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| ## Gene Content Dynamics | ||
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| These scripts will allow you to compare the gene content of a species between all pairs of metagenomic samples. | ||
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| Before running these scripts, you'll need to have run: | ||
| `merge_midas.py genes` [read more] (https://github.com/snayfach/MIDAS/blob/master/docs/merge_snvs.md). | ||
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| ####Command usage: | ||
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| ``` | ||
| gene_sharing.py --indir <PATH> --out <PATH> [options] | ||
| --indir PATH path to output from 'merge_midas.py genes' for one species | ||
| directory should be named according to a species_id and contains files 'genes_*.txt') | ||
| --out PATH path to output file | ||
| ``` | ||
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| ####Options: | ||
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| ``` | ||
| --max_genes INT Maximum number of genes to use. Useful for quick tests (use all) | ||
| --max_samples INT Maximum number of samples to use. Useful for quick tests (use all) | ||
| --distance {jaccard,euclidean,manhattan} | ||
| Metric to use for computing distances (jaccard) | ||
| --dtype {presabs,copynum} | ||
| Data type to use for comparing genes (presabs) | ||
| --cutoff FLOAT Cutoff to use for determining presence absence (0.35) | ||
| ``` | ||
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| ####Examples: | ||
| 1) Run with defaults: | ||
| `gene_sharing.py --indir /path/to/species --out distances.txt` | ||
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| 2) Run a quick test: | ||
| `gene_sharing.py --indir /path/to/species --out distances.txt --max_genes 1000 --max_samples 10` | ||
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| 3) Use a different distance metric: | ||
| `gene_sharing.py --indir /path/to/species --out distances.txt --distance manhattan` | ||
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| 4) Use a lenient cutoff for determining gene presence absence: | ||
| `gene_sharing.py --indir /path/to/species --out distances.txt --cutoff 0.10` | ||
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| 5) Use a strict cutoff for determining gene presence absence: | ||
| `gene_sharing.py --indir /path/to/species --out distances.txt --cutoff 0.75` | ||
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| ####Output format: | ||
| sample1: first sample identifier | ||
| sample2: second sample identifier | ||
| count1: number of present genes in sample1 | ||
| count2: number of present genes in sample2 | ||
| count_either: number of genes in sample1 or sample2 | ||
| count_both: number of genes in sample1 and sample2 | ||
| distance: dissimilarity between gene sets | ||
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| ## Core-Genome Phylogenetic Trees | ||
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| This script will allow you to build strain-level phylogenetic trees using consensus-alleles found in the core-genome. | ||
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| The core-genome of a species is identified directly from the data by looking for genomic sites in the representative genome that have high coverage across multiple metagenomes. | ||
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| Before running these scripts, you'll need to have run `merge_midas.py snps` [read more] (https://github.com/snayfach/MIDAS/blob/master/docs/merge_snvs.md). | ||
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| ### Step 1. Call consensus alleles | ||
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| ####Command usage: | ||
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| ``` | ||
| call_consensus.py --indir <PATH> --out <PATH> [options] | ||
| --indir PATH path to output from 'merge_midas.py snps' for one species | ||
| directory should be named according to a species_id and contains files 'snps_*.txt') | ||
| --out PATH path to output file | ||
| ``` | ||
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| ####Options for selecting samples: | ||
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| ``` | ||
| --sample_depth FLOAT minimum average read depth per sample (0.0) | ||
| --sample_cov FLOAT fraction of reference sites covered by at least 1 read (0.0) | ||
| --max_samples INT maximum number of samples to process. | ||
| useful for quick tests (use all) | ||
| --keep_samples STR comma-separated list of samples to include | ||
| samples will still be subject to other filters | ||
| --exclude_samples STR | ||
| comma-separated list of samples to exclude. | ||
| samples will still be subject to other filters | ||
| ``` | ||
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| ####Options for selecting genomic sites: | ||
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| ``` | ||
| --site_depth INT minimum number of mapped reads per site (2) | ||
| --site_prev FLOAT site has at least <site_depth> coverage in at least <site_prev> proportion of samples (0.0) | ||
| a value of 1.0 will select sites that have sufficent coverage in all samples. | ||
| a value of 0.0 will select all sites, including those with low coverage in many samples | ||
| NAs recorded for included sites with less than <site_depth> in a sample | ||
| --site_maf FLOAT minimum average-minor-allele-frequency of site across samples (0.0) | ||
| setting this above zero (e.g. 0.01, 0.02, 0.05) will only retain variable sites | ||
| by default invariant sites are also retained. | ||
| --site_ratio FLOAT maximum ratio of site-depth to mean-genome-depth (None) | ||
| a value of 10 will filter genomic sites with 10x high coverage than the genomic background | ||
| --max_sites INT maximum number of sites to include in output (use all) | ||
| useful for quick tests | ||
| ``` | ||
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| ####Example command: | ||
| ``` | ||
| call_consensus.py --indir /path/to/snps --out consensus.fa --site_maf 0.01 --site_depth 5 --site_prev 0.90 --sample_depth 10 --sample_cov 0.40 --site_ratio | ||
| ``` | ||
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| This command will build a multi-FASTA of core-genome sequences | ||
| -core-genome sites defined as >=5 reads in >=90% of samples | ||
| -use only variable positions (>=1% minor allele frequency across samples) | ||
| -only include samples with sufficient data (>=10x mean-depth, >=40% of sites with >=1 mapped read) | ||
| -exclude sites with abnormal depth (>5x mean-depth or <1/5 mean-depth) | ||
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| ### Step 2. Build phylogenetic tree | ||
| Now simply use your favorite tool to build the phylogenetic tree. | ||
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| Example: | ||
| Download FastTree [here] (http://www.microbesonline.org/fasttree) | ||
| And, run: `FastTree -gtr -nt < consensus.fa > consensus.tree ` | ||
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| ### Step 3. Visualize tree | ||
| From a web-browser: [iTOL] (http://itol.embl.de/) | ||
| Or, from R: [ape] (https://cran.r-project.org/web/packages/ape) | ||
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