Our research interests include proteomics, mass spectrometry and advanced technology development, mapping protein interactions and topologies in biological systems and chemical biology. We are developing new mass spectrometry technology and chemical approaches that allow insight in protein interaction networks in vivo.
The Dunham lab develops and applies genomic tools to study genome evolution and genetic variation in yeast and humans. We utilize the budding yeasts as a testbed for technology development and as an experimentally tractable system for evolutionary genetics and genomics. By leveraging these systems in creative ways, we hope to learn in molecular detail how cells evolve and the mechanisms by which they do so, addressing important open questions on mutation spectrum, genome structure, mechanisms and consequences of copy number change, genetic interactions, evolution of gene expression, and other fundamental topics.
The lab is broadly organized into an experimental evolution group and a comparative functional genomics group. Many projects also intersect my long-standing interest in how gene and chromosome copy number variation contributes to adaptation, and the mechanisms by which such variation arises. When new technology to study these questions has been required, we have developed it, including methods for genome analysis and long term continuous culture.
Current projects include understanding the costs and benefits of aneuploidy, evolving hybrid yeasts, building new instruments for continuous culture, functionally characterizing centromeres and replication origins across species, and developing high throughput methods for measuring the impact of genetic variation in yeast and humans.
Experimental evolution of S. cerevisiae for caffeine tolerance alters multidrug resistance and TOR signaling pathways. Geck RC, Moresi NG, Anderson LM; yEvo Students; Brewer R, Renz TR, Taylor MB, Dunham MJ. G3. 2024 Jul 11:jkae148. doi: 10.1093/g3journal/jkae148. [Pubmed][G3][SRA][GitHub][PDF][bioRxiv]
Systematic profiling of ale yeast protein dynamics across fermentation and repitching. Garge RK, Geck RC, Armstrong JO, Dunn B, Boutz DR, Battenhouse A, Leutert M, Dang V, Jiang P, Kwiatkowski D, Peiser T, McElroy H, Marcotte EM, Dunham MJ. G3. 2024 Mar 6;14(3):jkad293. doi: 10.1093/g3journal/jkad293. PMID: 38135291 [Pubmed][G3][Shiny App][PDF][bioRxiv][Genes to Genomes blog][The Brü Lab podcast]
yEvo: a modular eukaryotic genetics and evolution research experience for high school students. Taylor MB*, Warwick AR*, Skophammer R, Boyer JM, Geck RC, Gunkelman K, Walson M, Rowley PA#, Dunham MJ# (*co-first authors, #co-corresponding authors). Ecol Evol. 2024 Jan 7;14(1):e10811. doi: 10.1002/ece3.10811. eCollection 2024 Jan. PMID: 38192907 [Pubmed][Ecology and Evolution][PDF][bioRxiv]
Caffeine-tolerant mutations selected through an at-home yeast experimental evolution teaching lab. Moresi NG, Geck RC, Skophammer R, Godin D, yEvo Students, Taylor MB, Dunham MJ. MicroPubl Biol. 2023 Feb 9;2023:10.17912/micropub.biology.000749. doi: 10.17912/micropub.biology.000749. eCollection 2023. PMID: 36855741 [Pubmed][microPublication Biology][PDF][bioRxiv][This Week in Microbiology podcast]
Functional interpretation, cataloging, and analysis of 1,341 glucose-6-phosphate dehydrogenase variants. Geck RC, Powell NR, Dunham MJ. AJHG. 2023 Feb 2;110:1–12. [Pubmed][AJHG][G6PDcat on GitHub][PDF][letter to the editor response][GitHub][PDF][bioRxiv][All of Us][All of Us in Spanish]
yEvo: Experimental evolution in high school classrooms selects for novel mutations and epistatic interactions that impact clotrimazole resistance in S. cerevisiae. Taylor MB, Skophammer R, Warwick AR, Geck RC, Boyer JM, yEvo Students, Walson M, Large CRL, Hickey AS, Rowley PA#, Dunham MJ# (#co-corresponding authors). G3. 2022 Nov 4;12(11):jkac246. doi: 10.1093/g3journal/jkac246. [Pubmed][G3][PDF][SRA][bioRxiv]
PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment. Yeh CC*, Amorosi CJ*, Showman S, Dunham MJ. (*co-first authors). Bioinformatics. 2022 May 13;38(10):2927-2929. doi: 10.1093/bioinformatics/btac165. [Pubmed][Bioinformatics][GitHub][bioRxiv]
