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On February 23, 2018, PubMed Central Canada (PMC Canada) will be taken offline permanently. No author manuscripts will be deleted, and the approximately 2,900 manuscripts authored by Canadian Institutes of Health Research (CIHR)-funded researchers currently in the archive will be copied to the National Research Council’s (NRC) Digital Repository over the coming months. These manuscripts along with all other content will also remain publicly searchable on PubMed Central (US) and Europe PubMed Central, meaning such manuscripts will continue to be compliant with the Tri-Agency Open Access Policy on Publications.

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1.  A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution 
eLife  null;5:e16970.
Understanding chromatin function requires knowing the precise location of nucleosomes. MNase-seq methods have been widely applied to characterize nucleosome organization in vivo, but generally lack the accuracy to determine the precise nucleosome positions. Here we develop a computational approach leveraging digestion variability to determine nucleosome positions at a base-pair resolution from MNase-seq data. We generate a variability template as a simple error model for how MNase digestion affects the mapping of individual nucleosomes. Applied to both yeast and human cells, this analysis reveals that alternatively positioned nucleosomes are prevalent and create significant heterogeneity in a cell population. We show that the periodic occurrences of dinucleotide sequences relative to nucleosome dyads can be directly determined from genome-wide nucleosome positions from MNase-seq. Alternatively positioned nucleosomes near transcription start sites likely represent different states of promoter nucleosomes during transcription initiation. Our method can be applied to map nucleosome positions in diverse organisms at base-pair resolution.
eLife digest
Plants, animals and other eukaryotes wrap their DNA around complexes of proteins called histones to form repeating units known as nucleosomes. The interaction between histones and DNA is strong, and so the DNA region inside a nucleosome has limited access to other proteins, including those that drive the expression of genes.
Moving a nucleosome slightly can change the access to its DNA and significantly impact how the genes in the region are regulated. Nevertheless, determining the position of nucleosomes accurately or testing how nucleosomes are different between individual cells are challenging tasks. Most methods for identifying nucleosomes use an enzyme called micrococcal nuclease (or MNase for short) to break down the DNA that isn’t protected in nucleosomes, followed by high-throughput DNA sequencing to identify the DNA fragments that remain. However, this technique, known as MNase-seq, is limited because it only measures an average location of the nucleosomes across millions of cells.
Now, Zhou, Blocker et al. have developed a new computational approach to identify nucleosome positions more accurately using MNase-seq data obtained from both yeast and human cells. This approach revealed that in more than half of the yeast genome, a given nucleosome is found at slightly different positions in different cells. Nucleosomes positioned near the beginning of a gene mark it open or closed for binding by the cell’s gene expression machinery. Zhou, Blocker et al. suggest that the nucleosomes’ positions influence how gene expression starts via a multi-step process.
Following on from this work, the next step is to use the newly developed method to study how nucleosome positions change when other regulators of gene activity bind and when genes are activated or repressed.
PMCID: PMC5094857  PMID: 27623011
nucleosome position; MNase-seq; gene regulation; template-based model; heterogenity; base-pair resolution; Human; S. cerevisiae
2.  Getting Started in Probabilistic Graphical Models 
PLoS Computational Biology  2007;3(12):e252.
PMCID: PMC2134967  PMID: 18069887
3.  Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen 
Molecular Biology of the Cell  2016;27(8):1383-1396.
Steady-state and transiently perturbed nitrogen-limited chemostats show that nitrogen abundance is a primary signal controlling nitrogen-responsive gene expression. When cells experience an increase in nitrogen, some transcripts are rapidly degraded, suggesting that accelerated mRNA degradation contributes to remodeling of gene expression.
Cell growth rate is regulated in response to the abundance and molecular form of essential nutrients. In Saccharomyces cerevisiae (budding yeast), the molecular form of environmental nitrogen is a major determinant of cell growth rate, supporting growth rates that vary at least threefold. Transcriptional control of nitrogen use is mediated in large part by nitrogen catabolite repression (NCR), which results in the repression of specific transcripts in the presence of a preferred nitrogen source that supports a fast growth rate, such as glutamine, that are otherwise expressed in the presence of a nonpreferred nitrogen source, such as proline, which supports a slower growth rate. Differential expression of the NCR regulon and additional nitrogen-responsive genes results in >500 transcripts that are differentially expressed in cells growing in the presence of different nitrogen sources in batch cultures. Here we find that in growth rate–controlled cultures using nitrogen-limited chemostats, gene expression programs are strikingly similar regardless of nitrogen source. NCR expression is derepressed in all nitrogen-limiting chemostat conditions regardless of nitrogen source, and in these conditions, only 34 transcripts exhibit nitrogen source–specific differential gene expression. Addition of either the preferred nitrogen source, glutamine, or the nonpreferred nitrogen source, proline, to cells growing in nitrogen-limited chemostats results in rapid, dose-dependent repression of the NCR regulon. Using a novel means of computational normalization to compare global gene expression programs in steady-state and dynamic conditions, we find evidence that the addition of nitrogen to nitrogen-limited cells results in the transient overproduction of transcripts required for protein translation. Simultaneously, we find that that accelerated mRNA degradation underlies the rapid clearing of a subset of transcripts, which is most pronounced for the highly expressed NCR-regulated permease genes GAP1, MEP2, DAL5, PUT4, and DIP5. Our results reveal novel aspects of nitrogen-regulated gene expression and highlight the need for a quantitative approach to study how the cell coordinates protein translation and nitrogen assimilation to optimize cell growth in different environments.
PMCID: PMC4831890  PMID: 26941329
4.  Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state 
eLife  null;3:e03915.
The conserved Musashi (Msi) family of RNA binding proteins are expressed in stem/progenitor and cancer cells, but generally absent from differentiated cells, consistent with a role in cell state regulation. We found that Msi genes are rarely mutated but frequently overexpressed in human cancers and are associated with an epithelial-luminal cell state. Using ribosome profiling and RNA-seq analysis, we found that Msi proteins regulate translation of genes implicated in epithelial cell biology and epithelial-to-mesenchymal transition (EMT), and promote an epithelial splicing pattern. Overexpression of Msi proteins inhibited the translation of Jagged1, a factor required for EMT, and repressed EMT in cell culture and in mammary gland in vivo. Knockdown of Msis in epithelial cancer cells promoted loss of epithelial identity. Our results show that mammalian Msi proteins contribute to an epithelial gene expression program in neural and mammary cell types.
eLife digest
All living things start life as a single cell, but many organisms develop into a collection of different, specialized cells. Most of the cells in an organism can only divide to make more of the same type of cell; however, stem cells are different because they can ‘differentiate’ and develop into several different cell types.
