89 research outputs found
DATASETS - Pervasive iron limitation at subsurface chlorophyll maxima of the California Current
No full textUPDATE: This record is not current. Please see new record at https://doi.org/10.5281/zenodo.1495558S.L Hogle - Nov 24, 2018-----------------------------------Derived datasets presented here are licensed under the CC0 1.0 Universal license (see LICENSE.txt) and have been compiled from sources including:1. Pacific Fisheries Environmental Laboratory (PFEL) [https://www.pfeg.noaa.gov/]2. The North Pacific Gyre Oscillation Index (NPGO) from Emanuele Di Lorenzo at Georgia Tech [http://www.o3d.org/npgo/enso.html]3. California Coopeative Fisheries Oceanic Fisheries Investigations (CalCOFI) [http://calcofi.org/]4. iMicrobe (Project ID: CAM_P_0001069) [https://imicrobe.us]5. UCSD Datazoo Research Project [http://oceaninformatics.ucsd.edu/datazoo/catalogs/ccelter/sources/1758]6. The authors' (SL Hogle et al.) own workIf using these original and/or derived datasets in your work pleaseconsider citing the original sources.S.L Hogle - March 27, 2018</div
CODE - Pervasive iron limitation at subsurface chlorophyll maxima of the California Current
No full textUPDATE: This record is not current. Please see new record at https://doi.org/10.5281/zenodo.1495504S.L Hogle - Nov 24, 2018-----------------------------------Code necessary to reproduce analyses from "Pervasive influence of iron limitation in subsurface chlorophyll maximum layers of the California Current" by Hogle SL et al.jupyter_notebooksJupyter notebook files needed to generate all figures and reproduce all analyses from the manuscript. In each notebook calls that generate the respective main or supplementary figure are labeled as such.The data files that the notebooks reference are located at the following URL under the CC0 License: https://doi.org/10.6084/m9.figshare.6033761</div
Heme in the marine environment: from cells to the iron cycle
No full textHemes are iron containing heterocyclic molecules important in many cellular processes. In the marine environment, hemes participate as enzymatic cofactors in biogeochemically significant processes like photosynthesis, respiration, and nitrate assimilation. Further, hemoproteins, hemes, and their analogs appear to be iron sources for some marine bacterioplankton under certain conditions. Current oceanographic analytical methodologies allow for the extraction and measurement of heme b from marine material, and a handful of studies have begun to examine the distribution of heme b in ocean basins. The study of heme in the marine environment is still in its infancy, but some trends can be gleaned from the work that has been published so far. In this review, we summarize what is known or might be inferred about the roles of heme in marine microbes as well as the few studies on heme in the marine environment that have been conducted to date. We conclude by presenting some future questions and challenges for the field
Metadata and data for https://doi.org/10.1128/AEM.03128-15
No full textPublication reference: Hogle SL, Cameron Thrash J, Dupont CL, Barbeau KA. Trace metal acquisition by marine heterotrophic bacterioplankton with contrasting trophic strategies. Appl Environ Microbiol. 2016. 82:1613–24.
File Descriptions: Supplemental_material.pdf - Supplemental methods, results, tables, figures, and referencesDataset1.xlsx - Genome features, Genome completion, environmental characteristics, transporter abundanceDataset2.xlsx - CDD/PFAM/COGs used to identify metal transportersDataset3.xlsx - Predicted Fur boxesDataset4.xlsx - COGs used for phylogenetic tree constructionDataset5.xlsx - TonB dependent receptor genome neighborhoods and Markov Clustering parametersDataset6.xlsx - Particle associated versus free-living lifestyle assignments and references used to make assignments
scripts - folder with scripts and markdown used to do analysis
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MARMICRODB database for taxonomic classification of (marine) metagenomes
No full textThis database (MARMICRODB) was introduced in the following publication JW Becker, SL Hogle, K Rosendo, and SW Chisholm. 2019. Co-culture and biogeography of Prochlorococcus and SAR11. ISME J. doi:10.1038/s41396-019-0365-4. For a detailed description of this database please see the original publication and its associated supplementary material.
