Bio

Academic Appointments


Professional Education


  • PhD, Yale University, Molecular, Cellular and Developmental Biology (2007)

Research & Scholarship

Current Research and Scholarly Interests


I have over a decade’s worth of experience in developing and applying high-throughput and high-resolution genomics analysis tools and procedures, in particular in the context of studying genomic sequence variation in brain development and function.
I have been involved on numerous occasions in using a large-scale and high-throughput setup for genomics analyses as well as carrying out analyses over several levels of genomics and epigenomics information. This includes participation in the ENCODE and 1000 Genomes projects, for the latter as a member of both the analytical and structural variation groups.
I have experience with developing and applying state-of-the-art and emerging genomics and epigenomics technologies (array and next-generation-sequencing based) for the analysis of gene expression, genomic DNA sequence and structure, DNA methylation and chromatin modification, in human cells and human cell culture systems, including stem cell culture models. For example I was co-first author of the paper in Science (Korbel, Urban, Affourtit et al., 2007, PMID 17901297) on developing next-generation-sequencing based paired-end mapping of CNVs and SVs, an approach that is now a standard part of whole-human-genome sequencing projects such as the 1000 Genomes Project. Paired-end mapping is also a critical component of advanced RNA-Seq approaches, mapping of transposable elements and the study of long-range chromatin interactions using the HiC method.
Two main, and connected, directions of research in my laboratory are the investigation of the molecular effects of large genome variants during neuronal development using iPSC model systems and the study of the nature and effects of somatic genome variation in the brain using tissue culture models and primary tissue samples.

Teaching

2013-14 Courses


Graduate and Fellowship Programs


Publications

Journal Articles


  • Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals GENOME RESEARCH Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., Haudenschild, C. D., Beckman, K. B., Shi, J., Mei, R., Urban, A. E., Montgomery, S. B., Levinson, D. F., Koller, D. 2014; 24 (1): 14-24

    Abstract

    Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation-by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

    View details for DOI 10.1101/gr.155192.113

    View details for Web of Science ID 000329163500002

    View details for PubMedID 24092820

  • Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing. Molecular psychiatry Mostafavi, S., Battle, A., Zhu, X., Potash, J. B., Weissman, M. M., Shi, J., Beckman, K., Haudenschild, C., McCormick, C., Mei, R., Gameroff, M. J., Gindes, H., Adams, P., Goes, F. S., Mondimore, F. M., Mackinnon, D. F., Notes, L., Schweizer, B., Furman, D., Montgomery, S. B., Urban, A. E., Koller, D., Levinson, D. F. 2013

    Abstract

    A study of genome-wide gene expression in major depressive disorder (MDD) was undertaken in a large population-based sample to determine whether altered expression levels of genes and pathways could provide insights into biological mechanisms that are relevant to this disorder. Gene expression studies have the potential to detect changes that may be because of differences in common or rare genomic sequence variation, environmental factors or their interaction. We recruited a European ancestry sample of 463 individuals with recurrent MDD and 459 controls, obtained self-report and semi-structured interview data about psychiatric and medical history and other environmental variables, sequenced RNA from whole blood and genotyped a genome-wide panel of common single-nucleotide polymorphisms. We used analytical methods to identify MDD-related genes and pathways using all of these sources of information. In analyses of association between MDD and expression levels of 13 857 single autosomal genes, accounting for multiple technical, physiological and environmental covariates, a significant excess of low P-values was observed, but there was no significant single-gene association after genome-wide correction. Pathway-based analyses of expression data detected significant association of MDD with increased expression of genes in the interferon α/β signaling pathway. This finding could not be explained by potentially confounding diseases and medications (including antidepressants) or by computationally estimated proportions of white blood cell types. Although cause-effect relationships cannot be determined from these data, the results support the hypothesis that altered immune signaling has a role in the pathogenesis, manifestation, and/or the persistence and progression of MDD.Molecular Psychiatry advance online publication, 3 December 2013; doi:10.1038/mp.2013.161.

    View details for DOI 10.1038/mp.2013.161

    View details for PubMedID 24296977

  • Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge. PloS one Mostafavi, S., Battle, A., Zhu, X., Urban, A. E., Levinson, D., Montgomery, S. B., Koller, D. 2013; 8 (7)

    Abstract

    Transcriptomic assays that measure expression levels are widely used to study the manifestation of environmental or genetic variations in cellular processes. RNA-sequencing in particular has the potential to considerably improve such understanding because of its capacity to assay the entire transcriptome, including novel transcriptional events. However, as with earlier expression assays, analysis of RNA-sequencing data requires carefully accounting for factors that may introduce systematic, confounding variability in the expression measurements, resulting in spurious correlations. Here, we consider the problem of modeling and removing the effects of known and hidden confounding factors from RNA-sequencing data. We describe a unified residual framework that encapsulates existing approaches, and using this framework, present a novel method, HCP (Hidden Covariates with Prior). HCP uses a more informed assumption about the confounding factors, and performs as well or better than existing approaches while having a much lower computational cost. Our experiments demonstrate that accounting for known and hidden factors with appropriate models improves the quality of RNA-sequencing data in two very different tasks: detecting genetic variations that are associated with nearby expression variations (cis-eQTLs), and constructing accurate co-expression networks.

    View details for DOI 10.1371/journal.pone.0068141

    View details for PubMedID 23874524

  • Child Development and Structural Variation in the Human Genome CHILD DEVELOPMENT Zhang, Y., Haraksingh, R., Grubert, F., Abyzov, A., Gerstein, M., Weissman, S., Urban, A. E. 2013; 84 (1): 34-48

    Abstract

    Structural variation of the human genome sequence is the insertion, deletion, or rearrangement of stretches of DNA sequence sized from around 1,000 to millions of base pairs. Over the past few years, structural variation has been shown to be far more common in human genomes than previously thought. Very little is currently known about the effects of structural variation on normal child development, but such effects could be of considerable significance. This review provides an overview of the phenomenon of structural variation in the human genome sequence, describing the novel genomics technologies that are revolutionizing the way structural variation is studied and giving examples of genomic structural variations that affect child development.

    View details for DOI 10.1111/cdev.12051

    View details for Web of Science ID 000314112000003

    View details for PubMedID 23311762

  • Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells NATURE Abyzov, A., Mariani, J., Palejev, D., Zhang, Y., Haney, M. S., Tomasini, L., Ferrandino, A. F., Belmaker, L. A., Szekely, A., Wilson, M., Kocabas, A., Calixto, N. E., Grigorenko, E. L., Huttner, A., Chawarska, K., Weissman, S., Urban, A. E., Gerstein, M., Vaccarino, F. M. 2012; 492 (7429): 438-?

    Abstract

    Reprogramming somatic cells into induced pluripotent stem cells (iPSCs) has been suspected of causing de novo copy number variation. To explore this issue, here we perform a whole-genome and transcriptome analysis of 20 human iPSC lines derived from the primary skin fibroblasts of seven individuals using next-generation sequencing. We find that, on average, an iPSC line manifests two copy number variants (CNVs) not apparent in the fibroblasts from which the iPSC was derived. Using PCR and digital droplet PCR, we show that at least 50% of those CNVs are present as low-frequency somatic genomic variants in parental fibroblasts (that is, the fibroblasts from which each corresponding human iPSC line is derived), and are manifested in iPSC lines owing to their clonal origin. Hence, reprogramming does not necessarily lead to de novo CNVs in iPSCs, because most of the line-manifested CNVs reflect somatic mosaicism in the human skin. Moreover, our findings demonstrate that clonal expansion, and iPSC lines in particular, can be used as a discovery tool to reliably detect low-frequency CNVs in the tissue of origin. Overall, we estimate that approximately 30% of the fibroblast cells have somatic CNVs in their genomes, suggesting widespread somatic mosaicism in the human body. Our study paves the way to understanding the fundamental question of the extent to which cells of the human body normally acquire structural alterations in their DNA post-zygotically.

    View details for DOI 10.1038/nature11629

    View details for Web of Science ID 000312488200058

    View details for PubMedID 23160490

  • Copy Number Variation detection from 1000 Genomes project exon capture sequencing data BMC BIOINFORMATICS Wu, J., Grzeda, K. R., Stewart, C., Grubert, F., Urban, A. E., Snyder, M. P., Marth, G. T. 2012; 13

    Abstract

    DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.As members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%.This study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.

    View details for DOI 10.1186/1471-2105-13-305

    View details for Web of Science ID 000314688600001

    View details for PubMedID 23157288

  • Extensive genetic variation in somatic human tissues PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA O'Huallachain, M., Karczewski, K. J., Weissman, S. M., Urban, A. E., Snyder, M. P. 2012; 109 (44): 18018-18023

    Abstract

    Genetic variation between individuals has been extensively investigated, but differences between tissues within individuals are far less understood. It is commonly assumed that all healthy cells that arise from the same zygote possess the same genomic content, with a few known exceptions in the immune system and germ line. However, a growing body of evidence shows that genomic variation exists between differentiated tissues. We investigated the scope of somatic genomic variation between tissues within humans. Analysis of copy number variation by high-resolution array-comparative genomic hybridization in diverse tissues from six unrelated subjects reveals a significant number of intraindividual genomic changes between tissues. Many (79%) of these events affect genes. Our results have important consequences for understanding normal genetic and phenotypic variation within individuals, and they have significant implications for both the etiology of genetic diseases such as cancer and for immortalized cell lines that might be used in research and therapeutics.

