|1.||Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.|
|Angiuoli SV, White JR, Matalka M, White O, Fricke WF.|
|PLoS One. 2011;6(10):e26624. Epub 2011 Oct 19.|
|PMID: 22028928 [PubMed - in process]|
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.
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.
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.
|2.||First Survey of the Wheat Chromosome 5A Composition through a Next Generation Sequencing Approach.|
|Vitulo N, Albiero A, Forcato C, Campagna D, Dal Pero F, Bagnaresi P, Colaiacovo M, Faccioli P, Lamontanara A, Simková H, Kubaláková M, Perrotta G, Facella P, Lopez L, Pietrella M, Gianese G, Doležel J, Giuliano G, Cattivelli L, Valle G, Stanca AM.|
|PLoS One. 2011;6(10):e26421. Epub 2011 Oct 18.|
|PMID: 22028874 [PubMed - in process]|
|3.||The distal hereditary motor neuropathies.|
|Rossor AM, Kalmar B, Greensmith L, Reilly MM.|
|J Neurol Neurosurg Psychiatry. 2011 Oct 25. [Epub ahead of print]|
|PMID: 22028385 [PubMed - as supplied by publisher]|
|4.||Next-generation sequencing identifies novel microRNAs in peripheral blood of lung cancer patients.|
|Keller A, Backes C, Leidinger P, Kefer N, Boisguerin V, Barbacioru C, Vogel B, Matzas M, Huwer H, Katus HA, Stähler C, Meder B, Meese E.|
|Mol Biosyst. 2011 Oct 25. [Epub ahead of print]|
|PMID: 22027949 [PubMed - as supplied by publisher]|
MicroRNAs (miRNAs) are increasingly envisaged as biomarkers for various tumor and non-tumor diseases. MiRNA biomarker identification is, as of now, mostly performed in a candidate approach, limiting discovery to annotated miRNAs and ignoring unknown ones with potential diagnostic value. Here, we applied high-throughput SOLiD transcriptome sequencing of miRNAs expressed in human peripheral blood of patients with lung cancer. We developed a bioinformatics pipeline to generate profiles of miRNA markers and to detect novel miRNAs with diagnostic information. Applying our approach, we detected 76 previously unknown miRNAs and 41 novel mature forms of known precursors. In addition, we identified 32 annotated and seven unknown miRNAs that were significantly altered in cancer patients. These results demonstrate that deep sequencing of small RNAs bears high potential to quantify miRNAs in peripheral blood and to identify previously unknown miRNAs serving as biomarker for lung cancer.
|5.||Next generation sequencing in epigenetics: Insights and challenges.|
|Meaburn E, Schulz R.|
|Semin Cell Dev Biol. 2011 Oct 19. [Epub ahead of print]|
|PMID: 22027613 [PubMed - as supplied by publisher]|