The database MARMICRODB is comprised of 56 million sequence non-redundant protein sequences and was designed for use with the protein homology classifier Kaiju [1]. To ensure maximum representation of marine bacteria, archaea, and microbial eukaryotes, we included translated genes/transcripts from 5397 representative “specI” species clusters from the proGenomes database [2]; 113 transcriptomes from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) [3]; 10 509 metagenome assembled genomes from the Tara Oceans expedition [4,5], the Red Sea [6], the Baltic Sea [7], and other aquatic and terrestrial sources [8]; 994 isolate genomes from the Genomic Encyclopedia of Bacteria and Archaea [9]; 7492 viral genomes from NCBI RefSeq [10]; 786 bacterial and archaeal genomes from MarRef [11]; and 677 marine single cell genomes [12]. The taxonomic composition of this database was intended to predominantly reflect that of the marine environment, while minimizing (but not excluding) the representation of clinical, industrial, and terrestrial host-associated samples. We excluded protein sequences from our custom database with lengths less than 20 and greater than 20 000 amino acids, removed non-standard amino acid residues, and condensed redundant protein sequences to a single representative sequence to which we assigned a lowest common ancestor taxonomy identifier from the NCBI taxonomy database [13].
MARMICRODB is optimized for metagenomic samples from the marine environment, in particular planktonic microbes from the pelagic euphotic zone. I expect this database will also be useful for classifying other types of marine metagenomic samples (for example mesopelagic, bathypelagic, benthic, marine host-associated), but it has not been tested as such. It would probably also be useful in other non-marine environments, but again I haven't tested it. I carefully selected genomes to quantify Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116. The processing of the other marine groups was largely automated and unsupervised. Taxonomy for other groups was copied over from the GTDB [14,15] and NCBI [13] so any errors in those databases will be propagated to MARMICRODB. In most cases MARMICRODB can probably just be used as is, but if you have a favorite organism/clade that I didn't list above then you may want to spend some time curating those genomes o (ie checking for contamination, dereplicating, building a genome phylogeny for custom taxonomy node assignment). Currently the taxonomy is hardcoded in the MARMICRODB.fmi index, but if you would like to modify MARMICRODB by adding or removing genomes, or reconfiguring taxonomic ranks the names.dmp and nodes.dmp files can easily be modified as well as the fasta file of protein sequences. However, the Kaiju index will need to be rebuilt, and you will need a high performance compute cluster to do this. The explanation for the Kaiju custom database format can be found here.
1. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Comms 2016;7:11257.
2. Mende DR, Letunic I, Huerta-Cepas J, Li SS, Forslund K, Sunagawa S, et al. proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes. Nucleic Acids Res. 2017;45:D529–34.
3. Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol 2014;12:e1001889.
4. Tully BJ, Sachdeva R, Graham ED, Heidelberg JF. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 2017;5:e3558.
5. Tully BJ, Graham ED, Heidelberg JF. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 2018;5:170203–8.
6. Haroon MF, Thompson LR, Parks DH, Hugenholtz P, Stingl U. A catalogue of 136 microbial draft genomes from Red Sea metagenomes. Sci. Data 2016;3:160050.
7. Hugerth LW, Larsson J, Alneberg J, Lindh MV, Legrand C, Pinhassi J, et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biology 2015;16:279.
8. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Ben J Woodcroft, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nature Microbiology 2017;2:1533–42.
9. Mukherjee S, Seshadri R, Varghese NJ, Eloe-Fadrosh EA, Meier-Kolthoff JP, Göker M, et al. 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nature Biotechnology 35:676–83.
10. Haft DH, DiCuccio M, Badretdin A, Brover V, Chetvernin V, O’Neill K, et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res. 2017;46:D851–60.
11. Klemetsen T, Raknes IA, Fu J, Agafonov A, Balasundaram SV, Tartari G, et al. The MAR databases: development and implementation of databases specific for marine metagenomics. Nucleic Acids Res. 2017;46:D692–9.
12. Berube PM, Biller SJ, Hackl T, Hogle SL, Satinsky BM, Becker JW, et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Sci. Data 2018;5:180154.