Massively parallel characterization of CYP2C9 variant enzyme activity and abundance. Amorosi CJ, Chiasson MA, McDonald MG, Wong LH, Sitko KA, Boyle G, Kowalski JP, Rettie AE, Fowler DM#, Dunham MJ# (co-corresponding authors). Am J Hum Genet. 2021 Sep 2;108(9):1735-1751. doi: 10.1016/j.ajhg.2021.07.001. [Pubmed][AJHG][PDF][bioRxiv][Science in Seattle]
Multiplexing Mutation Rate Assessment: Determining Pathogenicity of Msh2 Variants in S. cerevisiae. Ollodart AR, Yeh CC, Miller AW, Shirts BH, Gordon AS, Dunham MJ. Genetics. 2021 Apr 12;iyab058. doi: 10.1093/genetics/iyab058. [Pubmed][Genetics][PDF][bioRxiv][BioProject]
Transposable element mobilization in interspecific yeast hybrids. Heil CS, Patterson K, Hickey AS, Alcantara E, Dunham MJ. Genome Biol Evol. 2021 Mar 1;13(3):evab033. doi: 10.1093/gbe/evab033. [Pubmed][GBE][PMC][PDF][bioRxiv][BioProject]
additional publications available via Google Scholar
The long-term goal of our laboratory is to understand the evolution, pathology and mechanisms of recent gene duplication and DNA transposition within the human genome.
Our research specifically addresses a new paradigm that has emerged in the past few years in which particular regions of the human genome have been shown active in the acquisition, duplication and dispersal of large gene-containing genomic segments.
We hypothesize that these ‘jumping genomic segments’ are part of an ongoing evolutionary process that results in a novel form of large-scale variation in human genomic DNA and contributes rapidly to primate gene evolution.
The large blocks of sequence similarity generated by this process, we further propose provide the substrates for aberrant recombination, thereby leading to recurrent and potentially pathogenic chromosomal structural rearrangements.
The general aims of our research are
1) to investigate the molecular mechanism(s) responsible for such duplications;
2) to evaluate their role in the evolution of the higher primate genome; and
3) to assess their impact in contributing to polymorphism of both normal human chromosomes and chromosomes associated with genetic instability diseases.
Our approach has been to combine bioinformatics, large-scale comparative sequencing, phylogenetics and high-resolution FISH methods to address these questions.
We are committed to the further characterization of these complex regions of the genome and the development of assays to correlate their dynamic structure with chromosome function, gene evolution and human disease. My research philosophy combines various disciplines (evolutionary biology, human genetics/genomics and bioinformatics) to understand the mechanisms and consequences of novel forms of variation in the human genome. Such a synergism of various disciplines provides a powerful strategy to address biological processes of genome evolution. The development of tools and the conditions required to pursue such a holistic approach, with respect to studies of genome evolution, are unprecedented. With the advent of the information age, current large-scale sequencing of genomes and the development of powerful bioinformatics tools, such ‘complex’ and mulitfaceted research objectives will become increasingly tractable endeavors.
My overall goal is to contribute to this new era of genomics sciences as it applies to evolution and medicine and to impart the value of this scientific design, through teaching and mentorship, to the next generation of scientists.
Girirajan S, Rosenfeld JA, Cooper GM, Antonacci F, Siswara P, Itsara A, Vives L, Walsh T, McCarthy SE, Baker C, Mefford HC, Kidd JM, Browning SR, Browning BL, Dickel DE, Levy DL, Ballif BC, Platky K, Farber DM, Gowans GC, Wetherbee JJ, Asamoah A, Weaver DD, Mark PR, Dickerson J, Garg BP, Ellingwood SA, Smith R, Banks VC, Smith W, McDonald MT, Hoo JJ, French BN, Hudson C, Johnson JP, Ozmore JR, Moeschler JB, Surti U, Escobar LF, El-Khechen D, Gorski JL, Kussmann J, Salbert B, Lacassie Y, Biser A, McDonald-McGinn DM, Zackai EH, Deardorff MA, Shaikh TH, Haan E, Friend KL, Fichera M, Romano C, Gecz J, Delisi LE, Sebat J, King MC, Shaffer LG, Eichler EE. (2010). A recurrent 16p12.1 microdeletion supports a two-hit model for severe developmental delay. Nat Genet Mar;42(3):203-9.
