| #!/usr/bin/env python | |
| """ | |
| GTF.py | |
| Kamil Slowikowski | |
| December 24, 2013 | |
| Read GFF/GTF files. Works with gzip compressed files and pandas. | |
| http://useast.ensembl.org/info/website/upload/gff.html |
SAM and BAM filtering one-liners
@author: David Fredman, [email protected] (sans poly-A tail)
@dependencies: http://sourceforge.net/projects/bamtools/ and http://samtools.sourceforge.net/
Please extend with additional/faster/better solutions via a pull request!
BWA mapping (using piping for minimal disk I/O)
| import sys | |
| ########################################################## | |
| #requires snakemake, python3, pyfasta to be installed | |
| #save this file and provide all the binaries and their path | |
| #in variables below. | |
| #to run flux pipeline: | |
| #snakemake run_flux_pipeline | |
| #to run rsem pipeline: | |
| #snakemake run_rsem_pipeline |
| \documentclass{article} | |
| % General document formatting | |
| \usepackage[margin=0.7in]{geometry} | |
| \usepackage[parfill]{parskip} | |
| \usepackage[utf8]{inputenc} | |
| % Related to math | |
| \usepackage{amsmath,amssymb,amsfonts,amsthm} | |
| \begin{document} |
| // Here is an extremely simple version of work scheduling for multiple | |
| // processors. | |
| // | |
| // The Problem: | |
| // We have a lot of numbers that need to be math'ed. Doing this on one | |
| // CPU core is slow. We have 4 CPU cores. We would thus like to use those | |
| // cores to do math, because it will be a little less slow (ideally | |
| // 4 times faster actually). | |
| // | |
| // The Solution: |
| boot.cell.names <- read.table("quants_boot_rows.txt", strings=FALSE)[,1] | |
| gene.names <- read.table("quants_mat_cols.txt", strings=FALSE)[,1] | |
| cell.names <- read.table("quants_mat_rows.txt", strings=FALSE)[,1] | |
| library(jsonlite) | |
| jsonPath <- file.path("../cmd_info.json") | |
| cmd_info <- jsonlite::fromJSON(jsonPath) | |
| num.cells <- length(cell.names) | |
| num.genes <- length(gene.names) |
| #### 1. Sign up at GitHub.com ################################################ | |
| ## If you do not have a GitHub account, sign up here: | |
| ## https://github.com/join | |
| # ---------------------------------------------------------------------------- | |
| #### 2. Install git ########################################################## | |
| ## If you do not have git installed, please do so: |
| # get the data as per https://www.cedricscherer.com/2019/05/17/the-evolution-of-a-ggplot-ep.-1/#code | |
| library(tidyverse) | |
| devtools::source_gist("https://gist.github.com/Z3tt/301bb0c7e3565111770121af2bd60c11") | |
| # convert to data.frame, attach and sort region | |
| df <- as.data.frame(df_ratios) | |
| attach(df) | |
| # get rid of NAs | |
| df <- df[ ! is.na(student_ratio),] |
