Difference between revisions of "Estimating and decreasing technical biases in transcriptomic data"

From Master Projects
Jump to: navigation, search
(New page: {{Masterproject |Master name=Bioinformatics |Student name=Simon van Leeuwen |Project start date=2013/02/04 |Project end date=2013/09/04 |Supervisor=Irina Pulyakhina |Second supervisor=Pete...)
 
 
(One intermediate revision by the same user not shown)
Line 8: Line 8:
 
|Second reader=Anton Feenstra
 
|Second reader=Anton Feenstra
 
|Company=LUMC/Human Genetics
 
|Company=LUMC/Human Genetics
|Finished=No
+
|Thesis title=Estimating and decreasing technical biases in transcriptomic data
 +
|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.

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



..................................

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.