Alkan C, Kidd JM, Marques-Bonet T, Aksay G, Antonacci F, Hormozdiari F, Kitzman JO, Baker C, Malig M, Mutlu O, Sahinalp SC, Gibbs RA, Eichler EE. (2009). Personalized copy number and segmental duplication maps using next-generation sequencing. Nat Genet Oct;41(10):1061–7.
Marques-Bonet T, Kidd JM, Ventura M, Graves TA, Cheng Z, Hillier LW, Jiang Z, Baker C, Malfavon-Borja R, Fulton LA, Alkan C, Aksay G, Girirajan S, Siswara P, Chen L, Cardone MF, Navarro A, Mardis ER, Wilson RK, Eichler EE. (2009). A burst of segmental duplications in the genome of the African great ape ancestor. Nature Feb 12;457(7231):877–81.
Itsara A, Cooper GM, Baker C, Girirajan S, Li J, Absher D, Krauss RM, Myers RM, Ridker PM, Chasman DI, Mefford H, Ying P, Nickerson DA, Eichler EE. (2009). Population analysis of large copy number variants and hotspots of human genetic disease. Am J Hum Genet. Feb;84(2):148–61.
Kidd JM, Cooper GM, Donahue WF, Hayden HS, Sampas N, Graves T, Hansen N, Teague B, Alkan C, Antonacci F, Haugen E, Zerr T, Yamada NA, Tsang P, Newman TL, Tuzun E, Cheng Z, Ebling HM, Tusneem N, David R, Gillett W, Phelps KA, Weaver M, Saranga D, Brand A, Tao W, Gustafson E, McKernan K, Chen L, Malig M, Smith JD, Korn JM, McCarroll SA, Altshuler DA, Peiffer DA, Dorschner M, Stamatoyannopoulos J, Schwartz D, Nickerson DA, Mullikin JC, Wilson RK, Bruhn L, Olson MV, Kaul R, Smith DR, Eichler EE. (2008). Mapping and sequencing of structural variation from eight human genomes. Nature May 1;453(7191):56–64.
Jiang Z, Tang H, Ventura M, Cardone MF, Marques-Bonet T, She X, Pevzner P, Eichler EE. (2007). Ancestral reconstruction of segmental duplications reveals punctuated cores of human genome evolution. Nat Genet Nov;39(11):1361–1368 (7 Oct 2007).
The Feder lab aims to uncover how the rapid evolution of pathogens and cancers within people exacerbates disease, and how a better understanding of this intra-host evolution can be harnessed to improve human health. We are particularly interested in how the complex environment of the human body shapes this process across spatial scales. From cellular coinfection mediating viral interaction, to heterogeneous tumor microenvironments creating distinct environmental niches, and to the diverse conditions that pathogens face in different organ systems, space is a critical driver of intra-host evolution, and failure to interrogate its effects limits our ability to understand disease. While spatially-resolved data is increasingly collected at higher resolution and frequency, our analytical tools to leverage these spatial data have not kept pace. We are meeting this need by developing new quantitative approaches to understand spatially-resolved intra-host genetic data across viral, bacterial and somatic domains of life.
State-dependent evolutionary models reveal modes of solid tumour growth
Elevated HIV viral load is associated with higher recombination rate in vivo
Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity
Our lab seeks to understand how genotype specifies phenotype by developing new technologies. We answer questions like how does a protein’s sequence encode its fold and function? How do changes in a person’s genome sequence influence disease risk, prognosis and treatment? How do gene expression patterns combine with protein activity to define cellular processes like growth, migration and communication?
We are global leaders in high-throughput, sequencing-based assays; we have deep expertise in large-scale experimental approaches and computational analyses. We have led the use of functional genomics data to interpret the clinical consequences of human genetic variation. And, by illuminating the effects of genetic variation at unprecedented scale, we have impacted diverse fields including protein design and engineering, protein structure determination, protein allostery and regulation, molecular and viral evolution, drug resistance, computational variant effect prediction, and the analysis of human genetic variants.
Our broad research goal is to help provide the computational methods necessary to achieve a complete, quantitative understanding of how cells function at the molecular level. Such an understanding will require three things: a “parts list”, or catalogue of all cellular molecules; a “wiring diagram” that specifies the interactions that occur between those molecules; and, finally, quantitative models of systems of interacting molecules. The advent of large-scale genome sequencing is bringing the possibility of completing the parts list within view, although substantial work remains to be done. Most current research in molecular biology is directed at filling the wiring diagram (which may be taken as specifying molecular “function”). The modeling of molecular systems, still in its infancy, will become increasingly important as the wiring diagram approaches completion and our ability to accurately quantitate cellular molecules improve.
