Columbia University in the City of New York

Tavazoie Lab

Major Contributions

IPOD: a technology for global in vivo mapping of protein-DNA interactions. We developed a technology called In vivo protein occupancy display (IPOD) that enables comprehensive monitoring of bacterial transcriptional regulatory interactions. IPOD enables monitoring of thousands of dynamic protein-DNA interaction sites across the genome, revealing co-regulated regulons responsive to environmental and genetic perturbations. By applying IPOD to E. coli, we have found that the E. coli chromosome contains hundreds of kilo-base scale regions, bound by nucleoid proteins, that, through their transcriptional silencing effect, appear to function as prokaryotic analogs of eukaryotic heterochromatin.


Dynamic landscape of protein occupancy across the Escherichia coli chromosome.
PLoS Biology 19(6):e3001306 2021 (bioRxiv Jan. 2020)
Freddolino, P., Amemiya, H.M., Goss, T.J., Tavazoie, S.

Global protein occupancy landscape of a bacterial genome
Molecular Cell. 2009 Jul 31;35(2):247-53. PDF
Vora T, Hottes AK, Tavazoie S

In vivo mRNA display technology enables high-throughput proteomic analysis by DNA-sequencing. We developed a technology called In vivo mRNA display that enables high-throughput proteomics using next-generation sequencing as a readout. This is achieved by coupling in vivo expressed proteins to their encoding mRNAs using the high affinity interaction between a phage coat protein and its cognate RNA stem-loop. This technology bypasses the labor, cost, sensitivity, and throughput limitations of mass-spectrometry to advance a variety of proteomics applications.

In vivo mRNA display enables large-scale proteomics by next generation sequencing.
PNAS 117(43):26710 2020
Oikonomou, P., Salatino, R., Tavazoie, S.

PETRI-seq: A technology for high-throughput bacterial single-cell RNA sequencing. We developed PETRI-seq, the first high-throughput prokaryotic single-cell RNA-sequencing technology. PETRI-seq enables single-cell RNA sequencing of tens of thousands of bacteria with capture-rates that permit identification of rare cell-states.

Prokaryotic Single-Cell RNA Sequencing by in Situ Combinatorial Indexing.
Nature Microbiology
May 25, 2020
(bioRxiv Dec. 6, 2019)

Blattman, S.B., Jiang, W., Oikonomou, P., Tavazoie, S.


CRISPR-interference technology for rapid comprehensive genetic interrogation of bacterial phenotypes. We developed a technology called CALM that enables rapid generation of near-comprehensive CRISPR-interference libraries in bacteria. The massive scale of CALM libraries can produce a broad range of inhibition, providing high sensitivity for mapping the genetic basis of bacterial phenotypes, including the complex genetics of antibiotic sensitivity as demonstrated here.

Comprehensive genome-wide perturbations via CRISPR adaptation reveal complex genetics of antibiotic sensitivity.
Cell 180(5):1002 2020
Jiang, W, Oikonomou, P., Tavazoie, S.

Mutations to translation-related processes lead to extreme antibiotic persistence.  We utilized laboratory evolution for antibiotic persistence in E. coli to discover mutations that increase persistence frequency to multiple antibiotics. This study revealed that mutations targeting the process of translation result in population heterogeneity and consequent >100-fold increase in persistence rate.

Extreme Antibiotic Persistence via Heterogeneity-Generating Mutations Targeting Translation.
mSystems 5(1), pii: e00847-19 Januray 21, 2020
Khare, A., Tavazoie, S.

Discovery of stochastic tuning: a mechanism for adaptive reprogramming of gene expression by trial & error. Using a combination of theoretical and experimental studies, we discovered a new principle of cellular adaptation we call stochastic tuning. Stochastic tuning is a versatile alternative to conventional gene regulation, enabling cells to adapt to unfamiliar environments through active reinforcement of random gene expression changes that improve cellular health. Stochastic tuning may be a widespread phenomenon in eukaryotes, enabling cells to optimize their gene expression states beyond the precision possible with conventional regulatory regulators such as transcription factors and RNA binding proteins.

Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry.
eLife 2018;7:e31867 DOI: 10.7554/eLife.31867 (bioRxiv, May 2017)
Freddolino, P., Yang, J., Momen-Roknabadi, A., Tavazoie, S.

