Genomic insights into the coupling of a Chlorella-like microeukaryote and sulfur bacteria in the chemocline of permanently stratified Lake Cadagno
Saini et al. ISMEJ 2023 https://www.nature.com/articles/s41396-023-01396-y
Meromictic Lake Cadagno is situated at 1921 m within Swiss Alps. Lake Cadagno is permanently stratisfied into three zones called 1) mixolimnion (upper oxic), 2) monimolimnion (lower anoxic), and 3) in between oxic-anoxic interface called chemocline which harbours a persistent microbial bloom. This study focussed on eukaryotic microbes, and investigated the microbial bloom using Shotgun DNA sequencing (Illumina HighSeq 4000). Following pipeline was used to obtain near complete genome of Chlorella-like microbial eukaryote from metagenomics dataset.
Raw reads are available at the NCBI under the sequence read archive SUB11916861 and under the accessions SRR21025699, SRR21025700, SRR21025701, and SRR21025702. The GenBank ID for the Chlorella-like MAG is JAOAOU000000000.1. The assembled contigs from raw reads, MAGs, R scriptsincluding files to generate figures for the manuscript and other data were deposited in the Zenodo (https://zenodo.org/record/7505505).
There are total eight metagenomics samples collected from the water column of Lake Cadagno. Three from mixolimnion (5m, 9m, 11m), four from chemocline (13m, 15mw, 15mm, 15.5m), and one from monimolimnion (17m). The samples were collected from different meter depths of the Lake Cadagno. Chemocline metagenomics samples were primarily used in this study to obtain the near complete genome of Chlorella-like microbial eukaryote.
For metagenomics sequencing, 20L lake water was collected from the chemocline (oxic-anoxic boundry; 13-15.5 m) of Lake Cadagno. The biomass was captured on 0.2μm filters, and after DNA extractions samples were sent for Shotgun sequencing. Metagenomics sample collection is in parallel to 16S rRNA gene sequencing mentioned in ASM mbio article (ASM mBIO).
Required tools with installation links: Anvio: https://anvio.org/install/ | BBtools https://jgi.doe.gov/data-and-tools/software-tools/bbtools/ | Spades https://github.com/ablab/spades
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4.1 Trimming of reads prior to assembly to remove low quality calls
bbduk.sh in1=read1.fq in2=read2.fq out1=clean1.fq out2=clean2.fq -
4.2 Error-correction and normalization of reads
bbnorm.sh in=<input> out=<reads to keep> outt=<reads to toss> hist=<histogram output> -
5.1 Assembly of raw reads using SPAdes
spades.py -1 R1.fastq -2 R2.fastq -o name_of_output_folder -t 32 --meta -
5.2 Contig names were simplified using Anvio with minimum length of 1KB
anvi-script-reformat-fasta ./spades.contigs.fasta -o ./renamed.contigs.fa --min-len 1000 --simplify-names --report ./Spades_13m/name_conversions.txt
- 6.1 CONCOCT based competitive binning using Anvio
Required tools with installation links: Anvio: https://anvio.org/install/ | Concoct https://anaconda.org/bioconda/concoct | Bowtie2 and Samtools (Already installed with Anvio).
Following is the example of Spades Assembly of 15.5m sample of Lake Cadagno mapped to different samples (5m, 9m, 11m, 13m, 15mw, 15mm, 15.5m, 17m). There are total of eight metagenomics samples collected from the Lake. Four samples are from the chemocline (13m, 15mw, 15mm, 17m).