    View details for DOI 10.1073/pnas.1213736109

    View details for Web of Science ID 000311149900070

    View details for PubMedID 23043118

  • A Role of Genomic Copy Number Variation in the Complex Behavioral Phenotype of Alcohol Dependence: A Commentary ALCOHOLISM-CLINICAL AND EXPERIMENTAL RESEARCH Urban, A. E. 2012; 36 (9): 1483-1486

    Abstract

    In their paper "Copy number variations in 6q14.1 and 5q13.2 are associated with alcohol dependence" Lin and colleagues report on the association between alcohol dependence and 2 duplication CNVs in the genome sequence, one containing 8 genes within its boundaries and another that contains no genes. In this commentary, I point out some of the opportunities and challenges that arise from such a finding.

    View details for DOI 10.1111/j.1530-0277.2012.01915.x

    View details for Web of Science ID 000308435200003

    View details for PubMedID 22909245

  • Mutations in DNMT1 cause autosomal dominant cerebellar ataxia, deafness and narcolepsy HUMAN MOLECULAR GENETICS Winkelmann, J., Lin, L., Schormair, B., Kornum, B. R., Faraco, J., Plazzi, G., Melberg, A., Cornelio, F., Urban, A. E., Pizza, F., Poli, F., Grubert, F., Wieland, T., Graf, E., Hallmayer, J., Strom, T. M., Mignot, E. 2012; 21 (10): 2205-2210

    Abstract

    Autosomal dominant cerebellar ataxia, deafness and narcolepsy (ADCA-DN) is characterized by late onset (30-40 years old) cerebellar ataxia, sensory neuronal deafness, narcolepsy-cataplexy and dementia. We performed exome sequencing in five individuals from three ADCA-DN kindreds and identified DNMT1 as the only gene with mutations found in all five affected individuals. Sanger sequencing confirmed the de novo mutation p.Ala570Val in one family, and showed co-segregation of p.Val606Phe and p.Ala570Val, with the ADCA-DN phenotype, in two other kindreds. An additional ADCA-DN kindred with a p.GLY605Ala mutation was subsequently identified. Narcolepsy and deafness were the first symptoms to appear in all pedigrees, followed by ataxia. DNMT1 is a widely expressed DNA methyltransferase maintaining methylation patterns in development, and mediating transcriptional repression by direct binding to HDAC2. It is also highly expressed in immune cells and required for the differentiation of CD4+ into T regulatory cells. Mutations in exon 20 of this gene were recently reported to cause hereditary sensory neuropathy with dementia and hearing loss (HSAN1). Our mutations are all located in exon 21 and in very close spatial proximity, suggesting distinct phenotypes depending on mutation location within this gene.

    View details for DOI 10.1093/hmg/dds035

    View details for Web of Science ID 000303333700006

    View details for PubMedID 22328086

  • Genome-Wide Mapping of Copy Number Variation in Humans: Comparative Analysis of High Resolution Array Platforms PLOS ONE Haraksingh, R. R., Abyzov, A., Gerstein, M., Urban, A. E., Snyder, M. 2011; 6 (11)

    Abstract

    Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.

    View details for DOI 10.1371/journal.pone.0027859

    View details for Web of Science ID 000298168100021

    View details for PubMedID 22140474

  • A Comprehensive Map of Mobile Element Insertion Polymorphisms in Humans PLOS GENETICS Stewart, C., Kural, D., Stroemberg, M. P., Walker, J. A., Konkel, M. K., Stuetz, A. M., Urban, A. E., Grubert, F., Lam, H. Y., Lee, W., Busby, M., Indap, A. R., Garrison, E., Huff, C., Xing, J., Snyder, M. P., Jorde, L. B., Batzer, M. A., Korbel, J. O., Marth, G. T. 2011; 7 (8)

    Abstract

    As a consequence of the accumulation of insertion events over evolutionary time, mobile elements now comprise nearly half of the human genome. The Alu, L1, and SVA mobile element families are still duplicating, generating variation between individual genomes. Mobile element insertions (MEI) have been identified as causes for genetic diseases, including hemophilia, neurofibromatosis, and various cancers. Here we present a comprehensive map of 7,380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods. This catalog enables us to systematically study mutation rates, population segregation, genomic distribution, and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals. Population allele frequencies of MEI and SNPs are described, broadly, by the same neutral ancestral processes despite vastly different mutation mechanisms and rates, except in coding regions where MEI are virtually absent, presumably due to strong negative selection. A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations.

    View details for DOI 10.1371/journal.pgen.1002236

    View details for Web of Science ID 000294297000031

    View details for PubMedID 21876680

  • Identification of genomic indels and structural variations using split reads BMC GENOMICS Zhang, Z. D., Du, J., Lam, H., Abyzov, A., Urban, A. E., Snyder, M., Gerstein, M. 2011; 12

    Abstract

    Recent studies have demonstrated the genetic significance of insertions, deletions, and other more complex structural variants (SVs) in the human population. With the development of the next-generation sequencing technologies, high-throughput surveys of SVs on the whole-genome level have become possible. Here we present split-read identification, calibrated (SRiC), a sequence-based method for SV detection.We start by mapping each read to the reference genome in standard fashion using gapped alignment. Then to identify SVs, we score each of the many initial mappings with an assessment strategy designed to take into account both sequencing and alignment errors (e.g. scoring more highly events gapped in the center of a read). All current SV calling methods have multilevel biases in their identifications due to both experimental and computational limitations (e.g. calling more deletions than insertions). A key aspect of our approach is that we calibrate all our calls against synthetic data sets generated from simulations of high-throughput sequencing (with realistic error models). This allows us to calculate sensitivity and the positive predictive value under different parameter-value scenarios and for different classes of events (e.g. long deletions vs. short insertions). We run our calculations on representative data from the 1000 Genomes Project. Coupling the observed numbers of events on chromosome 1 with the calibrations gleaned from the simulations (for different length events) allows us to construct a relatively unbiased estimate for the total number of SVs in the human genome across a wide range of length scales. We estimate in particular that an individual genome contains ~670,000 indels/SVs.Compared with the existing read-depth and read-pair approaches for SV identification, our method can pinpoint the exact breakpoints of SV events, reveal the actual sequence content of insertions, and cover the whole size spectrum for deletions. Moreover, with the advent of the third-generation sequencing technologies that produce longer reads, we expect our method to be even more useful.

    View details for DOI 10.1186/1471-2164-12-375

    View details for Web of Science ID 000294205500001

    View details for PubMedID 21787423

  • CNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing GENOME RESEARCH Abyzov, A., Urban, A. E., Snyder, M., Gerstein, M. 2011; 21 (6): 974-984

    Abstract

    Copy number variation (CNV) in the genome is a complex phenomenon, and not completely understood. We have developed a method, CNVnator, for CNV discovery and genotyping from read-depth (RD) analysis of personal genome sequencing. Our method is based on combining the established mean-shift approach with additional refinements (multiple-bandwidth partitioning and GC correction) to broaden the range of discovered CNVs. We calibrated CNVnator using the extensive validation performed by the 1000 Genomes Project. Because of this, we could use CNVnator for CNV discovery and genotyping in a population and characterization of atypical CNVs, such as de novo and multi-allelic events. Overall, for CNVs accessible by RD, CNVnator has high sensitivity (86%-96%), low false-discovery rate (3%-20%), high genotyping accuracy (93%-95%), and high resolution in breakpoint discovery (<200 bp in 90% of cases with high sequencing coverage). Furthermore, CNVnator is complementary in a straightforward way to split-read and read-pair approaches: It misses CNVs created by retrotransposable elements, but more than half of the validated CNVs that it identifies are not detected by split-read or read-pair. By genotyping CNVs in the CEPH, Yoruba, and Chinese-Japanese populations, we estimated that at least 11% of all CNV loci involve complex, multi-allelic events, a considerably higher estimate than reported earlier. Moreover, among these events, we observed cases with allele distribution strongly deviating from Hardy-Weinberg equilibrium, possibly implying selection on certain complex loci. Finally, by combining discovery and genotyping, we identified six potential de novo CNVs in two family trios.

