文件列表:

other_scripts (0, 2022-01-17)
other_scripts\RPKM_cal.py (1381, 2022-01-17)
other_scripts\fasta2relaxedphylip.py (667, 2022-01-17)
other_scripts\remove_small_seqs.pl (465, 2022-01-17)
slurm_scripts (0, 2022-01-17)
slurm_scripts\MicrobeCensus.slurm (921, 2022-01-17)
slurm_scripts\bwa-bbmap.slurm (1491, 2022-01-17)
slurm_scripts\bwa-samtools.slurm (1094, 2022-01-17)
slurm_scripts\cd-hit.slurm (1082, 2022-01-17)
slurm_scripts\checkm.slurm (758, 2022-01-17)
slurm_scripts\gtdbtk.slurm (533, 2022-01-17)
slurm_scripts\hmmsearch.slurm (538, 2022-01-17)
slurm_scripts\htseq.slurm (980, 2022-01-17)
slurm_scripts\iqtree.slurm (304, 2022-01-17)
slurm_scripts\mafft.slurm (338, 2022-01-17)
slurm_scripts\megahit-coassembly.slurm (573, 2022-01-17)
slurm_scripts\metaWRAP-BinRefinement.slurm (626, 2022-01-17)
slurm_scripts\metaWRAP-ReassembleBins.slurm (674, 2022-01-17)
slurm_scripts\metaWRAP-binning.slurm (717, 2022-01-17)
slurm_scripts\picard.slurm (655, 2022-01-17)
slurm_scripts\prodigal-meta.slurm (398, 2022-01-17)
slurm_scripts\prokka.slurm (508, 2022-01-17)
slurm_scripts\trimmomatic-loop.slurm (1146, 2022-01-17)

[](https://doi.org/10.1038/s41396-020-00889-4) # Genome-centric Multi-omic Analysis Workflow This repository contains a collection of code and scripts used in the paper **Mercury methylation by metabolically versatile and cosmopolitan marine bacteria** (DOI: [10.1038/s41396-020-00889-4](https://doi.org/10.1038/s41396-020-00889-4)) by Lin et al.. The links below the sub-headings lead to the scripts needed for the corresponding steps. Most of the scripts were developed for running on the [SLURM](https://slurm.schedmd.com/) workload manager. All code and scripts were created for and tested on [Spartan](https://dashboard.hpc.unimelb.edu.au/) HPC at The University of Melbourne. You may download and adapt the scripts to suit your own requirements. ## 1. Software used in this workflow ### Software that has been integrated into Spartan system - [Perl](https://www.perl.org/) - [Python3](https://www.python.org/) - [Trimmomatic](https://github.com/timflutre/trimmomatic) - [MEGAHIT](https://github.com/voutcn/megahit) - [Prokka](https://github.com/tseemann/prokka) - [Prodigal](https://github.com/hyattpd/Prodigal) - [HMMER](http://hmmer.org/) - [CD-HIT](https://github.com/weizhongli/cdhit) - [MAFFT](https://mafft.cbrc.jp/alignment/software/) - [IQ-TREE](http://www.iqtree.org/) - [bwa](http://bio-bwa.sourceforge.net/) - [BBMap](https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/) - [Pandas](https://pandas.pydata.org/) - [Numpy](https://numpy.org/) ### Software that needs to be installed manually - [metaWRAP](https://github.com/bxlab/metaWRAP) - [CheckM](https://ecogenomics.github.io/CheckM/) - [GTDB-Tk](https://github.com/Ecogenomics/GTDBTk) - [MicrobeCensus](https://github.com/snayfach/MicrobeCensus) - [Picard](https://broadinstitute.github.io/picard/) > Take the five samples from "SI047 S3" station as an example. ## 2. Raw Data Download ```bash ids="SRR3724469;SRR3724456;SRR3724482;SRR3724508;SRR3724533" # SRA accessions for the 5 metagenomic samples urls="SI047_ftp_urls.txt" # output out_dir="SI047_raw_data" # output # Using ENA API to retreive data urls for str in ${ids//;/ } ; do echo 