Tran Q, Abyzov A Bioinformatics 2021; 37(7):1015-1017
73.	Comprehensive identification of somatic nucleotide variants in human brain tissue. Wang Y, Bae T, Thorpe J, Sherman MA, Jones AG, Cho S, Daily K, Dou Y, Ganz J, Galor A, Lobon I, Pattni R, Rosenbluh C, Tomasi S, Tomasini L, Yang X, Zhou B, Akbarian S, Ball LL, Bizzotto S, Emery SB, Doan R, Fasching L, Jang Y, Juan D, Lizano E, Luquette LJ, Moldovan JB, Narurkar R, Oetjens MT, Rodin RE, Sekar S, Shin JH, Soriano E, Straub RE, Zhou W, Chess A, Gleeson JG, Marquès-Bonet T, Park PJ, Peters MA, Pevsner J, Walsh CA, Weinberger DR, Vaccarino FM, Moran JV, Urban AE, Kidd JM, Mills RE, Abyzov A Genome Biol 2021; 22(1):92
72.	Early developmental asymmetries in cell lineage trees in living individuals. Fasching L, Jang Y, Tomasi S, Schreiner J, Tomasini L, Brady MV, Bae T, Sarangi V, Vasmatzis N, Wang Y, Szekely A, Fernandez TV, Leckman JF, Abyzov A, Vaccarino FM Science 2021; 371(6535):1245-1248
71.	Landmarks of human embryonic development inscribed in somatic mutations. Bizzotto S, Dou Y, Ganz J, Doan RN, Kwon M, Bohrson CL, Kim SN, Bae T, Abyzov A, Park PJ, Walsh CA Science 2021; 371(6535):1249-1253
70.	Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia. Zhu X, Zhou B, Pattni R, Gleason K, Tan C, Kalinowski A, Sloan S, Fiston-Lavier AS, Mariani J, Petrov D, Barres BA, Duncan L, Abyzov A, Vogel H, Moran JV, Vaccarino FM, Tamminga CA, Levinson DF, Urban AE Nat Neurosci 2021; 24(2):186-196
69.	PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids. Jourdon A, Scuderi S, Capauto D, Abyzov A, Vaccarino FM Neuropsychopharmacology 2021; 46(1):70-85

2020

68.	Complex mosaic structural variations in human fetal brains. Sekar S, Tomasini L, Proukakis C, Bae T, Manlove L, Jang Y, Scuderi S, Zhou B, Kalyva M, Amiri A, Mariani J, Sedlazeck FJ, Urban AE, Vaccarino FM, Abyzov A Genome Res 2020; 30(12):1695-1704
67.	The role of somatic mosaicism in brain disease. Jourdon A, Fasching L, Scuderi S, Abyzov A, Vaccarino FM Curr Opin Genet Dev 2020; 65:84-90
66.	Adult diffuse glioma GWAS by molecular subtype identifies variants in D2HGDH and FAM20C. Eckel-Passow JE, Drucker KL, Kollmeyer TM, Kosel ML, Decker PA, Molinaro AM, Rice T, Praska CE, Clark L, Caron A, Abyzov A, Batzler A, Song JS, Pekmezci M, Hansen HM, McCoy LS, Bracci PM, Wiemels J, Wiencke JK, Francis S, Burns TC, Giannini C, Lachance DH, Wrensch M, Jenkins RB Neuro Oncol 2020; 22(11):1602-1613
65.	SCELLECTOR: ranking amplification bias in single cells using shallow sequencing. Sarangi V, Jourdon A, Bae T, Panda A, Vaccarino F, Abyzov A BMC Bioinformatics 2020; 21(1):521
64.	Cell Lineage Tracing and Cellular Diversity in Humans. Abyzov A, Vaccarino FM Annu Rev Genomics Hum Genet 2020; 21:101-116
63.	Neurological safety of oxaliplatin in patients with uncommon variants in Charcot-Marie-tooth disease genes. Le-Rademacher JG, Lopez CL, Kanwar R, Major-Elechi B, Abyzov A, Banck MS, Therneau TM, Sloan JA, Loprinzi CL, Beutler AS J Neurol Sci 2020; 411:116687
62.	Combining copy number, methylation markers, and mutations as a panel for endometrial cancer detection via intravaginal tampon collection. Sangtani A, Wang C, Weaver A, Hoppman NL, Kerr SE, Abyzov A, Shridhar V, Staub J, Kocher JA, Voss JS, Podratz KC, Wentzensen N, Kisiel JB, Sherman ME, Bakkum-Gamez JN Gynecol Oncol 2020; 156(2):387-392

