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      Open ocean and coastal strains of the N 2-fixing cyanobacterium UCYN-A have distinct transcriptomes

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          Abstract

          Decades of research on marine N 2 fixation focused on Trichodesmium, which are generally free-living cyanobacteria, but in recent years the endosymbiotic cyanobacterium Candidatus Atelocyanobacterium thalassa (UCYN-A) has received increasing attention. However, few studies have shed light on the influence of the host versus the habitat on UCYN-A N 2 fixation and overall metabolism. Here we compared transcriptomes from natural populations of UCYN-A from oligotrophic open-ocean versus nutrient-rich coastal waters, using a microarray that targets the full genomes of UCYN-A1 and UCYN-A2 and known genes for UCYN-A3. We found that UCYN-A2, usually regarded as adapted to coastal environments, was transcriptionally very active in the open ocean and appeared to be less impacted by habitat change than UCYN-A1. Moreover, for genes with 24 h periodic expression we observed strong but inverse correlations among UCYN-A1, A2, and A3 to oxygen and chlorophyll, which suggests distinct host-symbiont relationships. Across habitats and sublineages, genes for N 2 fixation and energy production had high transcript levels, and, intriguingly, were among the minority of genes that kept the same schedule of diel expression. This might indicate different regulatory mechanisms for genes that are critical to the symbiosis for the exchange of nitrogen for carbon from the host. Our results underscore the importance of N 2 fixation in UCYN-A symbioses across habitats, with consequences for community interactions and global biogeochemical cycles.

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          Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

          In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.
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            Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

            In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.
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              A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

              When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2 May 2023
                2023
                : 18
                : 5
                : e0272674
                Affiliations
                [1 ] Department of Ocean Sciences, University of California Santa Cruz, Santa Cruz, California, United States of America
                [2 ] Departamento de Bioquímica y Biología Molecular, Campus de Excelencia Internacional Agroalimentario, Universidad de Córdoba, Córdoba, Spain
                Friedrich Schiller University, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-22-20768
                10.1371/journal.pone.0272674
                10153697
                3b596109-125b-4d0b-9fac-d64566184428
                © 2023 Muñoz-Marín et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 July 2022
                : 18 February 2023
                Page count
                Figures: 4, Tables: 0, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100011264, FP7 People: Marie-Curie Actions;
                Award ID: FP7-PIOF-GA-2013-625188
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 824082
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 724220
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 329108
                Award Recipient :
                This work was supported by the Simons Foundation ( https://www.simonsfoundation.org/) through awards to J.P.Z. (SCOPE awards #329108 and #724220 and Life Sciences award #824082). M.M.M was supported by a Marie Curie International Outgoing Fellowship( https://marie-sklodowska-curie-actions.ec.europa.eu/) within the 7th European Community Framework Programme (FP7-PIOF-GA-2013-625188). The funders had no role in study design, data collection or analysis, the decision to publish, or preparation of the manuscript.
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                \Once the manuscript is published, all data will be made publicly accessible without restriction at NCBI GEO using accessions provided in the manuscript: GPL32341 for the microarray design, GSE206403 for the Stn. ALOHA microarray data. Before publication, reviewers can view the data at NCBI GEO using the secure token provided in the manuscript: uhybigoivjwrrof [The re-analyzed data from Scripps Pier, already published in Muñoz-Marín et al. 2019, are already available to the public at NCBI GEO (accession GSE100124).] Our analysis scripts are already available in the GitHub repository https://github.com/jdmagasin/UCYN-A-metatranscriptomes."

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