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

S1 (Role)—Understand the role of computation and data mining in hypothesis-driven processes within the life sciences
S2 (Concepts)—Understand computational concepts used in bioinformatics, e.g., meaning of algorithm, bioinformatics file formats
S3 (Statistics)—Know statistical concepts used in bioinformatics, e.g., E-value, z-scores, t test, type-1 error, type-2 error, employ R
S4 (Access genomic)—Know how to access genomic data, e.g., in NCBI nucleotide databases
S5 (Tools genomic)—Be able to use bioinformatics tools to analyze genomic data, e.g., BLASTN, genome browser
S6 (Access expression)—Know how to access gene expression data, e.g., in UniGene, GEO, SRA
S7 (Tools expression)—Be able to use bioinformatics tools to analyze gene expression data, e.g., GeneSifter, David, ORF Finder
S8 (Access proteomic)—Know how to access proteomic data, e.g., in NCBI protein databases
S9 (Tools proteomic)—Be able to use bioinformatics tools to examine protein structure and function, e.g., BLASTP, Cn3D, PyMol
S10 (Access metabolomic)—Know how to access metabolomic and systems biology data, e.g., in the Human Metabolome Database
S11 (Pathways)—Be able to use bioinformatics tools to examine the flow of molecules within pathways/networks, e.g., Gene Ontology, KEGG
S12 (Metagenomics)—Be able to use bioinformatics tools to examine metagenomics data, e.g., MEGA, MUSCLE
S13 (Scripting)—Know how to write short computer programs as part of the scientific discovery process, e.g., write a script to analyze sequence data
S14 (Software)—Be able to use software packages to manipulate and analyze bioinformatics data, e.g., Geneious, Vector NTI Express, spreadsheets
S15 (Computational environment)—Operate in a variety of computational environments to manipulate and analyze bioinformatics data, e.g., Mac OS, Windows, web- or cloud-based, Unix/Linux command line