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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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LSTrAP-Cloud: A User-Friendly Cloud Computing Pipeline to Infer Coexpression Networks.

Qiao Wen Tan1, William Goh1, Marek Mutwil1

  • 1School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.

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|April 23, 2020
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Summary
This summary is machine-generated.

This study introduces LSTrAP-Cloud, a user-friendly pipeline for gene function prediction. It helps biologists identify genes involved in specific biological processes using RNA sequencing data and gene coexpression networks.

Keywords:
RNAcloudcoexpressionmetabolismsequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene function prediction is crucial for understanding biological processes.
  • The increasing availability of genomic data necessitates efficient tools for functional analysis.

Purpose of the Study:

  • To present the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud) as a tool for gene function prediction.
  • To demonstrate the pipeline's utility in identifying genes associated with specific biological pathways.

Main Methods:

  • Utilizing Google Colaboratory for a cloud-based, user-friendly pipeline.
  • Processing quality-controlled RNA sequencing data from the European Nucleotide Archive.
  • Generating gene coexpression networks from transcriptomic data.

Main Results:

  • The LSTrAP-Cloud pipeline successfully identifies functionally related genes.
  • A case study on *Nicotiana tabacum* demonstrated the identification of enzymes, transporters, and transcription factors in the nicotine biosynthesis pathway.

Conclusions:

  • LSTrAP-Cloud facilitates gene function prediction by creating gene coexpression networks.
  • The pipeline is applicable to any organism with a sequenced genome and available RNA sequencing data, aiding biological discovery.