    View details for DOI 10.1101/gr.114876.110

    View details for Web of Science ID 000291153400017

    View details for PubMedID 21324876

  • The promise of stem cell research for neuropsychiatric disorders JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY Vaccarino, F. M., Urban, A. E., Stevens, H. E., Szekely, A., Abyzov, A., Grigorenko, E. L., Gerstein, M., Weissman, S. 2011; 52 (4): 504-516

    Abstract

    The study of the developing brain has begun to shed light on the underpinnings of both early and adult onset neuropsychiatric disorders. Neuroimaging of the human brain across developmental time points and the use of model animal systems have combined to reveal brain systems and gene products that may play a role in autism spectrum disorders, attention deficit hyperactivity disorder, obsessive compulsive disorder and many other neurodevelopmental conditions. However, precisely how genes may function in human brain development and how they interact with each other leading to psychiatric disorders is unknown. Because of an increasing understanding of neural stem cells and how the nervous system subsequently develops from these cells, we have now the ability to study disorders of the nervous system in a new way - by rewinding and reviewing the development of human neural cells. Induced pluripotent stem cells (iPSCs), developed from mature somatic cells, have allowed the development of specific cells in patients to be observed in real time. Moreover, they have allowed some neuronal-specific abnormalities to be corrected with pharmacological intervention in tissue culture. These exciting advances based on the use of iPSCs hold great promise for understanding, diagnosing and, possibly, treating psychiatric disorders. Specifically, examination of iPSCs from typically developing individuals will reveal how basic cellular processes and genetic differences contribute to individually unique nervous systems. Moreover, by comparing iPSCs from typically developing individuals and patients, differences at stem cell stages, through neural differentiation, and into the development of functional neurons may be identified that will reveal opportunities for intervention. The application of such techniques to early onset neuropsychiatric disorders is still on the horizon but has become a reality of current research efforts as a consequence of the revelations of many years of basic developmental neurobiological science.

    View details for DOI 10.1111/j.1469-7610.2010.02348.x

    View details for Web of Science ID 000288461400010

    View details for PubMedID 21204834

  • Mapping copy number variation by population-scale genome sequencing NATURE Mills, R. E., Walter, K., Stewart, C., Handsaker, R. E., Chen, K., Alkan, C., Abyzov, A., Yoon, S. C., Ye, K., Cheetham, R. K., Chinwalla, A., Conrad, D. F., Fu, Y., Grubert, F., Hajirasouliha, I., Hormozdiari, F., Iakoucheva, L. M., Iqbal, Z., Kang, S., Kidd, J. M., Konkel, M. K., Korn, J., Khurana, E., Kural, D., Lam, H. Y., Leng, J., Li, R., Li, Y., Lin, C., Luo, R., Mu, X. J., Nemesh, J., Peckham, H. E., Rausch, T., Scally, A., Shi, X., Stromberg, M. P., Stuetz, A. M., Urban, A. E., Walker, J. A., Wu, J., Zhang, Y., Zhang, Z. D., Batzer, M. A., Ding, L., Marth, G. T., McVean, G., Sebat, J., Snyder, M., Wang, J., Ye, K., Eichler, E. E., Gerstein, M. B., Hurles, M. E., Lee, C., McCarroll, S. A., Korbel, J. O. 2011; 470 (7332): 59-65

    Abstract

    Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

    View details for DOI 10.1038/nature09708

    View details for Web of Science ID 000286886400033

    View details for PubMedID 21293372

  • A map of human genome variation from population-scale sequencing NATURE Altshuler, D., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., Clark, A. G., Collins, F. S., De La Vega, F. M., Donnelly, P., Egholm, M., Flicek, P., Gabriel, S. B., Gibbs, R. A., Knoppers, B. M., Lander, E. S., Lehrach, H., Mardis, E. R., McVean, G. A., Nickerson, D., Peltonen, L., Schafer, A. J., Sherry, S. T., Wang, J., Wilson, R. K., Gibbs, R. A., Deiros, D., Metzker, M., Muzny, D., Reid, J., Wheeler, D., Wang, J., Li, J., Jian, M., Li, G., Li, R., Liang, H., Tian, G., Wang, B., Wang, J., Wang, W., Yang, H., Zhang, X., Zheng, H., Lander, E. S., Altshuler, D. L., Ambrogio, L., Bloom, T., Cibulskis, K., Fennell, T. J., Gabriel, S. B., Jaffe, D. B., Shefler, E., Sougnez, C. L., Bentley, D. R., Gormley, N., Humphray, S., Kingsbury, Z., Koko-Gonzales, P., Stone, J., McKernan, K. J., Costa, G. L., Ichikawa, J. K., Lee, C. C., Sudbrak, R., Lehrach, H., Borodina, T. A., Dahl, A., Davydov, A. N., Marquardt, P., Mertes, F., Nietfeld, W., Rosenstiel, P., Schreiber, S., Soldatov, A. V., Timmermann, B., Tolzmann, M., Egholm, M., Affourtit, J., Ashworth, D., Attiya, S., Bachorski, M., Buglione, E., Burke, A., Caprio, A., Celone, C., Clark, S., Conners, D., Desany, B., Gu, L., Guccione, L., Kao, K., Kebbel, A., Knowlton, J., Labrecque, M., McDade, L., Mealmaker, C., Minderman, M., Nawrocki, A., Niazi, F., Pareja, K., Ramenani, R., Riches, D., Song, W., Turcotte, C., Wang, S., Mardis, E. R., Dooling, D., Fulton, L., Fulton, R., Weinstock, G., Durbin, R. M., Burton, J., Carter, D. M., Churcher, C., Coffey, A., Cox, A., Palotie, A., Quail, M., Skelly, T., Stalker, J., Swerdlow, H. P., Turner, D., De Witte, A., Giles, S., Gibbs, R. A., Wheeler, D., Bainbridge, M., Challis, D., Sabo, A., Yu, F., Yu, J., Wang, J., Fang, X., Guo, X., Li, R., Li, Y., Luo, R., Tai, S., Wu, H., Zheng, H., Zheng, X., Zhou, Y., Yang, H., Marth, G. T., Garrison, E. P., Huang, W., Indap, A., Kural, D., Lee, W., Leong, W. F., Huang, W., Indap, A., Kural, D., Lee, W., Leong, W. F., Quinlan, A. R., Stewart, C., Stromberg, M. P., Ward, A. N., Wu, J., Lee, C., Mills, R. E., Shi, X., Daly, M. J., DePristo, M. A., Altshuler, D. L., Ball, A. D., Banks, E., Bloom, T., Browning, B. L., Cibulskis, K., Fennell, T. J., Garimella, K. V., Grossman, S. R., Handsaker, R. E., Hanna, M., Hartl, C., Jaffe, D. B., Kernytsky, A. M., Korn, J. M., Li, H., Maguire, J. R., McCarroll, S. A., McKenna, A., Nemesh, J. C., Philippakis, A. A., Poplin, R. E., Price, A., Rivas, M. A., Sabeti, P. C., Schaffner, S. F., Shefler, E., Shlyakhter, I. A., Cooper, D. N., Ball, E. V., Mort, M., Phillips, A. D., Stenson, P. D., Sebat, J., Makarov, V., Ye, K., Yoon, S. C., Bustamante, C. D., Clark, A. G., Boyko, A., Degenhardt, J., Gravel, S., Gutenkunst, R. N., Kaganovich, M., Keinan, A., Lacroute, P., Ma, X., Reynolds, A., Clarke, L., Flicek, P., Cunningham, F., Herrero, J., Keenen, S., Kulesha, E., Leinonen, R., McLaren, W., Radhakrishnan, R., Smith, R. E., Zalunin, V., Zheng-Bradley, X., Korbel, J. O., Stuetz, A. M., Humphray, S., Bauer, M., Cheetham, R. K., Cox, T., Eberle, M., James, T., Kahn, S., Murray, L., Ye, K., De La Vega, F. M., Fu, Y., Hyland, F. C., Manning, J. M., McLaughlin, S. F., Peckham, H. E., Sakarya, O., Sun, Y. A., Tsung, E. F., Batzer, M. A., Konkel, M. K., Walker, J. A., Sudbrak, R., Albrecht, M. W., Amstislavskiy, V. S., Herwig, R., Parkhomchuk, D. V., Sherry, S. T., Agarwala, R., Khouri, H., Morgulis, A. O., Paschall, J. E., Phan, L. D., Rotmistrovsky, K. E., Sanders, R. D., Shumway, M. F., Xiao, C., McVean, G. A., Auton, A., Iqbal, Z., Lunter, G., Marchini, J. L., Moutsianas, L., Myers, S., Tumian, A., Desany, B., Knight, J., Winer, R., Craig, D. W., Beckstrom-Sternberg, S. M., Christoforides, A., Kurdoglu, A. A., Pearson, J., Sinari, S. A., Tembe, W. D., Haussler, D., Hinrichs, A. S., Katzman, S. J., Kern, A., Kuhn, R. M., Przeworski, M., Hernandez, R. D., Howie, B., Kelley, J. L., Melton, S. C., Abecasis, G. R., Li, Y., Anderson, P., Blackwell, T., Chen, W., Cookson, W. O., Ding, J., Kang, H. M., Lathrop, M., Liang, L., Moffatt, M. F., Scheet, P., Sidore, C., Snyder, M., Zhan, X., Zoellner, S., Awadalla, P., Casals, F., Idaghdour, Y., Keebler, J., Stone, E. A., Zilversmit, M., Jorde, L., Xing, J., Eichler, E. E., Aksay, G., Alkan, C., Hajirasouliha, I., Hormozdiari, F., Kidd, J. M., Sahinalp, S. C., Sudmant, P. H., Mardis, E. R., Chen, K., Chinwalla, A., Ding, L., Koboldt, D. C., McLellan, M. D., Dooling, D., Weinstock, G., Wallis, J. W., Wendl, M. C., Zhang, Q., Durbin, R. M., Albers, C. A., Ayub, Q., Balasubramaniam, S., Barrett, J. C., Carter, D. M., Chen, Y., Conrad, D. F., Danecek, P., Dermitzakis, E. T., Hu, M., Huang, N., Hurles, M. E., Jin, H., Jostins, L., Keane, T. M., Keane, T. M., Le, S. Q., Lindsay, S., Long, Q., MacArthur, D. G., Montgomery, S. B., Parts, L., Stalker, J., Tyler-Smith, C., Walter, K., Zhang, Y., Gerstein, M. B., Snyder, M., Abyzov, A., Abyzov, A., Balasubramanian, S., Bjornson, R., Du, J., Grubert, F., Habegger, L., Haraksingh, R., Jee, J., Khurana, E., Lam, H. Y., Leng, J., Mu, X. J., Urban, A. E., Zhang, Z., Li, Y., Luo, R., Marth, G. T., Garrison, E. P., Kural, D., Quinlan, A. R., Stewart, C., Stromberg, M. P., Ward, A. N., Wu, J., Lee, C., Mills, R. E., Shi, X., McCarroll, S. A., Banks, E., DePristo, M. A., Handsaker, R. E., Hartl, C., Korn, J. M., Li, H., Nemesh, J. C., Sebat, J., Makarov, V., Ye, K., Yoon, S. C., Degenhardt, J., Kaganovich, M., Clarke, L., Smith, R. E., Zheng-Bradley, X., Korbel, J. O., Humphray, S., Cheetham, R. K., Eberle, M., Kahn, S., Murray, L., Ye, K., De La Vega, F. M., Fu, Y., Peckham, H. E., Sun, Y. A., Batzer, M. A., Konkel, M. K., Xiao, C., Iqbal, Z., Desany, B., Blackwell, T., Snyder, M., Xing, J., Eichler, E. E., Aksay, G., Alkan, C., Hajirasouliha, I., Hormozdiari, F., Kidd, J. M., Chen, K., Chinwalla, A., Ding, L., McLellan, M. D., Wallis, J. W., Hurles, M. E., Conrad, D. F., Walter, K., Zhang, Y., Gerstein, M. B., Snyder, M., Abyzov, A., Du, J., Grubert, F., Haraksingh, R., Jee, J., Khurana, E., Lam, H. Y., Leng, J., Mu, X. J., Urban, A. E., Zhang, Z., Gibbs, R. A., Bainbridge, M., Challis, D., Coafra, C., Dinh, H., Kovar, C., Lee, S., Muzny, D., Nazareth, L., Reid, J., Sabo, A., Yu, F., Yu, J., Marth, G. T., Garrison, E. P., Indap, A., Leong, W. F., Quinlan, A. R., Stewart, C., Ward, A. N., Wu, J., Cibulskis, K., Fennell, T. J., Gabriel, S. B., Garimella, K. V., Hartl, C., Shefler, E., Sougnez, C. L., Wilkinson, J., Clark, A. G., Gravel, S., Grubert, F., Clarke, L., Flicek, P., Smith, R. E., Zheng-Bradley, X., Sherry, S. T., Khouri, H. M., Paschall, J. E., Shumway, M. F., Xiao, C., McVean, G. A., Katzman, S. J., Abecasis, G. R., Blackwell, T., Mardis, E. R., Dooling, D., Fulton, L., Fulton, R., Koboldt, D. C., Durbin, R. M., Balasubramaniam, S., Coffey, A., Keane, T. M., MacArthur, D. G., Palotie, A., Scott, C., Stalker, J., Tyler-Smith, C., Gerstein, M. B., Balasubramanian, S., Chakravarti, A., Knoppers, B. M., Peltonen, L., Abecasis, G. R., Bustamante, C. D., Gharani, N., Gibbs, R. A., Jorde, L., Kaye, J. S., Kent, A., Li, T., McGuire, A. L., McVean, G. A., Ossorio, P. N., Rotimi, C. N., Su, Y., Toji, L. H., Tyler-Smith, C., Brooks, L. D., Felsenfeld, A. L., McEwen, J. E., Abdallah, A., Juenger, C. R., Clemm, N. C., Collins, F. S., Duncanson, A., Green, E. D., Guyer, M. S., Peterson, J. L., Schafer, A. J., Abecasis, G. R., Altshuler, D. L., Auton, A., Brooks, L. D., Durbin, R. M., Gibbs, R. A., Hurles, M. E., McVean, G. A. 2010; 467 (7319): 1061-1073

