Literature DB >> 11252573

From phenotype to genotype.

J T Streelman1, T D Kocher.   

Abstract

Advances in the field of genomics have made it possible to decipher the genetic and transcriptional changes that underlie differences among organisms. Here we discuss the merits and drawbacks of these strategies as they relate to evolutionary biology. We suggest that the molecular basis of natural variation can best be interpreted via the intersection of genomic and transcriptome approaches. We outline how this might be accomplished in practice and assemble the components of a method to comprehend the evolutionary interplay between genotype and phenotype.

Mesh:

Year:  2000        PMID: 11252573     DOI: 10.1046/j.1525-142x.2000.00056.x

Source DB:  PubMed          Journal:  Evol Dev        ISSN: 1520-541X            Impact factor:   1.930


  8 in total

1.  A microsatellite-based genetic linkage map of the cichlid fish, Astatotilapia burtoni (Teleostei): a comparison of genomic architectures among rapidly speciating cichlids.

Authors:  Matthias Sanetra; Frederico Henning; Shoji Fukamachi; Axel Meyer
Journal:  Genetics       Date:  2008-08-30       Impact factor: 4.562

2.  ReCGiP, a database of reproduction candidate genes in pigs based on bibliomics.

Authors:  Lun Yang; Xiangzhe Zhang; Jian Chen; Qishan Wang; Lishan Wang; Yue Jiang; Yuchun Pan
Journal:  Reprod Biol Endocrinol       Date:  2010-08-14       Impact factor: 5.211

3.  The functional genomic response of developing embryonic submandibular glands to NF-kappa B inhibition.

Authors:  M Melnick; H Chen; Y Min Zhou; T Jaskoll
Journal:  BMC Dev Biol       Date:  2001-10-25       Impact factor: 1.978

4.  Contribution of transcriptional regulation to natural variations in Arabidopsis.

Authors:  Wenqiong J Chen; Sherman H Chang; Matthew E Hudson; Wai-King Kwan; Jingqiu Li; Bram Estes; Daniel Knoll; Liang Shi; Tong Zhu
Journal:  Genome Biol       Date:  2005-03-15       Impact factor: 13.583

5.  Blood-based analysis of type-2 diabetes mellitus susceptibility genes identifies specific transcript variants with deregulated expression and association with disease risk.

Authors:  Maria-Ioanna Christodoulou; Margaritis Avgeris; Ioanna Kokkinopoulou; Eirini Maratou; Panayota Mitrou; Christos K Kontos; Efthimios Pappas; Eleni Boutati; Andreas Scorilas; Emmanuel G Fragoulis
Journal:  Sci Rep       Date:  2019-02-06       Impact factor: 4.379

6.  The translational network for metabolic disease - from protein interaction to disease co-occurrence.

Authors:  Yonghyun Nam; Dong-Gi Lee; Sunjoo Bang; Ju Han Kim; Jae-Hoon Kim; Hyunjung Shin
Journal:  BMC Bioinformatics       Date:  2019-11-13       Impact factor: 3.169

7.  Evolutionary history of human disease genes reveals phenotypic connections and comorbidity among genetic diseases.

Authors:  Solip Park; Jae-Seong Yang; Jinho Kim; Young-Eun Shin; Jihye Hwang; Juyong Park; Sung Key Jang; Sanguk Kim
Journal:  Sci Rep       Date:  2012-10-22       Impact factor: 4.379

8.  Evaluating whole transcriptome amplification for gene profiling experiments using RNA-Seq.

Authors:  Sheena L Faherty; C Ryan Campbell; Peter A Larsen; Anne D Yoder
Journal:  BMC Biotechnol       Date:  2015-07-30       Impact factor: 2.563

  8 in total

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