Useful links

The below list is from my scientific experience and perspective. Hope it helps!

Useful omics tools:

  • MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis [link]
  • Qiita: rapid, web-enabled microbiome meta-analysis [link]
  • REVIGO [link] and WEGO [link] for GO annotation and visualization.
  • BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud [link]
  • MitoMiner 4.0 - A database of mammalian mitochondrial localisation evidence, phenotypes and diseases [link]
  • LipiDex: An Integrated Software Package for High-Confidence Lipid Identification [link]
  • MetaboList: Annotation of Metabolites from Liquid Chromatography-Mass Spectrometry Data [link]

Good data handling and analysis packages

  • car: Companion to Applied Regression [link]: I sometimes use this package to reassign the name of the columns in the data set.
  • forcats for handling categorical variables, especially for visualization [link]. E.g., fct_rev(fct_infreq(x)) for "ordered from top to bottom, highest count to lowest".

Visualization tools:

  • from data to viz (with code) [link]
  • DIVE: Turn your data into stories without writing code [link]

Good books:

  • Metabolic profiling series from Springer. Click here for the recent version.

Good papers:

  • From mass to metabolite in human untargeted metabolomics: recent advances in annotation of metabolites applying liquid chromatography-mass spectrometry data [link]
  • Bad practices in evaluation methodology relevant to class-imbalanced problems [link]

Good courses:

  • Khan academy [link]
  • DataCamp [link]
  • MIT Computational biology [link]


  • Common transition words and phrases [link]
  • Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data [link]
  • Writing for a Nature journal [link]
  • The Conversation - evidence-based news [link]