    Abstract

    The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.

    View details for DOI 10.1038/nature09534

    View details for Web of Science ID 000283548600039

    View details for PubMedID 20981092

  • Behavior, Brain, and Genome in Genomic Disorders: Finding the Correspondences JOURNAL OF DEVELOPMENTAL AND BEHAVIORAL PEDIATRICS Grigorenko, E. L., Urban, A. E., Mencl, E. 2010; 31 (7): 602-609

    Abstract

    Within the last decade or so, there has been an acceleration of research attempting to connect specific genetic lesions to the patterns of brain structure and activation. This article comments on observations that have been made based on these recent data and discusses their importance for the field of investigations into developmental disorders.In making these observations, the authors focus on one specific genomic lesion, the well-studied, yet still incompletely understood, 22q11.2 deletion syndrome.The authors demonstrate the degree of variability in the phenotype that occurs at both the brain and behavioral levels of genomic disorders and describe how this variability is, on close inspection, represented at the genomic level.The authors emphasize the importance of combining genetic/genomic analyses and neuroimaging for research and for future clinical diagnostic purposes and for the purposes of developing individualized, patient-tailored treatment and remediation approaches.

    View details for DOI 10.1097/DBP.0b013e3181f5a0a1

    View details for Web of Science ID 000281561700011

    View details for PubMedID 20814258

  • Variation in Transcription Factor Binding Among Humans SCIENCE Kasowski, M., Grubert, F., Heffelfinger, C., Hariharan, M., Asabere, A., Waszak, S. M., Habegger, L., Rozowsky, J., Shi, M., Urban, A. E., Hong, M., Karczewski, K. J., Huber, W., Weissman, S. M., Gerstein, M. B., Korbel, J. O., Snyder, M. 2010; 328 (5975): 232-235

    Abstract

    Differences in gene expression may play a major role in speciation and phenotypic diversity. We examined genome-wide differences in transcription factor (TF) binding in several humans and a single chimpanzee by using chromatin immunoprecipitation followed by sequencing. The binding sites of RNA polymerase II (PolII) and a key regulator of immune responses, nuclear factor kappaB (p65), were mapped in 10 lymphoblastoid cell lines, and 25 and 7.5% of the respective binding regions were found to differ between individuals. Binding differences were frequently associated with single-nucleotide polymorphisms and genomic structural variants, and these differences were often correlated with differences in gene expression, suggesting functional consequences of binding variation. Furthermore, comparing PolII binding between humans and chimpanzee suggests extensive divergence in TF binding. Our results indicate that many differences in individuals and species occur at the level of TF binding, and they provide insight into the genetic events responsible for these differences.

    View details for DOI 10.1126/science.1183621

    View details for Web of Science ID 000276459600043

    View details for PubMedID 20299548

  • EBNA1 regulates cellular gene expression by binding cellular promoters PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Canaan, A., Haviv, I., Urban, A. E., Schulz, V. P., Hartman, S., Zhang, Z., Palejev, D., Deisseroth, A. B., Lacy, J., Snyder, M., Gerstein, M., Weissman, S. M. 2009; 106 (52): 22421-22426

    Abstract

    Epstein-Barr virus (EBV) is associated with several types of lymphomas and epithelial tumors including Burkitt's lymphoma (BL), HIV-associated lymphoma, posttransplant lymphoproliferative disorder, and nasopharyngeal carcinoma. EBV nuclear antigen 1 (EBNA1) is expressed in all EBV associated tumors and is required for latency and transformation. EBNA1 initiates latent viral replication in B cells, maintains the viral genome copy number, and regulates transcription of other EBV-encoded latent genes. These activities are mediated through the ability of EBNA1 to bind viral-DNA. To further elucidate the role of EBNA1 in the host cell, we have examined the effect of EBNA1 on cellular gene expression by microarray analysis using the B cell BJAB and the epithelial 293 cell lines transfected with EBNA1. Analysis of the data revealed distinct profiles of cellular gene changes in BJAB and 293 cell lines. Subsequently, chromatin immune-precipitation revealed a direct binding of EBNA1 to cellular promoters. We have correlated EBNA1 bound promoters with changes in gene expression. Sequence analysis of the 100 promoters most enriched revealed a DNA motif that differs from the EBNA1 binding site in the EBV genome.

