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8 results for “Phaeocystis”
Phaeocystis globosa colonial gene expression
<p>Data and analysis for the paper: </p> <p><strong>Differential gene expression supports a resource-intensive, defensive role for colony production in the bloom-forming haptophyte, <em>Phaeocystis globosa</em></strong></p> <p>by: Margaret Mars Brisbin and Satoshi Mitarai</p> <p>The <em>Phaeocystis globosa</em> CCMP1528 transcriptome used in the study (phaeocystisglobosa_euk_seqs.fasta or pg_euk_seqs_altnames.fasta) was assembled with trimmed sequencing reads from 8 biological replicates (4 colonial replicates and 4 solitary replicates) with the Trinity software (v2.3.2).</p> <p>Raw sequencing reads are available from the NCBI SRA with accession numbers: SRR7811979–SRR7811986.</p> <p>Before assembling the transcriptome, reads were quality filtered and trimmed with the Trimmomatic software (v3.36) using the command:</p> <pre><code>java -jar $TRIM/trimmomatic-0.36.jar PE -phred33 $DATA2/S${SLURM_ARRAY_TASK_ID}_S*_R1_001.fastq.gz \ $DATA2/S${SLURM_ARRAY_TASK_ID}_S*_R2_001.fastq.gz \ $OUT/S${SLURM_ARRAY_TASK_ID}_1_paired.fq $OUT/S${SLURM_ARRAY_TASK_ID}_1_unpaired.fq \ $OUT/S${SLURM_ARRAY_TASK_ID}_2_paired.fq $OUT/S${SLURM_ARRAY_TASK_ID}_2_unpaired.fq \ ILLUMINACLIP:$TRIM/adapters/NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36</code></pre> <p>Trimmed reads were mapped to the ERCC reference sequences for Mix1 and mapped reads were filtered using the following commands from bowtie2 (v2.2.6), samtools, and bedtools: </p> <pre><code>bowtie2 -t -x $REF \ -1 $DATA/S${SLURM_ARRAY_TASK_ID}_1_paired.fq \ -2 $DATA/S${SLURM_ARRAY_TASK_ID}_2_paired.fq \ -S $OUT/S${SLURM_ARRAY_TASK_ID}_ercc.sam samtools view -bS $DATA/S${SLURM_ARRAY_TASK_ID}_ercc.sam >$DATA/S${SLURM_ARRAY_TASK_ID}.bam samtools sort $DATA/S${SLURM_ARRAY_TASK_ID}.bam $DATA/S${SLURM_ARRAY_TASK_ID}_sorted samtools view -b -f 13 S${SLURM_ARRAY_TASK_ID}_sorted.bam > S${SLURM_ARRAY_TASK_ID}_unmapped.bam samtools sort -n $DATA/S${SLURM_ARRAY_TASK_ID}_unmapped.bam $DATA/S${SLURM_ARRAY_TASK_ID}.qsort bedtools bamtofastq -i $DATA/S${SLURM_ARRAY_TASK_ID}.qsort.bam -fq $DATA/S${SLURM_ARRAY_TASK_ID}_1_paired.fq -fq2 $DATA/S${SLURM_ARRAY_TASK_ID}_2_paired.fq</code></pre> <p>The resulting Trimmed reads without ERCC sequences were used to make the transcriptome assembly: </p> <pre><code>Trinity --seqType fq --max_memory 475G \ --left $DATA2/C1_1_paired.fq,$DATA2/C2_1_paired.fq,$DATA2/C3_1_paired.fq,$DATA2/C4_1_paired.fq,$DATA2/S1_1_paired.fq,$DATA2/S2_1_paired.fq,$DATA2/S3_1_paired.fq,$DATA2/S4_1_paired.fq \ --right $DATA2/C1_2_paired.fq,$DATA2/C2_1_paired.fq,$DATA2/C3_2_paired.fq,$DATA2/C4_2_paired.fq,$DATA2/S1_2_paired.fq,$DATA2/S2_2_paired.fq,$DATA2/S3_2_paired.fq,$DATA2/S4_2_paired.fq \ --CPU 12</code></pre> <p>The Trinity assembly was dereplicated with CD-HIT-EST (v2016-0304) at 95% : </p> <pre><code>cd-hit-est -i $DATA/Trinity.fasta -o Trinity_Pg_clustered_95 -c 0.95 -n 8 -p 1 -g 1 -M 200000 -T 8 -d 40</code></pre> <p>The Trinity assembly was filtered to remove bacterial contamination by first running a blastn(v2.6.0+) against the nr/nt NCBI database:</p> <pre><code>blastn -query $DATA/Trinity_Pg_clustered_95.fasta -task blastn -db $REF -num_threads 12 -max_target_seqs 1 -outfmt 5 > TrinityBlast.xml</code></pre> <p>and then removing bacterial reads with custom python scripts included here: TrinityBlastXML.ipynb and FIlterTrinityEukNotEuk.ipynb </p> <p>RSEM (v1.2.22) was run with the final transcriptome assembly (phaeocystisglobosa_euk_seqs.fasta or pg_euk_seqs_altnames.fasta): </p> <pre><code>rsem-calculate-expression --bowtie2 --paired-end \ $DATA/C${SLURM_ARRAY_TASK_ID}_1_paired.fq \ $DATA/C${SLURM_ARRAY_TASK_ID}_2_paired.fq \ $REF/rsemref_longISO/pg_euks_RSEMref \ $REF/rsemout_longISO/C${SLURM_ARRAY_TASK_ID} rsem-calculate-expression --bowtie2 --paired-end \ $DATA/S${SLURM_ARRAY_TASK_ID}_1_paired.fq \ $DATA/S${SLURM_ARRAY_TASK_ID}_2_paired.fq \ $REF/rsemref_longISO/pg_euks_RSEMref \ $REF/rsemout_longISO/S${SLURM_ARRAY_TASK_ID} </code></pre> <p>The resulting data files are: C*.genes.results and S*.genes.results which were used with DESeq2 in the R environment to analyze different gene expression. The code for these analyses is available in html and R markdown (PhaeoColSol_DE.html, PhaeoColSol_DE.Rmd). </p> <p>The transcriptome assembly was annotated with the Dammit software (v1.0rc2), which wraps Transdecoder, HMMER, and BUSCO, and by submitting the translated amino acid sequences to GhostKOALA. </p> <p>The raw pfam Dammit annotation results are included: pg_euk_seqs.fasta.x.pfam.gff3. These results were parsed with the script: Pfam_gffParsing.ipynb. The resulting file, pfam_parsed_annotation.csv, is used in the script PhaeoColSol_DE.Rmd with pfam2go4R.txt for GO enrichment analysis. The script shinycolsol.Rmd creates an interactive plot of GO enrichment results. </p> <p>The GhostKOALA results are user_ko.csv, and are used in the script PhaeoColSol_DE.Rmd for KEGG pathway enrichment analysis. </p>
