Abstract
As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes involved in a biological process that they might be interested in, by using a single gene of interest as bait. The LSTrAP-Cloud is based on Google Colaboratory, and provides user-friendly tools that process quality-control RNA sequencing data streamed from the European Nucleotide Archive. The LSTRAP-Cloud outputs a gene coexpression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters, and transcription factors involved in the synthesis, transport, and regulation of nicotine can be identified using our pipeline.
Highlights
Genome sequencing and assembly are becoming more accessible in terms of cost and computational resources required due to the advances in technology and algorithms [1]
The improvement in transcript estimation algorithms has greatly reduced the amount of time and resources required to estimate gene expression from RNA-sequencing data
We have demonstrated with the LSTrAP-Lite pipeline that analysis of large-scale transcriptomic data was possible on a small computer costing less than 50 USD [24]
Summary
Genome sequencing and assembly are becoming more accessible in terms of cost and computational resources required due to the advances in technology and algorithms [1]. Despite extensive efforts over the decades, only 12% of genes have been characterised in the most studied model plant Arabidopsis thaliana [2]. This is because gene characterization is a time- and labor-intensive process hindered by various obstacles such as the lethality of mutants involving essential genes, or no observable mutant phenotype due to functional redundancy caused by large gene families [3,4,5,6,7]. Rhee and Mutwil [3]) To this end, newly sequenced genomes are mostly annotated using sequence similarity approaches, which annotate novel genes based on the sequence similarity to characterized genes. Classical approaches to gene function prediction are powerful but require other methods to complement them [3]
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