Tuesday, May 14, 2013

Eukaryotes are microbes too

Dr. Holly Bik gave a presentation last week on the difficulties of visualization of data in the biology field.  Dr. Bik explained her personal research on the marine species called “Nematodes” (which are also in our drinking water too!).  She estimated that there are around 4000 marine species actually sequenced and described.  There is an estimated 1-100 million different species of Nematodes and per 1 m2 of the sea floor the abundance of a Nematodes can range anywhere from 100,000 to 84 million. Due to the vast amount of Nematode species there are Dr. Bik explained to us the difficulties of visualizing all the data.  There are many different approaches used currently, the most common separating the nematodes into “OTUs”, operational taxonomic units.  The OUT is a classification that separates species based upon rRNA.  Dr. Bik told us that it is difficult using this classification system because there could can as many as 10,000 copies of rRNA and therefore it is hard to make inferences.  Dr. Bik continued on explaining that a way to combat this is to make “cutoffs” based on species similarity in OUT “clouds”.  These clouds and cutoffs create a way to better cluster species into different branches on a tree. 
                  Dr. Bik introduced us to a new sort of “pipeline” for metagenomic data analysis called “phylosift”.  She says that it is an innovative new way to help sort through species data and organize them in a more effective way.  Dr. Bik claims that the current system for visualizing and organizing data is inefficient and difficult to understand, which led to the creation of phylosift. It was very interesting to hear her different ideas and ways she would like to better improve the bioinformatics field. I also really enjoyed the lecture because I didn’t know there was such a problem; I honestly thought that the way current things were going were okay.  I sincerely hope that in the next few years the field of bioinformatics is heavily emphasized, because it is crucial that we can visualize and interpret data we collect from experiments in an effective manor. 

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