'ftp://ftp.sra.ebi.ac.uk/vol1/fastq/'${str:0:6}'/00'${str:0-1}'/'$str'/'$str'_1.fastq.gz' >> $urls; echo 'ftp://ftp.sra.ebi.ac.uk/vol1/fastq/'${str:0:6}'/00'${str:0-1}'/'$str'/'$str'_2.fastq.gz' >> $urls; done # Download raw fastq files to $out_dir wget -b -q -i $urls -P $out_dir ``` ## 3. Read trimming [trimmomatic-loop.slurm](slurm_scripts/trimmomatic-loop.slurm) ```bash input="SI047_raw_data" output="SI047_clean_data" sbatch trimmomatic-loop.slurm $input $output ``` ## 4. Assembly ### MEGAHIT [megahit-coassembly.slurm](slurm_scripts/megahit-coassembly.slurm) ```bash input="SI047_clean_data" output="SI047_megahit" sbatch megahit-coassembly.slurm $input $output ``` ### Remove small contigs [remove_small_seqs.pl](other_scripts/remove_small_seqs.pl) ```bash input="SI047_megahit/final.contigs.fa" output="SI047_megahit/SI047.2k.fa" perl remove_small_seqs.pl 2000 $input > $output ``` ## 5. Binning * `metaWRAP` conda env is required ### Initial Binning [metaWRAP-binning.slurm](slurm_scripts/metaWRAP-binning.slurm) ```bash input_fa="SI047_megahit/SI047.2k.fa" input_fq="SI047_clean_data" output="SI047_binning_out" sbatch metaWRAP-binning $input_fa $input_fq $output # Using --metabat1 --metabat2 --maxbin2 ``` ### Bins Refinement [metaWRAP-BinRefinement.slurm](slurm_scripts/metaWRAP-BinRefinement.slurm) ```bash input="SI047_binning_out" output="SI047_bins_refined" sbatch metaWRAP-BinRefinement.slurm $input $output # Integrating the output from 3 binners ``` ### Bins Reassembly [metaWRAP-ReassembleBins.slurm](slurm_scripts/metaWRAP-ReassembleBins.slurm) ```bash # Pick up some bins of interest to process reassembly mkdir SI047_bins_interest cp SI047_bins_refined/metawrap_70_10_bins/bin5.fa SI047_bins_interest cp SI047_bins_refined/metawrap_70_10_bins/bin12.fa SI047_bins_interest # Running metaWRAP input_reads_dir="SI047_bins_refined/metawrap_70_10_bins" input_bins_dir="SI047_bins_interest" output="SI047_bins_reassembled" sbatch metaWRAP-ReassembleBins.slurm $input_reads_dir $input_bins_dir $output ``` ### Final Bins ```bash mkdir SI047_final_bins cp SI047_bins_refined/metawrap_70_10_bins/*fa SI047_final_bins cp -rf SI047_bins_reassembled/reassembled_bins/*fa SI047_final_bins ``` ## 6. Bins Annotation [prokka.slurm](slurm_scripts/prokka.slurm) ```bash input="SI047_final_bins" output="SI047_final_bins_prokka" for fa in $input/*.fa; sbatch prokka.slurm $fa $output done ``` ## 7. Bins Quality Assessment & Classification ### CheckM [checkm.slurm](slurm_scripts/checkm.slurm) ```bash input="SI047_final_bins" output="SI047_final_bins_checkm" sbatch checkm.slurm $input $output ``` ### GTDB-Tk [gtdbtk.slurm](slurm_scripts/gtdbtk.slurm) ```bash input="SI047_final_bins" output="SI047_final_bins_gtdbtk" sbatch gtdbtk.slurm $input $output ``` ## 8. Searching for target genes ### HMM Search > The hmm database for HgcA proteins is needed for this step. `HgcA.hmm` is available upon request. [hmmsearch.slurm](slurm_scripts/hmmsearch.slurm) ```bash input="SI047_final_bins_prokka" output="SI047_HgcA_search" find $input -name *.faa | while read faa do sbatch hmmsearch.slurm $faa $output done ``` ### Reduce Redundancy [cd-hit.slurm](slurm_scripts/cd-hit.slurm) ```bash cat SI047_HgcA_search/*faa > SI047_hgcA_all.faa # need some outgroups input="SI047_hgcA_all.faa" output="SI047_hgcA.faa" sbatch