EUKulele databases for Phaeocystis mapping (Krinos et al. 2023)
<p>Supplementary dataset for Krinos et al. 2023, <a href="https://www.biorxiv.org/content/10.1101/2023.07.30.551153v1">Missing microbial eukaryotes and misleading meta-omic conclusions</a>, <em>bioRxiv</em>. Each zipped folder is a different database containing some subset of the <em>Phaeocystis</em> references used to map against the <em>Tara</em> Oceans metagenomes. These folders contain a combined database and taxonomy table derived from other sequencing dataset resources which can be used directly with the <a href="https://github.com/akrinos/2022-euk-diversity">EUKulele software</a>.</p> <ul> <li>`marmmetsp_all_phaeo.tar.gz` - full database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014), and all <em>Phaeocystis</em> references as listed in Krinos et al. (2023)</li> <li>`marmmetsp_all_colonies.tar.gz` - database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014), and <em>Phaeocystis</em> references known to form colonies (<em>Phaeocystis antarctica</em>, <em>Phaeocystis pouchetii</em>, <em>Phaeocystis globosa</em>)</li> <li>`marmmetsp_all_free.tar.gz` - database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014), and <em>Phaeocystis</em> references known <strong>not</strong> to form colonies (<em>Phaeocystis rex</em>, <em>Phaeocystis jahnii</em>, <em>Phaeocystis cordata</em>)</li> </ul>
Data from: What doesn’t kill you makes you stronger: Fear of predation widens the thermal niche of the harmful algal bloom species Phaeocystis globosa
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Exploring the roles of iron and irradiance in dynamics of diatoms and Phaeocystis in the Amundsen Sea continental shelf water
<p>The Amundsen Sea continental shelf (ACS) water ecosystem is expected to undergo changes since the increasing melt rate of glaciers and decreasing sea ice extent by global warming would lead to the mitigation of iron and light limitation. We investigated how diatoms and Phaeocystis, two dominant taxa, and primary production in the ACS water would respond to variations in iron and light availabilities by using a 1-D pelagic ecosystem model. In the model, we added sea ice effects that reduce light penetration and optimized model parameters for diatoms and Phaeocystis. The results from our model showed good agreement with 20-year observations of Chl-a as well as the biomass proportion of diatoms and Phaeocystis and nutrient distributions during the growing season. Our model experimental results suggest that the current moderate iron and high light conditions favor the growth of Phaeocystis over diatoms. Moreover, as iron increases, the organic carbon exudation by phytoplankton increases more rapidly than net primary production (NPP), leading to a decline in phytoplankton biomass. On the other hand, irradiance plays a role in controlling NPP in terms of photoinhibition which is reduced by increasing iron. Increases in both iron and irradiance lead to an advance in the timing of the bloom peak (surface Chl-a maximum) due to increases in phytoplankton carbon loss and photoinhibition. Our results imply that the dominance of Phaeocystis can continue and that the carbon uptake capacity of the ACS in the summer seasons might increase given that iron availability will increase with future climate change.</p>
Exploring the roles of iron and irradiance in dynamics of diatoms and Phaeocystis in the Amundsen Sea continental shelf water
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Response of Phaeocystis antarctica under changing iron conditions
GEO Series GSE102608. Phaeocystis antarctica. 15 samples. Type: Expression profiling by high throughput sequencing.
Differences between colonial cells of Phaeocystis globosa from two Strains (GX and ST)
GEO Series GSE141662. Phaeocystis globosa. 6 samples. Type: Expression profiling by high throughput sequencing.
Differences between single isolated cells and colonial cells in heteromorphic life cycle of Phaeocystis globosa：Morphology, Physiology and Transcriptome
GEO Series GSE140985. Phaeocystis globosa. 6 samples. Type: Expression profiling by high throughput sequencing.
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