    View details for DOI 10.1073/pnas.0911676106

    View details for Web of Science ID 000273178700069

    View details for PubMedID 20080792

  • The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Korbel, J. O., Tirosh-Wagner, T., Urban, A. E., Chen, X., Kasowski, M., Dai, L., Grubert, F., Erdman, C., Gao, M. C., Lange, K., Sobel, E. M., Barlow, G. M., Aylsworth, A. S., Carpenter, N. J., Clark, R. D., Cohen, M. Y., Doran, E., Falik-Zaccai, T., Lewin, S. O., Lott, I. T., McGillivray, B. C., Moeschler, J. B., Pettenati, M. J., Pueschel, S. M., Rao, K. W., Shaffer, L. G., Shohat, M., Van Riper, A. J., Warburton, D., Weissman, S., Gerstein, M. B., Snyder, M., Korenberg, J. R. 2009; 106 (29): 12031-12036

    Abstract

    Down syndrome (DS), or trisomy 21, is a common disorder associated with several complex clinical phenotypes. Although several hypotheses have been put forward, it is unclear as to whether particular gene loci on chromosome 21 (HSA21) are sufficient to cause DS and its associated features. Here we present a high-resolution genetic map of DS phenotypes based on an analysis of 30 subjects carrying rare segmental trisomies of various regions of HSA21. By using state-of-the-art genomics technologies we mapped segmental trisomies at exon-level resolution and identified discrete regions of 1.8-16.3 Mb likely to be involved in the development of 8 DS phenotypes, 4 of which are congenital malformations, including acute megakaryocytic leukemia, transient myeloproliferative disorder, Hirschsprung disease, duodenal stenosis, imperforate anus, severe mental retardation, DS-Alzheimer Disease, and DS-specific congenital heart disease (DSCHD). Our DS-phenotypic maps located DSCHD to a <2-Mb interval. Furthermore, the map enabled us to present evidence against the necessary involvement of other loci as well as specific hypotheses that have been put forward in relation to the etiology of DS-i.e., the presence of a single DS consensus region and the sufficiency of DSCR1 and DYRK1A, or APP, in causing several severe DS phenotypes. Our study demonstrates the value of combining advanced genomics with cohorts of rare patients for studying DS, a prototype for the role of copy-number variation in complex disease.

    View details for DOI 10.1073/pnas.0813248106

    View details for Web of Science ID 000268178400040

    View details for PubMedID 19597142

  • Distinct Genomic Aberrations Associated with ERG Rearranged Prostate Cancer GENES CHROMOSOMES & CANCER Demichelis, F., Setlur, S. R., Beroukhim, R., Perner, S., Korbel, J. O., LaFargue, C. J., Pflueger, D., Pina, C., Hofer, M. D., Sboner, A., Svensson, M. A., Rickman, D. S., Urban, A., Snyder, M., Meyerson, M., Lee, C., Gerstein, M. B., Kuefer, R., Rubin, M. A. 2009; 48 (4): 366-380

    Abstract

    Emerging molecular and clinical data suggest that ETS fusion prostate cancer represents a distinct molecular subclass, driven most commonly by a hormonally regulated promoter and characterized by an aggressive natural history. The study of the genomic landscape of prostate cancer in the light of ETS fusion events is required to understand the foundation of this molecularly and clinically distinct subtype. We performed genome-wide profiling of 49 primary prostate cancers and identified 20 recurrent chromosomal copy number aberrations, mainly occurring as genomic losses. Co-occurring events included losses at 19q13.32 and 1p22.1. We discovered three genomic events associated with ERG rearranged prostate cancer, affecting 6q, 7q, and 16q. 6q loss in nonrearranged prostate cancer is accompanied by gene expression deregulation in an independent dataset and by protein deregulation of MYO6. To analyze copy number alterations within the ETS genes, we performed a comprehensive analysis of all 27 ETS genes and of the 3 Mbp genomic area between ERG and TMPRSS2 (21q) with an unprecedented resolution (30 bp). We demonstrate that high-resolution tiling arrays can be used to pin-point breakpoints leading to fusion events. This study provides further support to define a distinct molecular subtype of prostate cancer based on the presence of ETS gene rearrangements.

    View details for DOI 10.1002/gcc.20647

    View details for Web of Science ID 000263572700007

    View details for PubMedID 19156837

  • Analysis of copy number variants and segmental duplications in the human genome: Evidence for a change in the process of formation in recent evolutionary history GENOME RESEARCH Kim, P. M., Lam, H. Y., Urban, A. E., Korbel, J. O., Affourtit, J., Grubert, F., Chen, X., Weissman, S., Snyder, M., Gerstein, M. B. 2008; 18 (12): 1865-1874

    Abstract

    Segmental duplications (SDs) are operationally defined as >1 kb stretches of duplicated DNA with high sequence identity. They arise from copy number variants (CNVs) fixed in the population. To investigate the formation of SDs and CNVs, we examine their large-scale patterns of co-occurrence with different repeats. Alu elements, a major class of genomic repeats, had previously been identified as prime drivers of SD formation. We also observe this association; however, we find that it sharply decreases for younger SDs. Continuing this trend, we find only weak associations of CNVs with Alus. Similarly, we find an association of SDs with processed pseudogenes, which is decreasing for younger SDs and absent entirely for CNVs. Next, we find that SDs are significantly co-localized with each other, resulting in a highly skewed "power-law" distribution and chromosomal hotspots. We also observe a significant association of CNVs with SDs, but find that an SD-mediated mechanism only accounts for some CNVs (<28%). Overall, our results imply that a shift in predominant formation mechanism occurred in recent history: approximately 40 million years ago, during the "Alu burst" in retrotransposition activity, non-allelic homologous recombination, first mediated by Alus and then the by newly formed CNVs themselves, was the main driver of genome rearrangements; however, its relative importance has decreased markedly since then, with proportionally more events now stemming from other repeats and from non-homologous end-joining. In addition to a coarse-grained analysis, we performed targeted sequencing of 67 CNVs and then analyzed a combined set of 270 CNVs (540 breakpoints) to verify our conclusions.

    View details for DOI 10.1101/gr.081422.108

    View details for Web of Science ID 000261398900002

    View details for PubMedID 18842824

  • High-Resolution Copy-Number Variation Map Reflects Human Olfactory Receptor Diversity and Evolution PLOS GENETICS Hasin, Y., Olender, T., Khen, M., Gonzaga-Jauregui, C., Kim, P. M., Urban, A. E., Snyder, M., Gerstein, M. B., Lancet, D., Korbel, J. O. 2008; 4 (11)

    Abstract

    Olfactory receptors (ORs), which are involved in odorant recognition, form the largest mammalian protein superfamily. The genomic content of OR genes is considerably reduced in humans, as reflected by the relatively small repertoire size and the high fraction ( approximately 55%) of human pseudogenes. Since several recent low-resolution surveys suggested that OR genomic loci are frequently affected by copy-number variants (CNVs), we hypothesized that CNVs may play an important role in the evolution of the human olfactory repertoire. We used high-resolution oligonucleotide tiling microarrays to detect CNVs across 851 OR gene and pseudogene loci. Examining genomic DNA from 25 individuals with ancestry from three populations, we identified 93 OR gene loci and 151 pseudogene loci affected by CNVs, generating a mosaic of OR dosages across persons. Our data suggest that approximately 50% of the CNVs involve more than one OR, with the largest CNV spanning 11 loci. In contrast to earlier reports, we observe that CNVs are more frequent among OR pseudogenes than among intact genes, presumably due to both selective constraints and CNV formation biases. Furthermore, our results show an enrichment of CNVs among ORs with a close human paralog or lacking a one-to-one ortholog in chimpanzee. Interestingly, among the latter we observed an enrichment in CNV losses over gains, a finding potentially related to the known diminution of the human OR repertoire. Quantitative PCR experiments performed for 122 sampled ORs agreed well with the microarray results and uncovered 23 additional CNVs. Importantly, these experiments allowed us to uncover nine common deletion alleles that affect 15 OR genes and five pseudogenes. Comparison to the chimpanzee reference genome revealed that all of the deletion alleles are human derived, therefore indicating a profound effect of human-specific deletions on the individual OR gene content. Furthermore, these deletion alleles may be used in future genetic association studies of olfactory inter-individual differences.

    View details for DOI 10.1371/journal.pgen.1000249

    View details for Web of Science ID 000261481000004

    View details for PubMedID 18989455

  • A procedure for highly specific, sensitive, and unbiased whole-genome amplification PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Pan, X., Urban, A. E., Palejev, D., Schulz, V., Grubert, F., Hu, Y., Snyder, M., Weissman, S. M. 2008; 105 (40): 15499-15504

    Abstract

    Highly specific amplification of complex DNA pools without bias or template-independent products (TIPs) remains a challenge. We have developed a method using phi29 DNA polymerase and trehalose and optimized control of amplification to create micrograms of specific amplicons without TIPs from down to subfemtograms of DNA. With an input of as little as 0.5-2.5 ng of human gDNA or a few cells, the product could be close to native DNA in locus representation. The amplicons from 5 and 0.5 ng of DNA faithfully demonstrated all previously known heterozygous segmental duplications and deletions (3 Mb to 18 kb) located on chromosome 22 and even a homozygous deletion smaller than 1 kb with high-resolution chromosome-wide comparative genomic hybridization. With 550k Infinium BeadChip SNP typing, the >99.7% accuracy was compared favorably with results on unamplified DNA. Importantly, underrepresentation of chromosome termini that occurred with GenomiPhi v2 was greatly rescued with the present procedure, and the call rate and accuracy of SNP typing were also improved for the amplicons with a 0.5-ng, partially degraded DNA input. In addition, the amplification proceeded logarithmically in terms of total yield before saturation; the intact cells was amplified >50 times more efficiently than an equivalent amount of extracted DNA; and the locus imbalance for amplicons with 0.1 ng or lower input of DNA was variable, whereas for higher input it was largely reproducible. This procedure facilitates genomic analysis with single cells or other traces of DNA, and generates products suitable for analysis by massively parallel sequencing as well as microarray hybridization.