A key step in the development of an embryo is called the epithelial-to-mesenchymal transition, in which an epithelial cell—a cell type that normally lines body surfaces and cavities—begins to crawl away from the tissue it is in and starts to differentiate. This transition also allows cancer cells to leave tumors and spread around the body, in a process known as metastasis.
In mammals, two proteins called Musashi1 and Musashi2 are abundant in stem cells and brain cancers, but are rarely found in specialized tissues and cells. Katz, Li et al. now find that the Musashi proteins are also often overexpressed in human breast, lung, and prostate tumors. In addition, Musashi proteins are much less abundant in cells that have completed an epithelial-to-mesenchymal transition.
When Katz, Li et al. artificially reduced the amounts of Musashi proteins in breast cancer cells, the cells migrated and dispersed, as if becoming mesenchymal cells. Furthermore, many of the genes normally used in epithelial cells were switched off. In comparison, artificially increasing the levels of Musashi proteins halted the movement of mesenchymal cells and led to increased levels of genes used in epithelial cells, as if they were reverting to epithelial cells. Therefore, it appears that the Musashi proteins prevent epithelial cells from developing mesenchymal properties.
Katz, Li et al. investigated how Musashi proteins work at the molecular level by studying neural and mammary cells in mice. This revealed that Musashi proteins control the steps that lead to the epithelial-to-mesenchymal transition by binding to the tail end of the RNA molecules that include the instructions to make certain proteins. This affects how often these proteins can be made from the RNA molecules. Katz, Li et al. suggest that Musashi proteins may similarly control the behavior of progenitor and stem cells in many other tissues as well; however, further study is needed to confirm this.
PMCID: PMC4381951  PMID: 25380226
cancer genomics; translational regulation; alternative splicing; epithelial–mesenchymal transition; human; mouse
5.  Correction: Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates 
PLoS ONE  2013;8(10):10.1371/annotation/9f5465d9-e9fa-4a80-84ca-9c9a3f6e82c7.
PMCID: PMC3794061
6.  Defining the essential function of yeast Hsf1 reveals a compact transcriptional program for maintaining eukaryotic proteostasis 
Molecular cell  2016;63(1):60-71.
Despite its eponymous association with the heat shock response, yeast heat shock factor 1 (Hsf1) is essential even at low temperatures. Here we show that engineered nuclear export of Hsf1 results in cytotoxicity associated with massive protein aggregation. Genome-wide analysis revealed that Hsf1 nuclear export immediately decreased basal transcription and mRNA expression of 18 genes, which predominately encode chaperones. Strikingly, rescuing basal expression of Hsp70 and Hsp90 chaperones enabled robust cell growth in the complete absence of Hsf1. With the exception of chaperone gene induction, the vast majority of the heat shock response was Hsf1-independent. By comparative analysis of mammalian cell lines, we found that only heat shock-induced but not basal expression of chaperones is dependent on the mammalian Hsf1 homolog (HSF1). Our work reveals that yeast chaperone gene expression is an essential housekeeping mechanism and provides a roadmap for defining the function of HSF1 as a driver of oncogenesis.
Graphical Abstarct
PMCID: PMC4938784  PMID: 27320198
7.  Network Sampling and Classification:An Investigation of Network Model Representations 
Decision support systems  2011;51(3):506-518.
Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of network metrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed.
PMCID: PMC3110739  PMID: 21666773
connectivity pattern; network type; network metrics; network sampling; network classification
8.  An Entropy Approach to Disclosure Risk Assessment: Lessons from Real Applications and Simulated Domains 
Decision support systems  2011;51(1):10-20.
We live in an increasingly mobile world, which leads to the duplication of information across domains. Though organizations attempt to obscure the identities of their constituents when sharing information for worthwhile purposes, such as basic research, the uncoordinated nature of such environment can lead to privacy vulnerabilities. For instance, disparate healthcare providers can collect information on the same patient. Federal policy requires that such providers share “de-identified” sensitive data, such as biomedical (e.g., clinical and genomic) records. But at the same time, such providers can share identified information, devoid of sensitive biomedical data, for administrative functions. On a provider-by-provider basis, the biomedical and identified records appear unrelated, however, links can be established when multiple providers’ databases are studied jointly. The problem, known as trail disclosure, is a generalized phenomenon and occurs because an individual’s location access pattern can be matched across the shared databases. Due to technical and legal constraints, it is often difficult to coordinate between providers and thus it is critical to assess the disclosure risk in distributed environments, so that we can develop techniques to mitigate such risks. Research on privacy protection has so far focused on developing technologies to suppress or encrypt identifiers associated with sensitive information. There is growing body of work on the formal assessment of the disclosure risk of database entries in publicly shared databases, but a less attention has been paid to the distributed setting. In this research, we review the trail disclosure problem in several domains with known vulnerabilities and show that disclosure risk is influenced by the distribution of how people visit service providers. Based on empirical evidence, we propose an entropy metric for assessing such risk in shared databases prior to their release. This metric assesses risk by leveraging the statistical characteristics of a visit distribution, as opposed to person-level data. It is computationally efficient and superior to existing risk assessment methods, which rely on ad hoc assessment that are often computationally expensive and unreliable. We evaluate our approach on a range of location access patterns in simulated environments. Our results demonstrate the approach is effective at estimating trail disclosure risks and the amount of self-information contained in a distributed system is one of the main driving factors.
PMCID: PMC3107517  PMID: 21647242
disclosure; distributed systems; information theory; information privacy; risk analysis
9.  A multivariate computational method to analyze high-content RNAi screening data 
Journal of biomolecular screening  2015;20(8):985-997.
High-content screening (HCS) using RNA interference (RNAi) in combination with automated microscopy is a powerful investigative tool to explore complex biological processes. However, despite the plethora of data generated from these screens, little progress has been made in analyzing HC data using multivariate methods that exploit the full richness of multidimensional data. We developed a novel multivariate method for HCS, Multivariate Robust Analysis Method (M-RAM), integrating image feature selection with ranking of perturbations for hit identification, and applied this method to a HC RNAi screen to discover novel components of the DNA damage response in an osteosarcoma cell line. M-RAM automatically selects the most informative phenotypic readouts and time points to facilitate the more efficient design of follow-up experiments and enhance biological understanding. Our method outperforms univariate hit identification and identifies relevant genes that these approaches would have missed. We found that statistical cell-to-cell variation in phenotypic responses is an important predictor of ‘hits’ in RNAi-directed image-based screens. Genes that we identified as modulators of DNA damage signaling in U2OS cells include B-Raf, a cancer driver gene in multiple tumor types, whose role in DNA damage signaling we confirm experimentally, and multiple subunits of protein kinase A.
