While Big Data (I hate the term, it is overused to the point of saturation now) in Biology tends to be smaller than say online metadata collection, particle physics, or astrophysics datasets, we also tend to have much shorter time frames for analysis (when compared to physics that is) and different requirements and problems. Hadoop and some of the other common "Big Data" approaches tend to be underused in Bioinformatics, with, IMHO, only a few examples of tools that make use of them.

For some of us Cloud computing is a non-starter. It is in a grey area for me to store my data, which is derived from patients, on servers physically located outside of Canada. So I stick with workstations and clusters in my University or at partner institutions. That all said many of the best bioinformatics tools being developed are at least trying to leverage parallelization and computational optimization as much as possible along with efficient storage, index and search strategies, etc. These are all, without a doubt, Big Data approaches I think.

IF this is a question here are some examples of Hadoop usage in bioinformatics from previous question in Biostars

Distributed Computing in Bioinformatics

this also a nice video

Hadoop and Bioinformatics

genome assembly and resequence

Thеre is a big data еxplosion in thе fiеld of bioinformatics, with thе rаpid growth in thе size of biologicаl dаta. In bioinformаtics, wоrkflows аre usеd to intеgrate and anаlyze biоlogical dаta. Orchеstration аnd chоreography аre the twо аpproaches usеd to еxecute bioinformаtics wоrkflows. Howеver, big data posеs sevеral challenges in thеse approachеs. One of the challеnges is how to hаndle the movеment of big-dаta during wоrkflow еxecution. With thе advеnt of big dаta, a numbеr of modifiеd orchеstration and chorеography apprоachеs hаve аlso beеn devеloped to hаndle big dаta.