scholarly journals Detection of allele-specific gene expression on Next Generation Sequencing data

2012 ◽  
Vol 18 (B) ◽  
pp. 130
Author(s):  
Vladan Mijatovic ◽  
Luciano Xumerle ◽  
Alberto Ferrarini ◽  
Ilaria Iacobucci ◽  
Chiara Pighi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Specific Gene ◽  
Next Generation ◽  
Sequencing Data ◽  
Specific Gene Expression ◽  
Allele Specific ◽  
Generation Sequencing

scholarly journals ASEQ: fast allele-specific studies from next-generation sequencing data

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Alessandro Romanel ◽  
Sara Lago ◽  
Davide Prandi ◽  
Andrea Sboner ◽  
Francesca Demichelis
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Allele Specific ◽  
Generation Sequencing

scholarly journals Wx: a neural network-based feature selection algorithm for next-generation sequencing data

2017 ◽  
Author(s):  
Sungsoo Park ◽  
Bonggun Shin ◽  
Yoonjung Choi ◽  
Kilsoo Kang ◽  
Keunsoo Kang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Next Generation Sequencing ◽  
Supplementary Information ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Selection Algorithm ◽  
Sequencing Data ◽  
Optimal Set ◽  
Generation Sequencing

AbstractMotivationNext-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. For example, an NGS-based transcriptomic assay called RNA-seq can be used to estimate the abundance of approximately 190,000 transcripts together. As the cost of next-generation sequencing sharply declines, researchers in many fields have been conducting research using NGS. The amount of information produced by NGS has made it difficult for researchers to choose the optimal set of target genes (or genomic loci).ResultsWe have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker (universal gene-expression biomarkers) candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method.Availabilityhttps://github.com/deargen/[email protected] informationSupplementary data are available at online.

Sign in / Sign up

Export Citation Format

Share Document