scholarly journals Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data

2013 ◽  
Vol 37 (5) ◽  
pp. 1250-1258 ◽  
Author(s):  
MARC LOHSE ◽  
AXEL NAGEL ◽  
THOMAS HERTER ◽  
PATRICK MAY ◽  
MICHAEL SCHRODA ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Sequence Data ◽  
Web Server ◽  
Genome Scale ◽  
Plant Sequence

Genome-scale sequence data processing and epigenetic analysis of DNA methylation

2013 ◽  
Vol 35 (6) ◽  
pp. 685-694
Author(s):  
Ting-Zhang WANG ◽  
Gao SHAN ◽  
Jian-Hong XU ◽  
Qing-Zhong XUE
Keyword(s):  
Dna Methylation ◽  
Data Processing ◽  
Sequence Data ◽  
Epigenetic Analysis ◽  
Genome Scale ◽  
Scale Sequence

scholarly journals META-pipe cloud setup and execution

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2060
Author(s):  
Aleksandr Agafonov ◽  
Kimmo Mattila ◽  
Cuong Duong Tuan ◽  
Lars Tiede ◽  
Inge Alexander Raknes ◽  
...  
Keyword(s):  
Functional Annotation ◽  
High Performance ◽  
Sequence Data ◽  
Metagenomic Data ◽  
Taxonomic Profiling ◽  
Geographically Distributed ◽  
Computationally Intensive ◽  
High Performance Computing Cluster ◽  
And Storage ◽  
Performance Computing

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

scholarly journals Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle

2021 ◽  
Vol 11 ◽  
Author(s):  
Bryan Irvine M. Lopez ◽  
Narae An ◽  
Krishnamoorthy Srikanth ◽  
Seunghwan Lee ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Prediction Accuracy ◽  
Sequence Data ◽  
Predictive Performance ◽  
Genomic Region ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Marbling Score ◽  
Muscle Area ◽  
Snp Panel

Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes (i.e., backfat thickness, carcass weight, longissimus muscle area, and marbling score), 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation [intergenic (IGR), intron (ITR), regulatory (REG), synonymous (SYN), and non-synonymous (NSY)] to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set (7324 animals) used for predictive variant detection and the cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for the pre-selection of variants. Each set of pre-selected SNPs with different density (1000, 3000, 5000, or 10,000) were added to the 50 k genotypes separately and the predictive performance of each set of genotypes was assessed using the genomic best linear unbiased prediction (GBLUP). Results showed that the predictive performance of the customized Hanwoo 50 k SNP panel can be improved by the addition of pre-selected variants from the WGS data, particularly 3000 variants from each trait, which is then sufficient to improve the prediction accuracy for all traits. When 12,000 pre-selected variants (3000 variants from each trait) were added to the 50 k genotypes, the prediction accuracies increased by 9.9, 9.2, 6.4, and 4.7% for backfat thickness, carcass weight, longissimus muscle area, and marbling score compared to the regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region.

scholarly journals Protein Interaction Verification and Functional Annotation by Integrated Analysis of Genome-Scale Data

2002 ◽  
Vol 9 (5) ◽  
pp. 1133-1143 ◽  
Author(s):  
Patrick Kemmeren ◽  
Nynke L. van Berkum ◽  
Jaak Vilo ◽  
Theo Bijma ◽  
Rogier Donders ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Functional Annotation ◽  
Integrated Analysis ◽  
Genome Scale ◽  
Scale Data

scholarly journals MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks

2010 ◽  
Vol 38 (Web Server) ◽  
pp. W132-W137 ◽  
Author(s):  
L. Cottret ◽  
D. Wildridge ◽  
F. Vinson ◽  
M. P. Barrett ◽  
H. Charles ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Web Server ◽  
Genome Scale

scholarly journals PANNZER2: a rapid functional annotation web server

2018 ◽  
Vol 46 (W1) ◽  
pp. W84-W88 ◽  
Author(s):  
Petri Törönen ◽  
Alan Medlar ◽  
Liisa Holm
Keyword(s):  
Functional Annotation ◽  
Web Server

scholarly journals Genome-scale DNA sequence data and the evolutionary history of placental mammals

Data in Brief ◽  
2018 ◽  
Vol 18 ◽  
pp. 1972-1975 ◽  
Author(s):  
Shaoyuan Wu ◽  
Scott Edwards ◽  
Liang Liu
Keyword(s):  
Dna Sequence ◽  
Evolutionary History ◽  
Sequence Data ◽  
Dna Sequence Data ◽  
History Of ◽  
Genome Scale
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