bowtie2-build ../Anvio.15_5m.S.contigs.fa ./Anvio.15_5m.S.contigs
#15.5m Assembly mapped to 5m sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119834_R1.fastq \
-2 Sample_119834_R2.fastq \
-S lib_15_5m_mapped_5m_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_5m_r.sam > lib_15_5m_mapped_5m_r-RAW.bam
#15.5m Assembly mapped to 9m sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119835_R1.fastq \
-2 Sample_119835_R2.fastq \
-S lib_15_5m_mapped_9m_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_9m_r.sam > lib_15_5m_mapped_9m_r-RAW.bam
#15.5m Assembly mapped to 11m sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119836_R1.fastq \
-2 Sample_119836_R2.fastq \
-S lib_15_5m_mapped_11m_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_11m_r.sam > lib_15_5m_mapped_11m_r-RAW.bam
#15.5m Assembly mapped to 13m sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119837_R1.fastq \
-2 Sample_119837_R2.fastq \
-S lib_15_5m_mapped_13m_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_13m_r.sam > lib_15_5m_mapped_13m_r-RAW.bam
#15.5m Assembly mapped to 15mw sample raw reads (whole water sample)
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119838_R1.fastq \
-2 Sample_119838_R2.fastq \
-S lib_15_5m_mapped_15mw_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_15mw_r.sam > lib_15_5m_mapped_15mw_r-RAW.bam
#15.5m Assembly mapped to 15mm sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119839_R1.fastq \
-2 Sample_119839_R2.fastq \
-S lib_15_5m_mapped_15mm_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_15mm_r.sam > lib_15_5m_mapped_15mm_r-RAW.bam
#15.5m Assembly mapped to 15.5m sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119840_R1.fastq \
-2 Sample_119840_R2.fastq \
-S lib_15_5m_mapped_15_5m_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_15_5m_r.sam > lib_15_5m_mapped_15_5m_r-RAW.bam
#15.5m Assembly mapped to 17m sample raw reads
bowtie2 --threads 32 -x ./Anvio.15_5m.S.contigs -1 Sample_119841_R1.fastq \
-2 Sample_119841_R2.fastq \
-S lib_15_5m_mapped_17m_r.sam
samtools view -F 4 -bS lib_15_5m_mapped_17m_r.sam > lib_15_5m_mapped_17m_r-RAW.bam
#Sorting RAW bam using Anvio
anvi-init-bam lib_15_5m_mapped_15mw_r-RAW.bam -o lib_15_5m_mapped_15mw_r.bam
anvi-init-bam lib_15_5m_mapped_15mm_r-RAW.bam -o lib_15_5m_mapped_15mm_r.bam
anvi-init-bam lib_15_5m_mapped_15_5m_r-RAW.bam -o lib_15_5m_mapped_15_5m_r.bam
anvi-init-bam lib_15_5m_mapped_17m_r-RAW.bam -o lib_15_5m_mapped_17m_r.bam
anvi-init-bam lib_15_5m_mapped_9m_r-RAW.bam -o lib_15_5m_mapped_9m_r.bam
anvi-init-bam lib_15_5m_mapped_5m_r-RAW.bam -o lib_15_5m_mapped_5m_r.bam
anvi-init-bam lib_15_5m_mapped_13m_r-RAW.bam -o lib_15_5m_mapped_13m_r.bam
anvi-init-bam lib_15_5m_mapped_11m_r-RAW.bam -o lib_15_5m_mapped_11m_r.bam
#Generating Anvio profiles using BAM files
anvi-profile -i lib_15_5m_mapped_11m_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_13m_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_15mm_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_15mw_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_15_5m_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_17m_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_5m_r.bam -c ../Anvio.15_5m.S.contigs.db -T 32
anvi-profile -i lib_15_5m_mapped_9m_r.bam -c ../Anvio.15_5m.S.contigs.db - -T 32
anvi-merge ./lib_15_5m_mapped_5m_r/PROFILE.db ./lib_15_5m_mapped_9m_r/PROFILE.db ./lib_15_5m_mapped_11m_r/PROFILE.db ./lib_15_5m_mapped_13m_r/PROFILE.db \
./lib_15_5m_mapped_15mm_r/PROFILE.db ./lib_15_5m_mapped_15mw_r/PROFILE.db ./lib_15_5m_mapped_15_5m_r/PROFILE.db \
./lib_15_5m_mapped_17m_r/PROFILE.db -c ../Anvio.15_5m.S.contigs.db -o ../All_SAMPLES-MERGED_P
#Concoct binning inside Anvio using coverage information stored from all the samples.
anvi-cluster-contigs -p ./All_SAMPLES-MERGED_P/PROFILE.db -c ../Anvio.15_5m.S.contigs.db -C Bins_concoct_15_5m --driver concoct --just-do-it -T 32
- 6.2 CONCOCT based non competitive binning (optional)
Required tools with installation links: Anvio: https://anvio.org/install/ | Concoct | Bowtie2 and Samtools (Already installed with Anvio).
