Skip to content
Snippets Groups Projects
Commit dd3373bc authored by Paul Costea's avatar Paul Costea
Browse files

readmeu udpate

parent 65b7e10d
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 28 additions and 153 deletions
README.md
+ 28
− 153
View file @ dd3373bc
......@@ -9,7 +9,7 @@ Download
Via Git:
git clone git@git.embl.de:costea/metaSNV.git
git clone git@git.embl.de:rmuench/metaSNP.git
or [download](https://git.embl.de/rmuench/metaSNP/repository/archive.zip?ref=master) a zip file of the repository.
......@@ -64,11 +64,12 @@ Workflow:
=========
## Required Files:
* **'all\_samples'** = a list of all BAM files, one /path/2/sample.bam per line (no duplicates)
* **'ref\_db'** = the reference database in fasta format (f.i. multi-sequence fasta)
* **'all\_samples'** = a list of all BAM files, one /path/2/sample.bam per line (no duplicates)
* **'ref\_db'** = the reference database in fasta format (f.i. multi-sequence fasta)
* **'gen\_pos'** = a list with start and end positions for each sequence in the reference (format: `sequence\_id start end`)
## Optional Files:
* **'db\_ann'** = a gene annotation file for the reference database.
* **'db\_ann'** = a gene annotation file for the reference database (format: ).
## To use one of the provided reference databases:
......@@ -76,40 +77,12 @@ Workflow:
## 2. Run metaSNV
metaSNV.py project_dir/ all_samples ref_db ref_fasta [options]
metaSNV.py project_dir/ all_samples ref_db [options]
## 3. Part II: Post-Processing (Filtering & Analysis)
### a) Filtering:
Note: requires SNP calling (Part II) to be done!
Caution: Perform this step seperately for individual SNPs and population SNPs.
usage: metaSNP_filtering.py
positional arguments:
perc_FILE input file with horizontal genome (taxon) coverage (breadth) per sample (percentage covered)
cov_FILE input file with average genome (taxon) coverage
(depth) per sample (average number reads per site)
snp_FILE input files from SNP calling
all_samples list of input BAM files, one per line
output_dir/ output folder
optional arguments:
-h, --help show this help message and exit
-p PERC, --perc PERC Coverage breadth: Horizontal coverage cutoff
(percentage taxon covered) per sample (default: 40.0)
-c COV, --cov COV Coverage depth: Average vertical coverage cutoff per
taxon, per sample (default: 5.0)
-m MINSAMPLES, --minsamples MINSAMPLES
Minimum number of sample required to pass the coverage
cutoffs per genome (default: 2)
-s SNPC, --snpc SNPC FILTERING STEP II: SNP coverage cutoff (default: 5.0)
-i SNPI, --snpi SNPI FILTERING STEP II: SNP occurence (incidence) cutoff
within samples_of_interest (default: 0.5)
### b) Analysis:
> TODO: include R scripts for computing pairwise distances and visualization
metaSNV_post.py project_dir [options]
Example Tutorial
================
......@@ -123,137 +96,39 @@ Example Tutorial
$ cd EXAMPLE
$ ./getSamplesScript.sh
## 3. Initiate a new project in the parent directory
$ metaSNV_New tutorial
## 4. Generate the 'all_samples' file
$ find `pwd`/EXAMPLE/samples -name “*.bam” > tutorial/all_samples
## 5. Prepare and run the coverage estimation
$ metaSNV_COV tutorial/ tutorial/all_samples > runCoverage
$ bash runCoverage
## 6. Perform a work load balancing step for run time optimization.
## 3. Make sample list
$ metaSNV_OPT tutorial/ db/Genomev9_definitions 5
$ bash runCoverage
$ find `pwd`/EXAMPLE/samples -name “*.bam” > sample_list
## 7. Prepare and run the SNV calling step
## 4. Run the SNV calling step
$ metaSNV_SNP tutorial/ tutorial/all_samples db/RepGenomesv9.fna -a db/RefOrganismDB_v9_gene.clean -l tutorial/bestsplits/ > runSNPcall
$ bash runSNPcall
$ python metaSNV.py tutorial sample_list db/freeze9.genomes.RepGenomesv9.fna --threads 8 --ctg_len db/freeze9.len.def.bed
## 8. Run the post processing / filtering steps
### a) Compute allele frequencies for each position that pass the given thresholds.
## 5. Run filtering and post processing
$ metaSNV_filtering.py tutorial/tutorial.all_perc.tab tutorial/tutorial.all_cov.tab tutorial/snpCaller/called_SNVs.best_split_* tutorial/all_samples tutorial/filtered/pop/
### b) Compute pair-wise distances between samples on their SNP profiles and create a PCoA plot.
$ python metaSNV_post.py tutorial
Voila! Your distances will be in the tutorial/distances folder. Enjoy!
