Katz
Laura A.
Katz
Laura A.
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ArticleBroadly sampled multigene trees of eukaryotes(BioMed Central, 2008-01-18) Yoon, Hwan Su ; Grant, Jessica ; Tekle, Yonas I. ; Wu, Min ; Chaon, Benjamin C. ; Cole, Jeffrey C. ; Logsdon, John M. ; Patterson, David J. ; Bhattacharya, Debashish ; Katz, Laura A.Our understanding of the eukaryotic tree of life and the tremendous diversity of microbial eukaryotes is in flux as additional genes and diverse taxa are sampled for molecular analyses. Despite instability in many analyses, there is an increasing trend to classify eukaryotic diversity into six major supergroups: the 'Amoebozoa', 'Chromalveolata', 'Excavata', 'Opisthokonta', 'Plantae', and 'Rhizaria'. Previous molecular analyses have often suffered from either a broad taxon sampling using only single-gene data or have used multigene data with a limited sample of taxa. This study has two major aims: (1) to place taxa represented by 72 sequences, 61 of which have not been characterized previously, onto a well-sampled multigene genealogy, and (2) to evaluate the support for the six putative supergroups using two taxon-rich data sets and a variety of phylogenetic approaches.
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DatasetCTD data from 39 stations from R/V Cape Hatteras cruise CH0112 in the Northwest Atlantic Continental Shelf in 2012 (CiliateSequencing project)(Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2019-11-06) McManus, George B. ; Katz, Laura A.CTD data from 39 stations from R/V Cape Hatteras cruise CH0112 in the Northwest Atlantic Continental Shelf in 2012. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/3959
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DatasetCurrent velocities from ADCP from an R/V Lowell Weicker cruise in Fisher's Island Sound (NY/CT) in May 2012(Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2018-12-07) McManus, George B. ; Katz, Laura A.This dataset contains northward and eastward components of current velocity measured by Acoustic Doppler Current Profiler (ADCP). Velocity components are given at every 0.5 meter depth. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/3713
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DatasetCTD data from 5 stations collected on an R/V Lowell Weicker cruise in Fisher's Island Sound (NY/CT) in 2012(Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2018-12-07) McManus, George B. ; Katz, Laura A.Temperature, salinity, fluorescence, and sigma-t density measured by CTD are reported for 5 stations in Fisher's Island Sound. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/3714
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DatasetTemperature and salinity measured at the UConn dock in Groton, CT, USA, associated with DNA collection(Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2021-06-28) McManus, George ; Katz, Laura A. ; Santoferrara, LucianaThis dataset includes temperature and salinity measurements collected from the UConn dock in Groton CT, USA (41˚ 18' 59" N, 72˚ 03'39" W), in association with DNA collection. Measurements were made weekly from January 2020 to April 2021. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/854554
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ArticleEvaluating support for the current classification of eukaryotic diversity(Public Library of Science (PLoS), 2006-12-22) Parfrey, Laura Wegener ; Barbero, Erika ; Lasser, Elyse ; Dunthorn, Micah ; Bhattacharya, Debashish ; Patterson, David J. ; Katz, Laura A.Perspectives on the classification of eukaryotic diversity have changed rapidly in recent years, as the four eukaryotic groups within the five-kingdom classification—plants, animals, fungi, and protists—have been transformed through numerous permutations into the current system of six ‘‘supergroups.’’ The intent of the supergroup classification system is to unite microbial and macroscopic eukaryotes based on phylogenetic inference. This supergroup approach is increasing in popularity in the literature and is appearing in introductory biology textbooks. We evaluate the stability and support for the current six-supergroup classification of eukaryotes based on molecular genealogies. We assess three aspects of each supergroup: (1) the stability of its taxonomy, (2) the support for monophyly (single evolutionary origin) in molecular analyses targeting a supergroup, and (3) the support for monophyly when a supergroup is included as an out-group in phylogenetic studies targeting other taxa. Our analysis demonstrates that supergroup taxonomies are unstable and that support for groups varies tremendously, indicating that the current classification scheme of eukaryotes is likely premature. We highlight several trends contributing to the instability and discuss the requirements for establishing robust clades within the eukaryotic tree of life.