#Mapping followed by Binning
#Creating BAM file
bowtie2-build ./renamed.contigs.fa ./output_file
bowtie2 --threads 16 -x ./output_file -1 R1.fastq -2 R2.fastq -S output_file.sam
samtools view -F 4 -bS ./output_file.sam > ./output_file-RAW.bam
#Sorting and indexing BAM files
anvi-init-bam ./output_file-RAW.bam -o ./output_file.bam
#CONCOCT based non-competitve binning steps
cut_up_fasta.py ./renamed.contigs.fa -c 10000 -o 0 --merge_last -b renamed.contigs_10k.bed > renamed.contigs_10k.fa
concoct_coverage_table.py renamed.contigs_10k.bed ./output_file.bam > coverage_table.tsv
concoct --composition_file renamed.contigs_10k.fa --coverage_file coverage_table.tsv -b ./output_folder_name/ -t 8
merge_cutup_clustering.py ./output_folder_name/clustering_gt1000.csv > ./output_folder_name/clustering_merged.csv
extract_fasta_bins.py ./renamed.contigs.fa ./output_folder_name/clustering_merged.csv --output_path ./output_folder_name/
Required tools with installation links: CAT: https://github.com/dutilh/CAT | BUSCO: https://busco.ezlab.org/busco_userguide.html | Phyloflash http://hrgv.github.io/phyloFlash/usage.html | CoverM https://github.com/wwood/CoverM
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7.1 CAT BAT Taxonomic Classification
#Requires high memory: 250GB RAM with 16 cores CAT bins -b /folder_containing_bins/ -s .fa -d ../CAT_database.2021-07-24/ -t ../CAT_taxonomy.2021-07-24/ -n 16 --block_size 20 --index_chunks 1 #Getting taxonomy for each MAG CAT add_names -i out.BAT.bin2classification.txt -o GT_tax.txt -t ../CAT_taxonomy.2021-07-24/ --only_official -
8.1 Classification and Quality Assessment of MAGs using BUSCO
busco --in /folder_containing_bins/ --mode genome --cpu 16 --out busco_output_folder -
8.2 CoverM based Relative Abundance assessment of MAGs
coverm genome --coupled /Sample_R1.fastq \ /Sample_R2.fastq \ --genome-fasta-extension .fa --genome-fasta-directory /Coverm_bins/ -m relative_abundance --threads 16 -o Main_MAGs_abundance.tsv -
8.3 Overall Eukaryotic Community Composition using 18S
phyloFlash.pl -lib run01 -read1 reads_F.fq.gz -read2 reads_R.fq.gz -readlength 150
Important: After running busco, we detected the Chlorophyta genome in all four samples of the chemocline (>90% complete). This is the stage you can detect if there are eukaryotic MAGs in your samples
Required tools with installation links: dRep: https://drep.readthedocs.io/en/latest/installation.html | Bedtools https://anaconda.org/bioconda/bedtools | Bowtie2, Samtools, Spades (Already installed previously).
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9.1 dRep MESH clustering of Eukaryotic MAGs detected by BUSCO.
