Forum:Skills for industry career in bioinformatics
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2.0 years ago
m.engalla ▴ 10


I am wondering which technical and soft skills are most required (and commonly missing in applicants) for a career in bioinformatics and data analysis in industry? Thank you.

industry • 1.8k views
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2.0 years ago

It's a boring answer, but the answer is it depends.

For hard skills generally for every job you will need to know linux, bash, and version control (usually git). Data analysis jobs generally prefer an interpreted language (Python and/or R). Software dev jobs will look for proficiency in a compiled statically typed language like C++ or Rust. More jobs want experience in pipeline development in nextflow or snakemake. Most companies like to see proficiency with an HPC or AWS/GCP. Also having knowledge of SQL, SQL-like, and NoSQL databases on these services or in general will give you a leg up.

As for general domain knowledge, depending on what job you look for they may lean more into algorithms, machine learning, statistics, or pure biological knowledge. The job will generally be tailored to a certain discipline in computational biology too, like proteomics, transcriptomics, genomics, etc. There are also differing sequencing modalities that may be more important to know based on the job like Illumina, Nanopore, PacBio, etc.

For soft skills you will be collaborating a lot in industry, so ability to communicate to general audiences is really important. You would be surprised at how many brilliant computational biologists fumble because they can't explain their work to an audience of bench scientists. It's also important to show projects that you have "delivered" on, since most companies will judge you based on their timely completion.

Entering edit mode
2.0 years ago
ahmad mousavi ▴ 800

You need at least following as a expert person in omics era :

  • basic of cells and molecular biology
  • NGS technology for transcriptome and genome basic and NGS data
  • Linux, Bash programming
  • R and python programming
  • Data analysis of RNA-SEQ, ChiP-SEQ, miRNA, DNA-SEQ and single cell and other omics types.
  • Being familiar with Machine learning algorithms

There many other parts, but many companies needs such skills.


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