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Deliverables: Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit. Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don’t overlook these. Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page. |
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Evaluation: NA: This unit is not evaluated for course marks. |
This unit collects short explorations of various databases. It is probably best not to work through the units all in one go, but to go through them in context of an actual use case, when you need information from one of them. Currently we have SGD and STRING.
Yeast happens to have a very well maintained model organism database - a Web resource dedicated to Saccharomyces cerevisiae. Where such dedicated resources are available, they are very useful for the community. For the general case however, we need to work with one of the large, general data providers - the NCBI and the EBI. But in order to get a sense of the type of data that is available, let’s explore the SGD database.
Task…
Access the information page on Mbp1 at the Saccharomyces Genome Database.
Browse through the Summary page and note the available information: you should see:
information about the gene and the protein;
Information about it’s roles in the cell curated at the Gene Ontology database;
Information about knock-out phenotypes; (Amazing. Would you have imagined that this is a non-essential gene?)
Information about protein-protein interactions;
Regulation and expression;
A curators’ summary of our understanding of the protein. Mandatory reading.
And key references.
Access the Protein tab and note the much more detailed information.
Domains and their classification;
Sequence;
Shared domains;
and much more…
You will notice that some of this information relates to the molecule itself, and some of it relates to its relationship with other molecules. Some of it is stored at SGD, and some of it is cross-referenced from other databases. And we have textual data, numeric data, and images.
If we would be working on yeast, most data we need is right here: curated, kept current and consistent, referenced to the literature and ready to use. But if you are working on a different species - some “MYSPE”- you need to integrate data yourself, from data sources such as the NCBI, or UniProt. The upside is that most of the information like this is available for many, many species. The downside is that you have to integrate information from many different sources essentially “by hand”.
Task…
YAR014C YBR040W YBR200W YCL027W YCR089W YDL223C YDR085C YDR141C YER125W YER133W YER149C YHR102W YHR135C YHR158C YIL129C YKL048C YKL189W YKR031C YLL021W YLR229C YLR313C YLR332W YMR232W YNL154C YNR032C-A YOL111C YOR326W YPL123C YPR194C
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The essence of our “new” view of molcular biology is the study of interactions: after characterizing biomolecules individually, we are assembling networks of relationships through protein-protein and other interaction experiments. But visualizing the results is not trivial since we need to display genes as networks, define attributes of the nodes and edges and encode them in our visualization, develop quantitative measures that help us mine the data for information, and map the results back into the network to evaluate the influence of the network topology (gene “neigborhoods”) on our findings. Databases strive to build integrated viewers for this kind of data. However there is much that still needs to be done. Have a look at this article that discusses the gap between what one would need to do, and what is offered:
Jeanquartier,
Fleur, Claire Jean-Quartier, and Andreas Holzinger. (2015).
“Integrated web visualizations for protein-protein interaction
databases”. Bmc Bioinformatics 16:195 .
[PMID: 26077899]
[DOI: 10.1186/s12859-015-0615-z]
The online resource that comes out as the best is the one at the STRING database.
Task…
Szklarczyk,
Damian et al.. (2019). “STRING v11: protein-protein
association networks with increased coverage, supporting functional
discovery in genome-wide experimental datasets”. Nucleic Acids
Research 47(D1):D607–D613 .
[PMID: 30476243]
[DOI: 10.1093/nar/gky1131]
In summary, STRING is a convincingly well built tool to explore functional relationships between proteins.
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