A systems biology framework for discovering the genetic determinants of antagonism in multi-species bacterial communities. We used a set of unbiased genome-scale approaches to systematically define the factors that contribute to antagonism in a two-species model system of P. aeruginosa and E. coli. We found that iron sequestration led to exploitative competition, while phenazine exposure led to interference competition. Laboratory evolution experiments revealed adaptive strategies to bypass these strategies. This study demonstrates the power of agnostic systems biology approaches in revealing the molecular basis of interference and competition in multi-species communities.

Multifactorial competition and resistance in a two-species bacterial system.
PLoS Genetics 2015 11(12):e1005715
Khare, A., Tavazoie, S.


Discovery that the RNA-binding protein HNRNPA2B1 is a nuclear reader of the m(6)A modifications on mRNAs. In this collaborative work we find that the RNA-binding protein HNRNPA2B1 binds m(6)A modified RNAs in vivo within the nucleus and leads to similar alternative splicing effects as perturbations to the m(6)A writer METTL3.

HNRNPA2B1 is a mediator of m6A-dependent nuclear RNA processing events.
Cell 2015 162(6):1299-308
Alarcon, C.R., Goodarzi, H., Lee, H, Liu, X., Tavazoie, S., Tavazoie, S.F.

Discovery of a structural RNA regulatory element controlling mRNA stability and breast cancer metastasis.  We utilized a set of computational and experimental studies to probe global transcriptomes and discover a structural RNA element that regulates mRNA stability and breast cancer metastasis through binding of the RNA binding protein TARBP2.

Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins.
Nature. 2014 513, 255-260
Goodarzi, H., Zhang, S., Buss, C.G., Fish, L., Tavazoie, S., & Tavazoie, S.F.


High-throughput profiling of conserved human 3’UTR sequences for post-transcriptional activity. We generated a fluorescent reporter library of conserved human 3’UTR sequences and used FAC-sorting and next-generation sequencing to comprehensively profile their post-transcriptional contributions. This led to the discovery of a large set of linear and structural RNA elements that post-transcriptionally modulate the fates of human transcripts.

Systematic Identification of Regulatory Elements in Conserved 3′ UTRs of Human Transcripts.
Cell Reports. 2014 Mar 20.
Oikonomou, P., Goodarzi, H. & Tavazoie, S.

Synaptic state matching: a new mechanism of synaptic potentiation that enables self-supervised predictive learning in neural networks. In this theoretical work, we introduced the principle of ‘synaptic state matching’(SSM) which is a local, biologically plausible mechanism for synaptic potentiation and homeostasis in neural networks. We show that SSM, implemented in artificial neural networks, enables generation of stable predictive internal representations, leading to pattern completion, sequence-learning and unsupervised feature detection. SSM is a biologically plausible mechanism for the ‘predictive coding hypothesis’ and prescribes the low-level hardware required for self-supervised learning in natural and artificial intelligence systems. 

Synaptic state matching: a dynamical architecture for predictive internal representation and feature detection.
PLoS One. 2013 Aug 26;8(8):e72865. doi: 10.1371/journal.pone.0072865.
Tavazoie, S.

Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception.
Nature precedings (Aug 2011)
Tavazoie, S.


Rapid adaptation of bacteria to extreme environments through loss-of-function mutations. In this work, we compiled a set of diverse transposon-profiling experiments, previously published by our group, to show that loss-of-function mutations are a substantial contributor to adaptation of bacteria to extreme environments. Using these observations, we argue that often fitness bottlenecks are caused by regulatory and metabolic constraints that can be easily bypassed by loss-of-function of individual regulatory genes. This work has implications for diverse areas of investigation including emergence of antibiotic resistance, understanding limitations of unculturable bacteria, and engineering of synthetic microbes for extreme environments.

Bacterial adaptation through loss of function.
PLoS Genetics. 2013;9(7):e1003617. doi: 10.1371/journal.pgen.1003617.
Hottes, A.K., Freddolino, P.L., Khare, A., Donnell, Z.N., Liu, J.C., & Tavazoie, S.


Large increases in antibiotic persistence through dozens of easily acquired mutations. We used transposon mutagenesis and profiling to show that there exist dozens of genes in the E. coli genome that when disrupted cause substantially increased antibiotic persistence. The functions of these loci are informative of common mechanisms of persistence. The large number of loci and the ease with which mutations in them can cause loss of function suggest that rapid evolution of increased persistence-rate may be a common process of critical significance in the setting of therapeutic tolerance to antibiotics.