2019

61.	Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2. Zhou B, Ho SS, Greer SU, Spies N, Bell JM, Zhang X, Zhu X, Arthur JG, Byeon S, Pattni R, Saha I, Huang Y, Song G, Perrin D, Wong WH, Ji HP, Abyzov A, Urban AE Nucleic Acids Res 2019; 47(8):3846-3861
60.	Chromatin organization modulates the origin of heritable structural variations in human genome. Roychowdhury T, Abyzov A Nucleic Acids Res 2019; 47(6):2766-2777
59.	Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562. Zhou B, Ho SS, Greer SU, Zhu X, Bell JM, Arthur JG, Spies N, Zhang X, Byeon S, Pattni R, Ben-Efraim N, Haney MS, Haraksingh RR, Song G, Ji HP, Perrin D, Wong WH, Abyzov A, Urban AE Genome Res 2019; 29(3):472-484
58.	Molecular signatures of multiple myeloma progression through single cell RNA-Seq. Jang JS, Li Y, Mitra AK, Bi L, Abyzov A, van Wijnen AJ, Baughn LB, Van Ness B, Rajkumar V, Kumar S, Jen J Blood Cancer J 2019; 9(1):2

2018

57.	Revealing the brain's molecular architecture. Science 2018; 362(6420):1262-1263
56.	Transcriptome and epigenome landscape of human cortical development modeled in organoids. Amiri A, Coppola G, Scuderi S, Wu F, Roychowdhury T, Liu F, Pochareddy S, Shin Y, Safi A, Song L, Zhu Y, Sousa AMM, Gerstein M, Crawford GE, Sestan N, Abyzov A, Vaccarino FM Science 2018; 362(6420):eaat6720
55.	Molecular characterization of colorectal adenomas with and without malignancy reveals distinguishing genome, transcriptome and methylome alterations. Druliner BR, Wang P, Bae T, Baheti S, Slettedahl S, Mahoney D, Vasmatzis N, Xu H, Kim M, Bockol M, O'Brien D, Grill D, Warner N, Munoz-Gomez M, Kossick K, Johnson R, Mouchli M, Felmlee-Devine D, Washechek-Aletto J, Smyrk T, Oberg A, Wang J, Chia N, Abyzov A, Ahlquist D, Boardman LA Sci Rep 2018; 8(1):3161
54.	Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis. Bae T, Tomasini L, Mariani J, Zhou B, Roychowdhury T, Franjic D, Pletikos M, Pattni R, Chen BJ, Venturini E, Riley-Gillis B, Sestan N, Urban AE, Abyzov A, Vaccarino FM Science 2018; 359(6375):550-555
53.	Detection and Quantification of Mosaic Genomic DNA Variation in Primary Somatic Tissues Using ddPCR: Analysis of Mosaic Transposable-Element Insertions, Copy-Number Variants, and Single-Nucleotide Variants. Zhou B, Haney MS, Zhu X, Pattni R, Abyzov A, Urban AE Methods Mol Biol 2018; 1768:173-190

