Introduction to Bioinformatics & Genetics by Gamze Gursoy

The lecture was about Bioinformatics, defined as the process of developing tools to understand large and complex biological data. Some examples offered were the Human Genome Project and AlphaFold.

The goals of Bioinformatics are to manage data (for easy access), develop technological tools, and use both data and tools to emerge knowledge via interpretation. Now there's medical informatics which is really about developing and deploying IT based solutions for healthcare services delivery, planning, and management. But biomedical infomratics deals with biomedical data: genomic, transcriptomic, interactomic, exomic, and so on.

Discussion on the Central Dogma of Biology, DNA structure, nucleotides, genes, proteins, exons, introns, and so on. Only 1% of our DNA is genes which code for about 25,000 proteins. This is not the case with bacteria which have ~90% coding regions. There's really no consensus on why we have this much redundancy.

RNA is like DNA but has Uracil instead of an Adenine. DNA → RNA Polymerase → mRNA → Ribosome → Protein. Note that there's not much tech to actually sequence RNA! You rely on reverse transcription (RNA → DNA) to measure how much mRNA exists for a given gene. Discussion on codons. Gene have introns and exons (latter being the coding regions). Introns are spliced out prior to translation. You can have alternative splicing: you pick up different exons from the same gene. Discussion on how codon redundancy can lead or not lead to 'things' happening (e.g. missense, nonsense). Discussion on human genome variation via deletion, translocation, etc.

We inherit genes in chunks called haploblocks that you can trace all the way to the MRCA. Discussion on GWAS and what they're used for. Discussion on databases like GenBank, EMBL (several by data type (e.g. metabolic pathways), others by organism). Genomics vs. Genetics is something that trips up people. Both deal with 'genetic information' in various fashions and various scopes (Genetics deals with heredity, though). Discussion on gene expression, enhancers and promoters, and how to analyze transcriptomics data (more in Yufeng Chen's upcoming lecture!)