Difference between revisions of "Estimating and decreasing technical biases in transcriptomic data"
From Master Projects
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|Company=LUMC/Human Genetics | |Company=LUMC/Human Genetics | ||
|Thesis title=Estimating and decreasing technical biases in transcriptomic data | |Thesis title=Estimating and decreasing technical biases in transcriptomic data | ||
| − | |Finished= | + | |Finished=Yes |
|Thesis=Thesis.pdf | |Thesis=Thesis.pdf | ||
|Poster=Posternaam.pdf | |Poster=Posternaam.pdf | ||
}} | }} | ||
RNAseq data is rather complex, and its analysis is complicated by several biases, and the most well-known are sequencability and 5'-3' bias. The focus of this project is to find correction methods for the sequencability bias and the 5'-3' bias. As a final step, the two corrections will be combined in a single pipeline and its applicability proven on datasets of different type and complexity. | RNAseq data is rather complex, and its analysis is complicated by several biases, and the most well-known are sequencability and 5'-3' bias. The focus of this project is to find correction methods for the sequencability bias and the 5'-3' bias. As a final step, the two corrections will be combined in a single pipeline and its applicability proven on datasets of different type and complexity. | ||
Latest revision as of 13:57, 29 June 2015
| has title::Estimating and decreasing technical biases in transcriptomic data | |
|---|---|
status: finished
| |
| Master: | project within::Bioinformatics |
| Student name: | student name::Simon van Leeuwen |
| Dates | |
| Start | start date:=2013/02/04 |
| End | end date:=2013/09/04 |
| Supervision | |
| Supervisor: | Irina Pulyakhina |
| Second supervisor: | Peter-Bram 't Hoen |
| Second reader: | has second reader::Anton Feenstra |
| Company: | has company::LUMC/Human Genetics |
| Thesis: | has thesis::Media:Thesis.pdf |
| Poster: | has poster::Media:Posternaam.pdf |
Signature supervisor
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Abstract
RNAseq data is rather complex, and its analysis is complicated by several biases, and the most well-known are sequencability and 5'-3' bias. The focus of this project is to find correction methods for the sequencability bias and the 5'-3' bias. As a final step, the two corrections will be combined in a single pipeline and its applicability proven on datasets of different type and complexity.