    View details for DOI 10.1073/pnas.0808028105

    View details for Web of Science ID 000260360500052

    View details for PubMedID 18832167

  • MEDME: An experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment GENOME RESEARCH Pelizzola, M., Koga, Y., Urban, A. E., Krauthammer, M., Weissman, S., Halaban, R., Molinaro, A. M. 2008; 18 (10): 1652-1659

    Abstract

    DNA methylation is an important component of epigenetic modifications that influences the transcriptional machinery and is aberrant in many human diseases. Several methods have been developed to map DNA methylation for either limited regions or genome-wide. In particular, antibodies specific for methylated CpG have been successfully applied in genome-wide studies. However, despite the relevance of the obtained results, the interpretation of antibody enrichment is not trivial. Of greatest importance, the coupling of antibody-enriched methylated fragments with microarrays generates DNA methylation estimates that are not linearly related to the true methylation level. Here, we present an experimental and analytical methodology, MEDME (modeling experimental data with MeDIP enrichment), to obtain enhanced estimates that better describe the true values of DNA methylation level throughout the genome. We propose an experimental scenario for evaluating the true relationship in a high-throughput setting and a model-based analysis to predict the absolute and relative DNA methylation levels. We successfully applied this model to evaluate DNA methylation status of normal human melanocytes compared to a melanoma cell strain. Despite the low resolution typical of methods based on immunoprecipitation, we show that model-derived estimates of DNA methylation provide relatively high correlation with measured absolute and relative levels, as validated by bisulfite genomic DNA sequencing. Importantly, the model-derived DNA methylation estimates simplify the interpretation of the results both at single-loci and at chromosome-wide levels.

    View details for DOI 10.1101/gr.080721.108

    View details for Web of Science ID 000259700800012

    View details for PubMedID 18765822

  • The current excitement about copy-number variation: how it relates to gene duplications and protein families CURRENT OPINION IN STRUCTURAL BIOLOGY Korbel, J. O., Kim, P. M., Chen, X., Urban, A. E., Weissman, S., Snyder, M., Gerstein, M. B. 2008; 18 (3): 366-374

    Abstract

    Following recent technological advances there has been an increasing interest in genome structural variants (SVs), in particular copy-number variants (CNVs)--large-scale duplications and deletions. Although not immediately evident, CNV surveys make a conceptual connection between the fields of population genetics and protein families, in particular with regard to the stability and expandability of families. The mechanisms giving rise to CNVs can be considered as fundamental processes underlying gene duplication and loss; duplicated genes being the results of 'successful' copies, fixed and maintained in the population. Conversely, many 'unsuccessful' duplicates remain in the genome as pseudogenes. Here, we survey studies on CNVs, highlighting issues related to protein families. In particular, CNVs tend to affect specific gene functional categories, such as those associated with environmental response, and are depleted in genes related to basic cellular processes. Furthermore, CNVs occur more often at the periphery of the protein interaction network. In comparison, protein families associated with successful and unsuccessful duplicates are associated with similar functional categories but are differentially placed in the interaction network. These trends are likely reflective of CNV formation biases and natural selection, both of which differentially influence distinct protein families.

    View details for DOI 10.1016/j.sbi.2008.02.005

    View details for Web of Science ID 000257539100013

    View details for PubMedID 18511261

  • Systematic analysis of transcribed loci in ENCODE regions using RACE sequencing reveals extensive transcription in the human genome GENOME BIOLOGY QianWu, J., Du, J., Rozowsky, J., Zhang, Z., Urban, A. E., Euskirchen, G., Weissman, S., Gerstein, M., Snyder, M. 2008; 9 (1)

    Abstract

    Recent studies of the mammalian transcriptome have revealed a large number of additional transcribed regions and extraordinary complexity in transcript diversity. However, there is still much uncertainty regarding precisely what portion of the genome is transcribed, the exact structures of these novel transcripts, and the levels of the transcripts produced.We have interrogated the transcribed loci in 420 selected ENCyclopedia Of DNA Elements (ENCODE) regions using rapid amplification of cDNA ends (RACE) sequencing. We analyzed annotated known gene regions, but primarily we focused on novel transcriptionally active regions (TARs), which were previously identified by high-density oligonucleotide tiling arrays and on random regions that were not believed to be transcribed. We found RACE sequencing to be very sensitive and were able to detect low levels of transcripts in specific cell types that were not detectable by microarrays. We also observed many instances of sense-antisense transcripts; further analysis suggests that many of the antisense transcripts (but not all) may be artifacts generated from the reverse transcription reaction. Our results show that the majority of the novel TARs analyzed (60%) are connected to other novel TARs or known exons. Of previously unannotated random regions, 17% were shown to produce overlapping transcripts. Furthermore, it is estimated that 9% of the novel transcripts encode proteins.We conclude that RACE sequencing is an efficient, sensitive, and highly accurate method for characterization of the transcriptome of specific cell/tissue types. Using this method, it appears that much of the genome is represented in polyA+ RNA. Moreover, a fraction of the novel RNAs can encode protein and are likely to be functional.

    View details for DOI 10.1186/gb-2008-9-1-r3

    View details for Web of Science ID 000253779800011

    View details for PubMedID 18173853

  • Paired-end mapping reveals extensive structural variation in the human genome SCIENCE Korbel, J. O., Urban, A. E., Affourtit, J. P., Godwin, B., Grubert, F., Simons, J. F., Kim, P. M., Palejev, D., Carriero, N. J., Du, L., Taillon, B. E., Chen, Z., Tanzer, A., Saunders, A. C., Chi, J., Yang, F., Carter, N. P., Hurles, M. E., Weissman, S. M., Harkins, T. T., Gerstein, M. B., Egholm, M., Snyder, M. 2007; 318 (5849): 420-426

    Abstract

    Structural variation of the genome involves kilobase- to megabase-sized deletions, duplications, insertions, inversions, and complex combinations of rearrangements. We introduce high-throughput and massive paired-end mapping (PEM), a large-scale genome-sequencing method to identify structural variants (SVs) approximately 3 kilobases (kb) or larger that combines the rescue and capture of paired ends of 3-kb fragments, massive 454 sequencing, and a computational approach to map DNA reads onto a reference genome. PEM was used to map SVs in an African and in a putatively European individual and identified shared and divergent SVs relative to the reference genome. Overall, we fine-mapped more than 1000 SVs and documented that the number of SVs among humans is much larger than initially hypothesized; many of the SVs potentially affect gene function. The breakpoint junction sequences of more than 200 SVs were determined with a novel pooling strategy and computational analysis. Our analysis provided insights into the mechanisms of SV formation in humans.

