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Author

Jens Allmer

Bio: Jens Allmer is an academic researcher from İzmir Institute of Technology. The author has contributed to research in topics: MiRBase & Regulation of gene expression. The author has an hindex of 19, co-authored 75 publications receiving 3465 citations. Previous affiliations of Jens Allmer include İzmir University of Economics & University of Pennsylvania.


Papers
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Journal ArticleDOI
Sabeeha S. Merchant1, Simon E. Prochnik2, Olivier Vallon3, Elizabeth H. Harris4, Steven J. Karpowicz1, George B. Witman5, Astrid Terry2, Asaf Salamov2, Lillian K. Fritz-Laylin6, Laurence Maréchal-Drouard7, Wallace F. Marshall8, Liang-Hu Qu9, David R. Nelson10, Anton A. Sanderfoot11, Martin H. Spalding12, Vladimir V. Kapitonov13, Qinghu Ren, Patrick J. Ferris14, Erika Lindquist2, Harris Shapiro2, Susan Lucas2, Jane Grimwood15, Jeremy Schmutz15, Pierre Cardol16, Pierre Cardol3, Heriberto Cerutti17, Guillaume Chanfreau1, Chun-Long Chen9, Valérie Cognat7, Martin T. Croft18, Rachel M. Dent6, Susan K. Dutcher19, Emilio Fernández20, Hideya Fukuzawa21, David González-Ballester22, Diego González-Halphen23, Armin Hallmann, Marc Hanikenne16, Michael Hippler24, William Inwood6, Kamel Jabbari25, Ming Kalanon26, Richard Kuras3, Paul A. Lefebvre11, Stéphane D. Lemaire27, Alexey V. Lobanov17, Martin Lohr28, Andrea L Manuell29, Iris Meier30, Laurens Mets31, Maria Mittag32, Telsa M. Mittelmeier33, James V. Moroney34, Jeffrey L. Moseley22, Carolyn A. Napoli33, Aurora M. Nedelcu35, Krishna K. Niyogi6, Sergey V. Novoselov17, Ian T. Paulsen, Greg Pazour5, Saul Purton36, Jean-Philippe Ral7, Diego Mauricio Riaño-Pachón37, Wayne R. Riekhof, Linda A. Rymarquis38, Michael Schroda, David B. Stern39, James G. Umen14, Robert D. Willows40, Nedra F. Wilson41, Sara L. Zimmer39, Jens Allmer42, Janneke Balk18, Katerina Bisova43, Chong-Jian Chen9, Marek Eliáš44, Karla C Gendler33, Charles R. Hauser45, Mary Rose Lamb46, Heidi K. Ledford6, Joanne C. Long1, Jun Minagawa47, M. Dudley Page1, Junmin Pan48, Wirulda Pootakham22, Sanja Roje49, Annkatrin Rose50, Eric Stahlberg30, Aimee M. Terauchi1, Pinfen Yang51, Steven G. Ball7, Chris Bowler25, Carol L. Dieckmann33, Vadim N. Gladyshev17, Pamela J. Green38, Richard A. Jorgensen33, Stephen P. Mayfield29, Bernd Mueller-Roeber37, Sathish Rajamani30, Richard T. Sayre30, Peter Brokstein2, Inna Dubchak2, David Goodstein2, Leila Hornick2, Y. Wayne Huang2, Jinal Jhaveri2, Yigong Luo2, Diego Martinez2, Wing Chi Abby Ngau2, Bobby Otillar2, Alexander Poliakov2, Aaron Porter2, Lukasz Szajkowski2, Gregory Werner2, Kemin Zhou2, Igor V. Grigoriev2, Daniel S. Rokhsar2, Daniel S. Rokhsar6, Arthur R. Grossman22 
University of California, Los Angeles1, United States Department of Energy2, University of Paris3, Duke University4, University of Massachusetts Medical School5, University of California, Berkeley6, Centre national de la recherche scientifique7, University of California, San Francisco8, Sun Yat-sen University9, University of Tennessee Health Science Center10, University of Minnesota11, Iowa State University12, Genetic Information Research Institute13, Salk Institute for Biological Studies14, Stanford University15, University of Liège16, University of Nebraska–Lincoln17, University of Cambridge18, Washington University in St. Louis19, University of Córdoba (Spain)20, Kyoto University21, Carnegie Institution for Science22, National Autonomous University of Mexico23, University of Münster24, École Normale Supérieure25, University of Melbourne26, University of Paris-Sud27, University of Mainz28, Scripps Research Institute29, Ohio State University30, University of Chicago31, University of Jena32, University of Arizona33, Louisiana State University34, University of New Brunswick35, University College London36, University of Potsdam37, Delaware Biotechnology Institute38, Boyce Thompson Institute for Plant Research39, Macquarie University40, Oklahoma State University Center for Health Sciences41, İzmir University of Economics42, Academy of Sciences of the Czech Republic43, Charles University in Prague44, St. Edward's University45, University of Puget Sound46, Hokkaido University47, Tsinghua University48, Washington State University49, Appalachian State University50, Marquette University51
12 Oct 2007-Science
TL;DR: Analyses of the Chlamydomonas genome advance the understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella.
Abstract: Chlamydomonas reinhardtii is a unicellular green alga whose lineage diverged from land plants over 1 billion years ago. It is a model system for studying chloroplast-based photosynthesis, as well as the structure, assembly, and function of eukaryotic flagella (cilia), which were inherited from the common ancestor of plants and animals, but lost in land plants. We sequenced the approximately 120-megabase nuclear genome of Chlamydomonas and performed comparative phylogenomic analyses, identifying genes encoding uncharacterized proteins that are likely associated with the function and biogenesis of chloroplasts or eukaryotic flagella. Analyses of the Chlamydomonas genome advance our understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella.