2017

52.	Inferring modes of evolution from colorectal cancer with residual polyp of origin. Kim M, Druliner BR, Vasmatzis N, Bae T, Chia N, Abyzov A, Boardman LA Oncotarget 2017; 9(6):6780-6792
51.	Patient-reported (EORTC QLQ-CIPN20) versus physician-reported (CTCAE) quantification of oxaliplatin- and paclitaxel/carboplatin-induced peripheral neuropathy in NCCTG/Alliance clinical trials. Le-Rademacher J, Kanwar R, Seisler D, Pachman DR, Qin R, Abyzov A, Ruddy KJ, Banck MS, Lavoie Smith EM, Dorsey SG, Aaronson NK, Sloan J, Loprinzi CL, Beutler AS Support Care Cancer 2017; 25(11):3537-3544
50.	Landscape and variation of novel retroduplications in 26 human populations. Zhang Y, Li S, Abyzov A, Gerstein MB PLoS Comput Biol 2017; 13(6):e1005567
49.	Human induced pluripotent stem cells for modelling neurodevelopmental disorders. Ardhanareeswaran K, Mariani J, Coppola G, Abyzov A, Vaccarino FM Nat Rev Neurol 2017; 13(5):265-278
48.	Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network. McConnell MJ, Moran JV, Abyzov A, Akbarian S, Bae T, Cortes-Ciriano I, Erwin JA, Fasching L, Flasch DA, Freed D, Ganz J, Jaffe AE, Kwan KY, Kwon M, Lodato MA, Mills RE, Paquola ACM, Rodin RE, Rosenbluh C, Sestan N, Sherman MA, Shin JH, Song S, Straub RE, Thorpe J, Weinberger DR, Urban AE, Zhou B, Gage FH, Lehner T, Senthil G, Walsh CA, Chess A, Courchesne E, Gleeson JG, Kidd JM, Park PJ, Pevsner J, Vaccarino FM Science 2017; 356(6336):eaal1641
47.	Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans. Haraksingh RR, Abyzov A, Urban AE BMC Genomics 2017; 18(1):321
46.	One thousand somatic SNVs per skin fibroblast cell set baseline of mosaic mutational load with patterns that suggest proliferative origin. Abyzov A, Tomasini L, Zhou B, Vasmatzis N, Coppola G, Amenduni M, Pattni R, Wilson M, Gerstein M, Weissman S, Urban AE, Vaccarino FM Genome Res 2017; 27(4):512-523
45.	Genomic Mosaicism in Neurons and Other Cell Types Abyzov A, Urban AE, Vaccarino FM Principles and Approaches for Discovery and Validation of Somatic Mosaicism in the Human Brain., Springer New York: Springer Nature; 2017; Chapter 1.; 3-24p

2016

44.	Colorectal Cancer with Residual Polyp of Origin: A Model of Malignant Transformation. Druliner BR, Rashtak S, Ruan X, Bae T, Vasmatzis N, O'Brien D, Johnson R, Felmlee-Devine D, Washechek-Aletto J, Basu N, Liu H, Smyrk T, Abyzov A, Boardman LA Transl Oncol 2016; 9(4):280-6
43.	Elevated variant density around SV breakpoints in germline lineage lends support to error-prone replication hypothesis. Dhokarh D, Abyzov A Genome Res 2016; 26(7):874-81
42.	Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Mitra AK, Mukherjee UK, Harding T, Jang JS, Stessman H, Li Y, Abyzov A, Jen J, Kumar S, Rajkumar V, Van Ness B Leukemia 2016; 30(5):1094-102
41.	A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals. Chen J, Rozowsky J, Galeev TR, Harmanci A, Kitchen R, Bedford J, Abyzov A, Kong Y, Regan L, Gerstein M Nat Commun 2016; 7:11101
40.	Testing of candidate single nucleotide variants associated with paclitaxel neuropathy in the trial NCCTG N08C1 (Alliance). Boora GK, Kanwar R, Kulkarni AA, Abyzov A, Sloan J, Ruddy KJ, Banck MS, Loprinzi CL, Beutler AS Cancer Med 2016; 5(4):631-9
39.	Understanding genome structural variations. Abyzov A, Li S, Gerstein MB Oncotarget 2016; 7(7):7370-1