dRep compare output_directory -g path/to/genomes/*.fasta -
9.2 Concatenation of similar eukaryotic genomes
#Added the sample name to contigs of MAGs before concatenation sed ’s/^>/>15m_/g’ finename.fna > Chlorophyta_file_modified.fna #Concatenate all Eukaryotic MAGs together cat Chlorophyta_13m_164_meta_mod.fa Chlorophyta_15mw_87_mod.fa Chlorophyta_15mm_94_meta_mod.fa Chlorophyta_15_5m_12_meta_mod.fa > Compiled_Chlorophyta.fa -
9.3 Mapping Chlorophyta specific raw reads from 13m, 15mw, 15mm, and 15.5m sample
bowtie2-build ./Compiled_Chlorophyta.fa ./Compiled_Chlorophyta bowtie2 --threads 16 -x ./Compiled_Chlorophyta -1 13m_R1.fastq -2 13m_R2.fastq -S 13m_output_file.sam samtools view -F 4 -bS 13m_output_file.sam > 13m_output_file-RAW.sam #Perform the same step with other three samples (15mw, 15mm, and 15.5m) -
9.4 Extracting Chlorophyta specific raw reads (R1 and R2) from BAM files
bamToFastq -i output_file-RAW.bam -fq lib_13m_mapped.1.fastq -fq2 lib_13m_mapped.2.fastq -
9.5 Assembling Eukaryotic Genome of Interest raw reads (We only used 13m, and 15.5m because of high N50 value).
#raw reads from two samples was taken spades.py -1 lib_13m_mapped.1.fastq -2 lib_13m_mapped.2.fastq -1 lib_15_5m_mapped.1.fastq -2 lib_15_5m_mapped.2.fastq -o ./Anvio2/Spades_13_15p5_Map_GBII
Required tools with installation links: Anvio | Concoct | Bowtie2, Samtools | BBMap (Already installed with Anvio).
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10.1 Anvio based rebinning of Chlorophyta Genome
#Generate contig database of Chlorophyta Assembly anvi-gen-contigs-database -f ./Spades_13_15p5_Map_GBII/Anvio_13_15p5_GBII.contigs.fa -o ./mapping2/contigs_Anvio13m_run2.db -n 'An example contigs database' #HMMS RUN anvi-run-hmms -c ./mapping2/contigs_Anvio13m_run2.db #Create profile database with each sample anvi-profile -i ./mapping2/lib_13m_mapped_F_13_15p5.bam -c ./mapping2/contigs_Anvio13m_run2.db -o ./mapping2/P_An_13_w13_run2 -T 16 anvi-profile -i ./mapping2/lib_15mw_mapped_F_13_15p5.bam -c ./mapping2/contigs_Anvio13m_run2.db -o ./mapping2/P_An_13_w13_15mw_run2 -T 16 anvi-profile -i ./mapping2/lib_15_5m_mapped_F_13_15p5.bam -c ./mapping2/contigs_Anvio13m_run2.db -o ./mapping2/P_An_13_15p5_run2 -T 4 anvi-profile -i ./mapping2/lib_15mm_mapped_F_13_15p5.bam -c ./mapping2/contigs_Anvio13m_run2.db -o ./mapping2/P_An_13_15mm_run2 -T 16 #Merge Anvio Profiles anvi-merge ./mapping2/P_An_13_w13_run2/PROFILE.db ./mapping2/P_An_13_w13_15mw_run2/PROFILE.db ./mapping2/P_An_13_15p5_run2/PROFILE.db ./mapping2/P_An_13_15mm_run2/PROFILE.db -o ./mapping2/SAMPLES-MERGED_run2 -c ./mapping2/contigs_Anvio13m_run2.db #Refine assembly by Anvio interface anvi-refine -p ./mapping2/SAMPLES-MERGED_run2/PROFILE.db -c ./mapping2/contigs_Anvio13m_run2.db --server-only -P 8080 anvi-cluster-contigs -p ./mapping2/SAMPLES-MERGED_run2/PROFILE.db -c ./mapping2/contigs_Anvio13m_run2.db -C CONCOCT2 --driver CONCOCT -T 8 --just-do-it anvi-summarize -p ./mapping2/SAMPLES-MERGED_run2/PROFILE.db -c ./mapping2/contigs_Anvio13m_run2.db -o ./mapping2/SAMPLES-SUMMARY_run23 -C CONCOCT2 -
10.2 Removal of contigs using command line (optional)
