scholarly journals A High-Throughput Data Mining of Single Nucleotide Polymorphisms in Coffea Species Expressed Sequence Tags Suggests Differential Homeologous Gene Expression in the Allotetraploid Coffea arabica

2010 ◽  
Vol 154 (3) ◽  
pp. 1053-1066 ◽  
Author(s):  
Ramon Oliveira Vidal ◽  
Jorge Maurício Costa Mondego ◽  
David Pot ◽  
Alinne Batista Ambrósio ◽  
Alan Carvalho Andrade ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
High Throughput ◽  
Expressed Sequence Tags ◽  
Coffea Arabica ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
High Throughput Data ◽  
Homeologous Gene ◽  
Expressed Sequence

Population structure of pigs determined by single nucleotide polymorphisms observed in assembled expressed sequence tags

2011 ◽  
Vol 83 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Toshimi MATSUMOTO ◽  
Naohiko OKUMURA ◽  
Hirohide UENISHI ◽  
Takeshi HAYASHI ◽  
Noriyuki HAMASIMA ◽  
...  
Keyword(s):  
Population Structure ◽  
Single Nucleotide Polymorphisms ◽  
Expressed Sequence Tags ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Expressed Sequence

scholarly journals 30 Mutli-omic data integration in quantitative genetics

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 15-15
Author(s):  
Gota Morota
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
High Throughput ◽  
Quantitative Genetics ◽  
Copy Number ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Number Variation ◽  
Omic Data Integration ◽  
Omic Data

Abstract The advent of high-throughput technologies has generated diverse omic data including single-nucleotide polymorphisms, copy-number variation, gene expression, methylation, and metabolites. The next major challenge is how to integrate those multi-omic data for downstream analyses to enhance our biological insights. This emerging approach is known as multi-omic data integration, which is in contrast to studying each omic data type independently. I will discuss challenging issues in developing algorithms and methods for multi-omic data integration. The particular focus will be given to the potential for combining diverse types of FAANG data and the utility of multi-omic data integration in association analysis and phenotypic prediction.

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