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

Structured Abstract Motivation Despite the abundance of species with transcriptomic data, a significant number of the species still lack genomes, making it difficult to study gene function and expression in these organisms. While de novo transcriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily-selected or similar experimental conditions which might fail to capture condition-specific transcripts. Results We developed the Large-Scale Transcriptome Assembly Pipeline for de novo assembled transcripts (LSTrAP- denovo ) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP- denovo can (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses. Availability and Implementation LSTrAP- denovo is easy to implement, written in python, and is freely available at https://github.com/pengkenlim/LSTrAP-denovo/ . Supplementary Information Supplementary data are available in the forms of supplementary figures, supplementary tables, and supplementary methods.