2015

38.	The PsychENCODE project. Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, Crawford GE, Jaffe AE, Pinto D, Dracheva S, Geschwind DH, Mill J, Nairn AC, Abyzov A, Pochareddy S, Prabhakar S, Weissman S, Sullivan PF, State MW, Weng Z, Peters MA, White KP, Gerstein MB, Amiri A, Armoskus C, Ashley-Koch AE, Bae T, Beckel-Mitchener A, Berman BP, Coetzee GA, Coppola G, Francoeur N, Fromer M, Gao R, Grennan K, Herstein J, Kavanagh DH, Ivanov NA, Jiang Y, Kitchen RR, Kozlenkov A, Kundakovic M, Li M, Li Z, Liu S, Mangravite LM, Mattei E, Markenscoff-Papadimitriou E, Navarro FC, North N, Omberg L, Panchision D, Parikshak N, Poschmann J, Price AJ, Purcaro M, Reddy TE, Roussos P, Schreiner S, Scuderi S, Sebra R, Shibata M, Shieh AW, Skarica M, Sun W, Swarup V, Thomas A, Tsuji J, van Bakel H, Wang D, Wang Y, Wang K, Werling DM, Willsey AJ, Witt H, Won H, Wong CC, Wray GA, Wu EY, Xu X, Yao L, Senthil G, Lehner T, Sklar P, Sestan N Nat Neurosci 2015; 18(12):1707-12
37.	An integrated map of structural variation in 2,504 human genomes. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH, Konkel MK, Malhotra A, Stütz AM, Shi X, Casale FP, Chen J, Hormozdiari F, Dayama G, Chen K, Malig M, Chaisson MJP, Walter K, Meiers S, Kashin S, Garrison E, Auton A, Lam HYK, Mu XJ, Alkan C, Antaki D, Bae T, Cerveira E, Chines P, Chong Z, Clarke L, Dal E, Ding L, Emery S, Fan X, Gujral M, Kahveci F, Kidd JM, Kong Y, Lameijer EW, McCarthy S, Flicek P, Gibbs RA, Marth G, Mason CE, Menelaou A, Muzny DM, Nelson BJ, Noor A, Parrish NF, Pendleton M, Quitadamo A, Raeder B, Schadt EE, Romanovitch M, Schlattl A, Sebra R, Shabalin AA, Untergasser A, Walker JA, Wang M, Yu F, Zhang C, Zhang J, Zheng-Bradley X, Zhou W, Zichner T, Sebat J, Batzer MA, McCarroll SA, Mills RE, Gerstein MB, Bashir A, Stegle O, Devine SE, Lee C, Eichler EE, Korbel JO Nature 2015; 526(7571):75-81
36.	A global reference for human genetic variation. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR Nature 2015; 526(7571):68-74
35.	MetaSV: an accurate and integrative structural-variant caller for next generation sequencing. Mohiyuddin M, Mu JC, Li J, Bani Asadi N, Gerstein MB, Abyzov A, Wong WH, Lam HY Bioinformatics 2015; 31(16):2741-4
34.	FOXG1-Dependent Dysregulation of GABA/Glutamate Neuron Differentiation in Autism Spectrum Disorders. Mariani J, Coppola G, Zhang P, Abyzov A, Provini L, Tomasini L, Amenduni M, Szekely A, Palejev D, Wilson M, Gerstein M, Grigorenko EL, Chawarska K, Pelphrey KA, Howe JR, Vaccarino FM Cell 2015; 162(2):375-390
33.	Analysis of deletion breakpoints from 1,092 humans reveals details of mutation mechanisms. Abyzov A, Li S, Kim DR, Mohiyuddin M, Stütz AM, Parrish NF, Mu XJ, Clark W, Chen K, Hurles M, Korbel JO, Lam HY, Lee C, Gerstein MB Nat Commun 2015; 6:7256
32.	VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications. Mu JC, Mohiyuddin M, Li J, Bani Asadi N, Gerstein MB, Abyzov A, Wong WH, Lam HY Bioinformatics 2015; 31(9):1469-71