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ArticleThe Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) : illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing(Public Library of Science, 2014-06-24) Keeling, Patrick J. ; Burki, Fabien ; Wilcox, Heather M. ; Allam, Bassem ; Allen, Eric E. ; Amaral-Zettler, Linda A. ; Armbrust, E. Virginia ; Archibald, John M. ; Bharti, Arvind K. ; Bell, Callum J. ; Beszteri, Bank ; Bidle, Kay D. ; Cameron, Connor T. ; Campbell, Lisa ; Caron, David A. ; Cattolico, Rose Ann ; Collier, Jackie L. ; Coyne, Kathryn J. ; Davy, Simon K. ; Deschamps, Phillipe ; Dyhrman, Sonya T. ; Edvardsen, Bente ; Gates, Ruth D. ; Gobler, Christopher J. ; Greenwood, Spencer J. ; Guida, Stephanie M. ; Jacobi, Jennifer L. ; Jakobsen, Kjetill S. ; James, Erick R. ; Jenkins, Bethany D. ; John, Uwe ; Johnson, Matthew D. ; Juhl, Andrew R. ; Kamp, Anja ; Katz, Laura A. ; Kiene, Ronald P. ; Kudryavtsev, Alexander N. ; Leander, Brian S. ; Lin, Senjie ; Lovejoy, Connie ; Lynn, Denis ; Marchetti, Adrian ; McManus, George ; Nedelcu, Aurora M. ; Menden-Deuer, Susanne ; Miceli, Cristina ; Mock, Thomas ; Montresor, Marina ; Moran, Mary Ann ; Murray, Shauna A. ; Nadathur, Govind ; Nagai, Satoshi ; Ngam, Peter B. ; Palenik, Brian ; Pawlowski, Jan ; Petroni, Giulio ; Piganeau, Gwenael ; Posewitz, Matthew C. ; Rengefors, Karin ; Romano, Giovanna ; Rumpho, Mary E. ; Rynearson, Tatiana A. ; Schilling, Kelly B. ; Schroeder, Declan C. ; Simpson, Alastair G. B. ; Slamovits, Claudio H. ; Smith, David R. ; Smith, G. Jason ; Smith, Sarah R. ; Sosik, Heidi M. ; Stief, Peter ; Theriot, Edward ; Twary, Scott N. ; Umale, Pooja E. ; Vaulot, Daniel ; Wawrik, Boris ; Wheeler, Glen L. ; Wilson, William H. ; Xu, Yan ; Zingone, Adriana ; Worden, Alexandra Z.Microbial ecology is plagued by problems of an abstract nature. Cell sizes are so small and population sizes so large that both are virtually incomprehensible. Niches are so far from our everyday experience as to make their very definition elusive. Organisms that may be abundant and critical to our survival are little understood, seldom described and/or cultured, and sometimes yet to be even seen. One way to confront these problems is to use data of an even more abstract nature: molecular sequence data. Massive environmental nucleic acid sequencing, such as metagenomics or metatranscriptomics, promises functional analysis of microbial communities as a whole, without prior knowledge of which organisms are in the environment or exactly how they are interacting. But sequence-based ecological studies nearly always use a comparative approach, and that requires relevant reference sequences, which are an extremely limited resource when it comes to microbial eukaryotes. In practice, this means sequence databases need to be populated with enormous quantities of data for which we have some certainties about the source. Most important is the taxonomic identity of the organism from which a sequence is derived and as much functional identification of the encoded proteins as possible. In an ideal world, such information would be available as a large set of complete, well-curated, and annotated genomes for all the major organisms from the environment in question. Reality substantially diverges from this ideal, but at least for bacterial molecular ecology, there is a database consisting of thousands of complete genomes from a wide range of taxa, supplemented by a phylogeny-driven approach to diversifying genomics. For eukaryotes, the number of available genomes is far, far fewer, and we have relied much more heavily on random growth of sequence databases, raising the question as to whether this is fit for purpose.
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DatasetCTD profiles collected with RV Connecticut on the Continental shelf and slope south of Montauk, NY on 14-15 June 2022.(Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2022-09-16) McManus, George ; Santoferrara, Luciana ; Katz, Laura A.Seven stations on the shelf and slope south of Montauk, NY on 14-15 June 2022 were sampled. Basic hydrographic properties, including temperature, salinity, dissolved oxygen, and pH were measured and rosette samples were taken for chlorophyll, nutrients, and microplankton. Water was filtered for metagenomics and metatranscriptomics and picked individual microplankters for sequencing. Three to ten depths were sampled at each station. Our shallowest station was on the shelf at 38m and the deepest was at the slope edge at 2400m. Due to limitations of the CTD our deepest sampled depth was 1150m. At the two shallowest stations, we also deployed a Wetstar fluorometer for phytoplankton fluorescence. The CTD data were processed with SeaBird's Seasoft program and converted to spreadsheet format. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/879380
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PreprintBroadly sampled multigene analyses yield a well-resolved eukaryotic tree of life( 2010-06-01) Parfrey, Laura Wegener ; Grant, Jessica ; Tekle, Yonas I. ; Lasek-Nesselquist, Erica ; Morrison, Hilary G. ; Sogin, Mitchell L. ; Patterson, David J. ; Katz, Laura A.An accurate reconstruction of the eukaryotic tree of life is essential to identify the innovations underlying the diversity of microbial and macroscopic (e.g. plants and animals) eukaryotes. Previous work has divided eukaryotic diversity into a small number of high-level ‘supergroups’, many of which receive strong support in phylogenomic analyses. However, the abundance of data in phylogenomic analyses can lead to highly supported but incorrect relationships due to systematic phylogenetic error. Further, the paucity of major eukaryotic lineages (19 or fewer) included in these genomic studies may exaggerate systematic error and reduces power to evaluate hypotheses. Here, we use a taxon-rich strategy to assess eukaryotic relationships. We show that analyses emphasizing broad taxonomic sampling (up to 451 taxa representing 72 major lineages) combined with a moderate number of genes yield a well-resolved eukaryotic tree of life. The consistency across analyses with varying numbers of taxa (88-451) and levels of missing data (17-69%) supports the accuracy of the resulting topologies. The resulting stable topology emerges without the removal of rapidly evolving genes or taxa, a practice common to phylogenomic analyses. Several major groups are stable and strongly supported in these analyses (e.g. SAR, Rhizaria, Excavata), while the proposed supergroup ‘Chromalveolata’ is rejected. Further, extensive instability among photosynthetic lineages suggests the presence of systematic biases including endosymbiotic gene transfer from symbiont (nucleus or plastid) to host. Our analyses demonstrate that stable topologies of ancient evolutionary relationships can be achieved with broad taxonomic sampling and a moderate number of genes. Finally, taxonrich analyses such as presented here provide a method for testing the accuracy of relationships that receive high bootstrap support in phylogenomic analyses and enable placement of the multitude of lineages that lack genome scale data.