Bioinformatics

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• Case Studies
  • Paleontology
  • Healthcare
    • Pharmaceuticals
    • Drug Discovery
      • Protein Folding & Discovery ...Google DeepMind AlphaFold
  • Life Sciences
• COVID-19
• Bio-inspired Computing
• COVID-19
• DIY Human CRISPR Guide | The Odin
• Machine learning spots natural selection at work in human genome | Amy Maxmen
• DeepVariant: Highly Accurate Genomes With Deep Neural Networks | Mark DePristo and Ryan Poplin, Google Brain Team

an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques.

Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as a specific analysis "pipelines" that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences, called proteomics. Wikipedia

Proteins

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Proteins are made up of hundreds or thousands of amino acids, and these amino acid sequences specify the protein's structure and function. But understanding just how to build these sequences to create novel proteins has been challenging. Past work has resulted in methods that can specify structure, but function has been more elusive. Machine learning reveals recipe for building artificial proteins | Emily Ayshford - University of Chicago - PhysOrg

What Came First, Cells or Viruses?

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• Markov Model (Chain, Discrete Time, Continuous Time, Hidden)
• Phylogenetic Hidden Markov Models | Adam Siepel and David Haussler
• Phylogeny Programs | Joe Felsenstein - University of Washington Here are 392 phylogeny packages and 54 free web servers ... PHYLIP (the PHYLogeny Inference Package) Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites and fragments, distance matrices, and discrete characters.

Virus & Consciousness

• An Ancient Virus May Be Responsible for Human Consciousness | Rafi Letzter - Live Science
• Unraveling the Human Genome: 6 Molecular Milestones | Stephanie Pappas - Live Science

Long ago, a virus bound its genetic code to the genome of four-limbed animals. That snippet of code is still very much alive in humans' brains today, where it does the very viral task of packaging up genetic information and sending it from nerve cells to their neighbors in little capsules that look a whole lot like viruses themselves. And these little packages of information might be critical elements of how nerves communicate and reorganize over time — tasks thought to be necessary for higher-order thinking...

Though it may sound surprising that bits of human genetic code come from viruses, it's actually more common than you might think: A review published in Cell in 2016 found that between 40 and 80 percent of the human genome arrived from some archaic viral invasion.