13. Federhen S. The NCBI Taxonomy database. Nucleic Acids Res. 2011;40:D136–43.
14. DH Parks, M Chuvochina, DW Waite, C Rinke, A Skarshewski, PA Chaumeil, P Hugenholtz. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 2018
15. GTDB-Tk: a toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes. https://github.com/Ecogenomics/GTDBTk
File descriptions
------------------------
MARMICRODB_catalog.tsv
Tabular file of NCBI assembly accessions and associated taxonomic information for every genome in MARMICRODB. Also includes literature references for each genome where available. Header description:
genome: Unique identifier for each genome
full_name: full organism name where available
source: literature reference where available
taxid: NCBI taxonomy ID for the assembly accession
MARMICRODBtaxid: taxonomy ID used in the custom Kaiju database
lineage_assignment: taxonomic lineage assignment from NCBI
domain: archaea, bacteria, or eukaryote
taxgroup: short descriptive group
taxclade: higher resolution clade assignment where available
habitat_source: whether genome derives from marine or aquatic source
sequence_type: isolate, single cell genome (sag), metagenome assembled genome (mag), or transcriptome in case of eukaryotes
assembly_ftp: NCBI ftp for assembly
gbk_acc: assembly genbank or refseq accession number
gtdb_taxonomy: taxonomic lineage assignment from GTDB-Tk v0.1.3 against GTDB v83
MARMICRODB_kronaplot.html
Interactive Kronaplot for the exploration of taxonomic composition of MARMICRODB
MARMICRODB.faa.bz2
Fasta file of all protein sequences in MARMICRODB
scripts.tar.gz
directory containing scripts for generating Kaiju formatted database
phylogenies.tar.gz
directory containing detailed phylogenies for SAR11, Prochlorococcus, SAR86, and SAR116
MARMICRODB.fmi
Kaiju index for MARMICRODB
nodes.dmp
nodes file for taxonomic assignment with Kaiju
names.dmp
names file for generating Kaiju reports
I typically run Kaiju like:
kaiju -z 20 -a greedy -e 5 -m 11 -s 65 -E 0.05 -x \
-t nodes.dmp -f MARMICRODB.fmi \
-i inputfile_R1.fastq.gz \
-j inputfile_R2.fastq.gz \
-o MYOUTPUT.kaiju
To obtain a parseable report that lists the custom taxonomic ranks from the nodes.dmp and names.dmp files run kaiju2krona on the output.
kaiju2krona -t nodes.dmp -n names.dmp -i MYOUTPUT.kaiju -o MYOUTPUT.kaiju.krona
This report shows counts assigned to each node in the custom taxonomy and will also include the names for each rank. It is trivial to parse programmatically.</p
CODE - Pervasive iron limitation at subsurface chlorophyll maxima of the California Current
No full textData sets for use with R and Python code are available from 10.5281/zenodo.1495558
Abstract: Subsurface chlorophyll maximum layers (SCMLs) are nearly ubiquitous in stratified water columns and exist at horizontal scales ranging from the submesoscale to the extent of oligotrophic gyres. These layers of heightened chlorophyll and/or phytoplankton concentrations are generally thought to be a consequence of a balance between light energy from above and a limiting nutrient flux from below, typically nitrate (NO3). Here we present multiple lines of evidence demonstrating that iron (Fe) limits or with light colimits phytoplankton communities in SCMLs along a primary productivity gradient from coastal to oligotrophic offshore waters in the southern California Current ecosystem. SCML phytoplankton responded markedly to added Fe or Fe/light in experimental incubations and transcripts of diatom and picoeukaryote Fe stress genes were strikingly abundant in SCML metatranscriptomes. Using a biogeochemical proxy with data from a 40-y time series, we find that diatoms growing in California Current SCMLs are persistently Fe deficient during the spring and summer growing season.We also find that the spatial extent of Fe deficiency within California Current SCMLs has significantly increased over the last 25 y in line with a regional climate index. Finally, we show that diatom Fe deficiency may be common in the subsurface of major upwelling zones worldwide. Our results have important implications for our understanding of the biogeochemical consequences of marine SCML formation and maintenance.</p
slhogle/hambiRNAseq: Archive code and data in Zenodo
No full textThe purpose of this release is to generate a citeable Zenodo do
Long read genome assembly of species from the synthetic HAMBI bacterial community
No full text<p>We present complete genome sequences of 30 bacterial species that can be used to construct defined synthetic communities that stably form in the laboratory under controlled conditions.</p><p>This repository contains the final genome assemblies, logs, reports/summaries, scripts, and associated computational methods. All genomes were circularized (or in the case of <i>Agrobacterium tumefaciens</i> properly linearized) with the recovery of some small plasmids.</p><p>30 Species from the HAMBI community were sent for Oxford Nanopore or PacBio HiFi sequencing at <a href="https://www.seqcenter.com/">SeqCenter</a> early November 2022. I assembled multiple subsets of the read data for each genome and generated consensus assemblies using <a href="https://github.com/rrwick/Trycycler">Trycycler</a>.</p><p>All sequence data is available under BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1047486">PRJNA1047486</a></p><p>This is a mirror of the repository here: <a href="https://gitlab.utu.fi/slhogl/hambiLongRead">https://gitlab.utu.fi/slhogl/hambiLongRead</a></p>
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