Finding our way through phenotypes.
Finding our way through phenotypes.
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DOI：
10.1371/journal.pbio.1002033
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发表时间：
2015-01
期刊：
影响因子：
9.8
通讯作者：
Mabee P
中科院分区：
文献类型：
--
作者：
Deans AR;Lewis SE;Huala E;Anzaldo SS;Ashburner M;Balhoff JP;Blackburn DC;Blake JA;Burleigh JG;Chanet B;Cooper LD;Courtot M;Csösz S;Cui H;Dahdul W;Das S;Dececchi TA;Dettai A;Diogo R;Druzinsky RE;Dumontier M;Franz NM;Friedrich F;Gkoutos GV;Haendel M;Harmon LJ;Hayamizu TF;He Y;Hines HM;Ibrahim N;Jackson LM;Jaiswal P;James-Zorn C;Köhler S;Lecointre G;Lapp H;Lawrence CJ;Le Novère N;Lundberg JG;Macklin J;Mast AR;Midford PE;Mikó I;Mungall CJ;Oellrich A;Osumi-Sutherland D;Parkinson H;Ramírez MJ;Richter S;Robinson PN;Ruttenberg A;Schulz KS;Segerdell E;Seltmann KC;Sharkey MJ;Smith AD;Smith B;Specht CD;Squires RB;Thacker RW;Thessen A;Fernandez-Triana J;Vihinen M;Vize PD;Vogt L;Wall CE;Walls RL;Westerfeld M;Wharton RA;Wirkner CS;Woolley JB;Yoder MJ;Zorn AM;Mabee P
想象一下，如果我们可以像计算基因组数据一样轻松地计算表型数据，这篇文章呼吁努力实现这一愿景，并讨论潜在的好处。尽管有大量多方面的努力来理解表型数据的广阔前景，但它们目前的形式抑制了生产性数据分析。缺乏一个社区范围内的，基于共识的，人类和机器可解释的语言来描述表型及其基因组和环境背景可能是生物学中许多关键领域整合的最紧迫的科学瓶颈，包括基因组学，系统生物学，发展，医学，进化，生态学和系统分类学。在这里，我们调查了当前的表型组学景观，包括数据资源和处理，以及在准确捕获表型相关数据描述方面所取得的进展。我们提出了一个跨领域的整合，可计算表型将使一个例子，我们呼吁更广泛的生物学社区，出版商和相关的资助机构，以支持努力克服今天的数据障碍，促进分析再现性。
Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits. Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.
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