    View details for DOI 10.1126/science.1149504

    View details for Web of Science ID 000250230400038

    View details for PubMedID 17901297

  • Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project NATURE Birney, E., Stamatoyannopoulos, J. A., Dutta, A., Guigo, R., Gingeras, T. R., Margulies, E. H., Weng, Z., Snyder, M., Dermitzakis, E. T., Stamatoyannopoulos, J. A., Thurman, R. E., Kuehn, M. S., Taylor, C. M., Neph, S., Koch, C. M., Asthana, S., Malhotra, A., Adzhubei, I., Greenbaum, J. A., Andrews, R. M., Flicek, P., Boyle, P. J., Cao, H., Carter, N. P., Clelland, G. K., Davis, S., Day, N., Dhami, P., Dillon, S. C., Dorschner, M. O., Fiegler, H., Giresi, P. G., Goldy, J., Hawrylycz, M., Haydock, A., Humbert, R., James, K. D., Johnson, B. E., Johnson, E. M., Frum, T. T., Rosenzweig, E. R., Karnani, N., Lee, K., Lefebvre, G. C., Navas, P. A., Neri, F., Parker, S. C., Sabo, P. J., Sandstrom, R., Shafer, A., Vetrie, D., Weaver, M., Wilcox, S., Yu, M., Collins, F. S., Dekker, J., Lieb, J. D., Tullius, T. D., Crawford, G. E., Sunyaev, S., Noble, W. S., Dunham, I., Dutta, A., Guigo, R., Denoeud, F., Reymond, A., Kapranov, P., Rozowsky, J., Zheng, D., Castelo, R., Frankish, A., Harrow, J., Ghosh, S., Sandelin, A., Hofacker, I. L., Baertsch, R., Keefe, D., Flicek, P., Dike, S., Cheng, J., Hirsch, H. A., Sekinger, E. A., Lagarde, J., Abril, J. F., Shahab, A., Flamm, C., Fried, C., Hackermueller, J., Hertel, J., Lindemeyer, M., Missal, K., Tanzer, A., Washietl, S., Korbel, J., Emanuelsson, O., Pedersen, J. S., Holroyd, N., Taylor, R., Swarbreck, D., Matthews, N., Dickson, M. C., Thomas, D. J., Weirauch, M. T., Gilbert, J., Drenkow, J., Bell, I., Zhao, X., Srinivasan, K. G., Sung, W., Ooi, H. S., Chiu, K. P., Foissac, S., Alioto, T., Brent, M., Pachter, L., Tress, M. L., Valencia, A., Choo, S. W., Choo, C. Y., Ucla, C., Manzano, C., Wyss, C., Cheung, E., Clark, T. G., Brown, J. B., Ganesh, M., Patel, S., Tammana, H., Chrast, J., Henrichsen, C. N., Kai, C., Kawai, J., Nagalakshmi, U., Wu, J., Lian, Z., Lian, J., Newburger, P., Zhang, X., Bickel, P., Mattick, J. S., Carninci, P., Hayashizaki, Y., Weissman, S., Dermitzakis, E. T., Margulies, E. H., Hubbard, T., Myers, R. M., Rogers, J., Stadler, P. F., Lowe, T. M., Wei, C., Ruan, Y., Snyder, M., Birney, E., Struhl, K., Gerstein, M., Antonarakis, S. E., Gingeras, T. R., Brown, J. B., Flicek, P., Fu, Y., Keefe, D., Birney, E., Denoeud, F., Gerstein, M., Green, E. D., Kapranov, P., Karaoez, U., Myers, R. M., Noble, W. S., Reymond, A., Rozowsky, J., Struhl, K., Siepel, A., Stamatoyannopoulos, J. A., Taylor, C. M., Taylor, J., Thurman, R. E., Tullius, T. D., Washietl, S., Zheng, D., Liefer, L. A., Wetterstrand, K. A., Good, P. J., Feingold, E. A., Guyer, M. S., Collins, F. S., Margulies, E. H., Cooper, G. M., Asimenos, G., Thomas, D. J., Dewey, C. N., Siepel, A., Birney, E., Keefe, D., Hou, M., Taylor, J., Nikolaev, S., Montoya-Burgos, J. I., Loeytynoja, A., Whelan, S., Pardi, F., Massingham, T., Brown, J. B., Huang, H., Zhang, N. R., Bickel, P., Holmes, I., Mullikin, J. C., Ureta-Vidal, A., Paten, B., Seringhaus, M., Church, D., Rosenbloom, K., Kent, W. J., Stone, E. A., Gerstein, M., Antonarakis, S. E., Batzoglou, S., Goldman, N., Hardison, R. C., Haussler, D., Miller, W., Pachter, L., Green, E. D., Sidow, A., Weng, Z., Trinklein, N. D., Fu, Y., Zhang, Z. D., Karaoez, U., Barrera, L., Stuart, R., Zheng, D., Ghosh, S., Flicek, P., King, D. C., Taylor, J., Ameur, A., Enroth, S., Bieda, M. C., Koch, C. M., Hirsch, H. A., Wei, C., Cheng, J., Kim, J., Bhinge, A. A., Giresi, P. G., Jiang, N., Liu, J., Yao, F., Sung, W., Chiu, K. P., Vega, V. B., Lee, C. W., Ng, P., Shahab, A., Sekinger, E. A., Yang, A., Moqtaderi, Z., Zhu, Z., Xu, X., Squazzo, S., Oberley, M. J., Inman, D., Singer, M. A., Richmond, T. A., Munn, K. J., Rada-Iglesias, A., Wallerman, O., Komorowski, J., Clelland, G. K., Wilcox, S., Dillon, S. C., Andrews, R. M., Fowler, J. C., Couttet, P., James, K. D., Lefebvre, G. C., Bruce, A. W., Dovey, O. M., Ellis, P. D., Dhami, P., Langford, C. F., Carter, N. P., Vetrie, D., Kapranov, P., Nix, D. A., Bell, I., Patel, S., Rozowsky, J., Euskirchen, G., Hartman, S., Lian, J., Wu, J., Urban, A. E., Kraus, P., Van Calcar, S., Heintzman, N., Kim, T. H., Wang, K., Qu, C., Hon, G., Luna, R., Glass, C. K., Rosenfeld, M. G., Force Aldred, S., Cooper, S. J., Halees, A., Lin, J. M., Shulha, H. P., Zhang, X., Xu, M., Haidar, J. N., Yu, Y., Birney, E., Weissman, S., Ruan, Y., Lieb, J. D., Iyer, V. R., Green, R. D., Gingeras, T. R., Wadelius, C., Dunham, I., Struhl, K., Hardison, R. C., Gerstein, M., Farnham, P. J., Myers, R. M., Ren, B., Snyder, M., Thomas, D. J., Rosenbloom, K., Harte, R. A., Hinrichs, A. S., Trumbower, H., Clawson, H., Hillman-Jackson, J., Zweig, A. S., Smith, K., Thakkapallayil, A., Barber, G., Kuhn, R. M., Karolchik, D., Haussler, D., Kent, W. J., Dermitzakis, E. T., Armengol, L., Bird, C. P., Clark, T. G., Cooper, G. M., de Bakker, P. I., Kern, A. D., Lopez-Bigas, N., Martin, J. D., Stranger, B. E., Thomas, D. J., Woodroffe, A., Batzoglou, S., Davydov, E., Dimas, A., Eyras, E., Hallgrimsdottir, I. B., Hardison, R. C., Huppert, J., Sidow, A., Taylor, J., Trumbower, H., Zody, M. C., Guigo, R., Mullikin, J. C., Abecasis, G. R., Estivill, X., Birney, E., Bouffard, G. G., Guan, X., Hansen, N. F., Idol, J. R., Maduro, V. V., Maskeri, B., McDowell, J. C., Park, M., Thomas, P. J., Young, A. C., Blakesley, R. W., Muzny, D. M., Sodergren, E., Wheeler, D. A., Worley, K. C., Jiang, H., Weinstock, G. M., Gibbs, R. A., Graves, T., Fulton, R., Mardis, E. R., Wilson, R. K., Clamp, M., Cuff, J., Gnerre, S., Jaffe, D. B., Chang, J. L., Lindblad-Toh, K., Lander, E. S., Koriabine, M., Nefedov, M., Osoegawa, K., Yoshinaga, Y., Zhu, B., de Jong, P. J. 2007; 447 (7146): 799-816

    Abstract

    We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

    View details for DOI 10.1038/nature05874

    View details for Web of Science ID 000247207500034

    View details for PubMedID 17571346

  • Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Korbel, J. O., Urban, A. E., Grubert, F., Du, J., Royce, T. E., Starr, P., Zhong, G., Emanuel, B. S., Weissman, S. M., Snyder, M., Gerstein, M. B. 2007; 104 (24): 10110-10115

    Abstract

    Copy-number variants (CNVs) are an abundant form of genetic variation in humans. However, approaches for determining exact CNV breakpoint sequences (physical deletion or duplication boundaries) across individuals, crucial for associating genotype to phenotype, have been lacking so far, and the vast majority of CNVs have been reported with approximate genomic coordinates only. Here, we report an approach, called BreakPtr, for fine-mapping CNVs (available from http://breakptr.gersteinlab.org). We statistically integrate both sequence characteristics and data from high-resolution comparative genome hybridization experiments in a discrete-valued, bivariate hidden Markov model. Incorporation of nucleotide-sequence information allows us to take into account the fact that recently duplicated sequences (e.g., segmental duplications) often coincide with breakpoints. In anticipation of an upcoming increase in CNV data, we developed an iterative, "active" approach to initially scoring with a preliminary model, performing targeted validations, retraining the model, and then rescoring, and a flexible parameterization system that intuitively collapses from a full model of 2,503 parameters to a core one of only 10. Using our approach, we accurately mapped >400 breakpoints on chromosome 22 and a region of chromosome 11, refining the boundaries of many previously approximately mapped CNVs. Four predicted breakpoints flanked known disease-associated deletions. We validated an additional four predicted CNV breakpoints by sequencing. Overall, our results suggest a predictive resolution of approximately 300 bp. This level of resolution enables more precise correlations between CNVs and across individuals than previously possible, allowing the study of CNV population frequencies. Further, it enabled us to demonstrate a clear Mendelian pattern of inheritance for one of the CNVs.