2013

31.	Analysis of variable retroduplications in human populations suggests coupling of retrotransposition to cell division. Abyzov A, Iskow R, Gokcumen O, Radke DW, Balasubramanian S, Pei B, Habegger L, Lee C, Gerstein M Genome Res 2013; 23(12):2042-52
30.	Integrative annotation of variants from 1092 humans: application to cancer genomics. Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, Sboner A, Lochovsky L, Chen J, Harmanci A, Das J, Abyzov A, Balasubramanian S, Beal K, Chakravarty D, Challis D, Chen Y, Clarke D, Clarke L, Cunningham F, Evani US, Flicek P, Fragoza R, Garrison E, Gibbs R, Gümüş ZH, Herrero J, Kitabayashi N, Kong Y, Lage K, Liluashvili V, Lipkin SM, MacArthur DG, Marth G, Muzny D, Pers TH, Ritchie GRS, Rosenfeld JA, Sisu C, Wei X, Wilson M, Xue Y, Yu F, Dermitzakis ET, Yu H, Rubin MA, Tyler-Smith C, Gerstein M Science 2013; 342(6154):1235587
29.	Child development and structural variation in the human genome. Zhang Y, Haraksingh R, Grubert F, Abyzov A, Gerstein M, Weissman S, Urban AE Child Dev 2013; 84(1):34-48

2012

28.	Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells. Abyzov A, Mariani J, Palejev D, Zhang Y, Haney MS, Tomasini L, Ferrandino AF, Rosenberg Belmaker LA, Szekely A, Wilson M, Kocabas A, Calixto NE, Grigorenko EL, Huttner A, Chawarska K, Weissman S, Urban AE, Gerstein M, Vaccarino FM Nature 2012; 492(7429):438-42
27.	An integrated map of genetic variation from 1,092 human genomes. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA Nature 2012; 491(7422):56-65
26.	Architecture of the human regulatory network derived from ENCODE data. Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, Min R, Alves P, Abyzov A, Addleman N, Bhardwaj N, Boyle AP, Cayting P, Charos A, Chen DZ, Cheng Y, Clarke D, Eastman C, Euskirchen G, Frietze S, Fu Y, Gertz J, Grubert F, Harmanci A, Jain P, Kasowski M, Lacroute P, Leng JJ, Lian J, Monahan H, O'Geen H, Ouyang Z, Partridge EC, Patacsil D, Pauli F, Raha D, Ramirez L, Reddy TE, Reed B, Shi M, Slifer T, Wang J, Wu L, Yang X, Yip KY, Zilberman-Schapira G, Batzoglou S, Sidow A, Farnham PJ, Myers RM, Weissman SM, Snyder M Nature 2012; 489(7414):91-100
25.	An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489(7414):57-74
24.	Regulatory element copy number differences shape primate expression profiles. Iskow RC, Gokcumen O, Abyzov A, Malukiewicz J, Zhu Q, Sukumar AT, Pai AA, Mills RE, Habegger L, Cusanovich DA, Rubel MA, Perry GH, Gerstein M, Stone AC, Gilad Y, Lee C Proc Natl Acad Sci U S A 2012; 109(31):12656-61

2011

23.	Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms. Haraksingh RR, Abyzov A, Gerstein M, Urban AE, Snyder M PLoS One 2011; 6(11):e27859
22.	Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions. Bhardwaj N, Abyzov A, Clarke D, Shou C, Gerstein MB Protein Sci 2011; 20(10):1745-54
21.	AlleleSeq: analysis of allele-specific expression and binding in a network framework. Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, Bhardwaj N, Rubin M, Snyder M, Gerstein M Mol Syst Biol 2011; 7:522
20.	Identification of genomic indels and structural variations using split reads. Zhang ZD, Du J, Lam H, Abyzov A, Urban AE, Snyder M, Gerstein M BMC Genomics 2011; 12:375
19.	CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Abyzov A, Urban AE, Snyder M, Gerstein M Genome Res 2011; 21(6):974-84
18.	Annual Research Review: The promise of stem cell research for neuropsychiatric disorders. Vaccarino FM, Urban AE, Stevens HE, Szekely A, Abyzov A, Grigorenko EL, Gerstein M, Weissman S J Child Psychol Psychiatry 2011; 52(4):504-16
17.	AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision. Abyzov A, Gerstein M Bioinformatics 2011; 27(5):595-603
16.	Mapping copy number variation by population-scale genome sequencing. Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, Abyzov A, Yoon SC, Ye K, Cheetham RK, Chinwalla A, Conrad DF, Fu Y, Grubert F, Hajirasouliha I, Hormozdiari F, Iakoucheva LM, Iqbal Z, Kang S, Kidd JM, Konkel MK, Korn J, Khurana E, Kural D, Lam HY, Leng J, Li R, Li Y, Lin CY, Luo R, Mu XJ, Nemesh J, Peckham HE, Rausch T, Scally A, Shi X, Stromberg MP, Stütz AM, Urban AE, Walker JA, Wu J, Zhang Y, Zhang ZD, Batzer MA, Ding L, Marth GT, McVean G, Sebat J, Snyder M, Wang J, Ye K, Eichler EE, Gerstein MB, Hurles ME, Lee C, McCarroll SA, Korbel JO Nature 2011; 470(7332):59-65