2,554 citations

Journal ArticleDOI
TL;DR: Iron‐deprivation induces a transition from photoheterotrophic to primarily heterotrophic metabolism, indicating that a hierarchy for iron allocations within organelles of a single cell exists that is closely linked with the metabolic state of the cell.
Abstract: The basic question addressed in this study is how energy metabolism is adjusted to cope with iron deficiency in Chlamydomonas reinhardtii. To investigate the impact of iron deficiency on bioenergetic pathways, comparative proteomics was combined with spectroscopic as well as voltametric oxygen measurements to assess protein dynamics linked to functional properties of respiratory and photosynthetic machineries. Although photosynthetic electron transfer is largely compromised under iron deficiency, our quantitative and spectroscopic data revealed that the functional antenna size of photosystem II (PSII) significantly increased. Concomitantly, stress-related chloroplast polypeptides, like 2-cys peroxiredoxin and a stress-inducible light-harvesting protein, LhcSR3, as well as a novel light-harvesting protein and several proteins of unknown function were induced under iron-deprivation. Respiratory oxygen consumption did not decrease and accordingly, polypeptides of respiratory complexes, harboring numerous iron-sulfur clusters, were only slightly diminished or even increased under low iron. Consequently, iron-deprivation induces a transition from photoheterotrophic to primarily heterotrophic metabolism, indicating that a hierarchy for iron allocations within organelles of a single cell exists that is closely linked with the metabolic state of the cell.

176 citations

Journal ArticleDOI
TL;DR: De novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum and many algorithms have been proposed and a selection of them are detailed in this article.
Abstract: Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

109 citations

Journal ArticleDOI
TL;DR: An extensible framework is presented, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods and demonstrates that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.
Abstract: MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.As the experimental discovery of microRNAs (miRNAs) is cumbersome, computational tools have been developed for the prediction of pre-miRNAs. Here the authors develop a framework to assess the performance of existing and novel pre-miRNA prediction tools and provide guidelines for selecting an appropriate approach for a given data set.

72 citations

Journal ArticleDOI
TL;DR: A new high‐throughput computational strategy was established that improves genomic data mining from MS experiments, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.
Abstract: A new high-throughput computational strategy was established that improves genomic data mining from MS experiments. The MS/MS data were analyzed by the SEQUEST search algorithm and a combination of de novo amino acid sequencing in conjunction with an error-tolerant database search tool, operating on a 256 processor computer cluster. The error-tolerant search tool, previously established as GenomicPeptideFinder (GPF), enables detection of intron-split and/or alternatively spliced peptides from MS/MS data when deduced from genomic DNA. Isolated thylakoid membranes from the eukaryotic green alga Chlamydomonas reinhardtii were separated by 1-D SDS gel electrophoresis, protein bands were excised from the gel, digested in-gel with trypsin and analyzed by coupling nano-flow LC with MS/MS. The concerted action of SEQUEST and GPF allowed identification of 2622 distinct peptides. In total 448 peptides were identified by GPF analysis alone, including 98 intron-split peptides, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.

