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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Teaching genomics to life science undergraduates using cloud computing platforms with open datasets.

Toryn M Poolman1, Andrea Townsend-Nicholson1, Amanda Cain1

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Biochemistry and Molecular Biology Education : a Bimonthly Publication of the International Union of Biochemistry and Molecular Biology
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Biochemistry students completed computational metagenomics research projects using open datasets and Google Colaboratory when lab work was impossible. This provided valuable data science experience and skills development.

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Area of Science:

  • Biochemistry Education
  • Computational Biology
  • Bioinformatics

Background:

  • Laboratory-based research projects were infeasible for biochemistry undergraduates during the COVID-19 pandemic.
  • The final year of a biochemistry degree typically involves hands-on research experience.

Purpose of the Study:

  • To provide undergraduate biochemistry students with computational research experience using open datasets.
  • To enable students with limited computing experience to explore diverse metagenomic datasets.
  • To leverage virtual computing environments for accessible research projects.

Main Methods:

  • Utilized Google Colaboratory (Colab) as a virtual computing environment for data analysis.
  • Employed QIIME2 for the analysis of raw sequencing data and visualization generation.
  • Integrated Google Cloud Compute for projects requiring substantial computational resources.
  • Facilitated the exploration of approximately 60 diverse, published metagenomic datasets.

Main Results:

  • Successfully provided computational research projects to 80 biochemistry undergraduates with minimal prior computing experience.
  • Enabled students to perform meta-analyses and analyze large datasets with numerous subjects and factors.
  • Demonstrated the feasibility of using Colab for retrieving, analyzing, and visualizing metagenomic data.

Conclusions:

  • Google Colaboratory offers a viable framework for delivering computational research projects in biochemistry education.
  • Reanalyzing public data and developing data science skills can be integrated into future biochemistry curricula.
  • Colab supports the development of data skills in multiple programming languages, enhancing student preparedness.