PMCID: PMC5377593  PMID: 25918037
high-content screening; RNAi screening; multivariate data analysis; feature selection; hit identification
10.  Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) 
Klionsky, Daniel J | Abdelmohsen, Kotb | Abe, Akihisa | Abedin, Md Joynal | Abeliovich, Hagai | Acevedo Arozena, Abraham | Adachi, Hiroaki | Adams, Christopher M | Adams, Peter D | Adeli, Khosrow | Adhihetty, Peter J | Adler, Sharon G | Agam, Galila | Agarwal, Rajesh | Aghi, Manish K | Agnello, Maria | Agostinis, Patrizia | Aguilar, Patricia V | Aguirre-Ghiso, Julio | Airoldi, Edoardo M | Ait-Si-Ali, Slimane | Akematsu, Takahiko | Akporiaye, Emmanuel T | Al-Rubeai, Mohamed | Albaiceta, Guillermo M | Albanese, Chris | Albani, Diego | Albert, Matthew L | Aldudo, Jesus | Algül, Hana | Alirezaei, Mehrdad | Alloza, Iraide | Almasan, Alexandru | Almonte-Beceril, Maylin | Alnemri, Emad S | Alonso, Covadonga | Altan-Bonnet, Nihal | Altieri, Dario C | Alvarez, Silvia | Alvarez-Erviti, Lydia | Alves, Sandro | Amadoro, Giuseppina | Amano, Atsuo | Amantini, Consuelo | Ambrosio, Santiago | Amelio, Ivano | Amer, Amal O | Amessou, Mohamed | Amon, Angelika | An, Zhenyi | Anania, Frank A | Andersen, Stig U | Andley, Usha P | Andreadi, Catherine K | Andrieu-Abadie, Nathalie | Anel, Alberto | Ann, David K | Anoopkumar-Dukie, Shailendra | Antonioli, Manuela | Aoki, Hiroshi | Apostolova, Nadezda | Aquila, Saveria | Aquilano, Katia | Araki, Koichi | Arama, Eli | Aranda, Agustin | Araya, Jun | Arcaro, Alexandre | Arias, Esperanza | Arimoto, Hirokazu | Ariosa, Aileen R | Armstrong, Jane L | Arnould, Thierry | Arsov, Ivica | Asanuma, Katsuhiko | Askanas, Valerie | Asselin, Eric | Atarashi, Ryuichiro | Atherton, Sally S | Atkin, Julie D | Attardi, Laura D | Auberger, Patrick | Auburger, Georg | Aurelian, Laure | Autelli, Riccardo | Avagliano, Laura | Avantaggiati, Maria Laura | Avrahami, Limor | Awale, Suresh | Azad, Neelam | Bachetti, Tiziana | Backer, Jonathan M | Bae, Dong-Hun | Bae, Jae-sung | Bae, Ok-Nam | Bae, Soo Han | Baehrecke, Eric H | Baek, Seung-Hoon | Baghdiguian, Stephen | Bagniewska-Zadworna, Agnieszka | Bai, Hua | Bai, Jie | Bai, Xue-Yuan | Bailly, Yannick | Balaji, Kithiganahalli Narayanaswamy | Balduini, Walter | Ballabio, Andrea | Balzan, Rena | Banerjee, Rajkumar | Bánhegyi, Gábor | Bao, Haijun | Barbeau, Benoit | Barrachina, Maria D | Barreiro, Esther | Bartel, Bonnie | Bartolomé, Alberto | Bassham, Diane C | Bassi, Maria Teresa | Bast, Robert C | Basu, Alakananda | Batista, Maria Teresa | Batoko, Henri | Battino, Maurizio | Bauckman, Kyle | Baumgarner, Bradley L | Bayer, K Ulrich | Beale, Rupert | Beaulieu, Jean-François | Beck, George R. | Becker, Christoph | Beckham, J David | Bédard, Pierre-André | Bednarski, Patrick J | Begley, Thomas J | Behl, Christian | Behrends, Christian | Behrens, Georg MN | Behrns, Kevin E | Bejarano, Eloy | Belaid, Amine | Belleudi, Francesca | Bénard, Giovanni | Berchem, Guy | Bergamaschi, Daniele | Bergami, Matteo | Berkhout, Ben | Berliocchi, Laura | Bernard, Amélie | Bernard, Monique | Bernassola, Francesca | Bertolotti, Anne | Bess, Amanda S | Besteiro, Sébastien | Bettuzzi, Saverio | Bhalla, Savita | Bhattacharyya, Shalmoli | Bhutia, Sujit K | Biagosch, Caroline | Bianchi, Michele Wolfe | Biard-Piechaczyk, Martine | Billes, Viktor | Bincoletto, Claudia | Bingol, Baris | Bird, Sara W | Bitoun, Marc | Bjedov, Ivana | Blackstone, Craig | Blanc, Lionel | Blanco, Guillermo A | Blomhoff, Heidi Kiil | Boada-Romero, Emilio | Böckler, Stefan | Boes, Marianne | Boesze-Battaglia, Kathleen | Boise, Lawrence H | Bolino, Alessandra | Boman, Andrea | Bonaldo, Paolo | Bordi, Matteo | Bosch, Jürgen | Botana, Luis M | Botti, Joelle | Bou, German | Bouché, Marina | Bouchecareilh, Marion | Boucher, Marie-Josée | Boulton, Michael E | Bouret, Sebastien G | Boya, Patricia | Boyer-Guittaut, Michaël | Bozhkov, Peter V | Brady, Nathan | Braga, Vania MM | Brancolini, Claudio | Braus, Gerhard H | Bravo-San Pedro, José M | Brennan, Lisa A | Bresnick, Emery H | Brest, Patrick | Bridges, Dave | Bringer, Marie-Agnès | Brini, Marisa | Brito, Glauber C | Brodin, Bertha | Brookes, Paul S | Brown, Eric J | Brown, Karen | Broxmeyer, Hal E | Bruhat, Alain | Brum, Patricia Chakur | Brumell, John H | Brunetti-Pierri, Nicola | Bryson-Richardson, Robert J | Buch, Shilpa | Buchan, Alastair M | Budak, Hikmet | Bulavin, Dmitry V | Bultman, Scott J | Bultynck, Geert | Bumbasirevic, Vladimir | Burelle, Yan | Burke, Robert E | Burmeister, Margit | Bütikofer, Peter | Caberlotto, Laura | Cadwell, Ken | Cahova, Monika | Cai, Dongsheng | Cai, Jingjing | Cai, Qian | Calatayud, Sara | Camougrand, Nadine | Campanella, Michelangelo | Campbell, Grant R | Campbell, Matthew | Campello, Silvia | Candau, Robin | Caniggia, Isabella | Cantoni, Lavinia | Cao, Lizhi | Caplan, Allan B | Caraglia, Michele | Cardinali, Claudio | Cardoso, Sandra Morais | Carew, Jennifer S | Carleton, Laura A | Carlin, Cathleen R | Carloni, Silvia | Carlsson, Sven R | Carmona-Gutierrez, Didac | Carneiro, Leticia AM | Carnevali, Oliana | Carra, Serena | Carrier, Alice | Carroll, Bernadette | Casas, Caty | Casas, Josefina | Cassinelli, Giuliana | Castets, Perrine | Castro-Obregon, Susana | Cavallini, Gabriella | Ceccherini, Isabella | Cecconi, Francesco | Cederbaum, Arthur I | Ceña, Valentín | Cenci, Simone | Cerella, Claudia | Cervia, Davide | Cetrullo, Silvia | Chaachouay, Hassan | Chae, Han-Jung | Chagin, Andrei S | Chai, Chee-Yin | Chakrabarti, Gopal | Chamilos, Georgios | Chan, Edmond YW | Chan, Matthew TV | Chandra, Dhyan | Chandra, Pallavi | Chang, Chih-Peng | Chang, Raymond Chuen-Chung | Chang, Ta Yuan | Chatham, John C | Chatterjee, Saurabh | Chauhan, Santosh | Che, Yongsheng | Cheetham, Michael E | Cheluvappa, Rajkumar | Chen, Chun-Jung | Chen, Gang | Chen, Guang-Chao | Chen, Guoqiang | Chen, Hongzhuan | Chen, Jeff W | Chen, Jian-Kang | Chen, Min | Chen, Mingzhou | Chen, Peiwen | Chen, Qi | Chen, Quan | Chen, Shang-Der | Chen, Si | Chen, Steve S-L | Chen, Wei | Chen, Wei-Jung | Chen, Wen Qiang | Chen, Wenli | Chen, Xiangmei | Chen, Yau-Hung | Chen, Ye-Guang | Chen, Yin | Chen, Yingyu | Chen, Yongshun | Chen, Yu-Jen | Chen, Yue-Qin | Chen, Yujie | Chen, Zhen | Chen, Zhong | Cheng, Alan | Cheng, Christopher HK | Cheng, Hua | Cheong, Heesun | Cherry, Sara | Chesney, Jason | Cheung, Chun Hei Antonio | Chevet, Eric | Chi, Hsiang Cheng | Chi, Sung-Gil | Chiacchiera, Fulvio | Chiang, Hui-Ling | Chiarelli, Roberto | Chiariello, Mario | Chieppa, Marcello | Chin, Lih-Shen | Chiong, Mario | Chiu, Gigi NC | Cho, Dong-Hyung | Cho, Ssang-Goo | Cho, William C | Cho, Yong-Yeon | Cho, Young-Seok | Choi, Augustine MK | Choi, Eui-Ju | Choi, Eun-Kyoung | Choi, Jayoung | Choi, Mary E | Choi, Seung-Il | Chou, Tsui-Fen | Chouaib, Salem | Choubey, Divaker | Choubey, Vinay | Chow, Kuan-Chih | Chowdhury, Kamal | Chu, Charleen T | Chuang, Tsung-Hsien | Chun, Taehoon | Chung, Hyewon | Chung, Taijoon | Chung, Yuen-Li | Chwae, Yong-Joon | Cianfanelli, Valentina | Ciarcia, Roberto | Ciechomska, Iwona A | Ciriolo, Maria Rosa | Cirone, Mara | Claerhout, Sofie | Clague, Michael J | Clària, Joan | Clarke, Peter GH | Clarke, Robert | Clementi, Emilio | Cleyrat, Cédric | Cnop, Miriam | Coccia, Eliana M | Cocco, Tiziana | Codogno, Patrice | Coers, Jörn | Cohen, Ezra EW | Colecchia, David | Coletto, Luisa | Coll, Núria S | Colucci-Guyon, Emma | Comincini, Sergio | Condello, Maria | Cook, Katherine L | Coombs, Graham H | Cooper, Cynthia D | Cooper, J Mark | Coppens, Isabelle | Corasaniti, Maria Tiziana | Corazzari, Marco | Corbalan, Ramon | Corcelle-Termeau, Elisabeth | Cordero, Mario D | Corral-Ramos, Cristina | Corti, Olga | Cossarizza, Andrea | Costelli, Paola | Costes, Safia | Cotman, Susan L | Coto-Montes, Ana | Cottet, Sandra | Couve, Eduardo | Covey, Lori R | Cowart, L Ashley | Cox, Jeffery S | Coxon, Fraser P | Coyne, Carolyn B | Cragg, Mark S | Craven, Rolf J | Crepaldi, Tiziana | Crespo, Jose L | Criollo, Alfredo | Crippa, Valeria | Cruz, Maria Teresa | Cuervo, Ana Maria | Cuezva, Jose M | Cui, Taixing | Cutillas, Pedro R | Czaja, Mark J | Czyzyk-Krzeska, Maria F | Dagda, Ruben K | Dahmen, Uta | Dai, Chunsun | Dai, Wenjie | Dai, Yun | Dalby, Kevin N | Dalla Valle, Luisa | Dalmasso, Guillaume | D'Amelio, Marcello | Damme, Markus | Darfeuille-Michaud, Arlette | Dargemont, Catherine | Darley-Usmar, Victor M | Dasarathy, Srinivasan | Dasgupta, Biplab | Dash, Srikanta | Dass, Crispin R | Davey, Hazel Marie | Davids, Lester M | Dávila, David | Davis, Roger J | Dawson, Ted M | Dawson, Valina L | Daza, Paula | de Belleroche, Jackie | de Figueiredo, Paul | de Figueiredo, Regina Celia Bressan Queiroz | de la Fuente, José | De Martino, Luisa | De Matteis, Antonella | De Meyer, Guido RY | De Milito, Angelo | De Santi, Mauro | de Souza, Wanderley | De Tata, Vincenzo | De Zio, Daniela | Debnath, Jayanta | Dechant, Reinhard | Decuypere, Jean-Paul | Deegan, Shane | Dehay, Benjamin | Del Bello, Barbara | Del Re, Dominic P | Delage-Mourroux, Régis | Delbridge, Lea MD | Deldicque, Louise | Delorme-Axford, Elizabeth | Deng, Yizhen | Dengjel, Joern | Denizot, Melanie | Dent, Paul | Der, Channing J | Deretic, Vojo | Derrien, Benoît | Deutsch, Eric | Devarenne, Timothy P | Devenish, Rodney J | Di Bartolomeo, Sabrina | Di Daniele, Nicola | Di Domenico, Fabio | Di Nardo, Alessia | Di Paola, Simone | Di Pietro, Antonio | Di Renzo, Livia | DiAntonio, Aaron | Díaz-Araya, Guillermo | Díaz-Laviada, Ines | Diaz-Meco, Maria T | Diaz-Nido, Javier | Dickey, Chad A | Dickson, Robert C | Diederich, Marc | Digard, Paul | Dikic, Ivan | Dinesh-Kumar, Savithrama P | Ding, Chan | Ding, Wen-Xing | Ding, Zufeng | Dini, Luciana | Distler, Jörg HW | Diwan, Abhinav | Djavaheri-Mergny, Mojgan | Dmytruk, Kostyantyn | Dobson, Renwick CJ | Doetsch, Volker | Dokladny, Karol | Dokudovskaya, Svetlana | Donadelli, Massimo | Dong, X Charlie | Dong, Xiaonan | Dong, Zheng | Donohue, Terrence M | Doran, Kelly S | D'Orazi, Gabriella | Dorn, Gerald W | Dosenko, Victor | Dridi, Sami | Drucker, Liat | Du, Jie | Du, Li-Lin | Du, Lihuan | du Toit, André | Dua, Priyamvada | Duan, Lei | Duann, Pu | Dubey, Vikash Kumar | Duchen, Michael R | Duchosal, Michel A | Duez, Helene | Dugail, Isabelle | Dumit, Verónica I | Duncan, Mara C | Dunlop, Elaine A | Dunn, William A | Dupont, Nicolas | Dupuis, Luc | Durán, Raúl V | Durcan, Thomas M | Duvezin-Caubet, Stéphane | Duvvuri, Umamaheswar | Eapen, Vinay | Ebrahimi-Fakhari, Darius | Echard, Arnaud | Eckhart, Leopold | Edelstein, Charles L | Edinger, Aimee L | Eichinger, Ludwig | Eisenberg, Tobias | Eisenberg-Lerner, Avital | Eissa, N Tony | El-Deiry, Wafik S | El-Khoury, Victoria | Elazar, Zvulun | Eldar-Finkelman, Hagit | Elliott, Chris JH | Emanuele, Enzo | Emmenegger, Urban | Engedal, Nikolai | Engelbrecht, Anna-Mart | Engelender, Simone | Enserink, Jorrit M | Erdmann, Ralf | Erenpreisa, Jekaterina | Eri, Rajaraman | Eriksen, Jason L | Erman, Andreja | Escalante, Ricardo | Eskelinen, Eeva-Liisa | Espert, Lucile | Esteban-Martínez, Lorena | Evans, Thomas J | Fabri, Mario | Fabrias, Gemma | Fabrizi, Cinzia | Facchiano, Antonio | Færgeman, Nils J | Faggioni, Alberto | Fairlie, W Douglas | Fan, Chunhai | Fan, Daping | Fan, Jie | Fang, Shengyun | Fanto, Manolis | Fanzani, Alessandro | Farkas, Thomas | Faure, Mathias | Favier, Francois B | Fearnhead, Howard | Federici, Massimo | Fei, Erkang | Felizardo, Tania C | Feng, Hua | Feng, Yibin | Feng, Yuchen | Ferguson, Thomas A | Fernández, Álvaro F | Fernandez-Barrena, Maite G | Fernandez-Checa, Jose C | Fernández-López, Arsenio | Fernandez-Zapico, Martin E | Feron, Olivier | Ferraro, Elisabetta | Ferreira-Halder, Carmen Veríssima | Fesus, Laszlo | Feuer, Ralph | Fiesel, Fabienne C | Filippi-Chiela, Eduardo C | Filomeni, Giuseppe | Fimia, Gian Maria | Fingert, John H | Finkbeiner, Steven | Finkel, Toren | Fiorito, Filomena | Fisher, Paul B | Flajolet, Marc | Flamigni, Flavio | Florey, Oliver | Florio, Salvatore | Floto, R Andres | Folini, Marco | Follo, Carlo | Fon, Edward A | Fornai, Francesco | Fortunato, Franco | Fraldi, Alessandro | Franco, Rodrigo | Francois, Arnaud | François, Aurélie | Frankel, Lisa B | Fraser, Iain DC | Frey, Norbert | Freyssenet, Damien G | Frezza, Christian | Friedman, Scott L | Frigo, Daniel E | Fu, Dongxu | Fuentes, José M | Fueyo, Juan | Fujitani, Yoshio | Fujiwara, Yuuki | Fujiya, Mikihiro | Fukuda, Mitsunori | Fulda, Simone | Fusco, Carmela | Gabryel, Bozena | Gaestel, Matthias | Gailly, Philippe | Gajewska, Malgorzata | Galadari, Sehamuddin | Galili, Gad | Galindo, Inmaculada | Galindo, Maria F | Galliciotti, Giovanna | Galluzzi, Lorenzo | Galluzzi, Luca | Galy, Vincent | Gammoh, Noor | Gandy, Sam | Ganesan, Anand K | Ganesan, Swamynathan | Ganley, Ian G | Gannagé, Monique | Gao, Fen-Biao | Gao, Feng | Gao, Jian-Xin | García Nannig, Lorena | García Véscovi, Eleonora | Garcia-Macía, Marina | Garcia-Ruiz, Carmen | Garg, Abhishek D | Garg, Pramod Kumar | Gargini, Ricardo | Gassen, Nils Christian | Gatica, Damián | Gatti, Evelina | Gavard, Julie | Gavathiotis, Evripidis | Ge, Liang | Ge, Pengfei | Ge, Shengfang | Gean, Po-Wu | Gelmetti, Vania | Genazzani, Armando A | Geng, Jiefei | Genschik, Pascal | Gerner, Lisa | Gestwicki, Jason E | Gewirtz, David A | Ghavami, Saeid | Ghigo, Eric | Ghosh, Debabrata | Giammarioli, Anna Maria | Giampieri, Francesca | Giampietri, Claudia | Giatromanolaki, Alexandra | Gibbings, Derrick J | Gibellini, Lara | Gibson, Spencer B | Ginet, Vanessa | Giordano, Antonio | Giorgini, Flaviano | Giovannetti, Elisa | Girardin, Stephen E | Gispert, Suzana | Giuliano, Sandy | Gladson, Candece L | Glavic, Alvaro | Gleave, Martin | Godefroy, Nelly | Gogal, Robert M | Gokulan, Kuppan | Goldman, Gustavo H | Goletti, Delia | Goligorsky, Michael S | Gomes, Aldrin V | Gomes, Ligia C | Gomez, Hernando | Gomez-Manzano, Candelaria | Gómez-Sánchez, Rubén | Gonçalves, Dawit AP | Goncu, Ebru | Gong, Qingqiu | Gongora, Céline | Gonzalez, Carlos B | Gonzalez-Alegre, Pedro | Gonzalez-Cabo, Pilar | González-Polo, Rosa Ana | Goping, Ing Swie | Gorbea, Carlos | Gorbunov, Nikolai V | Goring, Daphne R | Gorman, Adrienne M | Gorski, Sharon M | Goruppi, Sandro | Goto-Yamada, Shino | Gotor, Cecilia | Gottlieb, Roberta A | Gozes, Illana | Gozuacik, Devrim | Graba, Yacine | Graef, Martin | Granato, Giovanna E | Grant, Gary Dean | Grant, Steven | Gravina, Giovanni Luca | Green, Douglas R | Greenhough, Alexander | Greenwood, Michael T | Grimaldi, Benedetto | Gros, Frédéric | Grose, Charles | Groulx, Jean-Francois | Gruber, Florian | Grumati, Paolo | Grune, Tilman | Guan, Jun-Lin | Guan, Kun-Liang | Guerra, Barbara | Guillen, Carlos | Gulshan, Kailash | Gunst, Jan | Guo, Chuanyong | Guo, Lei | Guo, Ming | Guo, Wenjie | Guo, Xu-Guang | Gust, Andrea A | Gustafsson, Åsa B | Gutierrez, Elaine | Gutierrez, Maximiliano G | Gwak, Ho-Shin | Haas, Albert | Haber, James E | Hadano, Shinji | Hagedorn, Monica | Hahn, David R | Halayko, Andrew J | Hamacher-Brady, Anne | Hamada, Kozo | Hamai, Ahmed | Hamann, Andrea | Hamasaki, Maho | Hamer, Isabelle | Hamid, Qutayba | Hammond, Ester M | Han, Feng | Han, Weidong | Handa, James T | Hanover, John A | Hansen, Malene | Harada, Masaru | Harhaji-Trajkovic, Ljubica | Harper, J Wade | Harrath, Abdel Halim | Harris, Adrian L | Harris, James | Hasler, Udo | Hasselblatt, Peter | Hasui, Kazuhisa | Hawley, Robert G | Hawley, Teresa S | He, Congcong | He, Cynthia Y | He, Fengtian | He, Gu | He, Rong-Rong | He, Xian-Hui | He, You-Wen | He, Yu-Ying | Heath, Joan K | Hébert, Marie-Josée | Heinzen, Robert A | Helgason, Gudmundur Vignir | Hensel, Michael | Henske, Elizabeth P | Her, Chengtao | Herman, Paul K | Hernández, Agustín | Hernandez, Carlos | Hernández-Tiedra, Sonia | Hetz, Claudio | Hiesinger, P Robin | Higaki, Katsumi | Hilfiker, Sabine | Hill, Bradford G | Hill, Joseph A | Hill, William D | Hino, Keisuke | Hofius, Daniel | Hofman, Paul | Höglinger, Günter U | Höhfeld, Jörg | Holz, Marina K | Hong, Yonggeun | Hood, David A | Hoozemans, Jeroen JM | Hoppe, Thorsten | Hsu, Chin | Hsu, Chin-Yuan | Hsu, Li-Chung | Hu, Dong | Hu, Guochang | Hu, Hong-Ming | Hu, Hongbo | Hu, Ming Chang | Hu, Yu-Chen | Hu, Zhuo-Wei | Hua, Fang | Hua, Ya | Huang, Canhua | Huang, Huey-Lan | Huang, Kuo-How | Huang, Kuo-Yang | Huang, Shile | Huang, Shiqian | Huang, Wei-Pang | Huang, Yi-Ran | Huang, Yong | Huang, Yunfei | Huber, Tobias B | Huebbe, Patricia | Huh, Won-Ki | Hulmi, Juha J | Hur, Gang Min | Hurley, James H | Husak, Zvenyslava | Hussain, Sabah NA | Hussain, Salik | Hwang, Jung Jin | Hwang, Seungmin | Hwang, Thomas IS | Ichihara, Atsuhiro | Imai, Yuzuru | Imbriano, Carol | Inomata, Megumi | Into, Takeshi | Iovane, Valentina | Iovanna, Juan L | Iozzo, Renato V | Ip, Nancy Y | Irazoqui, Javier E | Iribarren, Pablo | Isaka, Yoshitaka | Isakovic, Aleksandra J | Ischiropoulos, Harry | Isenberg, Jeffrey S | Ishaq, Mohammad | Ishida, Hiroyuki | Ishii, Isao | Ishmael, Jane E | Isidoro, Ciro | Isobe, Ken-ichi | Isono, Erika | Issazadeh-Navikas, Shohreh | Itahana, Koji | Itakura, Eisuke | Ivanov, Andrei I | Iyer, Anand Krishnan V | Izquierdo, José M | Izumi, Yotaro | Izzo, Valentina | Jäättelä, Marja | Jaber, Nadia | Jackson, Daniel John | Jackson, William T | Jacob, Tony George | Jacques, Thomas S | Jagannath, Chinnaswamy | Jain, Ashish | Jana, Nihar Ranjan | Jang, Byoung Kuk | Jani, Alkesh | Janji, Bassam | Jannig, Paulo Roberto | Jansson, Patric J | Jean, Steve | Jendrach, Marina | Jeon, Ju-Hong | Jessen, Niels | Jeung, Eui-Bae | Jia, Kailiang | Jia, Lijun | Jiang, Hong | Jiang, Hongchi | Jiang, Liwen | Jiang, Teng | Jiang, Xiaoyan | Jiang, Xuejun | Jiang, Xuejun | Jiang, Ying | Jiang, Yongjun | Jiménez, Alberto | Jin, Cheng | Jin, Hongchuan | Jin, Lei | Jin, Meiyan | Jin, Shengkan | Jinwal, Umesh Kumar | Jo, Eun-Kyeong | Johansen, Terje | Johnson, Daniel E | Johnson, Gail VW | Johnson, James D | Jonasch, Eric | Jones, Chris | Joosten, Leo AB | Jordan, Joaquin | Joseph, Anna-Maria | Joseph, Bertrand | Joubert, Annie M | Ju, Dianwen | Ju, Jingfang | Juan, Hsueh-Fen | Juenemann, Katrin | Juhász, Gábor | Jung, Hye Seung | Jung, Jae U | Jung, Yong-Keun | Jungbluth, Heinz | Justice, Matthew J | Jutten, Barry | Kaakoush, Nadeem O | Kaarniranta, Kai | Kaasik, Allen | Kabuta, Tomohiro | Kaeffer, Bertrand | Kågedal, Katarina | Kahana, Alon | Kajimura, Shingo | Kakhlon, Or | Kalia, Manjula | Kalvakolanu, Dhan V | Kamada, Yoshiaki | Kambas, Konstantinos | Kaminskyy, Vitaliy O | Kampinga, Harm H | Kandouz, Mustapha | Kang, Chanhee | Kang, Rui | Kang, Tae-Cheon | Kanki, Tomotake | Kanneganti, Thirumala-Devi | Kanno, Haruo | Kanthasamy, Anumantha G | Kantorow, Marc | Kaparakis-Liaskos, Maria | Kapuy, Orsolya | Karantza, Vassiliki | Karim, Md Razaul | Karmakar, Parimal | Kaser, Arthur | Kaushik, Susmita | Kawula, Thomas | Kaynar, A Murat | Ke, Po-Yuan | Ke, Zun-Ji | Kehrl, John H | Keller, Kate E | Kemper, Jongsook Kim
Autophagy  2016;12(1):1-222.