Most of our research has been directed at constructing computational tools to support the acquisition of the parts list, in the form of a gene-annotated genome sequence. Common themes in this work include the development of appropriate probabilistic models for the type of data to be analyzed, the construction of efficient algorithms to carry out the probabilistic calculations, and the implementation of the algorithms in software which is then made widely available to the scientific user community. Probabilistic methods in particular have proven to be crucial in all of these areas, a reflection of the inherently probabilistic nature of such biological processes as meiotic recombination and sequence evolution, as well as of laboratory data.
Reliable identification of the protein parts list from a genome sequence remains an unsolved problem, which we are attacking on several fronts including improved probabilistic modeling of the genomic sequence, comparison to evolutionarily related sequences, and more effective utilization of available experimental data. Some of our work draws on our experience with sequence data processing in order to assemble expressed sequenced tags (ESTs), partial gene sequences that have been generated in a number of sequencing laboratories and submitted in unassembled form to the public databases. We recently used our EST assemblies to conclude that the number of genes in the human genome is substantially lower (about 35,000) than had been previously thought; we are applying the assemblies to make more reliable inferences of protein coding sequences, to catalogue alternative splicing (which can result in multiple proteins being encoded by the same gene), and to discover polymorphisms (differences in sequence between individuals). We are also beginning to undertake systematic experimental tests of our gene predictions.
The availability of sequence data from evolutionarily related organisms provides a powerful tool for identifying genes and illuminating their function. Through comparisons of yeast, human and nematode sequences we observed a number of years ago that a substantial fraction of genes (approaching 50%) appeared unique to an organism and its close relatives, an observation that has been repeatedly borne out with each new genome sequence that has been obtained. Most likely many of the “unique” genes do in fact have evolutionary homologues in more distant organisms but are simply evolving too quickly for the relationship to be readily detected, and we have developed methods for more sensitive detection of evolutionariy conserved sequence features. Evolutionary data should in principle help to understand the “wiring diagram” of molecular interactions, since it is primarily molecular interactions (including the self-interactions which determine tertiary structure) that constrain the allowed residue substitutions. We are currently working on improved probabilistic models of sequence evolution in the hope that these will allow such functional inferences.
Neuwald AF, Green P (1994)
Detecting patterns in protein sequences.
J. Mol. Biol. 239:698-712.
Green P (1995)
Ancient conserved regions in gene sequences.
Curr. Opin. Struct. Biol. 4:404-412.
Ewing B, Green P (1998)
Basecalling of automated sequencer traces using Phred. II. Error probabilities.
Genome Res. 8:186-194.
Green P., Koonin E. (1999)
Genomes and evolution: glimpses of an emerging synthesis.
Curr. Opin. Genet. Dev. 9: 621-623.
Ewing B., Green P. (2000)
Analysis of expressed sequence tags indicates 35,000 human genes.
Nature Genetics 25: 232-234.
Stem-cell-based oocyte (egg) production
The perpetuity of life is carried by germ cells (oocytes and sperm). With the in vitro germ cell development technology, we are approaching the mysteries of germ cells. This progress holds great potential to elucidate the underlying causes of infertility, paving the way for the development of a novel therapy.
Stem-cell-based embryo model
Embryo development (Embryogenesis) is a sequential process in which single cell results in 37 trillion cells. With in vitro reconstitution of embryo development, we are uncovering the fundamental mechanisms of development. These understandings help us to answer why pregnancy fails in uterus and how we should overcome that threat.
High-throughput genomics
We are leveraging the power of advanced genomics technologies to open up new perspectives on biology. In addition to high-throughput experimental genomic technologies, we are developing tools that can record / read / predict / manipulate cellular fate to dissect and understand the complex interplay between the genome and the continuum of normal and abnormal development.
Hamazaki N, Kyogoku H, Araki H, Miura F, Horikawa C, Hamada N, Shimamoto S, Hikabe O, Nakashima K, Kitajima TS, Ito T, Leitch HG, Hayashi K. Reconstitution of the oocyte transcriptional network with transcription factors. Nature. 2021 Jan;589(7841):264-269. doi: 10.1038/s41586-020-3027-9. Epub 2020 Dec 16. PubMed PMID: 33328630.
