Up at 5AM: The 5AM Solutions Blog

It can seem like biobanking is a ‘cold black box’ process. Samples are processed, labeled stored and retrieved. For many involved in biobanking, it is not clear that processing and storing a biospecimen must be based on core scientific principals in order for the biospecimen to be useful for downstream applications. Biobanking also requires in infrastructure: equipment, biospecimen management systems that may include labels, label readers, and information systems for linking important data about the biospecimen with the actual specimen.

This is the first in a series of blog posts that will dive more deeply into the nuts and bolts of Data Science. Today we will talk a bit about statistics, but we will be talking tools, visualization strategies, and data representation in the context of specific problems.

It has recently been reported by Transparency Market Research that the biopreservation global market is expected to reach $5.74 billion annually by 2019. Extensive undertaking by biomedical research for new drugs and therapies will be one of the largest contributing factors to this growth. 

Biobanking has seen many changes over the past decade. Decentralized biobanks managed by spreadsheet have given way to institution-wide efforts that are managed through large scale information systems that can interoperate with laboratory information management systems (LIMS) and international databases that publish the resulting research. This trend is continuing through the use of tools like biolocator to aggregate information about biospecimens from many institutions to allow researchers from around the world to build effective sample sizes for even some of the rarest diseases.

Performing research for any disease takes hard work, but it is nearly impossible to conduct ground-breaking research and advance science in an expedient manner without a solid starting point--The specimens. The raw material for most of the good work to happen in the life sciences can likely be found in ample quantities of quality-controlled and well-catalogued specimens linked to information about the person from whom they came and their health status.

Organisms are pretty complicated things, especially multicellular organisms. But what if I told you that, in some ways, single-celled organisms are even more complicated than multicellular organisms? Yet, when we talk about identifying things, this is exactly the case.

2013 was an excellent year for our biobanking blog readership and we sincerely appreciate your support. We are truly excited about our future posts and look forward to your continued readership. In appreciation of your continued support, here are the top 5 most popular biobanking articles of 2013. Let us know which one is your favorite and make sure to check back in next week for our first biobanking article of 2014. 

After working as a data scientist in melanoma research for 6 years, I’ve come to appreciate the need for biobanking specimens, especially when studying rare diseases. The team I worked with at Yale would have never been able to collect the 150 specimens and blood samples needed to produce a large scale melanoma exome resequencing study without a concerted effort from physicians, patients, and biobankers to gather enough samples.

The future of optimal health and cost effective, efficient care is dependent on the development of personalized medicine. Historically, scientific discovery and delivery of care has been developed from a one dimensional view of patients that assumed all individuals with a disease are the same – a one size fits all diagnosis and treatment regime which, over the past several decades, has failed to produce superior therapies and health outcomes.

This is the first post in our data modeling series. Today we give a broad perspective for different ways to represent knowledge and data. Some of our posts have talked about ontologies, controlled vocabularies, data models, and other kinds of knowledge representation. All of these share some commonalities, and exist along the Ontology (or sometimes Semantic) Spectrum.