For years, Engineers and Biochemists have labored to produce high-throughput genome and transcriptome sequencing technologies and hybridization platforms to elucidate pathway information. More recently, progress has been made to up the rate of generation of protein-related data. Sophisticated robotics are being employed to screen hundreds of thousands of compounds as possible active ingredients in drugs against promising gene-inspired drug targets. It's all been great, right? Every year another order of magnitude of sequencing capacity, price-per-base reductions, and accuracy and length of reads improves. So what's holding back the flood of drug, treatment and cure discoveries? The current bottleneck is phenotype data.

Recently, it appeared there would be breakthroughs in this area as well with the push for mandated EMRs (electronic medical records). Yet, the early experience in trying to export large volumes of clinical data for association and candidate gene studies has been frustratingly unfruitful. The data suffers from two main deficiencies: 1) Sparseness, and 2) Noisiness. Most of this phenotype data collected during remedial clinical visits is focused on a particular diagnosis or disease type. For example, information about disease depletion in a group of potential study subjects is usually absent, and that sparseness severely limits the utility of these seemingly vast repositories of data. In addition, the data points that are often available are collected in non-standard ways and have large inter-observer variation. So, while the long-assumed to be impossible bottleneck of gathering molecular data has been broken to a large extent, the need for ways to systematically gather, organize and purify phenotype data will remain a large challenge for bioinformaticians for some time to come. The researchers at Bio::Neos have been knee deep in this type of data for more than a decade, and look forward to partnering with you in gathering and refining your study data sets.

Are you utilizing phenotype data in your research?

• Yes, of course!: 66.7%
• Just getting started with it: 0%
• No way! That data is too noisy: 33.3%

Recommended readings from the Bio::Neos team:

• Exome sequencing and analysis of induced pluripotent stem cells identify the cilia-related gene male germ cell-associated kinase (MAK) as a cause of retinitis pigmentosa. Tucker BA, et al. Proc Natl Acad Sci U S A. 2011 Aug 23;108(34):E569-76. Epub 2 (PMID:21825139)
• Colloquium papers: Numbering the hairs on our heads: the shared challenge and promise of phenomics. Houle D, et al. Proc Natl Acad Sci U S A. 2010 Jan 26;107 Suppl 1:1793-9. Epub 2009 Oct 26. (PMID:19858477)
• Phenomics: the next challenge. Houle D, et al. Nat Rev Genet. 2010 Dec;11(12):855-66. (PMID:21085204)