DOI: 10.5061/dryad.kh1893284
title: Annotation of genes encoding enzymes across marine phytoplankton genomes

description: Phytoplankton cells span a large size range, from picoplankton
 (&lt;2µm), nanoplankton (2 to 20µm), microplankton (20 to 200µm) to
 macroplankton (200 to &lt;2000µm). Cell size interacts with multiple
 selective pressures, including cellular metabolic rate, light absorption,
 nutrient uptake, cell nutrient quotas, trophic interactions and
 diffusional exchanges with the environment. Beyond simple size, cells of
 different shapes differ in surface area to volume ratio. For example, more
 elongated cells, such as pennate diatoms, have a larger surface area to
 volume ratio compared to more rounded cells, such as centric diatoms, of
 equivalent biovolume, which can in turn influence diffusional exchanges
 between cells and their environment. We assembled metadata on diverse
 marine phytoplankters, in parallel with genomic or transcriptomic data
 annotations to identify genes encoding enzymes, to facilitate analyses of
 genomic patterns of encoded enzymes across diverse taxa, sizes, growth
 forms and origins of strains.
description: MetaData.csv contains information on the site and latitude of
 origin, cell size, genome size, presence of flagella and colony formation
 assembled for 146 diverse marine phytoplankters from citations listed in
 MetaData.csv.  We used an automated pipeline implemented through Snakemake
 to pass gene sequences from the downloaded genomes and/or transcriptomes
 from the 146 phytoplankters, in .fasta format, to the eggNOG 5.0
 database.  We then used eggNOG-Mapper 2.0.6  and the DIAMOND algorithm to
 annotate potential orthologs in each analyzed genome or transcriptome,
 using the following parameters: seed_ortholog_evalue = 0.001,
 seed_ortholog_score = 60, tax_scope = "auto", go_evidence =
 "non-electronic", query_cover = 20 and subject_cover =
 0. The output of automatically annotated
 orthologs, from each genome or transcriptome, from the bioinformatic
 pipeline was compiled into
 <em>combinedHits.csv</em>. Definitions of
 annotations and variable names are listed in
 <em>CombinedHitsDataDictionary.csv</em>.
 Definitions of variable names from
 <em>MetaData.cs</em>v are listed in
 <em>MetaDataDictionary.csv</em>.
description: In <em>MetaData.csv</em>,
 <em>combinedHits</em>.<em>csv</em>,
 <em>MetaDataDictionary</em>.<em>csv</em> and
 <em>CombinedHitsDataDictionary</em>.<em>csv</em>,
 missing values are left blank to facilitate direct import into a dataframe
 under 'R'. .csv files are accessible through
 many software platforms including LibreOffice, but
 <em>combinedHits</em>.<em>csv</em> is large, so
 importing directly into 'R' is advisable.
 Files and Directory Structures: 
 GitHub repository:   https://github.com/FundyPhytoPhys/ROS_bioinfo/tree/master/ROSGenomicPatternsAcrossMarinePhytoplankton <br>contains the code used to generate:<br>Omar NM, Fleury K, Beardsall B, Prášil O, Campbell DA, 2022, Reactive Oxygen Species Production and Scavenging; Genomic Patterns Across Marine Phytoplankton, in review `R/ROSDataImport.Rmd` imports data from the Snakemake pipeline, to generate <em>CombinedHits.csv</em>. `R/ROSModels.Rmd` imports CombinedHits.csv and MetaData.csv, and generates the Poisson and Quasi-Poisson models used in the manuscript. `R/ROSGenomicPatternsBySubstrate.Rmd` generates the manuscript. The metadata catalog containing information about each organism can additionally be found at:<br> https://docs.google.com/spreadsheets/d/1kXm0GvMdTRbF12cG5Bps-2e_4MTjgUX7iUOjqiu-_c0/edit#gid=0 The Data Dictionary for the metadata catalog can additionally be found at:<br> https://docs.google.com/spreadsheets/d/1Gmg7jbvv8S4MuQ04CrH6Z0Nd2L-1_m38djRzW3W1k84/edit#gid=0 The Data dictionary for data used `R/ROSGenomicPatternsBySubstate.Rmd` (`DataProcessed/MergedData.csv`) can additionally be found at:<br> https://docs.google.com/spreadsheets/d/1lECellT86qCIs4bOzpCBZZPsijzV0MQFv7IQS7TS2HY/edit#gid=0