Package: condiga
ConDiGAConDiGA(ContigsDirectedGeneAnnotation) is an accurate taxonomic annotation pipeline from metagenomic data to construct accurate protein sequence databases for deep metaproteomic coverage.Setting up ConDiGAOption 1: Installing ConDiGA using conda (recommended)You can install ConDiGA from bioconda athttps://anaconda.org/bioconda/condiga. Make sure you havecondainstalled.# create conda environment and install condigacondacreate-ncondiga-cconda-forge-cbiocondacondiga# activate environmentcondaactivatecondigaOption 2: Installing ConDiGA using pipYou can install ConDiGA from PyPI athttps://pypi.org/project/condiga/. Make sure you havepipinstalled.pipinstallcondigaNote: If you use pip to setup ConDiGA, you will have to installMinimap2andTaxonKitmanually and add it to your system path. Irrespective of the package manager, if you want to use Kaiju results, you have todownload and setup the NCBI taxdump database for TaxonKit.Test the setupAfter setting up, run the following command to ensure thatcondigais working.condiga --helpUsageUsage: condiga [OPTIONS]

  ConDiGA: Contigs directed gene annotation for accurate protein sequence
  database construction in metaproteomics.

Options:
  -c, --contigs PATH              path to the contigs file  [required]
  -ta, --taxa PATH                path to the taxanomic classification results
                                  file  [required]
  -g, --genes PATH                path to the genes file  [required]
  -cov, --coverages PATH          path to the contig coverages file
                                  [required]
  -as, --assembly-summary PATH    path to the assembly_summary.txt file
                                  [required]
  -ra, --rel-abundance FLOAT RANGE
                                  minimum relative abundance cut-off
                                  [default: 0.0001; 0<=x<=1]
  -gc, --genome-coverage FLOAT RANGE
                                  minimum genome coverage cut-off  [default:
                                  0.001; 0<=x<=1]
  -mt, --map-threshold FLOAT RANGE
                                  minimum mapping length threshold cut-off
                                  [default: 0.5; 0<=x<=1]
  -t, --nthreads INTEGER          number of threads to use  [default: 8]
  -o, --output PATH               path to the output folder  [required]
  --help                          Show this message and exit.PreprocessingBefore running ConDiGA, you have to process your data as follows.Step 1: Assemble reads into contigsYou have to assemble your reads into contigs usingMEGAHITas follows. Currently, ConDiGA only supports MEGAHIT assemblies.megahit -1 Reads/reads_1.fq.gz -2 Reads/reads_2.fq.gz -o MEGAHIT_output -t 16Step 2: Taxonomically annotate contigsNext, you have to perform taxonomic annotation on your contigs. You can use any tool such asKraken2,Kaijuor evenBLAST.As an example, let's runKraken2as follows.$DBNAMEis the path to your Kraken database.kraken2 --threads 16 --db $DBNAME --use-names --output kraken_res_0.1.txt --confidence 0.1 --report kraken_report_0.1.txt MEGAHIT_output/final.contigs.faNow, you can run theconvertcommand to convert your result to a form that can be used as input tocondiga. The result will be saved to theKrakenfolder. Currently,convertsupports results from Kraken2, Kaiju and BLAST.mkdir Kraken
convert -i kraken_res_0.1.txt -t kraken -o KrakenNOTE:Since, different annotation tools output results in different formats, you have to format the annotation results usingcovertwhich will output the result in a standard format readable by ConDiGA.Step 3: Obtain coverage of contigsYou can useCoverMto get the coverage values of contigs as follows.coverm contig -1 Reads/reads_1.fastq -2 Reads/reads_2.fastq -r MEGAHIT_output/final.contigs.fa -o contig_coverage.tsv -t 16Step 4: Predict genes in contigsYou can predict the genes in the contigs usingMetaGeneMarkas follows. You will find the nucleotide and amino acid sequences of the predicted genes in a file namedfinal.contigs.fa.lst.gmhmmp -m MetaGeneMark_v1.mod final.contigs.faStep 5: Downloadassembly_summary.txtfile.You can download the assembly summary file for bacteria fromNCBIas follows.wget https://ftp.ncbi.nlm.nih.gov/genomes/genbank/bacteria/assembly_summary.txtRunning ConDiGAOnce you have preprocessed your data and obtained all the necessary files, you can runcondigaas follows.condiga -c final.contigs.fa -ta Kraken/kraken_result.txt -g final.contigs.fa.lst -cov contig_coverages.tsv -as assembly_summary.txt -o <output_folder>OutputThe output of ConDiGA will contain the following main files and folders.genes.species.mapped.xlsxcontains the gene annotation resultsall_genes.fnacontains nucleotide sequences of the predicted genesall_genes.faacontains amino acid sequences of the predicted genesall_genes.outputcontainsminimap2mapping results for the predicted genesAssembliescontains FASTA files of the downloaded reference genomesCitationIf you use ConDiGA in your work, please cite thebioRxiv preprintas follows.@article{Wu2023.04.19.537311,author={Enhui Wu and Vijini Mallawaarachchi and Jinzhi Zhao and Yi Yang and Hebin Liu and Xiaoqing Wang and Chengpin Shen and Yu Lin and Liang Qiao},title={Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics},elocation-id={2023.04.19.537311},year={2023},doi={10.1101/2023.04.19.537311},publisher={Cold Spring Harbor Laboratory},abstract={Microbiota are closely associated to human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. One of the main challenges is constructing a protein sequence database that best fits the microbiota sample. Herein, we proposed an accurate taxonomic annotation pipeline from metagenomic data for deep metaproteomic coverage, namely contigs directed gene annotation (ConDiGA). We mixed 12 known bacterial species to derive a synthetic microbial community to benchmark metagenomic and metaproteomic pipelines. With the optimized taxonomic annotation strategy by ConDiGA, we built a protein sequence database from the metagenomic data for metaproteomic analysis and identified about 12,000 protein groups, which was very close to the result obtained with the reference proteome protein sequence database of the 12 species. We also demonstrated the practicability of the method in real fecal samples, achieved deep proteome coverage of human gut microbiome, and compared the function and taxonomy of gut microbiota at metagenomic level and metaproteomic level. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database for metaproteomics. The unique dataset of metagenomic and the metaproteomic data of the 12 bacterial species is publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of real microbiota samples.Competing Interest StatementThe authors have declared no competing interest.},URL={https://www.biorxiv.org/content/early/2023/04/20/2023.04.19.537311},eprint={https://www.biorxiv.org/content/early/2023/04/20/2023.04.19.537311.full.pdf},journal={bioRxiv}}