PMCID: PMC4835977  PMID: 26799652
autolysosome; autophagosome; chaperone-mediated autophagy; flux; LC3; lysosome; macroautophagy; phagophore; stress; vacuole
11.  Reversible, specific, active aggregates of endogenous proteins assemble upon heat stress 
Cell  2015;162(6):1286-1298.
Heat causes protein misfolding and aggregation, and in eukaryotic cells triggers aggregation of proteins and RNA into stress granules. We have carried out extensive proteomic studies to quantify heat-triggered aggregation and subsequent disaggregation in budding yeast, identifying more than 170 endogenous proteins aggregating within minutes of heat shock in multiple subcellular compartments. We demonstrate that these aggregated proteins are not misfolded and destined for degradation. Stable-isotope labeling reveals that even severely aggregated endogenous proteins are disaggregated without degradation during recovery from shock, contrasting with the rapid degradation observed for exogenous thermolabile proteins. Although aggregation likely inactivates many cellular proteins, in the case of a heterotrimeric aminoacyl-tRNA synthetase complex, the aggregated proteins remain active with unaltered fidelity. We propose that most heat-induced aggregation of mature proteins reflects the operation of an adaptive, autoregulatory process of functionally significant aggregate assembly and disassembly that aids cellular adaptation to thermal stress.
PMCID: PMC4567705  PMID: 26359986
12.  Quantitative visualization of alternative exon expression from RNA-seq data 
Bioinformatics  2015;31(14):2400-2402.
Motivation: Analysis of RNA sequencing (RNA-Seq) data revealed that the vast majority of human genes express multiple mRNA isoforms, produced by alternative pre-mRNA splicing and other mechanisms, and that most alternative isoforms vary in expression between human tissues. As RNA-Seq datasets grow in size, it remains challenging to visualize isoform expression across multiple samples.
Results: To help address this problem, we present Sashimi plots, a quantitative visualization of aligned RNA-Seq reads that enables quantitative comparison of exon usage across samples or experimental conditions. Sashimi plots can be made using the Broad Integrated Genome Viewer or with a stand-alone command line program.
Availability and implementation: Software code and documentation freely available here:
Contact:, or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4542614  PMID: 25617416
13.  Scalable estimation strategies based on stochastic approximations: Classical results and new insights 
Statistics and computing  2015;25(4):781-795.
Estimation with large amounts of data can be facilitated by stochastic gradient methods, in which model parameters are updated sequentially using small batches of data at each step. Here, we review early work and modern results that illustrate the statistical properties of these methods, including convergence rates, stability, and asymptotic bias and variance. We then overview modern applications where these methods are useful, ranging from an online version of the EM algorithm to deep learning. In light of these results, we argue that stochastic gradient methods are poised to become benchmark principled estimation procedures for large data sets, especially those in the family of stable proximal methods, such as implicit stochastic gradient descent.
PMCID: PMC4484776  PMID: 26139959
maximum likelihood; exponential family; stochastic gradient descent methods; implicit stochastic gradient descent; recursive estimation; efficient estimation; optimal learning rate; asymptotic analysis; big data
14.  Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology 
We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and human, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast data sets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levels—a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model mis-specifications and non-ignorable missing data on estimates of the correlation between high dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought.
PMCID: PMC4418505  PMID: 25954056
high-throughput biology; inter-laboratory comparisons; structured covariance; measurement error; non-ignorable missing data; high-dimensional inference
15.  Generalized species sampling priors with latent Beta reinforcements 
Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet Process and the two parameters Poisson-Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array CGH data.
PMCID: PMC4392726  PMID: 25870462
Bayesian non-parametrics; Species Sampling Priors; Predictive Probability Functions; Random Partitions; MCMC; Genomics; Cancer
16.  Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis 
Cell reports  2014;7(3):705-714.
The fermentation of glucose in the presence of enough oxygen to support respiration, known as aerobic glycolysis, is believed to maximize growth rate. We measured increasing aerobic glycolysis during exponential growth at a constant rate, suggesting additional physiological roles for aerobic glycolysis. We investigated such roles in yeast batch cultures by quantifying metabolic fluxes, O2 consumption, CO2 production, amino-acids, mRNAs, proteins, post-translational modifications, and stress-sensitivity in the course of 9 doublings at a constant rate. During this course, the cells supported constant biomass-production rate with decreasing rates of respiration and ATP production but also decreased their heat- and oxidative-stress resistance. As the respiration rate decrease, so do the levels of enzymes catalyzing rate-determining reactions of the tricarboxylic-acid cycle (providing NADH for respiration) and of the mitochondrial folate-mediated NADPH production (required for oxidative defense). These findings suggest that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival.
PMCID: PMC4049626  PMID: 24767987
17.  Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast 
PLoS Genetics  2015;11(5):e1005206.
Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.
Author Summary
Cells respond to their environment by making proteins using transcription and translation of mRNA. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy and contain missing values. Here we show that when methods that account for noise are used to analyze much of the same data, mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels as commonly assumed, but rise much more rapidly. Regulation of translation achieves amplification of, rather than competition with, transcriptional signals. Our results suggest that for this set of conditions, mRNA sets protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.