#filter minimum length 2500 reformat.sh in=Chlophyta_2ks_nohit_filtered_bin.fa out=Chlophyta_2ks_nohit_filtered_bin2.fa minlength=2500 #Remove contigs with no-hits filterbyname.sh in=Chlophyta_2ksfiltered_bin.fa out=Chlophyta_2ks_nohit_filtered_bin.fa names=c_000000000762,c_000000000733, c_000000000503,c_000000000488,c_000000000717,c_000000000753,c_000000000834,c_000000000754,c_000000000786,c_000000000711, c_000000000784,c_000000000836,c_000000000832,c_000000000779,c_000000000781,c_000000000633,c_000000000726,c_000000000773, c_000000000683,c_000000000795,c_000000000725,c_000000000837,c_000000000770,c_000000000621,c_000000000742,c_000000000830, c_000000000748,c_000000000755,c_000000000732,c_000000000701,c_000000000330,c_000000000746,c_000000000602,c_000000000661, c_000000000769,c_000000000768,c_000000000715,c_000000000819,c_000000000841,c_000000000588,c_000000000644,c_000000000827, c_000000000844,c_000000000232,c_000000000824,c_000000000718,c_000000000817, c_000000000806,c_000000000314,c_000000000758,c_000000000774,c_000000000838,c_000000000804,c_000000000767, c_000000000678,c_000000000820,c_000000000568,c_000000000818 include=f -
10.3 Visualization of eukaryotic MAG using Blobtools
Required tools with installation links: Blobtools2 https://blobtoolkit.genomehubs.org/blobtools2/ | Diamond (Install with Anvio installation)
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10.3.1 Performing Blastx required for blobtools.
diamond blastx -query /Refined_Eukaryotic_genome.fa --db ./nr_Final2.dmnd --outfmt 6 qseqid staxids bitscore qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue --sensitive --max-target-seqs 1 --evalue 1e-25 --threads 16 > /home/users/s/saini7/scratch/MS2/Anvio_P/Refined_Eukaryotic_genome_dmnd.blastx.out -
10.3.2 Blobtools command line for generating plots
~/blobtoolkit/blobtools2/blobtools create --fasta ../Chlophyta_2ks_nohit_filtered_bin/Chlophyta_2ks_nohit_filtered_bin2.fa --cov ../lib_13_15_5m_mapped_F_13_15p5_filter_nohit.bam --hits ../Chlophyta_2ks_nohit_filtered_bin/Chlophyta_2ks_nohit_filtered_blast.out --taxdump ~/blobtoolkit/taxdump --threads 4 --replace /home/users/s/saini7/scratch/MS2/Anvio2/refined_bin/bin_by_bin/Chlorophyta_1/busco_chlo2k/busco/blob2
Required tools with installation links: Blast (Already installed with Anvio) | Seqtk https://github.com/lh3/seqtk | Novoplasty https://github.com/ndierckx/NOVOPlasty | Geseq https://chlorobox.mpimp-golm.mpg.de/geseq.html
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11.1 Performing BLAST using available NCBI chloroplast genomes
#Blast using NCBI Parachlorella kessleri chloroplast genome (NC_012978.1) blastn -db ~/scratch/Spades_15mw2/Anvio.15mw.S.contigs.fa -query NC_012978.1_para_chlorella.fa -outfmt 6 -max_target_seqs 1 > 15mw_PC_chloroplast.out #Blast using NCBI Cryptomonas curvata chloroplast genome (KY856939.1) blastn -db ~/scratch/15mm/Spades_15mm/Anvio.15mm.S.contigs.fa -query KY856939.1.fasta -outfmt 6 -max_target_seqs 1 > 15m_GT_chloroplast.out -
11.2 Extraction of contigs/prospective chloroplast genomes from main assemblies
#Contig with promising hit against Parachlorella kessleri chloroplast genome (NC_012978.1) printf "c_000000000152" | seqtk subseq ~/scratch/Spades_15mw2/Anvio.15mw.S.contigs.fa - > c_000000000152_PC_O_15mw.fa #Contig with promising hit against Cryptomonas curvata chloroplast genome (KY856939.1) printf "c_000000000134" | seqtk subseq ~/scratch/15mm/Spades_15mm/Anvio.15mm.S.contigs.fa - > c_000000000134_GT_15mm.fa -
11.3 Circularization of Chloroplast genomes by NOVOPlasty (Template)
