LSTrAP-Cloud : a user-friendly cloud computing pipeline to infer coexpression networks

• Main Authors: Tan, Qiao Wen, Goh, William, Mutwil, Marek
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2020
• Subjects: Science::Biological sciences; Cloud; RNA
• Online Access: https://hdl.handle.net/10356/145307
• Institution: Nanyang Technological University

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.