We presume most readers are not familiar with NOSQL databases. So, we need to introduce the concept first before delving into their potential benefits for bioinformaticians.
Let us consider the problem of deriving de Bruijn graph of a genome. Suppose you have a large mammalian genome and you like to build a de Bruijn graph from the genome sequence. While you were busy working in the lab, your country lost access to the credit market and you lost access to the cool 512 GB RAM computer as a result. Is that the end of the world?
Not really. Instead of relying on computer memory, you can build the graph in your hard drive. The access will not be as fast as the RAM-bases graph, but something is better than nothing. In fact, Hadoop technology discussed earlier in our blog indeed makes such a move. Moreover, a hard disk based method is immensely scalable, whereas adding RAM does not scale well unless your country has a no limit credit card :)
One easy way to build the graph is to use SQL database, where you add each node as a new entry. Each entry will have two fields - ‘node’ and ‘next’. Then you need to create an index over the nodes to traverse through any part of the graph rapidly.
That solution may work, but SQL databases were originally designed to hold large number of names, addresses and credit card numbers, not to store graphs. Therefore, the access to nodes and traversing through the graph will not be very fast, once your graph crosses a critical size. In fact, the indexing step itself can take very long for a large graph.
Over the last ten years, internet companies encountered other similar problems, where they realized that graph-friendly databases were needed. Thus came NOSQL databases. All that the bioinformatics community needs to do is to leverage on an existing infrastructure.
External links -