This repository contains code used for HRGM2 construction and HRGM2 data.
- trimmomatic_PE.py removes the adapter sequences and low-quality bases of Illumina sequencing samples
- reference_genome_removal_paired_gzin.py aligns sample reads against the human reference genome and removes the aligned reads as human contaminants
- minimap2_hifi.py removes human contaminants from PacBio HiFi sequencing samples
- metaSPAdes.py assembles quality-controlled reads as contigs using metaSPAdes
- megahit.py assembles quality-controlled reads as contigs using MEGAHIT
- hifiasm_meta.py assembles HiFi reads as contigs using hifiasm-meta
- pre_binning.py is a wrapper that can run the following codes at once
- build_contig_index.py builds an index to align with contig
- align_read_to_contig.py aligns reads to contig
- SAMtoBAM.py converts SAM to BAM
- sortingBAMfile.py sorts BAM
- indexingBAMfile.py indexes sorted BAM
- binning_pipeline.py is a wrapper that can run pipelines of MetaBAT2, MaxBin2.0, and CONCOCT at once
- metaBAT2_pipeline.py is a sub-wrapper that can run codes for binning using MetaBAT2
- depth_file.py generates a depth file from sorted BAM
- metaBAT2.py runs MetaBAT2 for binning from the depth file and contigs
- maxbin2_pipeline.py is a sub-wrapper that can run codes for binning using MaxBin2.0
- contig_abundance.py generates a contig abundance file from MetaBAT2 depth file
- maxbin2.py runs MaxBin2.0 for binning from the contig abundance file and contigs
- concoct_pipeline.py is a sub-wrapper that can run codes for binning using CONCOCT
- cut_contig.py cuts contigs into smaller parts
- coverage_table.py generates table with coverage depth information per sample and subcontig
- concoct.py runs CONCOCT for binning from the coverage table and contigs
- merge_subcontig_clustering.py merges subcontig clustering into original contig clustering
- extract_bin.py extracts bins as individual FASTA
- megahit_bin_change_header.py unifies the contig header format of CONCOCT with other binning tools when using MEGAHIT as an assembler
- metaWRAP.py combines the binning results from the three tools into a more robust bin set using the bin refinement module of MetaWRAP
- gunc_run_sample.py identifies genome chimerism using GUNC
- self_sketch_mash.py calculates the Mash distance between genomes belonging to the same order
- mash2cluster_0.1.py performs average-linkage-based hierarchical clustering with a 0.1 cutoff to establish preliminary clusters
- animf.py calculates average nucleotide identity (ANI) and coverage for each pair of genomes within each preliminary cluster
- ani_results_to_distance_dict.py applies coverage threshold and saves ANI results into a distance dictionary
- secondary_clustering.py conducts average-linkage-based hierarchical clustering with an ANI threshold of 95% to cluster genomes at the species level
- ani_results_to_distance_dict_cov0.81.py is identical to ani_results_to_distance_dict.py, except that it uses a coverage threshold of 0.81 to remove duplicate genomes
- non_redundant_clustering_sample_derep_ver2.py performs average-linkage-based hierarchical clustering within each species cluster to eliminate duplicate genomes
- gtdbtk_dataset.py runs GTDB-Tk classify_wf to assign GTDB taxonomic annotation to genomes
- make_all_run_prokka.py makes a shell script to run Prokka for 230,632 redundant genomes of HRGM2
- make_all_run_barrnap.py makes a shell script to run Barrnap for 230,632 redundant genomes of HRGM2
- summarize_rRNA.py counts the number of rRNA from Barrnap results
- make_all_run_tRNAscanSE.py makes a shell script to run tRNAscan-SE for 230,632 redundant genomes of HRGM2
- summarize_tRNA.py counts the number of tRNA from tRNAscan-SE results
- make_panaroo_inputs.py makes input lists to run Panaroo
- run_panaroo.py runs Panaroo to construct pan-genomes
- make_pangenome_faa.py obtains faa sequences corresponding to pan-genome references
- run_eggnog_mapper.py runs eggNOG-mapper for pan-genomes to annotate functions
- run_eggnog_mapper_cds_mode.py is identical to run_eggnog_mapper.py, except that it uses fna file of pan-genome and "--itype CDS" option
- run_rgi.py runs RGI for pan-genomes to predict antibiotic resistance genes
- panaroo_graph_to_node.py obtains nucleotide sequences and amino acid sequences of the pan-genome from the final graph
- size_1_node_summary.py is for the same purpose as panaroo_graph_to_node.py, specifically for singleton species
- remove_redundancy.py clusters identical proteins together
- length_filtering.py extracts protein families with lengths between 150 and 1,500 amino acids
- paralog_filtering.py extracts protein families with, on average, less than 1.5 copies per genome
- coreness.py computes coreness in a particular species of protein families
- coreness_filtering.py performs filtering based on a threshold determined by the size of the species cluster
- calc_uniqueness.py calculates the number of species that share a particular protein family
- core_uniqueness_filtering.py identifies unique core protein families for each species
- make_candidate_reads.py fragments the DNA sequences of the marker candidates into 150 bp segments
- make_align_coreness.py calculates coreness based on alignment
- find_markers_ver3.py finds species-specific markers based alignment coreness
- final_marker_filtering.py selects the top 200 markers, based on uniqueness and length, per species
- camisim_simulation.py generates simulated samples to compare taxonomic profiling methods
- run_classification.py runs various taxonomic profiling methods for simulated samples
- computational_resource.py summarizes the elapsed time and maximum RAM for each taxonomic profiling method
- summary_classification_evaluation_metrics.py compares predicted results with the answer and calculates precision, recall, and F1 score
- merge_metrics_summary.py merges the metric results per sample into one
- normalization_size.py summarizes the species sizes for use in length normalization
- summary_bray_curtis_similarity.py calculates Bray-Curtis similarity between the predicted profile and the real profile
- merge_similarity_summary.py merges the similarity results per sample into one
- prokka_CDS_parsing.py obtains protein sequences from Prokka results
- cluster_identical_proteins.py clusters identical proteins together
- extract_unique_proteins.py extracts protein sequences from the above clusters
- make_cluster_info_file.py makes metadata containing cluster member information for the HRGM2 protein family
- update_cluster_info_file.py adds a ‘the number of member proteins’ column to cluster_info.tsv
- make_taxonomic_map.py makes metadata containing taxonomy information for the HRGM2 protein family
- run_carveme.py runs CarveMe to reconstruct genome-scale metabolic models
- search_GEM_info.py calculates the number of metabolites/reactions/gene-associated reactions for GEMs
- run_smetana.py runs SMETANA to predict pairwise metabolic interactions for species
- calc_comp_pair_prop_taxonomy_rank.py calculates the proportion of cooperative and competitive pairs within the same and across different taxonomic clades
- cooperative_order_of_lmi_clades.py calculates cooperative pair proportion between LMI order and other orders
- calc_metabolite_dontation_to_LMI.py calculates the association between metabolite supply and the cooperative behavior of species toward LMI orders
- community_wise_smetana.py runs SMETANA to get MIP and MRO scores for a disease-associated species community and random communities
- species_pair_smetana_score.py calculates the SMETANA score for species pairs within the CD-associated species community
- essential_metabolites_from_main_donors.py investigates key metabolites delivered to the receiver species by the two main donors of CD-associated species community
- compare_donor_high_low_group.py investigates the relative abundances of receiver species and the abundance group of donor species in the CD samples
- summarize_crc_nutritional_requirement_sets.py investigates metabolites commonly required by CRC-associated species from the nutritional requirement set of SMETANA
- CRC_dominance_search.py investigates the dominance of species within CRC samples
This project is licensed under the Creative Commons Attribution 4.0 International License.