Large mutational target size for rapid emergence of bacterial persistence.
PNAS. 2012 Jul 16. [Epub ahead of print] PDF
Girgis, H.S., Harris, K., & Tavazoie, S.


A single amino-acid mutation causes a dramatic global shift in the fitness landscape of E. coli. Through extensive phenotypic analyses, we have characterized the systems-level consequences of a missense mutation in the global transcriptional terminator Rho of E. coli. We find that a single amino acid change in Rho results in a massive change in the fitness landscape of the cell, with widely discrepant fitness consequences of identical single locus mutations in rho* versus rho WT backgrounds. Our observations reveal the extent to which a single regulatory mutation can transform the entire fitness landscape of the cell, causing a massive change in the interpretation of individual mutations and altering the evolutionary trajectories which may be accessible to a bacterial population.

Fitness landscape transformation through a single amino acid change in the Rho terminator
PLoS Genetics, Vol. 8, No. 5. (2012), e1002744
Freddolino, P., Goodarzi, H., & Tavazoie, S.


TEISER: a structural RNA motif finder that enables transcriptome-wide discovery of post-transcriptional regulatory elements. In this work, we demonstrate the discovery of structural RNA regulatory elements that underlie post-transcriptional regulation of gene expression in mammalian genomes. To accomplish this, we developed a new algorithm for de novo discovery of structural RNA regulatory elements (TEISER) that utilizes context-free grammar representations that capture both sequence and structure. We take one of the top predictions of an mRNA stability element and using biochemistry, mass spectrometry and in vivo binding studies, show that it is bound by HNRPA2B1, a human RNA binding protein that binds this element and stabilizes a large number of its target genes. Our approach has revealed the putative involvement of a large family of structural RNA elements that control various fates of mRNA, including stability, splicing, localization, and translation.

Systematic discovery of structural elements governing stability of mammalian messenger RNAs
Nature 485, 264-268 (2012) PDF
Goodarzi, H., Najafabadi, H.S., Oikonomou, P., Greco, T.M., Fish, L., Salavati, R., Cristea, I.M., & Tavazoie, S.

Fitness profiling and experimental evolution reveal regulatory and metabolic adaptation of E. coli to a severe stress. In this work, we utilized global transposon profiling to understand the genetic basis of ethanol tolerance in E. coli. Among multiple adaptive pathways of osmoregulation and cell-wall biogenesis, we also discovered that loss-of-function mutations in specific regulators have the potential of reversing the flux through the TCA, leading to ethanol detoxification. By performing laboratory evolution experiments and phenotypic and metabolic analyses, we show that this hypothetical solution is actually utilized by E. coli in practice, leading to ethanol degradation by the TCA. This work demonstrates the flexibility of regulatory and metabolic networks in rewiring cell metabolism to enable rapid adaptation to extreme environments.

Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli
Mol Syst Biol. 2010 Jun 8; 6:378. PDF
Goodarzi H, Bennett BD, Amini S, Reaves ML, Hottes AK, Rabinowitz JD, Tavazoie S


Systematic discovery of regulatory and pathway perturbations across large cancer datasets. In this work, we describe the development and application of algorithms that reveal the transcriptional and post-transcriptional regulatory networks that are dysregulated across large cohorts of human cancer transcriptomes. Our information-theoretic analyses, across a diverse set of human cancers, revealed the majority of previously known cancer pathways along with many more novel predictions encompassing DNA and RNA regulatory elements and pathways. These analyses and the accompanying experimental validations demonstrate that agnostic systems biology approaches can be used to systematically discover regulatory networks and pathways that underlie oncogenesis and oncogenic progression.

Revealing global regulatory perturbations across human cancers
Molecular Cell. 2009 Dec 11; 36:900-911. PDF
Goodarzi, H, Elemento, O, Tavazoie S

Systematic genetic dissection of bacterial phenotypes within biofilms. In these studies, we devised a global transposon-based approach to determine the genetic basis of biofilm formation (E. coli) and biofilm-dependent antibiotic tolerance (p. aeruginosa). These studies revealed biophysical and biochemical insights into the unique nature of bacterial life within biofilms and the power of a global systematic approach for rapidly determining the genetic basis of complex phenotypes.

Genetic dissection of an exogenously induced biofilm in laboratory and clinical isolates of E. coli.
PLoS Pathogens 2009 May;5(5):e1000432. Epub 2009 May 15. PDF
Amini S, Goodarzi H, Tavazoie S

Fitness Landscape of Antibiotic Tolerance in Pseudomonas aeruginosa Biofilms
PLoS Pathogens 7(10):e1002298 (2011). PDF
Amini S., Hottes, A.K., Tavazoie, S.

ADAM: a technology for rapid discovery of adaptive mutations contributing to bacterial fitness. In this work, we describe the development of a technology (ADAM) for systematically identifying the subset of adaptive mutations that contribute to changes in fitness in laboratory evolved bacteria. This technology alleviates the labor-intensive task of separating driver and passenger mutations in a variety of natural and biomedically driven evolutionary trajectories.

Global discovery of adaptive mutations
Nature Methods. 2009 Aug; 6(8):581-3. PDF
Goodarzi H, Hottes AK, Tavazoie S


Discovery of predictive behavior in E. coli. This is the first demonstration of predictive behavior by microorganisms. Using a combination of theoretical, simulation, and experimental work, we show that the biochemical networks of the bacterium E. coli are able to carry out predictive behavior akin to animal nervous systems. This work was motivated by the observation that microbial habitats are highly structured in space and time and therefore the perception of change in one parameter (e.g. increase in temperature) can be highly predictive of an impending change in another parameter (e.g. decrease in oxygen). Utilizing an in silico ecology, we showed that such dynamically structured environments give rise to the evolution of complex biochemical networks that predict the short-term trajectory of their environments. We provided experimental evidence for such anticipatory behavior in responses of E. coli to changes in temperature and oxygen that mirror transitions between the outside environment and the mammalian gastrointestinal tract. Using laboratory evolution, we further demonstrated that circuits controlling predictive behavior can be rewired by exposing E. coli to dynamic environments with the opposite correlation to that of the native habitat. Predictive behavior in microbes has since been demonstrated in other microbes, including yeast, suggesting that it is a widespread phenomenon. These studies have fundamental implications for our understanding of microbial behaviors, and have reshaped even the most basic interpretation of laboratory experiments.

Predictive behavior within microbial genetic networks
Science (2008) 320:1313-1317, Epub 2008 May 8. PDF
Tagkopoulos I, Liu Y, Tavazoie S

Beyond homeostasis: a predictive-dynamic framework for understanding cellular behavior.
Annu Rev Cell Biol 2012. Vol. 28: 363-384
Freddolino, P.L., & Tavazoie, S.


FIRE: an information theoretic algorithm for sensitive discovery of DNA and RNA regulatory elements across gene expression datasets. In this work, we introduce a novel information-theoretic approach for discovering DNA and RNA regulatory elements that underlie global changes in gene expression. Our approach performs a course-grained comprehensive search for DNA and RNA sequence motifs that have high mutual information with large-scale observations of gene expression. In doing so, we are able to discover regulatory elements with exceptionally high sensitivity and near zero false discovery rates across organisms ranging from bacteria to human. Our algorithm is implemented in the FIRE (Finding Informative Regulatory Elements) software, available for local or server-based use ( FIRE has been used by hundreds of independent studies to reveal transcriptional and post-transcriptional regulatory programs underlying diverse physiological, developmental, and pathological processes.

A universal framework for regulatory element discovery across all genomes and data-types
Molecular Cell (2007) 28(2):337-50. PDF
Elemento O, Slonim N, Tavazoie, S

Revealing global regulatory perturbations across human cancers
Molecular Cell. 2009 Dec 11; 36:900-911. PDF
Goodarzi, H, Elemento, O, Tavazoie S

Molecular topography of an entire nervous system.
Cell Jul 2:S0092-8674(21)00758-3 2021
Taylor, S.R. et al.


Systematic interrogation of gene function and epistasis in bacterial chemotaxis using transposon profiling.  Here, we introduced an efficient framework for rapidly discovering the genetic basis of bacterial traits through the power of deep transposon mutagenesis, library selection, and parallel readout of transposon mutant spectra through microarray hybridization. In order to characterize the sensitivity of this technology, we applied it to one of the best understood bacterial behaviors-flagella mediated chemotaxis. Our strategy allowed a single graduate student to systematically survey the contribution of every gene in the E. coli genome to chemotaxis within the time-scale of a few weeks. This led to the identification of 95% of the roughly 50 known flagella and chemotaxis genes. Remarkably, we also discovered three dozen additional genes that clearly impact motility, many of which were genes of previously unknown function. By utilizing deep transposon libraries in the background of individual mutants, we were able to perform global epistasis analysis and place many of these novel genes within the context of known biochemical and regulatory pathways, including LPS signaling and cyclic di-GMP metabolism. The success of our systematic perturbation approach in revealing the genetic basis of a complex phenotype laid the groundwork for the development of many advances in the field such as CRISPR-based perturbation libraries that came years later.

