Huanan Market Metagenomic Data and Analyses

This repository provides data, sequences, and analyses of the metagenomic detection of mammalian wildlife at the Huanan Market in early 2020.

Article sections:

• (R1): SARS-CoV-2 genetic diversity linked to the Huanan market is consistent with market emergence
• (R2): Increased High SARS-CoV-2 positivity rate in and near a wildlife stall in the Huanan market
• (R3): Mammalian wildlife species detected in five SARS-CoV-2 positive samples from a wildlife stall and in other wildlife stalls
• (R4): Wildlife stalls and SARS-CoV-2 positive samples contain other mammalian viruses associated with the animal trade
• (R5): Reconstruction of mitochondrial genotypes of potential intermediate host species of SARS-CoV-2 within the Huanan market

Contents by folder

• metadata/: sample and species metadata (R)
• mitochondrial_mappings/: Read mapping counts and covered bases (breadth) for animal mtDNA. (R3, R5)
• mitochondrial_genotypes/: Animal mtDNA consensus genotypes and SNV VCFs. (R3, R5)
• sars2_mappings/: identification and quantification of SARS-CoV-2 reads. (R1, R2)
• sars2_phylogenetics/: SARS-CoV-2 phylogeny. (R1)
• viruses: identification and quantification of other viruses in the market. (R4)
• correlations/: computations of correlations between viruses and animals (R3)
• fastq_processing/: code for sample (pre)processing (R)
• figs/: directory where figure outputs are stored (R)
• figures_R/: R code to generate figures (R)
• summary_and_tables/: Supplementary tables (R)

Contents by topic

Sequences and genotypes of animals and viruses found at the market

Animals

• Mammalian mtDNA consensus genomes: ./mitochondrial_genotypes/lowconfidence_50_percent_mt_genomes.fasta and mediumconfidence_50_percent_mt_genomes.fasta.
• Script to generate mtDNA consensus genomes: ./mitochondrial_genotypes/mt_consensus_genomes.py.
• BAMs and SNVs of animal mtDNA mappings: ./mitochondrial_genotypes/bams_and_snvs/.
• mtDNA dereplicated databases: ./mitochondrial_mappings/mitochondria_dereplicated93_final.fasta.gz and ./mitochondrial_mappings/mitochondria_dereplicated98.fasta.gz.
• Scripts to count mapped reads and covered bases: ./mitochondrial_mappings/count_reads98.py and ./mitochondrial_mappings/count_reads93.py.
• Mapping mtDNA counts and covered bases: ./mitochondrial_mappings/mitochondrial_metazoa_counts_93.tsv and ./mitochondrial_mappings/mitochondrial_metazoa_coveredbases_93.tsv.

Viruses

• Viral sequences (bamboo rat CoV, raccoon dog amdovirus, civet kobuvirus, canine coronaviruses): ./viruses/market_mammalian_viruses.fna
• Influenza H9N2 fragmented genomes reconstructed from the market and trees: ./viruses/Influenza/*.fasta and ./viruses/Influenza/HA_tree.newick.

Sample Metadata

./metadata/Liu_etal_2023_with_sequencing.csv: Full metadata of all samples, linking samples to their primary metagenomic (untargeted) sequencing runs.

./metadata/Sequencing_run_info.tsv: Information on the 184 sequencing runs in Liu et al.

./metadata/Liu_PCR_results.csv: PCR CT values reported by Liu et al.

Contents by Figure

Figure 1

• sars2_phylogenetics/20210903.WHO.masked.fasta: Sequences used in Fig 1A tree.
• sars2_phylogenetics/HSM_sequences/*.fa: consensus FASTA sequences for HSM SARS-CoV-2 genomes.
• sars2_phylogenetics/combined_tMRCA.csv: tMRCA results (Fig 1C)
• R/Fig1C.R: Script to plot tMRCA distributions (Fig 1C)

Figure 2: SARS2 abundances

• sars2_mappings/count_reads_sars2.py: Script used to count the number of SARS-CoV-2 reads per sample.
• sars2_mappings/sars2_reads_post_trimming.tsv: counts of SARS-CoV-2 reads in each sample.
• R/Fig2ABC.R and R/Fig2DEFGH.R: Scripts to plot the different panels of Figure 2.

