Open vleifer685 opened 11 months ago
Hi Val,
To answer your question 1.
The BQ environment can be created and activated in any folder, mine was created in the ./opt/bamquery
parent folder.
You just need before running BamQuery to make sure you have installed all the dependencies and also activated the environment.
So, once you have activated the environment (BQ) you can run BamQuery.
(BQ) -bash:uger-c002:/BamQuery/Test_downloading_BQ 1029 $ python3 ${INSTALLDIR}/BamQuery/BamQuery.py --help
usage: BamQuery.py [-h] [--mode MODE] [--th_out TH_OUT] [--dbSNP DBSNP] [--c]
[--strandedness] [--light] [--sc] [--umi] [--var] [--maxmm]
[--overlap] [--plots] [--m] [--dev] [--t T]
path_to_input_folder name_exp genome_version
======== BamQuery ========
positional arguments:
path_to_input_folder Path to the input folder where to find
BAM_directories.tsv and peptides.tsv
name_exp BamQuery search Id
genome_version Genome human releases : v26_88 / v33_99 / v38_104;
Genome mouse releases : M24 / M30
optional arguments:
-h, --help show this help message and exit
--mode MODE BamQuery search mode : normal / translation
--th_out TH_OUT Threshold to assess expression comparation with other
tissues
--dbSNP DBSNP Human dbSNP : 149 / 151 / 155
--c Take into account the only common SNPs from the dbSNP
database chosen
--strandedness Take into account strandedness of the samples
--light Display only the count and norm count for peptides and
regions
--sc Query Single Cell Bam Files
--umi Count UMIs in Single Cell Bam Files
--var Keep Variants Alignments
--maxmm Enable STAR to generate a larger number of alignments
--overlap Count overlapping reads
--plots Plot biotype pie-charts
--m Mouse genome
--dev Save all temps files
--t T Specify the number of processing threads to run
BamQuery. The default is 4
To address question 2. The goal of BamQuery is to provide the RNA-seq expression of MAPs in the samples (your own samples) you may be interested in (normal, cancer). This expression is measured from the BAM files (of the samples) that should be provided as the list of BAMs. Therefore, to measure the expression, one of the steps of BamQuery is to align the coding sequences of the MAPs in the mouse genome in order to investigate those genomic locations in the BAM files and retrieve the expression information. In other words, mapping the MAP coding sequences to the mouse genome is not the goal of BamQuery, but a by-product. I hope this helps !
Hi, Thank you for this awesome program. I have two questions about running it:
Question 1 1) In what folder should I open the "BQ" python environment? I currently have two folders relevant to the analysis:
Folder 1: "opt", which contains a "BamQuery" folder and a "lib" folder. The BamQuery folder was downloaded from GitHub, and the lib folder contains a folder with the reference genome I want to use (m30). Folder 2, in a different location, "anaconda3" which contains all the packages/dependencies relevant to the analysis
My question is where I should open the python environment.
Question 2 2) The methods information page states that two inputs are required - the .tsv file with the list of MAPs, and a set of "BAM" files. I don't understand what the BAM files are supposed to contain. The only input data I have are a list of 4 MAPs (each 8-11 amino acids long) of interest that I wish to map to the mouse genome with BamQuery.