A comprehensive genetic characterization of bacterial motility
PLoS Genetics (2007) 3 (9): e154. PDF
Girgis H, Liu Y, Ryu W, Tavazoie S

Computational discovery of genes and gene modules underlying microbial traits. In this work, we introduce a computational framework that reveals the modular genetic architecture of bacterial traits by correlating the cross-species distribution of genes and phenotypes.  At the core of our approach is the simple assumption that proteins required for the expression of a phenotype are statistically enriched in the organisms that manifest the phenotype.  Since our approach does not require any prior knowledge of genes involved, it is ideal for determining the genetic basis of bacterial traits that are entirely uncharacterized.  We used information-theory to measure correlations between phylogenetic profiles (a vector of ones [zeros] for presence [absence], of a gene across the genomes), and phenotypic profiles (a vector of ones [zeros] for the presence [absence], of a trait across the genomes).  This yielded a highly sensitive framework that allowed successful genetic characterization of a variety of traits such as swimming motility, sporulation, and cellular morphology.  In addition to identifying the components of these modules, we showed that preferential co-inheritance of subsets of genes within them allows us to organize them into functional sub-modules.  For example, in the case of flagellar-mediated chemotaxis, our approach automatically revealed three independent sub-modules: the flagellar apparatus, the chemotaxis sensory/signal transduction network, and L/P rings which anchor the flagellum into the outer membrane of gram-negative bacteria, but which are absent from gram-positives.  With the massive increase in the rate of genome sequencing, approaches like the one introduced here allows efficient and rapid determination of genotype/phenotype relationships across diverse bacterial species of basic and clinical importance.

Ab initio genotype-phenotype association reveals the intrinsic modularity of genetic networks.
Molecular Systems Biology 2006; 2:2006.0005. Epub 2006 Jan 31. PDF
(Slonim N, Elemento O, equal contribution) and Tavazoie S

A cross-genomic approach for systematic mapping of phenotypic traits to genes.
Genome Research 2004 Jan; 14(1):109-15. PDF
Jim K, Parmar K, Singh M, Tavazoie S


Network-level conservation enables genome-wide discovery of conserved DNA/RNA regulatory elements without sequence alignment. A powerful approach for discovering DNA regulatory elements is to look for their conservation between closely related species.  Usually, such elements are identified through comparisons of individual genomic loci using alignments of regulatory regions from multiple species.  In these studies, we introduced a novel concept called ‘network-level conservation’ that extends this one-gene at a time comparison to the whole genome, by demanding that the network of regulatory interactions across the entire genome be conserved.  This strategy gave us unprecedented sensitivity to detect functional genomic elements, without the need for alignments, while requiring only two genomes.  Using network-level conservation, we generated comprehensive catalogues of regulatory elements for many model organisms including the very first global inventory of conserved elements between human and mouse.  These features also allowed us to compare regulatory element catalogues between distant phylogenetic groups.  Although we found many of the binding sites to be highly conserved in these comparisons, strikingly, the genes regulated by these elements are entirely different, and do not overlap any more than would be expected by chance.  In this way, our work revealed that there is extensive re-wiring of transcriptional networks even between relatively closely related species.  Subsequently, we found such re-wiring to also be a dominant feature in the evolution of post-transcriptional regulatory elements, including targets of microRNAs and RNA-binding proteins.

Whole-genome discovery of transcription factor binding sites by network-level conservation.
Genome Research 2004 Jan; 14(1):99-108. PDF
Pritsker M, Liu Y, Beer M, Tavazoie S

Fast and systematic genome-wide discovery of conserved regulatory elements using a non-alignment based approach.
Genome Biology (2005) 6(2):R18. Epub 2005 Jan 26. PDF
Elemento O, Tavazoie S

Revealing posttranscriptional regulatory elements through network-level conservation.
PLoS Computational Biology 2005 Dec; 1(7): e69 Epub 2005 Dec. 9. PDF
(Chan S, Elemento O, equal contribution) and Tavazoie S


Patterns of histone acetylation are predictive of gene expression dynamics in yeast. In eukaryotes, DNA is wrapped around histone proteins (within chromatin), and enzymatic modifications of histone tails is known to be important for many processes that operate on DNA—including transcription and DNA replication.  In collaboration with Michael Grunstein’s laboratory, we systematically explored the extent to which modifications (acetylation) of these histone tails is associated with changes in gene expression.  We observed that the patterns of multiple such modifications convey unique information about the expression state of genes.  Our observations are consistent with one dimension of the “histone code hypothesis”, namely that consistent modification patterns code for specific processes that are orchestrated on chromatin in order to affect processes such as transcription or DNA replication. 