Advanced usage (tools and scripts)
Advanced usage
==================================
If you are interested in using the pipeline in a more manual way (for example
the metaSNV caller stand alone), you will find the executables for the
individual steps in the `src/` directory.
metaSNV caller
--------------
Calls SNVs from samtools pileup format and generates two outputs.
usage: ./snpCall [options] < stdin.mpileup > std.out.popSNPs
Options:
-f, faidx indexed reference genome.
-g, gene annotation file.
-i, individual SNVs.
Note: Expecting samtools mpileup as standard input
### __Output__
1. Population SNVs (pSNVs):
Population wide variants that occur with a frequency of 1 % at positions with at least 4x coverage.
2. Individual specific SNVs (iSNVs):
Non population variants, that occur with a frequency of 10 % at positions with at least 10x coverage.
[qaCompute](https://github.com/CosteaPaul/qaTools)
-------------------------------------------------
Computes normal and span coverage from a bam/sam file.
Also counts unmapped and sub-par quality reads.
### __Parameters:__
m - Compute median coverage for each contig/chromosome.
Will make running a bit slower. Off by default.
q [INT] - Quality threshold. Any read with a mapping quality under
INT will be ignored when computing the coverage.
NOTE: bwa outputs mapping quality 0 for reads that map with
equal quality in multiple places. If you want to condier this,
set q to 0.
d - Print coverage histrogram over each individual contig/chromosome.
These details will be printed in file <output>.detail
p [INT] - Print coverage profile to bed file, averaged over given window size.
i - Silent run. Will not print running info to stdout.
If you want to run a lot of samples and would like to use the power of your cluster, we will print out the commands you need to
run and you can decide on how to schedule and manage them.
## 1. Get the first set of commands
s [INT] - Compute span coverage. (Use for mate pair libs)
Instead of actual read coverage, using the options will consider
the entire span of the insert as a read, if insert size is
lower than INT.
For an accurate estimation of span coverage, I recommend
setting an insert size limit INT around 3*std_dev of your lib's
insert size distribution.
$ python metaSNV.py tutorial sample_list db/freeze9.genomes.RepGenomesv9.fna --n_splits 8 --ctg_len db/freeze9.len.def.bed --print-commands
c [INT] - Maximum X coverage to consider in histogram.
Note the addition of the "--print-commnads". This will print out one-liners that you need to run. When done, run same again.
## 2. Get the second set of commands
$ python metaSNV.py tutorial sample_list db/freeze9.genomes.RepGenomesv9.fna --n_splits 8 --ctg_len db/freeze9.len.def.bed --print-commands
h [STR] - Use different header.
Because mappers sometimes break the headers or simply don't output them,
this is provieded as a non-kosher way around it. Use with care!
This will calculate the "load balancing" and give you the commands for running the SNV calling.
For more info on the parameteres try ./qaCompute
metaSNV_filtering.py
--------------------
usage: metaSNV filtering [-h] [-p PERC] [-c COV] [-m MINSAMPLES] [-s SNPC]
[-i SNPI]
perc_FILE cov_FILE snp_FILE [snp_FILE ...]
all_samples output_dir/
metaSNV filtering
positional arguments:
perc_FILE input file with horizontal genome (taxon) coverage
(breadth) per sample (percentage covered)
cov_FILE input file with average genome (taxon) coverage
(depth) per sample (average number reads per site)
snp_FILE input files from SNP calling
all_samples list of input BAM files, one per line
output_dir/ output folder
optional arguments:
-h, --help show this help message and exit
-p PERC, --perc PERC Coverage breadth: Horizontal coverage cutoff
(percentage taxon covered) per sample (default: 40.0)
-c COV, --cov COV Coverage depth: Average vertical coverage cutoff per
taxon, per sample (default: 5.0)
-m MINSAMPLES, --minsamples MINSAMPLES
Minimum number of sample that have to pass the
filtering criteria in order to write an output for the
representative Genome (default: 2)
-s SNPC, --snpc SNPC FILTERING STEP II: SNP coverage cutoff (default: 5.0)
-i SNPI, --snpi SNPI FILTERING STEP II: SNP occurence (incidence) cutoff
within samples_of_interest (default: 0.5)
## 3. Run post-processing as usual
$ python metaSNV_post.py tutorial
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment