Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

BACKGROUND: The widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. RESULTS: We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. CONCLUSIONS: Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Gene profiling--chances and challenges.

Microarray analysis has been emerged as a tool to characterize the overall reaction of cells in culture or tissue to different stimuli e.g. stressful events by analysing bulk RNA present at a particular time point. It has supplemented or even replaced more traditional methods like cDNA-bank sequencing or conventional differential display. The commercial availability of several different precoated arrays and the ease of handling has supported the broad distribution of this new technique. The basic protocol involves the hybridization of complementary strands of labelled DNA or RNA from cells/tissue with representations of known genes spotted onto a solid support (nylon, glass). Labelling can be radioactive (p32/33), by a hapten group (biotin, digoxigenin, aminoallyl) or by fluorescent (Cy3, Cy5 etc.) nucleotides. Detection is performed by autoradiography, chemiluminescence or fluorescence scanning. There are different setups of arrays available: either known genes/gene-groups (apoptosis, cytokines etc.) are spotted as PCR fragments, plasmids or synthetic oligonucleotides or representations of the known genome are directly synthesized as short sequence tags of 20-70 oligonucleotides on glass chips. The latter allow the identification of newly expressed genes whereas the former deal with known genes. Ideally, the intensity of the signal can be correlated with the relative expression of a known gene and allows the comparison with a standard. Problems arise from the quality of the sample material, the standardization of the protocols and the data management. Nevertheless, gene profiling by cDNA-arrays will definitely be integrated into routine screening programs.