Figure 2/3: mtDNA abundances

• mitochondrial_mappings/clustering98.py: Script for first clustering the MT reference database at 98% identity.
• mitochondrial_mappings/clustering93.py: Script for clustering the MT reference database at 93% identity.
• mitochondrial_mappings/count_reads98.py: Script for counting mtDNA reads after mapping to the 98% clustered database.
• mitochondrial_mappings/count_reads93.py: Script for counting mtDNA reads after mapping to the 93% clustered database (final species-level assignments).
• mitochondrial_mappings/mitochondria_dereplicated93_final.fasta.gz: the final 93% ANI dereplicated database used in this study.
• mitochondrial_mappings/mitochondrial_metazoa_counts_93.tsv: Read counts for animal mtDNA.
• mitochondrial_mappings/mitochondrial_metazoa_coveredbases_93.tsv: Covered bases count for animal mtDNA
• mitochondrial_mappings/species_descriptions_with_common_name.csv: Descriptions of species detected in this study.
• R/Fig3A.R and R/FigBCDE.R: Scripts to plot the different panels of Figure 3.

Figure 4: viruses

• viruses/Influenza/*.fasta: consensus Influenza H9N2 sequences identified in this study.
• viruses/Influenza/HA_tree.newick: Influenza H9N2 HA tree constructed.
• viruses/count_reads_viral.py: Script for filtering and counting reads mapped to the viral database.
• viruses/filtered_viral_counts_97_95_20_200.tsv: counts of mapped viral reads.
• viruses/market_mammalian_viruses.fna: FASTA files of the consensus genomes assembled for mammalian viruses from the market.
• viruses/plant_insect_phage_exclude.txt: List of viruses to ignore due to being phage / plant / insect viruses.
• viruses/viral_metadata.tsv: Metadata on various viruses.
• viruses/cluster_viral_references.py: Script to cluster the viral reference database.
• viruses/get_viral_consensus.py: Script to reconstruct viral consensus genomes from multiple samples.
• viruses/viral_db.list.txt: a list of all viral sequences included in the dereplicated mapping database.
• R/Fig4ABC.R: Script to plot panels A-C of Figure 4.

Figure 5: mtDNA phylogenetics

• mitochondrial_genotypes/bams_and_snvs: All mapped mammalian mtDNA BAMs from market samples.
• mitochondrial_genotypes/MT_Maps.ipynb: Script to create Figure 5a mtDNA genome maps.
• mitochondrial_genotypes/mt_consensus_genomes.py: Script to reconstruct mtDNA consensus genomes.
• mitochondrial_genotypes/lowconfidence_50_percent_mt_genomes.fasta: MT genomes obtained in this study, requiring just 1x read coverage.
• mitochondrial_genotypes/mediumconfidence_50_percent_mt_genomes.fasta: MT genomes obtained in this study, requiring 3x read coverage to call a base.
• mitochondrial_genotypes/mtDNA_RD_phylogenetics.clean.ipynb: Raccoon dog phylogenetics analysis notebook.
• mitochondrial_genotypes/raccoon_dog_cytB_all.duplicated.fasta: Raccon dog reference cytB alignment.

Supplementary: rRNA analysis

• mitochondrial_mappings/rRNA/count_reads93_rRNAs.py: Script to count reads excluding rRNA regions of the mitochondria.
• mitochondrial_mappings/rRNA/mt93_counts_rrna.tsv: Read counts ignoring rRNA regions.
• mitochondrial_mappings/rRNA/rRNA_analysis.ipynb: Script to identify rRNA regions.
• mitochondrial_mappings/rRNA/rrna_positions.tsv: positions of rRNA genes in the MT genomes.

Supplementary: Correlations

• correlations/Correlation.ipynb: Script to calculate correlations between animal sequence read abundances and SARS-CoV-2.

He et al. 2022 Cell data: Animal data from other markets

HeCell2022/: location of these files

• He2022_metadata.csv: Metadata about all of the files from that study
• cell2022_lowconfidence_mitochondria.fasta: FASTA file of consensus mitochondrial genomes with at least 1 read covering each position.
• cell2022_mediumconfidence_mitochondria.fasta: FASTA file of consensus mitochondrial genomes with at least 3 reads covering each position.
• scripts/mt_consensus_genomes_He2022.py: script used to generate Mitochondrial genomes above
• game_animals.xlsx: Additional metadata from He et al. 2022.

FASTQ processing

• fastq_processing/run_bbduk.sh: BBDuk commands used for quality filtering.
• fastq_processing/frequent_adapter_summary.tsv - Adapter and quality filtering summary per file. data/fastq_processing/: data related to FASTQ files
  • all_adapters.fa: list of adapters used for trimming
  • frequent_adapter_summary.tsv: results of adapter trimming, 5 most common adapters per library.
  • run_bbduk.sh: List of BBDuk commands run for each file for trimming.
  • bbduk_results/*.txt: Full adapter trimming output for each file.