Difference between revisions of "RNAMicro"
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'''Current status of application'''
'''What was done'''
'''What was done'''
Revision as of 15:50, 8 December 2014
Introduction to RNA Microarray
From Wikipedia, the free encyclopedia
"A hairpin loop from a pre-mRNA. Highlighted are the nucleobases (green) and the ribose-phosphate backbone (blue). Note that this is a single strand of RNA that folds back upon itself.
Ribonucleic acid (RNA) is a polymeric molecule. It is implicated in a varied sort of biological roles in coding, decoding, regulation, and expression of genes. DNA and RNA are nucleic acids, and, along with proteins and carbohydrates, constitute the three major macromolecules essential for all known forms of life. Like DNA, RNA is assembled as a chain of nucleotides, but unlike DNA it is more often found in nature as a single-strand folded unto itself, rather than a paired double-strand. Cellular organisms use messenger RNA (mRNA) to convey genetic information (using the letters G, A, U, and C to denote the nitrogenous bases guanine, adenine, uracil and cytosine) that directs synthesis of specific proteins. Many viruses encode their genetic information using an RNA genome.
Some RNA molecules play an active role within cells by catalyzing biological reactions, controlling gene expression, or sensing and communicating responses to cellular signals. One of these active processes is protein synthesis, a universal function whereby mRNA molecules direct the assembly of proteins on ribosomes. This process uses transfer RNA (tRNA) molecules to deliver amino acids to the ribosome, where ribosomal RNA (rRNA) links amino acids together to form protein".
"A microarray is a multiplex lab-on-a-chip. It is a 2D array on a solid substrate (usually a glass slide or silicon thin-film cell) that assays large amounts of biological material using high-throughput screening miniaturized, multiplexed and parallel processing and detection methods. The concept and methodology of microarrays was first introduced and illustrated in antibody microarrays (also referred to as antibody matrix) by Tse Wen Chang in 1983 in a scientific publication and a series of patents. The "gene chip" industry started to grow significantly after the 1995 Science Paper by the Ron Davis and Pat Brown labs at Stanford University. With the establishment of companies, such as Affymetrix, Agilent, Applied Microarrays, Arrayit, Illumina, and others, the technology of DNA microarrays has become the most sophisticated and the most widely used, while the use of protein, peptide and carbohydrate microarrays are expanding"
The actual visualization on Microarrays as in the example below, demonstrates that on the top and left we have a dendrogram. And them a correlation matrix demonstrates by the colors where the gene is active. If it is blue it means that only the control samples are active on that specific gene, and the more red it gets that means the more the gene is active on the examined sample. This way of visualization must be rethinked because it makes really hard to be demonstrate into an easy way and gathers a lot of information in a very confusing manner.
The new proposal
The objective it is to generate a new,simple,practical and fancy visualization for microarrays, in a way that it is made fast and broadly used and understandable by all. By this way we intend to create a more clean GUI where we could zoom in and out into 3 levels and have a table on the right side, that can be minimized.
- Version 1
Current status of application
What was done
- Decided to use HTML5 for heat map as making it with SVG is not possible because of big sizes of data.
- Did research on HTML5, especially canvas tag.
- Made a prototype of heat map, which is scaling the canvas to get the zoomed versions.
What should be done
- Refactor code not to scale the canvas but indeed to draw it each time we are moving the rectangle of zooming.
- Make possible to choose the RNA sequence we want to explore.
- Make a graph where more details are visible about specific RNA.
- Understand the RNA Data.
- Try to compact the data.
- Create a prototype
- Test the prototype
- Ask for improvements and feedback.
- Restart the prototyping -> developing -> testing cycle.
- Upload the final version to BIOJS.