15 Essential Skills a Bioinformatician should have, according to Rosenwald et al (2017) and Wilson Sayres et al (2018)

  1. Role — Understand the role of computation and data mining in hypothesis-driven processes within the life sciences
  2. Concepts — Understand computational concepts used in bioinformatics, e.g., meaning of algorithm, bioinformatics file formats
  3. Statistics — Know statistical concepts used in bioinformatics, e.g., E-value, z-scores, t test, type-1 error, type-2 error, employ R
  4. Access genomic — Know how to access genomic data, e.g., in NCBI nucleotide databases
  5. Tools genomic — Be able to use bioinformatics tools to analyze genomic data, e.g., BLASTN, genome browser
  6. Access expression — Know how to access gene expression data, e.g., in UniGene, GEO, SRA
  7. Tools expression — Be able to use bioinformatics tools to analyse gene expression data, e.g., GeneSifter, David, ORF Finder
  8. Access proteomic — Know how to access proteomic data, e.g., in NCBI protein databases
  9. S9 (Tools proteomic)—Be able to use bioinformatics tools to examine protein structure and function, e.g., BLASTP, Cn3D, PyMol
  10. Access metabolomic — Know how to access metabolomic and systems biology data, e.g., in the Human Metabolome Database
  11. Pathways — Be able to use bioinformatics tools to examine the flow of molecules within pathways/networks, e.g., Gene Ontology, KEGG
  12. Metagenomics — Be able to use bioinformatics tools to examine metagenomics data, e.g., MEGA, MUSCLE
  13. Scripting — Know how to write short computer programs as part of the scientific discovery process, e.g., write a script to analyse sequence data
  14. Software — Be able to use software packages to manipulate and analyse bioinformatics data, e.g., Geneious, Vector NTI Express, spreadsheets
  15. Computational environment — Operate in a variety of computational environments to manipulate and analyse bioinformatics data, e.g., Mac OS, Windows, web- or cloud-based, Unix/Linux command line