#Example script Project: ----------------------- Project name = CC_15_5m Type = chloro Genome Range = 80000-200000 K-mer = 33 Max memory = Extended log = 0 Save assembled reads = no Seed Input = ./c_000000000134_GT_15mm.fa Extend seed directly = yes Reference sequence = ./KY856939.1.fasta Variance detection = Chloroplast sequence = Dataset 1: ----------------------- Read Length = 151 Insert size = 300 Platform = illumina Single/Paired = PE Combined reads = Forward reads = /R1.fastq Reverse reads = /R2.fastq Store Hash = -
11.4 Visualization of Circularized Chloroplast Genomes using online GeSeq Platform
https://chlorobox.mpimp-golm.mpg.de/geseq.html
Required tools with installation links: EukMetaSanity https://github.com/cjneely10/EukMetaSanity | Orthologer https://orthologer.ezlab.org/ | KEGG https://www.kegg.jp/ghostkoala/ | Anvio | Eggnog https://github.com/eggnogdb/eggnog-mapper
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12.1 EukMetaSanity was used for gene prediction
yapim run -i directory_containing_genome -c run-config.yaml -p $EukMS_run -o name_of_out_put_directory #(needs to make the output directory manually prior to running code) -
12.2 Orthologer annotations of Chlorella-like MAG:
docker run -u $(id -u) -v $(pwd)/odb:/odbwork ezlabgva/orthologer:v3.0.2 orthomapper -c run -p LC_MAG -f input/Chlorella-like_MAG.faa -n 3041 -
12.3 EggNOG annotations of Chlorella genomes:
python ../emapper.py -m diamond --sensmode more-sensitive -i GCA_002245835.2_unplaced.scaf.1.Tier.faa --decorate_gff GCA_002245835.2_unplaced.scaf.1.Tier.gff3 -o GCA_002245835.2_unplaced.scaf.1.Tier --cpu 32 --target_taxa 3041
#Optionally
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12.4 Mapping of protein coding gene sequences (.faa) from EukMetaSanity to KEGG Pathways using GhostKoala Online
https://www.kegg.jp/ghostkoala/
OR
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12.5 Anvio Metabolism Prediction
anvi-setup-kegg-kofams --reset anvi-gen-contigs-database -f input.fa -o input.db -n 'An example contigs database' anvi-run-hmms -c input.db -T 4 anvi-run-kegg-kofams -c input.db -T 16 --just-do-it anvi-estimate-metabolism -c input.db -p ./All_SAMPLES-MERGED_P/PROFILE.db --add-coverage -O kegg_out anvi-interactive -c ./contigs_197.db -p ./All_SAMPLES-MERGED_P/PROFILE.db --server-only -P 8008
Required tools with installation links:
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13.1 Chloroplasts
#Concatenation of 18 marker genes were aligned using MAFFT and ambigious sequences were removed from Gblocks.List of Marker genes ATP synthase (atpA, atpB, atpC), large ribosomal subunits (rpl2, rpl5, rpl12, rpl14, rpl19, rpl23) and small ribosomal subunits (rps3, rps8, rps9, rps19), photosystem I (psaC) and photosystem II (psbA, psbB, psbE, psbH). https://mafft.cbrc.jp/alignment/software/ -
13.2 Microbial Eukaryote Nuclear Genome
#BUSCO (v5.2.2) was run in genome mode (BUSCO_Metaeuk workflow) on each genome assembly using the chlorophyta_odb10 dataset (1,519 markers). The identified single-copy genes that were shared across 100% of the species (with no duplicates across all species) were extracted. For each orthologous group, proteins were aligned using MAFFT (v7.505) 89 and trimmed using trimAl (v1.4 rev15) 90. The single alignments were concatenated with AMAS (v1.0) 91, and the resulting super-alignment was used to infer a maximum likelihood phylogeny with IQ-TREE (v2.1.2) 92 [selected_markers_with_links_and_description.txt] (https://github.com/JSSaini/Eukaryotic_Metagenomic_Lake_Cadagno/files/8986487/selected_markers_with_links_and_description.txt)