Literature DB >> 17202287

Computational approaches to phenotyping: high-throughput phenomics.

Yves A Lussier1, Yang Liu.   

Abstract

The recent completion of the Human Genome Project has made possible a high-throughput "systems approach" for accelerating the elucidation of molecular underpinnings of human diseases, and subsequent derivation of molecular-based strategies to more effectively prevent, diagnose, and treat these diseases. Although altered phenotypes are among the most reliable manifestations of altered gene functions, research using systematic analysis of phenotype relationships to study human biology is still in its infancy. This article focuses on the emerging field of high-throughput phenotyping (HTP) phenomics research, which aims to capitalize on novel high-throughput computation and informatics technology developments to derive genomewide molecular networks of genotype-phenotype associations, or "phenomic associations." The HTP phenomics research field faces the challenge of technological research and development to generate novel tools in computation and informatics that will allow researchers to amass, access, integrate, organize, and manage phenotypic databases across species and enable genomewide analysis to associate phenotypic information with genomic data at different scales of biology. Key state-of-the-art technological advancements critical for HTP phenomics research are covered in this review. In particular, we highlight the power of computational approaches to conduct large-scale phenomics studies.

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Year:  2007        PMID: 17202287      PMCID: PMC2647609          DOI: 10.1513/pats.200607-142JG

Source DB:  PubMed          Journal:  Proc Am Thorac Soc        ISSN: 1546-3222


  81 in total

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  19 in total

1.  A collaborative knowledge base for cognitive phenomics.

Authors:  F W Sabb; C E Bearden; D C Glahn; D S Parker; N Freimer; R M Bilder
Journal:  Mol Psychiatry       Date:  2008-01-08       Impact factor: 15.992

2.  Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations.

Authors:  Mayetri Gupta; Ching-Lung Cheung; Yi-Hsiang Hsu; Serkalem Demissie; L Adrienne Cupples; Douglas P Kiel; David Karasik
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3.  Automated multidimensional phenotypic profiling using large public microarray repositories.

Authors:  Min Xu; Wenyuan Li; Gareth M James; Michael R Mehan; Xianghong Jasmine Zhou
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-09       Impact factor: 11.205

4.  Identifying and mitigating biases in EHR laboratory tests.

Authors:  Rimma Pivovarov; David J Albers; Jorge L Sepulveda; Noémie Elhadad
Journal:  J Biomed Inform       Date:  2014-04-13       Impact factor: 6.317

Review 5.  Whole-Organism Cellular Pathology: A Systems Approach to Phenomics.

Authors:  K C Cheng; S R Katz; A Y Lin; X Xin; Y Ding
Journal:  Adv Genet       Date:  2016-07-29       Impact factor: 1.944

6.  The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease.

Authors:  Peter N Robinson; Sebastian Köhler; Sebastian Bauer; Dominik Seelow; Denise Horn; Stefan Mundlos
Journal:  Am J Hum Genet       Date:  2008-10-23       Impact factor: 11.025

7.  Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia.

Authors:  Xinan Yang; Yong Huang; James L Chen; Jianming Xie; Xiao Sun; Yves A Lussier
Journal:  BMC Bioinformatics       Date:  2009-09-17       Impact factor: 3.169

Review 8.  Chemobehavioural phenomics and behaviour-based psychiatric drug discovery in the zebrafish.

Authors:  David Kokel; Randall T Peterson
Journal:  Brief Funct Genomic Proteomic       Date:  2008-09-10

9.  Computer aided data acquisition tool for high-throughput phenotyping of plant populations.

Authors:  Raju Naik Vankadavath; Appibhai Jakir Hussain; Reddaiah Bodanapu; Eros Kharshiing; Pinjari Osman Basha; Soni Gupta; Yellamaraju Sreelakshmi; Rameshwar Sharma
Journal:  Plant Methods       Date:  2009-12-10       Impact factor: 4.993

10.  Analysis of AML genes in dysregulated molecular networks.

Authors:  Eunjung Lee; Hyunchul Jung; Predrag Radivojac; Jong-Won Kim; Doheon Lee
Journal:  BMC Bioinformatics       Date:  2009-09-17       Impact factor: 3.169

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