PMCID: PMC4423881  PMID: 25950722
18.  Deconvolution of mixing time series on a graph 
In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series.
PMCID: PMC4190096  PMID: 25309135
19.  Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data 
Molecular Biology and Evolution  2013;30(6):1438-1453.
A key goal in molecular evolution is to extract mechanistic insights from signatures of selection. A case study is codon usage, where despite many recent advances and hypotheses, two longstanding problems remain: the relative contribution of selection and mutation in determining codon frequencies and the relative contribution of translational speed and accuracy to selection. The relevant targets of selection—the rate of translation and of mistranslation of a codon per unit time in the cell—can only be related to mechanistic properties of the translational apparatus if the number of transcripts per cell is known, requiring use of gene expression measurements. Perhaps surprisingly, different gene-expression data sets yield markedly different estimates of selection. We show that this is largely due to measurement noise, notably due to differences between studies rather than instrument error or biological variability. We develop an analytical framework that explicitly models noise in expression in the context of the population-genetic model. Estimates of mutation and selection strength in budding yeast produced by this method are robust to the expression data set used and are substantially higher than estimates using a noise-blind approach. We introduce per-gene selection estimates that correlate well with previous scoring systems, such as the codon adaptation index, while now carrying an evolutionary interpretation. On average, selection for codon usage in budding yeast is weak, yet our estimates show that genes range from virtually unselected to average per-codon selection coefficients above the inverse population size. Our analytical framework may be generally useful for distinguishing biological signals from measurement noise in other applications that depend upon measurements of gene expression.
PMCID: PMC3649678  PMID: 23493257
selection; codon usage; gene expression; noise
The annals of applied statistics  2012;7(4):2431-2457.
We consider the problem of quantifying temporal coordination between multiple high-dimensional responses. We introduce a family of multi-way stochastic blockmodels suited for this problem, which avoids pre-processing steps such as binning and thresholding commonly adopted for this type of problems, in biology. We develop two inference procedures based on collapsed Gibbs sampling and variational methods. We provide a thorough evaluation of the proposed methods on simulated data, in terms of membership and blockmodel estimation, predictions out-of-sample, and run-time. We also quantify the effects of censoring procedures such as binning and thresholding on the estimation tasks. We use these models to carry out an empirical analysis of the functional mechanisms driving the coordination between gene expression and metabolite concentrations during carbon and nitrogen starvation, in S. cerevisiae.
PMCID: PMC3935422  PMID: 24587846
The annals of applied statistics  2009;4(2):615-644.
Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S = {A(1) : B(1), A(2) : B(2), …, A(N) : B(N)}, measures how well other pairs A : B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.
PMCID: PMC3935415  PMID: 24587838
Network analysis; Bayesian inference; variational approximation; ranking; information retrieval; data integration; Saccharomyces cerevisiae
22.  Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates 
PLoS ONE  2013;8(9):e75320.
Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection.
PMCID: PMC3783400  PMID: 24086506
23.  A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes 
Molecular Biology of the Cell  2012;23(10):1986-1997.
Transitions between the two phases of the cell growth cycle can account for the environmental stress response, the growth-rate response, and the cross-protection between slow growth and various types of stress factors. It is suggested that this mechanism is conserved across budding and fission yeast and normal human cells.
The respiratory metabolic cycle in budding yeast (Saccharomyces cerevisiae) consists of two phases that are most simply defined phenomenologically: low oxygen consumption (LOC) and high oxygen consumption (HOC). Each phase is associated with the periodic expression of thousands of genes, producing oscillating patterns of gene expression found in synchronized cultures and in single cells of slowly growing unsynchronized cultures. Systematic variation in the durations of the HOC and LOC phases can account quantitatively for well-studied transcriptional responses to growth rate differences. Here we show that a similar mechanism—transitions from the HOC phase to the LOC phase—can account for much of the common environmental stress response (ESR) and for the cross-protection by a preliminary heat stress (or slow growth rate) to subsequent lethal heat stress. Similar to the budding yeast metabolic cycle, we suggest that a metabolic cycle, coupled in a similar way to the ESR, in the distantly related fission yeast, Schizosaccharomyces pombe, and in humans can explain gene expression and respiratory patterns observed in these eukaryotes. Although metabolic cycling is associated with the G0/G1 phase of the cell division cycle of slowly growing budding yeast, transcriptional cycling was detected in the G2 phase of the division cycle in fission yeast, consistent with the idea that respiratory metabolic cycling occurs during the phases of the cell division cycle associated with mass accumulation in these divergent eukaryotes.
PMCID: PMC3350561  PMID: 22456505
24.  Systems-level dynamic analyses of fate change in murine embryonic stem cells 
Nature  2009;462(7271):358-362.
Molecular regulation of embryonic stem cell (ESC) fate involves a coordinated interaction between epigenetic1–4, transcriptional5–10 and translational11,12 mechanisms. It is unclear how these different molecular regulatory mechanisms interact to regulate changes in stem cell fate. Here we present a dynamic systems-level study of cell fate change in murine ESCs following a well-defined perturbation. Global changes in histone acetylation, chromatin-bound RNA polymerase II, messenger RNA (mRNA), and nuclear protein levels were measured over 5 days after downregulation of Nanog, a key pluripotency regulator13–15. Our data demonstrate how a single genetic perturbation leads to progressive widespread changes in several molecular regulatory layers, and provide a dynamic view of information flow in the epigenome, transcriptome and proteome. We observe that a large proportion of changes in nuclear protein levels are not accompanied by concordant changes in the expression of corresponding mRNAs, indicating important roles for translational and post-translational regulation of ESC fate. Gene-ontology analysis across different molecular layers indicates that although chromatin reconfiguration is important for altering cell fate, it is preceded by transcription-factor-mediated regulatory events. The temporal order of gene expression alterations shows the order of the regulatory network reconfiguration and offers further insight into the gene regulatory network. Our studies extend the conventional systems biology approach to include many molecular species, regulatory layers and temporal series, and underscore the complexity of the multilayer regulatory mechanisms responsible for changes in protein expression that determine stem cell fate.
PMCID: PMC3199216  PMID: 19924215
25.  Mixed Membership Stochastic Blockmodels 
Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.
PMCID: PMC3119541  PMID: 21701698
Hierarchical Bayes; Latent Variables; Mean-Field Approximation; Statistical Network Analysis; Social Networks; Protein Interaction Networks

Results 1-25 (31)