Murakami K, Hamazaki N, Hamada N, Nagamatsu G, Okamoto I, Ohta H, Nosaka Y, Ishikura Y, Kitajima TS, Semba Y, Kunisaki Y, Arai F, Akashi K, Saitou M, Kato K, Hayashi K. Generation of functional oocytes from male mice in vitro. Nature. 2023 Mar;615(7954):900-906. doi: 10.1038/s41586-023-05834-x. Epub 2023 Mar 15. PubMed PMID: 36922585.
Hikabe O, Hamazaki N, Nagamatsu G, Obata Y, Hirao Y, Hamada N, Shimamoto S, Imamura T, Nakashima K, Saitou M, Hayashi K. Reconstitution in vitro of the entire cycle of the mouse female germ line. Nature. 2016 Nov 10;539(7628):299-303. doi: 10.1038/nature20104. Epub 2016 Oct 17. PubMed PMID: 27750280.
Qiu C, Martin BK, Welsh IC, Daza RM, Le TM, Huang X, Nichols EK, Taylor ML, Fulton O, Oâ Day DR, Gomes AR, Ilcisin S, Srivatsan S, Deng X, Disteche CM, Noble WS, Hamazaki N, Moens CB, Kimelman D, Cao J, Schier AF, Spielmann M, Murray SA, Trapnell C, Shendure J. A single-cell transcriptional timelapse of mouse embryonic development, from gastrula to pup. bioRxiv. 2023 Apr 5;. doi: 10.1101/2023.04.05.535726. PubMed PMID: 37066300; PubMed Central PMCID: PMC10104014.
Choi J, Chen W, Minkina A, Chardon FM, Suiter CC, Regalado SG, Domcke S, Hamazaki N, Lee C, Martin B, Daza RM, Shendure J. A time-resolved, multi-symbol molecular recorder via sequential genome editing. Nature. 2022 Aug;608(7921):98-107. doi: 10.1038/s41586-022-04922-8. Epub 2022 Jul 6. PubMed PMID: 35794474; PubMed Central PMCID: PMC9352581.
full list: https://www.ncbi.nlm.nih.gov/myncbi/nobuhiko.hamazaki.1/bibliography/public/
I use population genetic theory and high-throughput biological sequence analysis to study recent evolutionary history in humans and other species. One of my primary research interests is the evolution of mutagenesis–I want to understand the forces that control DNA replication fidelity, the mutational breakdown of established traits, and the ultimate origin of new traits. Although DNA is replicated and repaired by highly conserved housekeeping pathways, the mutation rate appears to evolve surprisingly rapidly over evolutionary time. One way to see this is to compare the relative mutation rates of different 3-base-pair DNA motifs, expanding a one-dimensional “mutation rate” into a rich, multidimensional “mutation spectrum.” Due to changes in the mutation rates of particular DNA motifs, each human population and great ape species appears to have its own distinctive mutational spectrum that results from a unique set of mutational challenges and repair processes. My lab will work to decipher how this variation is genetically and environmentally determined and what evolutionary pressures (such as cancer, congenital disease, or life history) might be driving mutagenesis to change.
I am also broadly interested in the impact of demography, inbreeding, and hybridization on the dynamics of natural selection, particularly in the wake of gene flow between humans, Neanderthals, and other extinct hominids. I have developed a variety of computational methods for inferring population bottlenecks, divergence times, and admixture events at high resolution, and have written about the impact of Neanderthal interbreeding on the fitness of archaic and modern humans. My group will continue developing new statistical models that refine our understanding of how genomes and populations evolve, using methods derived from coalescent theory to visualize and extract the information contained in huge databases of whole genomes.
K. Harris and Jonathan K. Pritchard. “Rapid evolution of the human mutation spectrum.” eLife 6 (2017).
K. Harris and Rasmus Nielsen. “The genetic cost of Neanderthal introgression.” Genetics 203 (2016).
M. Raghavan,* M. Steinruecken,* K. Harris,* S. Schiffels,* S. Rasmussen,* M. DeGiorgio,* A. Albrechtsen,* C. Valdiosera,* M.C. Avila-Arcos,* A.-S. Malaspinas,* A. Eriksson, et al. “Genomic evidence for the Pleistocene and recent population history of Native Americans.” Science 349 (2015).
K. Harris. “Evidence for recent, population-specific evolution of the human mutation rate.” Proc Natl Acad Sci USA 112 (2015).
K. Harris and R. Nielsen. “Error-prone polymerase activity causes multinucleotide mutations in humans.” Genome Research 24 (2014).
