LSTrAP-Kingdom: an automated pipeline to generate annotated gene expression atlases for kingdoms of life

Abstract

Motivation There are now more than two million RNA sequencing experiments for plants, animals, bacteria and fungi publicly available, allowing us to study gene expression within and across species and kingdoms. However, the tools allowing the download, quality control and annotation of this data for more than one species at a time are currently missing. Results To remedy this, we present the Large-Scale Transcriptomic Analysis Pipeline in Kingdom of Life (LSTrAP-Kingdom) pipeline, which we used to process 134,521 RNA-seq samples, achieving ∼12,000 processed samples per day. Our pipeline generated quality-controlled, annotated gene expression matrices that rival the manually curated gene expression data in identifying functionally-related genes. Availability LSTrAP-Kingdom is available from: https://github.com/wirriamm/plants-pipeline and is fully implemented in Python and Bash. Supplementary information Supplementary data are available at Bioinformatics online.

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

“…However, the challenge of selecting the appropriate RNA-seq accessions for the dataset remains an issue in practice, as previously alluded to in the introduction. While it is possible to devise an approach to select for RNA-seq accessions from multiple experimental conditions and sample types via annotations inferred from uploaded metadata descriptions (as seen in Goh & Mutwil, 2021), these annotations might not be accurate and available for all accessions due to the lack of format standardization and fixed vocabulary imposed on uploaded metadata. In addition, experimental conditions and sample types applicable to describe RNA-seq accessions can differ significantly from species to species, making the development of said approach difficult.…”

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

“…We constructed a Pearson correlation coefficient (PCC) based co-expression network for all genes expressed in at least one transcriptome library with a TPM of 1 using the pcc.py script of LSTrAP v1.3 (Goh and Mutwil 2021 ). We converted the PCC-based co-expression network into a Highest Reciprocal Rank (HRR) co-expression network using parameters of a maximum HRR of 50 and a PCC cut-off of 0.5 with a second-level neighborhood.…”

PEO: Plant Expression Omnibus – a comparative transcriptomic database for 103 Archaeplastida

“…The complete list is available in Data S1. Gene expression matrices were obtained from a previous study from public databases via our pipeline using recommended thresholds of log 10 ‐normalized number of processed reads and percentage of reads pseudo‐aligned to the reference coding sequences (CDS) (Goh & Mutwil, 2021), which removed samples with low number and percentage of mapping reads. Gene expression was quantified as TPM via pseudoalignment to reference CDS of each species, using kallisto (Bray et al., 2016).…”

“…While the data is publicly available, the interfaces have not been specifically designed for bulk data acquisition for whole kingdoms. We addressed this by designing a parallel‐processing pipeline to automatically discover, download, and process RNA‐seq data for the whole plant kingdom in less than a month (Goh & Mutwil, 2021), making the provision and maintenance of a kingdom‐wide resource feasible.…”