    View details for DOI 10.1073/pnas.0703834104

    View details for Web of Science ID 000247363000036

    View details for PubMedID 17551006

  • Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome GENOME RESEARCH Emanuelsson, O., Nagalakshmi, U., Zheng, D., Rozowsky, J. S., Urban, A. E., Du, J., Lian, Z., Stolc, V., Weissman, S., Snyder, M., Gerstein, M. B. 2007; 17 (6): 886-897

    Abstract

    Genomic tiling microarrays have become a popular tool for interrogating the transcriptional activity of large regions of the genome in an unbiased fashion. There are several key parameters associated with each tiling experiment (e.g., experimental protocols and genomic tiling density). Here, we assess the role of these parameters as they are manifest in different tiling-array platforms used for transcription mapping. First, we analyze how a number of published tiling-array experiments agree with established gene annotation on human chromosome 22. We observe that the transcription detected from high-density arrays correlates substantially better with annotation than that from other array types. Next, we analyze the transcription-mapping performance of the two main high-density oligonucleotide array platforms in the ENCODE regions of the human genome. We hybridize identical biological samples and develop several ways of scoring the arrays and segmenting the genome into transcribed and nontranscribed regions, with the aim of making the platforms most comparable to each other. Finally, we develop a platform comparison approach based on agreement with known annotation. Overall, we find that the performance improves with more data points per locus, coupled with statistical scoring approaches that properly take advantage of this, where this larger number of data points arises from higher genomic tiling density and the use of replicate arrays and mismatches. While we do find significant differences in the performance of the two high-density platforms, we also find that they complement each other to some extent. Finally, our experiments reveal a significant amount of novel transcription outside of known genes, and an appreciable sample of this was validated by independent experiments.

    View details for DOI 10.1101/gr.5014606

    View details for Web of Science ID 000247226900020

    View details for PubMedID 17119069

  • High-resolution mapping of DNA copy alterations in human chromosome 22 using high-density tiling oligonucleotide arrays PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Urban, A. E., Korbel, J. O., Selzer, R., Richmond, T., Hacker, A., Popescu, G. V., Clubells, J. F., Green, R., Emanuel, B. S., Gerstein, M. B., Weissman, S. M., Snyder, M. 2006; 103 (12): 4534-4539

    Abstract

    Deletions and amplifications of the human genomic sequence (copy number polymorphisms) are the cause of numerous diseases and a potential cause of phenotypic variation in the normal population. Comparative genomic hybridization (CGH) has been developed as a useful tool for detecting alterations in DNA copy number that involve blocks of DNA several kilobases or larger in size. We have developed high-resolution CGH (HR-CGH) to detect accurately and with relatively little bias the presence and extent of chromosomal aberrations in human DNA. Maskless array synthesis was used to construct arrays containing 385,000 oligonucleotides with isothermal probes of 45-85 bp in length; arrays tiling the beta-globin locus and chromosome 22q were prepared. Arrays with a 9-bp tiling path were used to map a 622-bp heterozygous deletion in the beta-globin locus. Arrays with an 85-bp tiling path were used to analyze DNA from patients with copy number changes in the pericentromeric region of chromosome 22q. Heterozygous deletions and duplications as well as partial triploidies and partial tetraploidies of portions of chromosome 22q were mapped with high resolution (typically up to 200 bp) in each patient, and the precise breakpoints of two deletions were confirmed by DNA sequencing. Additional peaks potentially corresponding to known and novel additional CNPs were also observed. Our results demonstrate that HR-CGH allows the detection of copy number changes in the human genome at an unprecedented level of resolution.

    View details for DOI 10.1073/pnas.0511340103

    View details for Web of Science ID 000236362600039

    View details for PubMedID 16537408

  • Global identification of human transcribed sequences with genome tiling arrays SCIENCE Bertone, P., Stolc, V., Royce, T. E., Rozowsky, J. S., Urban, A. E., Zhu, X. W., Rinn, J. L., Tongprasit, W., Samanta, M., Weissman, S., Gerstein, M., Snyder, M. 2004; 306 (5705): 2242-2246

    Abstract

    Elucidating the transcribed regions of the genome constitutes a fundamental aspect of human biology, yet this remains an outstanding problem. To comprehensively identify coding sequences, we constructed a series of high-density oligonucleotide tiling arrays representing sense and antisense strands of the entire nonrepetitive sequence of the human genome. Transcribed sequences were located across the genome via hybridization to complementary DNA samples, reverse-transcribed from polyadenylated RNA obtained from human liver tissue. In addition to identifying many known and predicted genes, we found 10,595 transcribed sequences not detected by other methods. A large fraction of these are located in intergenic regions distal from previously annotated genes and exhibit significant homology to other mammalian proteins.

    View details for DOI 10.1126/science.1103388

    View details for Web of Science ID 000225950000042

    View details for PubMedID 15539566

  • The ENCODE (ENCyclopedia of DNA elements) Project SCIENCE Feingold, E. A., Good, P. J., Guyer, M. S., Kamholz, S., Liefer, L., Wetterstrand, K., Collins, F. S., Gingeras, T. R., Kampa, D., Sekinger, E. A., Cheng, J., Hirsch, H., Ghosh, S., Zhu, Z., Pate, S., Piccolboni, A., Yang, A., Tammana, H., Bekiranov, S., Kapranov, P., Harrison, R., Church, G., Struhl, K., Ren, B., Kim, T. H., Barrera, L. O., Qu, C., Van Calcar, S., Luna, R., Glass, C. K., Rosenfeld, M. G., Guigo, R., Antonarakis, S. E., Birney, E., Brent, M., Pachter, L., Reymond, A., Dermitzakis, E. T., Dewey, C., Keefe, D., Denoeud, F., Lagarde, J., Ashurst, J., Hubbard, T., Wesselink, J. J., Castelo, R., Eyras, E., Myers, R. M., Sidow, A., Batzoglou, S., Trinklein, N. D., Hartman, S. J., Aldred, S. F., Anton, E., Schroeder, D. I., Marticke, S. S., Nguyen, L., Schmutz, J., Grimwood, J., Dickson, M., Cooper, G. M., Stone, E. A., Asimenos, G., Brudno, M., Dutta, A., Karnani, N., Taylor, C. M., Kim, H. K., Robins, G., Stamatoyannopoulos, G., Stamatoyannopoulos, J. A., Dorschner, M., Sabo, P., Hawrytycz, M., Humbert, R., Wallace, J., Yu, M., Navas, P. A., McArthur, M., Noble, W. S., Dunham, I., Koch, C. M., Andrews, R. M., Clelland, G. K., Wilcox, S., Fowler, J. C., James, K. D., Groth, P., Dovey, O. M., ELLIS, P. D., Wraight, V. L., Mungall, A. J., Dhami, P., Fiegler, H., Langford, C. F., Carter, N. P., Vetrie, D., Snyder, M., Euskirchen, G., Urban, A. E., Nagalakshmi, U., Rinn, J., Popescu, G., Bertone, P., Hartman, S., Rozowsky, J., Emanuelsson, O., Royce, T., Chung, S., Gerstein, M., Lian, Z., Lian, J., Nakayama, Y., Weissman, S., Stoic, V., Tongprasit, W., Sethi, H., Jones, S., Marra, M., Shin, H., Schein, J., Clamp, M., Lindblad-Toh, K., Chang, J., Jaffe, D. B., Kamal, E. S., Lander, E. S., Mikkelsen, T. S., Vinson, J., Zody, M. C., de Jong, P. J., Osoegawa, K., Nefedov, M., Zhu, B., Baxevanis, A. D., Wolfsberg, T. G., Collins, F. S., Crawford, G. E., Holt, E., Vasicek, T. J., Zhou, D., Luo, S., Green, E. D., Bouffard, G. G., Margulies, E. H., Portnoy, M. E., Hansen, N. F., Thomas, P. J., Mcdowell, J. C., Maskeri, B., Young, A. C., Idol, J. R., Blakesley, R. W., Schuler, G., Miller, W., Hardison, R., Elnitski, L., Shah, P., Salzberg, S. L., Pertea, M., Majoros, W. H., Haussler, D., Thomas, D., Rosenbloom, K. R., Clawson, H., Siepe, A., Kent, W. J., Weng, Z., Jin, S., Halees, A., Burden, H., Karaoz, U., Fu, Y., Yu, Y., Ding, C., Cantor, C. R., Kingston, R. E., Dennis, J., Green, R. D., Singer, M. A., Richmond, T. A., Norton, J. E., Farnham, P. J., Oberley, M. J., INMAN, D. R., McCormick, M. R., Kim, H., Middle, C. L., Pirrung, M. C., Fu, X. D., Kwon, Y. S., Ye, Z., Dekker, J., Tabuchi, T. M., Gheldof, N., Dostie, J., Harvey, S. C. 2004; 306 (5696): 636-640

    Abstract

    The ENCyclopedia Of DNA Elements (ENCODE) Project aims to identify all functional elements in the human genome sequence. The pilot phase of the Project is focused on a specified 30 megabases (approximately 1%) of the human genome sequence and is organized as an international consortium of computational and laboratory-based scientists working to develop and apply high-throughput approaches for detecting all sequence elements that confer biological function. The results of this pilot phase will guide future efforts to analyze the entire human genome.

    View details for DOI 10.1126/science.1105136

    View details for Web of Science ID 000224756700037

    View details for PubMedID 15499007

  • Identification of novel functional elements in the human genome COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY Lian, Z., Euskirchen, G., Rinn, J., Martone, R., Bertone, P., Hartman, S., Royce, T., Nelson, K., Sayward, F., Luscombe, N., Yang, J., Li, J. L., Miller, P., Urban, A. E., Gerstein, M., Weissman, S., Snyder, M. 2003; 68: 317-322

    View details for Web of Science ID 000222969300037

    View details for PubMedID 15338632

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