2010

15.	A map of human genome variation from population-scale sequencing. Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA Nature 2010; 467(7319):1061-73
14.	Analysis of combinatorial regulation: scaling of partnerships between regulators with the number of governed targets. Bhardwaj N, Carson MB, Abyzov A, Yan KK, Lu H, Gerstein MB PLoS Comput Biol 2010; 6(5):e1000755
13.	RigidFinder: a fast and sensitive method to detect rigid blocks in large macromolecular complexes. Abyzov A, Bjornson R, Felipe M, Gerstein M Proteins 2010; 78(2):309-24

2009

12.	PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Korbel JO, Abyzov A, Mu XJ, Carriero N, Cayting P, Zhang Z, Snyder M, Gerstein MB Genome Biol 2009; 10(2):R23
11.	MSB: a mean-shift-based approach for the analysis of structural variation in the genome. Wang LY, Abyzov A, Korbel JO, Snyder M, Gerstein M Genome Res 2009; 19(1):106-17

2008

10.	An AP endonuclease 1-DNA polymerase beta complex: theoretical prediction of interacting surfaces. Abyzov A, Uzun A, Strauss PR, Ilyin VA PLoS Comput Biol 2008; 4(4):e1000066

2007

9.	UmuD and RecA directly modulate the mutagenic potential of the Y family DNA polymerase DinB. Godoy VG, Jarosz DF, Simon SM, Abyzov A, Ilyin V, Walker GC Mol Cell 2007; 28(6):1058-70
8.	A comprehensive analysis of non-sequential alignments between all protein structures. Abyzov A, Ilyin VA BMC Struct Biol 2007; 7:78
7.	Structure SNP (StSNP): a web server for mapping and modeling nsSNPs on protein structures with linkage to metabolic pathways. Uzun A, Leslin CM, Abyzov A, Ilyin V Nucleic Acids Res 2007; 35(Web Server issue):W384-92
6.	TOPOFIT-DB, a database of protein structural alignments based on the TOPOFIT method. Leslin CM, Abyzov A, Ilyin VA Nucleic Acids Res 2007; 35(Database issue):D317-21

2005

5.	Friend, an integrated analytical front-end application for bioinformatics. Abyzov A, Errami M, Leslin CM, Ilyin VA Bioinformatics 2005; 21(18):3677-8
4.	Active site prediction for comparative model structures with thematics. Shehadi IA, Abyzov A, Uzun A, Wei Y, Murga LF, Ilyin V, Ondrechen MJ J Bioinform Comput Biol 2005; 3(1):127-43

2004

3.	Structural exon database, SEDB, mapping exon boundaries on multiple protein structures. Leslin CM, Abyzov A, Ilyin VA Bioinformatics 2004; 20(11):1801-3
2.	Structural alignment of proteins by a novel TOPOFIT method, as a superimposition of common volumes at a topomax point. Ilyin VA, Abyzov A, Leslin CM Protein Sci 2004; 13(7):1865-74

2002

1.	Efficiency Profile Method To Study The Hit Efficiency Of Drift Chambers. Abyzov A, Bel'kov A, Lanyov A, Spiridonov A, Walter M, Hulsbergen W Particles and Nuclei, Letters 2002; 5 (114); :40-52

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RESEARCH

Tracing lineages and studying human development

Single cell studies

Cancer genomics

CNV and CNA analysis

Variant function

Analysis of mosaic variations in human

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