Additional scripts
Scripts used to perform analyses reported in the LSTrAP manuscript (Proost et al., under preparation) are found in ./helper
Obtain and prepare data
get_sra_ip.py
Script to download runs from Sequence Read Archive, requires the Aspera connect client to be installed and a open ssh key is required (can be obtained from the Apera connect package)
python3 get_sra_ip.py runs.list.txt ./output/directory /absolute/path/to/opensshkey
sra_to_fastq.py
Script to convert sra files into fastq. Sratools is required.
python3 sra_to_fastq.py /sra/files/directory /fastq/output/directory
Running LSTrAP on transcriptome data
To use LSTrAP on a de novo assembled transcriptome, a little pre-processing is required. Instead of the genome, a fasta file containing coding sequences can be used (remove UTRs). Using the helper script fasta_to_gff.py, a gff file suited for LSTrAP can be generated.
parse_gff.py
Script to remove splice variants from a GFF3 file, the longest one is retained.
# print to STDOUT
python3 parse_gff.py input.gff
# write to file
python parse_gff.py input.gff -o output.gff
python parse_gff.py input.gff --output output.gff
Quality control
htseq_count_stats.py, hisat2_stats.py and tophat_stats.py
These scripts will extract the statistics used to assess the quality of samples.
python3 htseq_count_stats.py ./path/to/htseq/files > output.txt
python3 tophat_stats.py ./path/to/tophat/output > output.txt
python3 hisat2_stats.py ./path/to/hisat2/output > output.txt
Plots and Graphs
Scripts to generate images similar to those presented in the publication. Example data, derived from the Sorghum bicolor case study, is included in the repository.
plot_network.py
Script that plots the co-expression neighborhood for a specific gene. A PCC cutoff of 0.7 is included by default, but users can override this setting using the --cutoff parameter. Matplotlib and networkx are required for this script.
# To draw plot to screen using a PCC cutoff of >= 0.8
python3 plot_network.py <PCC_TABLE> <GENE_ID> --cutoff 0.8
# Save as png
python3 plot_network.py <PCC_TABLE> <GENE_ID> --cutoff 0.8 --png output.png
# Set png dpi (for publication)
python3 plot_network.py <PCC_TABLE> <GENE_ID> --cutoff 0.8 --png output.png --dpi 900
matrix_heatmap.py
Script to draw a sample distance heatmap (with hierarchical clustering) based on a normalized expression matrix.
# To draw plot to screen
python3 matrix_heatmap.py ./data/sbi.expression.matrix.tpm.txt
# Hide labels (useful for large sets)
python3 matrix_heatmap.py ./data/sbi.expression.matrix.tpm.txt --hide_labels
# Save as png
python3 matrix_heatmap.py ./data/sbi.expression.matrix.tpm.txt --png output.png
# Set png dpi (for publication)
python3 matrix_heatmap.py ./data/sbi.expression.matrix.tpm.txt --png output.png --dpi 900
pca_plot.py
Script to perform a PCA analysis on any expression matrix.
python3 pca_plot.py ./data/sbi.expression.matrix.tpm.txt
pca_powerlaw.py
This script and the required data are included to recreate results from the manuscript (Proost et al., under review)
Script to perform a PCA analysis on the Sorghum bicolor data (case study) and draw the node degree distribution. The required data is included here as well. Note that this script requires sklearn and seaborn.
python3 pca_powerlaw.py ./data/sbi.expression.matrix.tpm.txt ./data/sbi_annotation.txt ./data/sbi.power_law.R07.txt
Utilities
merge_matrix.py
In case samples for one (!) species were processed in two or more batches, this script can be used to merge the expression matrices.
Note that to obtain co-expression networks using the merged matrix LSTrAP needs to be run, using the merged expression matrix, skipping all steps before the construction of co-expression.
Only merge raw matrices with raw, tpm with tpm and rpkm with rpkm!
python3 merge_matrix.py matrix_one.txt matrix_two.txt matrix_merged.txt