65 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
TL;DR: Phytozome provides a view of the evolutionary history of every plant gene at the level of sequence, gene structure, gene family and genome organization, while at the same time providing access to the sequences and functional annotations of a growing number of complete plant genomes.
Abstract: The number of sequenced plant genomes and associated genomic resources is growing rapidly with the advent of both an increased focus on plant genomics from funding agencies, and the application of inexpensive next generation sequencing. To interact with this increasing body of data, we have developed Phytozome (http://www.phytozome.net), a comparative hub for plant genome and gene family data and analysis. Phytozome provides a view of the evolutionary history of every plant gene at the level of sequence, gene structure, gene family and genome organization, while at the same time providing access to the sequences and functional annotations of a growing number (currently 25) of complete plant genomes, including all the land plants and selected algae sequenced at the Joint Genome Institute, as well as selected species sequenced elsewhere. Through a comprehensive plant genome database and web portal, these data and analyses are available to the broader plant science research community, providing powerful comparative genomics tools that help to link model systems with other plants of economic and ecological importance.

3,728 citations

Journal ArticleDOI
TL;DR: A brief summary of the current knowledge on oleaginous algae and their fatty acid and TAG biosynthesis, algal model systems and genomic approaches to a better understanding of TAG production, and a historical perspective and path forward for microalgae-based biofuel research and commercialization are provided.
Abstract: Microalgae represent an exceptionally diverse but highly specialized group of micro-organisms adapted to various ecological habitats. Many microalgae have the ability to produce substantial amounts (e.g. 20-50% dry cell weight) of triacylglycerols (TAG) as a storage lipid under photo-oxidative stress or other adverse environmental conditions. Fatty acids, the building blocks for TAGs and all other cellular lipids, are synthesized in the chloroplast using a single set of enzymes, of which acetyl CoA carboxylase (ACCase) is key in regulating fatty acid synthesis rates. However, the expression of genes involved in fatty acid synthesis is poorly understood in microalgae. Synthesis and sequestration of TAG into cytosolic lipid bodies appear to be a protective mechanism by which algal cells cope with stress conditions, but little is known about regulation of TAG formation at the molecular and cellular level. While the concept of using microalgae as an alternative and renewable source of lipid-rich biomass feedstock for biofuels has been explored over the past few decades, a scalable, commercially viable system has yet to emerge. Today, the production of algal oil is primarily confined to high-value specialty oils with nutritional value, rather than commodity oils for biofuel. This review provides a brief summary of the current knowledge on oleaginous algae and their fatty acid and TAG biosynthesis, algal model systems and genomic approaches to a better understanding of TAG production, and a historical perspective and path forward for microalgae-based biofuel research and commercialization.

3,479 citations

Journal ArticleDOI
TL;DR: A review of second generation biodiesel production systems using microalgae can be found in this paper, where the main advantages of second-generation microalgal systems are that they: (1) have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) can couple CO2-neutral fuel production with CO2 sequestration: (
Abstract: The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.

2,254 citations

Journal ArticleDOI
13 Aug 2010-Science
TL;DR: Although microalgae are not yet produced at large scale for bulk applications, recent advances—particularly in the methods of systems biology, genetic engineering, and biorefining—present opportunities to develop this process in a sustainable and economical way within the next 10 to 15 years.
Abstract: Microalgae are considered one of the most promising feedstocks for biofuels. The productivity of these photosynthetic microorganisms in converting carbon dioxide into carbon-rich lipids, only a step or two away from biodiesel, greatly exceeds that of agricultural oleaginous crops, without competing for arable land. Worldwide, research and demonstration programs are being carried out to develop the technology needed to expand algal lipid production from a craft to a major industrial process. Although microalgae are not yet produced at large scale for bulk applications, recent advances—particularly in the methods of systems biology, genetic engineering, and biorefining—present opportunities to develop this process in a sustainable and economical way within the next 10 to 15 years.

1,712 citations