High Throughput Sequencing (HTS) -- aka Next Generation Sequencing (NGS) -- is now achievable for almost all biological or bioinformatical laboratories around the world since the price dropped -- even faster than Moore's law -- in the last years. The reduction of sequencing costs dramatically increases the size of public databases and a bottle neck appeared in the data analysis steps.
Nowadays having HTS data, sequenced by your own laboratory or downloaded from public repository, is not a real problem any more. However the cost comes later, when the data must be computationally analysed, and this cost might be higher tan you think . As an example, one of the fastest (and in my opinion the best) RNA-seq aligner: STAR . The results from this software do not differer so much from other similar aligners, however, it give you the results in a few minutes instead of taking several days as do others. The bad site might be its high computational requirements.
Even thought is still not so cheap to buy (about 1.500$), most of labs can afford a 8 cores workstation with 5-8Gb of RAM. However the price can be a bit too high if you need to buy a machine with more than 30GBs of RAM, that's what STAR requires for aligning RNA-seq to human genome. Definitively, does not pay of to buy a super powerful workstation if you just need all its power for 5 minutes -- what STAR took me to align my Illumina-MiSeq RNA-seq data-- every time you want to align RNA-seq reads.
That is one of the reasons why we created BioCloud: To reduce computational prices of biological data analysis renting the computational power that you need just for the time you need. Without the need (and the waste of time) to install any programs, libraries or dependencies for most of the "omics data" analysis.
So do you plan to do NGS? Contact us! Even better if you do it before sequencing and we can assess you how to perform your experiments, sequencing techniques, protocols and replicates, in a way that the data that you are going to obtain is really adequate for your purposes. Because, as we will see in a future post, there is a big waste of money due to bad decisions during the experimental design; lack or bad replicates, wrong sequencing platform, low coverage, data redundancy and loose of sensitivity, etc.
As Ronald Fisher said; "To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of". So contact us and do not waste money!
 Sboner, A., Mu, X. J., Greenbaum, D., Auerbach, R. K., & Gerstein, M. B. (2011). The real cost of sequencing: higher than you think! Genome Biology, 12(8), 125. doi:10.1186/gb-2011-12-8-125
 Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., … Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England), 29(1), 15–21. doi:10.1093/bioinformatics/bts635