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Author

Nira Datta

Bio: Nira Datta is an academic researcher from University of Toronto. The author has contributed to research in topics: Histone H2A & Histone code. The author has an hindex of 9, co-authored 9 publications receiving 5091 citations.

Papers
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Journal ArticleDOI
30 Mar 2006-Nature
TL;DR: T tandem affinity purification was used to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae to identify protein–protein interactions, which will help future studies on individual proteins as well as functional genomics and systems biology.
Abstract: Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

2,975 citations

Journal ArticleDOI
TL;DR: It is shown that recruitment of Htz1 to chromatin requires the SWR-C, a TFIID-interacting protein that recognizes acetylated histone tails that promotes gene expression near silent heterochromatin.

629 citations

Journal ArticleDOI
26 Jan 2006-Nature
TL;DR: A three-protein complex containing the phosphatase Pph3 that regulates the phosphorylation status of γH2AX in vivo and efficiently dephosphorylatesγH 2AX in vitro is described.
Abstract: One of the earliest marks of a double-strand break (DSB) in eukaryotes is serine phosphorylation of the histone variant H2AX at the carboxy-terminal SQE motif to create gammaH2AX-containing nucleosomes. Budding-yeast histone H2A is phosphorylated in a similar manner by the checkpoint kinases Tel1 and Mec1 (ref. 2; orthologous to mammalian ATM and ATR, respectively) over a 50-kilobase region surrounding the DSB. This modification is important for recruiting numerous DSB-recognition and repair factors to the break site, including DNA damage checkpoint proteins, chromatin remodellers and cohesins. Multiple mechanisms for eliminating gammaH2AX as DNA repair completes are possible, including removal by histone exchange followed potentially by degradation, or, alternatively, dephosphorylation. Here we describe a three-protein complex (HTP-C, for histone H2A phosphatase complex) containing the phosphatase Pph3 that regulates the phosphorylation status of gammaH2AX in vivo and efficiently dephosphorylates gammaH2AX in vitro. gammaH2AX is lost from chromatin surrounding a DSB independently of the HTP-C, indicating that the phosphatase targets gammaH2AX after its displacement from DNA. The dephosphorylation of gammaH2AX by the HTP-C is necessary for efficient recovery from the DNA damage checkpoint.

500 citations

Journal ArticleDOI
TL;DR: This study systematically identified and estimated the relative abundance of stably associated polypeptides and RNA species using a combination of gel densitometry, protein mass spectrometry, and oligonucleotide microarray hybridization.

388 citations

Journal ArticleDOI
TL;DR: NuA4, the only essential histone acetyltransferase complex in Saccharomyces cerevisiae, acetylates the N-terminal tails of histones H4 and H2A.
Abstract: NuA4, the only essential histone acetyltransferase complex in Saccharomyces cerevisiae, acetylates the N-terminal tails of histones H4 and H2A. Affinity purification of NuA4 revealed the presence of three previously undescribed subunits, Vid21/Eaf1/Ydr359c, Swc4/Eaf2/Ygr002c, and Eaf7/Ynl136w. Experimental analyses revealed at least two functionally distinct sets of polypeptides in NuA4: (i) Vid21 and Yng2, and (ii) Eaf5 and Eaf7. Vid21 and Yng2 are required for bulk histone H4 acetylation and are functionally linked to the histone H2A variant Htz1 and the Swr1 ATPase complex (SWR-C) that assembles Htz1 into chromatin, whereas Eaf5 and Eaf7 have a different, as yet undefined, role. Mutations in Htz1, the SWR-C, and NuA4 cause defects in chromosome segregation that are consistent with genetic interactions we have observed between the genes encoding these proteins and genes encoding kinetochore components. Because SWR-C-dependent recruitment of Htz1 occurs in both transcribed and centromeric regions, a NuA4/SWR-C/Htz1 pathway may regulate both transcription and centromere function in S. cerevisiae.

284 citations


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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
30 Mar 2006-Nature
TL;DR: T tandem affinity purification was used to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae to identify protein–protein interactions, which will help future studies on individual proteins as well as functional genomics and systems biology.
Abstract: Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

2,975 citations

Journal ArticleDOI
TL;DR: This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest.
Abstract: Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

2,313 citations

Journal ArticleDOI
22 Jan 2010-Science
TL;DR: A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function.
Abstract: A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

2,225 citations

Journal ArticleDOI
TL;DR: This work addresses many aspects of remodeler biology: their targeting, mechanism, regulation, shared and unique properties, and specialization for particular biological processes.
Abstract: The packaging of chromosomal DNA by nucleosomes condenses and organizes the genome, but occludes many regulatory DNA elements. However, this constraint also allows nucleosomes and other chromatin components to actively participate in the regulation of transcription, chromosome segregation, DNA replication, and DNA repair. To enable dynamic access to packaged DNA and to tailor nucleosome composition in chromosomal regions, cells have evolved a set of specialized chromatin remodeling complexes (remodelers). Remodelers use the energy of ATP hydrolysis to move, destabilize, eject, or restructure nucleosomes. Here, we address many aspects of remodeler biology: their targeting, mechanism, regulation, shared and unique properties, and specialization for particular biological processes. We also address roles for remodelers in development, cancer, and human syndromes.

2,093 citations