# Analysis Description
Data were retrieved using the ENCODE portal
(https://doi.org/10.1093/nar/gkz1062). These include polyA+ RNAseq data
available for cytosolic and nuclear sub-cellular compartments originating from
common samples. Nextflow (version 25.04.6, https://doi.org/10.1038/nbt.3820)
and the nf-core rnaseq pipeline (https://doi.org/10.5281/zenodo.17909656) were
used to process the data. Moreover, fastp (https://doi.org/10.1002/imt2.107)
was used for read quality filtering and adapter trimming, SortMeRNA
(doi: 10.1093/bioinformatics/bts611) for ribosomal RNA filtering, STAR
(https://doi.org/10.1093/bioinformatics/bts635) for mapping reads to the
reference Genome (GRCh38), and RSEM (https://doi.org/10.1186/1471-2105-12-323)
for gene expression quantification. For further analysis, TPM values as
estimated by RSEM were utilized. Lowly expressed genes were filtered out based
on the average expression at each sample and sub-cellular compartment. Gene
filtering and graph visualization was done with the use of the R programming
language (version 4.3.3). The R environment utilized can be reproduced with a
docker image provided through Docker Hub (vntasis/r-env:base-4.3.3).