The aim of this lab is to investigate and document the relationships between several genes, enzymes and pathways. The data collection process is fully automated using Entrez.
3 pathways were chosen: Glycolysis, TCA cycle, and Pentose phosphate pathway.
4 enzymes were chosen for each pathway using their Enzyme Commission number (note that we chose one repetitive enzyme 4.1.1.49 in Glycolysis and TCA cycle):
| Pathway | Enzyme 1 | Enzyme 2 | Enzyme 3 | Enzyme 4 |
|---|---|---|---|---|
| Glycolysis | 4.1.1.49 | 5.3.1.9 | 5.4.2.2 | 1.8.1.4 |
| TCA cycle | 4.1.1.31 | 6.2.1.4 | 1.1.5.4 | 4.1.1.49 |
| Pentose Phosphate | 3.1.3.11 | 2.7.1.15 | 3.1.1.31 | 5.4.2.7 |
3 organisms were investigated: homo sapiens, drosophilia melanogaster (fruit fly), and escherichia coli K-12 MG1655
In this table we save the information of 4* 3 * 3 =36 genes.
Fields: id, name, description, organism, sequence
In this table we save the information of 3 pathways. The description for each of these pathways was found on the KEGG website
Fields: id, name, description
In this table we save the information of 11 enzymes (4 * 3 - 1 repetitive). Note that we chose EC number to be the primary key.
Fields: id, name, description, EC
We designed two associative tables.
The pathway_order is the order of occurance of the enzyme in the specific pathway.
Fields: id, pathway, enzyme, pathway_order
This table saves the relationship between the enzyems and the genes
Fields: id, ec, organism, gene
All processes except for the description of pathways are fully automated in this lab. To investigate other enzymes, simply edit the enzyme list at the top of the ipython file.
Entrez is the database mainly used in this lab and the most relevant result is returned in each case.
If this document does not compile with your default data rate limit, open jupyter notebook using the following command:
jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000000
- sqlite3 - in-process library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine.
- pandas - high-performance, easy-to-use data structures and data analysis tools
- BioPhython - a set of freely available tools for biological computation
- entrez - federated search engine, or web portal that allows users to search many discrete health sciences information