Mapping global histone acetylation patterns to gene expression.
Cell 2004 Jun 11; 117(6):721-33. PDF
Kurdistani SK, Tavazoie S, Grunstein M


The first demonstration of a machine-learning strategy to decode DNA regulatory logic, enabling prediction of gene expression dynamics from sequence alone. In this work, we introduced a computational framework that allows predictive modeling of gene expression dynamics through machine-learning models that map sequence to gene expression. This approach was the first to successfully predict global gene expression patterns based solely on local DNA sequences. This allowed us not only to predict the expression pattern of genes, but also to assess the extent to which the information within local DNA influenced gene expression patterns across microarray experiments. We designed our approach to function in a largely unbiased fashion with respect to previous biological knowledge, using only microarray expression data, genomic sequence, and minimal constraints. This systematic strategy revealed DNA regulatory elements that, in a combinatorial fashion, operate to affect gene-expression. We also discovered that the functionality of many of these elements requires precise spatial and pair-wise configurational constraints within regulatory regions. These findings addressed a major conundrum in the field: what determines the functional context of transcription-factor binding sites? Our ability to achieve robust predictions for >70% of genes established that, at least in yeast, most of the regulatory information resides in the ~800 base-pairs 5’ upstream of the genes. In this work, we went further to show that the same framework can successfully reveal DNA regulatory logic underlying temporal gene expression dynamics during C. elegans development. This work established that a sufficiently constrained machine-learning framework can reveal DNA cis-regulatory programs on a genomic scale.  As such, this work greatly influenced subsequent work in the field, and laid the foundation for systematic elucidation of such regulatory programs in mammalian genomes.

Predicting gene expression from sequence.
Cell 2004 Apr 16; 117(2):185-98. PDF
Beer MA, Tavazoie S


Systematic discovery of DNA regulatory elements and pathways underlying gene expression dynamics across the yeast cell-cycle. This study presented one of the first systematic analyses of microarray expression data.  Here, we implemented a novel machine-learning framework that utilized microarray expression data and genomic sequence information to identify co-expressed sets of genes and the DNA regulatory elements that implement their co-regulation. This approach revealed most of the known cell-cycle regulatory transcription factor binding sites in yeast, along with novel predictions that have since been experimentally validated by us and others.  The computational framework we introduced here was subsequently adopted and extended broadly by the community, leading to thousands of follow-up publications.  These analyses included: discovering co-expressed regulons by clustering of expression data, discovery and characterization of regulatory motifs in co-expressed genes, and statistical enrichment of gene-sets for functional categories, a predecessor to gene-set enrichment analysis. More broadly, we demonstrated that a largely agnostic machine-learning framework can be used to systematically reverse-engineer the regulatory network of a complex process, recapitulating decades of work across dozens of labs.

Systematic determination of genetic network architecture.
Nature Genetics 1999 22: 281-285. PDF
Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM


In vivo methylase protection: a technology for global in vivo monitoring of DNA-protein interactions in bacteria. In this work, we describe a new technology that enables in vivo monitoring of DNA-protein interactions in bacteria. Proteins when bound to DNA block access of DNA methylases to their target sites and this ‘methylase protection’ can be detected using methylation-sensitive restriction enzymes to reveal in vivo bound sites throughout the genome. Here, we showed dynamic occupancy patterns of two dozen sites as a function of genetic and environmental conditions in E. coli. Our work introduced the notion of clustering analysis on molecular profiling data, revealing regulons that show common dynamic patterns across these conditions. Clustering analysis was subsequently utilized by us and others to discover significant patterns across a variety of molecular profiling data, including microarray expression, proteomics, and metabolomics.

Quantitative whole-genome analysis of DNA-protein interactions by in vivo methylase protection in E. coli.
Nature Biotechnology 1998 16: 566-571. PDF
Tavazoie S, Church GM


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