K. Harris, S. Sheehan, J.A. Kamm, and Y.S. Song. “Decoding coalescent hidden Markov models in linear time.” Research in Computational Molecular Biology (2014).
S. Sheehan,* K. Harris,* and Y.S. Song. “Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach.” Genetics 194 (2013).
K. Harris and R. Nielsen. “Inferring demographic history from a spectrum of shared haplotype lengths.” PLoS Genetics (2013).
The focus of our research is on how the epigenome controls cell fates. One genome encodes the cellular functions for over 200 human cell types. We think the remarkable cellular specificities of different tissue/cell types within an individual are largely controlled by the epigenome. Epigenetic marks, including histone tail modifications and DNA methylation, are key identifiers of transcriptional output, and provide unique signatures of cellular identity. Using high-throughput experimental and computational technologies, we are trying to determine how the epigenome 1) confers pluripotency in hESCs and iPSCs; 2) controls transcription and cellular differentiation; and 3) provides a basis for many disease states. Similarly, we are trying to understand the consequences of epigenetic abnormalities in iPSCs.
In addition, histone modifications provide a remarkable means for annotating the genome. We and others have shown that histone modifications can identify cell-specific enhancer elements, promoter elements and regions of transcribed RNAs. We are continuing to develop unique epigenetic signatures throughout the human genome to better understand the aforementioned elements, and remaining “junk” DNA.
Egelhofer TA†, Minoda A†, Klugman S†, Kolasinska-Zwierz P, Alekseyenko AA, Cheung MS, Day DS, Gadel, S, Gorchakov, AA, Gu T, Kharchenko PV, Kuan, S, Latorre I, Linder-Basso D, Luu Y, Ngo Q, Perry M, Rechtsteiner A, Riddle NC, Schwartz YB, Shanower GA, Vielle A, Ahringer J, Elgin SCR, Kuroda MI, Pirrotta V, Ren B, Strome S, Park PJ^, Karpen G^, Hawkins RD^, and Lieb JD^ (2010) Assessment of histone-modification antibody quality. Nature Structural & Molecular Biology. Accepted. ^Co-corresponding Authors.
Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, Johnson BE, Fouse SD, Delaney A, Zhao Y, Olshen A, Ballinger T, Zhou X, Forsberg KJ, Gu J, Echipare L, O’Geen H, Lister R, Pelizzola M, Xi Y, Epstein CB, Bernstein BE, Hawkins RD, Ren B, Chung WY, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker JR, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, Costello JF. (2010) Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nature Biotechnology. 28(10), 852-862
Elo LL†, Järvenpää H†, Tuomela S†, Raghav S†, Ahlfors H, Laurila K, Gupta B, Lund RJ, Tahvanainen J, Hawkins RD, Oresic M, Lähdesmäki H, Rasool O, Rao KV, Aittokallio T, Lahesmaa R. (2010) Genome-wide profiling of interleukin-4 and STAT6 transcription factor regulation of human Th2 cell programming. Immunity 32: 852-862.
Hawkins RD†, Hon GC†, Ren B. (2010) Next-Generation Genomics: An Integrative Approach. Nature Reviews Genetics 11(7); 476-486.
Hawkins RD†, Hon GC †, Lee LK, Ngo Q, Lister R, Pelizzola M, Kuan S, Edsall LE, Ye Z, Espinoza C, Antosiewicz-Bourget J, Agarwahl S, Shen L, Ruotti V, Wang W, Stewart R, Thomson JA, Ecker JR, Ren B. (2010) Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. Cell Stem Cell 6: 279-491.
Lister R†, Pelizzola M†, Dowen RH, Hawkins RD, Hon GC, Tonti-Filippini J, Nery JR, Lee LK, Edsall LE, Antosiewicz-Bourget J, Ruotti V, Elwell A, Hernandez A, Stewart R, Millar AH, Thomson JA, Ren B, Ecker JR. (2009) Human DNA methylomes at single-base resolution reveal widespread cell-specific epigenetic signatures. Nature 462: 315-322.
Hon GC, Hawkins RD, Ren B. (2009) Predictive chromatin signatures in the mammalian genome. Hum Mol Genet.18(R2): R195-201.(Review)
Heintzman ND†, Hon G†, Hawkins RD†, Kheradpour P, Ching KA, Stuart RK, Harp LF, Ching CW, Liu H, Zhang X, Green RD, Crawford GE, Kellis M, and Ren B. (2009) Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459: 108-112.
†Equal contribution of work
