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Author

Jonathan S. Weissman

Bio: Jonathan S. Weissman is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topic(s): Ribosome profiling & Gene. The author has an hindex of 132, co-authored 357 publication(s) receiving 91528 citation(s). Previous affiliations of Jonathan S. Weissman include University of California, Berkeley & Carnegie Institution for Science.
Papers
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Journal ArticleDOI
Won-Ki Huh1, James V. Falvo1, Luke C. Gerke1, Adam S. Carroll1  +3 moreInstitutions (1)
16 Oct 2003-Nature
TL;DR: The construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.
Abstract: A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Here we describe the construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins. We classify these proteins, representing 75% of the yeast proteome, into 22 distinct subcellular localization categories, and provide localization information for 70% of previously unlocalized proteins. Analysis of this high-resolution, high-coverage localization data set in the context of transcriptional, genetic, and protein-protein interaction data helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.

4,071 citations


Journal ArticleDOI
Sina Ghaemmaghami1, Won-Ki Huh1, Kiowa Bower1, Russell W. Howson1  +4 moreInstitutions (1)
16 Oct 2003-Nature
TL;DR: A Saccharomyces cerevisiae fusion library is created where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location, and it is found that about 80% of the proteome is expressed during normal growth conditions.
Abstract: The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins--and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 10(6) molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

3,759 citations


Journal ArticleDOI
28 Feb 2013-Cell
TL;DR: This RNA-guided DNA recognition platform provides a simple approach for selectively perturbing gene expression on a genome-wide scale and can efficiently repress expression of targeted genes in Escherichia coli, with no detectable off-target effects.
Abstract: SUMMARY Targeted gene regulation on a genome-wide scale is a powerful strategy for interrogating, perturbing, and engineering cellular systems. Here, we develop a method for controlling gene expression based on Cas9, an RNA-guided DNA endonuclease from a type II CRISPR system. We show that a catalytically dead Cas9 lacking endonuclease activity, when coexpressed with a guide RNA, generates a DNA recognition complex that can specifically interfere with transcriptional elongation, RNA polymerase binding, or transcription factor binding. This system, which we call CRISPR interference (CRISPRi), can efficiently repress expression of targeted genes in Escherichia coli, with no detectable off-target effects. CRISPRi can be used to repress multiple target genes simultaneously, and its effects are reversible. We also show evidence that the system can be adapted for gene repression in mammalian cells. This RNA-guided DNA recognition platform provides a simple approach for selectively perturbing gene expression on a genome-wide scale.

3,499 citations


01 Jun 2010
TL;DR: Results show that changes in mRNA levels closely reflect the impact of miRNAs on gene expression and indicate that destabilization of target mRNAs is the predominant reason for reduced protein output.
Abstract: MicroRNAs (miRNAs) are endogenous approximately 22-nucleotide RNAs that mediate important gene-regulatory events by pairing to the mRNAs of protein-coding genes to direct their repression. Repression of these regulatory targets leads to decreased translational efficiency and/or decreased mRNA levels, but the relative contributions of these two outcomes have been largely unknown, particularly for endogenous targets expressed at low-to-moderate levels. Here, we use ribosome profiling to measure the overall effects on protein production and compare these to simultaneously measured effects on mRNA levels. For both ectopic and endogenous miRNA regulatory interactions, lowered mRNA levels account for most (>/=84%) of the decreased protein production. These results show that changes in mRNA levels closely reflect the impact of miRNAs on gene expression and indicate that destabilization of target mRNAs is the predominant reason for reduced protein output.

3,260 citations


Journal ArticleDOI
12 Aug 2010-Nature
Abstract: MicroRNAs (miRNAs) are endogenous approximately 22-nucleotide RNAs that mediate important gene-regulatory events by pairing to the mRNAs of protein-coding genes to direct their repression. Repression of these regulatory targets leads to decreased translational efficiency and/or decreased mRNA levels, but the relative contributions of these two outcomes have been largely unknown, particularly for endogenous targets expressed at low-to-moderate levels. Here, we use ribosome profiling to measure the overall effects on protein production and compare these to simultaneously measured effects on mRNA levels. For both ectopic and endogenous miRNA regulatory interactions, lowered mRNA levels account for most (>/=84%) of the decreased protein production. These results show that changes in mRNA levels closely reflect the impact of miRNAs on gene expression and indicate that destabilization of target mRNAs is the predominant reason for reduced protein output.

3,177 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations


Journal ArticleDOI
Thomas G. Dietterich1Institutions (1)
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.).

12,323 citations


Journal ArticleDOI
Jiirgen Cox1, Matthias Mann1Institutions (1)
TL;DR: MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data, detects peaks, isotope clusters and stable amino acid isotope–labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space and achieves mass accuracy in the p.p.b. range.
Abstract: Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

10,058 citations


Journal ArticleDOI
Cole Trapnell1, Adam Roberts2, Loyal A. Goff3, Loyal A. Goff4  +11 moreInstitutions (7)
TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Abstract: Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.

9,549 citations


Journal ArticleDOI
Tony Kouzarides1Institutions (1)
23 Feb 2007-Cell
TL;DR: The surface of nucleosomes is studded with a multiplicity of modifications that can dictate the higher-order chromatin structure in which DNA is packaged and can orchestrate the ordered recruitment of enzyme complexes to manipulate DNA.
Abstract: The surface of nucleosomes is studded with a multiplicity of modifications. At least eight different classes have been characterized to date and many different sites have been identified for each class. Operationally, modifications function either by disrupting chromatin contacts or by affecting the recruitment of nonhistone proteins to chromatin. Their presence on histones can dictate the higher-order chromatin structure in which DNA is packaged and can orchestrate the ordered recruitment of enzyme complexes to manipulate DNA. In this way, histone modifications have the potential to influence many fundamental biological processes, some of which may be epigenetically inherited.

9,366 citations


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Performance
Metrics

Author's H-index: 132

No. of papers from the Author in previous years
YearPapers
202125
202024
201927
201821
201719
201630