cd-hit.slurm $input $output ``` ## 9. Phylogenetic Tree ### Alignment [mafft.slurm](slurm_scripts/mafft.slurm) ```bash input="SI047_hgcA.faa" output="SI047_hgcA.aln.faa" sbatch mafft.slurm $input $output ``` ### Maximum Likelihood (ML) Tree [fasta2relaxedphylip.py](other_scripts/fasta2relaxedphylip.py) [iqtree.slurm](slurm_scripts/iqtree.slurm) ```bash # Change Fasta to Phylip format python fasta2relaxedphylip.py -i SI047_hgcA.aln.faa -o SI047_hgcA.aln.phy # Make Tree input="SI047_hgcA.aln.phy" sbatch mafft-iqtree.slurm $input ``` ## 10. Gene Abundance in metagenomic datasets [bwa-bbmap.slurm](slurm_scripts/bwa-bbmap.slurm) [MicrobeCensus.slurm](slurm_scripts/MicrobeCensus.slurm) ```bash input_fa="15hgcA.fna" input_metaG_fq="SI047_clean_data" out_dir_metaG="hgcA_metaG_abundance_SI047" out_dir_MC="SI047_MC" sbatch bwa-bbmap.slurm $input_fa $input_metaG_fq $out_dir sbatch MicrobeCensus.slurm $input_metaG_fq $out_dir_MC ``` ## 11. PRKM calculation for metatranscriptomic datasets ### Preparation * `htseq` conda env is required [prodigal.slurm](slurm_scripts/prodigal.slurm) [bwa-samtools.slurm](slurm_scripts/bwa-samtools.slurm) [picard.slurm](slurm_scripts/picard.slurm) [htseq.slurm](slurm_scripts/htseq.slurm) ```bash # ORF prediction input_fa="SI047_megahit/SI047.2k.fa" out_dir_prodigal="SI047_prodigal" sbatch prodigal.slurm $input_fa $out_dir_prodigal # mapping reads with bwa input_fa="SI047_megahit/SI047.2k.fa" input_metaT_fq="SI047_metaT_clean_data" # derived from a similar procedure to metagenomic clean data out_dir="SI047_metaT_mapping" sbatch bwa-samtools.slurm $input_fa $input_metaT_fq $out_dir # mapping and sort sbatch picard.slurm $out_dir # removing duplicates # counting mapped reads per gene input_bams_dir="SI047_metaT_mapping" input_gff="SI047_prodigal/SI047.gff" output_count="SI047_reads_count" sbatch htseq.slurm $input_bams_dir $input_gff $output_count # calculating gene lengths input_gff="SI047_prodigal/SI047.gff" cut -f4,5,9 $input_gff | sed 's/gene_id //g' | gawk '{print $3,$2-$1+1}' | tr ' ' '\t' > ${input_gff%.*}.gl.txt ``` ### RPKM table RPKM: Reads per kilo base per million mapped reads > **Formula** > > RPKM = C / ( (L/1000) * (N/1,000,000) ) = (10^9 * C)/(N * L) > > - C - number of reads mapped to a gene sequence > - L - gene length in base-pairs for a gene > - N - total number of mapped reads of a sample [RPKM_cal.py](other_scripts/RPKM_cal.py) [Pandas](https://pandas.pydata.org/) [Numpy](https://numpy.org/) ```bash module load pandas/0.23.4-intel-2016.u3-Python-3.5.2 # loading Python3 and Pandas module module load numpy/1.12.1-intel-2017.u2-Python-3.5.2 # loading Numpy module count_dir="SI047_reads_count" gene_len="SI047_prodigal/SI047.gl.txt" RPKM_out="SI047.rpkm.tsv" python RPKM_cal.py -c $count_dir -l $gene_len -o $RPKM_out ``` ## Copyright Heyu Lin [heyu.lin@student.unimelb.edu.au](mailto:heyu.lin@student.unimelb.edu.au) School of Earth Sciences, The University of Melbourne Please cite the article if the scripts are helpful in your research. Lin, H., Ascher, D.B., Myung, Y. *et al.* Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. *ISME J* (2021). [https://doi.org/10.1038/s41396-020-00889-4](https://doi.org/10.1038/s41396-020-00889-4)

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