Simply replace <<FILE>> with your properly formated VCF/BCF file name (2 places).
Required bcftools v. 1.2+.
paste \
<(bcftools query -f '[%SAMPLE\t]\n' <<FILE>> | head -1 | tr '\t' '\n') \
<(bcftools query -f '[%GT\t]\n' <<FILE>> | awk -v OFS="\t" '{for (i=1;i<=NF;i++) if ($i == "./.") sum[i]+=1 } END {for (i in sum) print i, sum[i] / NR }' | sort -k1,1n | cut -f 2)
Simply replace <<INPUT>>, <<OUTPUT>>, and <<PROP>> with the input file name, output file name, and proportion missing data at which points samples begin to get excluded, repectively. For example, 0.75 means that samples with greater than 75% missing data are filtered away. Requires bcftools v. 1.2+.
bcftools view -S ^<(paste <(bcftools query -f '[%SAMPLE\t]\n' <<INPUT>> | head -1 | tr '\t' '\n') <(bcftools query -f '[%GT\t]\n' <<INPUT>> | awk -v OFS="\t" '{for (i=1;i<=NF;i++) if ($i == "./.") sum[i]+=1 } END {for (i in sum) print i, sum[i] / NR }' | sort -k1,1n | cut -f 2) | awk '{ if ($2 > <<PROP>>) print $1 }') <<INPUT>> | bgzip > <<OUTPUT>>
shell one-liner that parses the fasta headers from the NCBI python genome. will likely work on other genomes from NCBI as well.
output fields:
Script to parse a NCBI GFF based on transcript IDs (e.g., XM_000..). These transcript IDs must not include the version suffix (.1, .2, etc.).
Columns returned:
Below is a ImageJ Python script that will read a user-provided image file and output the width and height, with units, to stdout. This macro is designed to be called from the command line using the ImageJ executable. With my Mac OSX computer running Fiji, the path is /Applications/Fiji.app/Contents/MacOS/ImageJ-macosx. This has not been tested elsewhere and may not work without some effort. It relies on the Bio-Formats plugin to read the file and was written to convert from Zeiss's .czi files, so no guarantee that it works with others as desired. It is especially important to note that this does not set the scale, but infers it based on the metadata stored in the .czi files. Therefore, it will probably not work well with other file types.
/Applications/Fiji.app/Contents/MacOS/ImageJ-macosx --headless get_image_dims.py input
| # install.packages(c("plotly", "reshape2", "ggdendro")) | |
| # devtools::install_github("sjmgarnier/viridis") | |
| library(ggplot2) | |
| library(ggdendro) | |
| library(plotly) | |
| library(viridis) | |
| # helper function for creating dendograms | |
| ggdend <- function(df) { |
SLiM is a newer, powerful piece of population genetic simulation software that is well documented, user-friendly, flexible, and has a pretty sweet GUI interface (plus command-line capability). The following script represents an initial dummy simulation situation I created as I got my feet wet with SLiM, and I added many notes to make it clear what each command was doing.
// in SLiM context are comments.
// set up a simple neutral simulation
initialize() {
initializeMutationRate(1e-7);
JBrowse is a handy genome browser and is especially useful for viewing the results of iterative rounds of MAKER. The documentation is decent, but for those not used to creating a data server, it can be difficult to understand. I struggled a bit at first.
When calling variants it can be very useful to know what impact a polymorphism might have on the biology of an organism. It is often difficult to extend a simple list of variants to specific phenotypes, but it is possible to broadly classify a variant based on its possible impact on protein coding genes (e.g., mis-sense mutations, etc.). A very useful tool for performing such an analysis is the Varient Effect Predictor (VEP) tool, which is produced by the folks at Ensembl. This tutorial will describe how to perform such an analysis on your organism of interest using variants stored in a VCF file and annotations from a GFF file.
VEP must be installed. Detailed information on this tool and its installation can be found here.Boa_constrictor_SGA_7C_scaffolds.fa: A genome FASTA file for your organism is requi