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Transcriptome Mining to Identify Genes of Interest: From Local Databases to Phylogenetic Inference

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Marine Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2498))

Abstract

The advancement in next-generation sequencing technologies and the dropping of sequencing costs have seen an increase in the amount of transcriptome data generated each year. These data are of big potential for identifying genes and molecular pathways of interest across a plethora of organisms. However, navigating these resources requires some bioinformatics and evolutionary skills. Here, we describe a protocol of transcriptome data mining for genes of interest, from the creation of a protein database to the inference of phylogenetic trees, which was used for marine protists, but can be used as general pipeline across different taxa.

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Acknowledgments

The authors thank Servier Medical Art (SMART) website (https://smart.servier.com/) by Servier for the elements of Fig. 1.

Author information

Authors and Affiliations

  1. Department of Biology, University of Naples Federico II, Botanic Garden of Naples, Naples, Italy

    Daniele De Luca

  2. Department of Ecosustainable Marine Biotechnology, Stazione Zoologica Anton Dohrn, Naples, Italy

    Chiara Lauritano

Authors
  1. Daniele De Luca
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  2. Chiara Lauritano
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Corresponding authors

Correspondence to Daniele De Luca or Chiara Lauritano .

Editor information

Editors and Affiliations

  1. Institute of Biosciences and BioResources (IBBR), National Research Council, Naples, Italy

    Cinzia Verde

  2. Institute of Biosciences and BioResources (IBBR), National Research Council, Naples, Italy

    Daniela Giordano

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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De Luca, D., Lauritano, C. (2022). Transcriptome Mining to Identify Genes of Interest: From Local Databases to Phylogenetic Inference. In: Verde, C., Giordano, D. (eds) Marine Genomics. Methods in Molecular Biology, vol 2498. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2313-8_3

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  • DOI: https://doi.org/10.1007/978-1-0716-2313-8_3

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2312-1

  • Online ISBN: 978-1-0716-2313-8

  • eBook Packages: Springer Protocols

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