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The association of genetic variation with disease and drug response, and improvements in nucleic acid technologies, have given great optimism for the impact of 'genomic medicine'. However, the formidable size [...]

The sensitivity of conventional DNA sequencing in tumor biopsies is limited by stromal contamination and by genetic heterogeneity within the cancer. Here, we show that microreactor-based pyrosequencing can detect rare cancer-associated sequence variations by independent and parallel sampling of multiple representatives of a given DNA fragment. This technology can thereby facilitate accurate molecular diagnosis of heterogeneous cancer specimens and enable patient selection for targeted cancer therapies. NOTE:: In the version of this article initially published, it should have been acknowledged that Jan F. Simons, in addition to Roman K. Thomas and Elizabeth Nickerson, contributed equally to this work. The error has been corrected in the HTML and PDF versions of the article., Author(s): Roman K Thomas [1, 2, 12]; Elizabeth Nickerson [3, 12]; Jan F Simons [3, 11, 12]; Pasi A Janne [1]; Torstein Tengs [1, 2]; Yuki Yuza [1]; Levi A [...]

Author(s): Peter Uetz [1, 2]; Loic Giot [1, 3]; Gerard Cagney [2]; Traci A. Mansfield [3]; Richard S. Judson [3]; James R. Knight [3]; Daniel Lockshon [2]; Vaibhav Narayan [3]; [...]

Abstract Background Whole genome amplification is an increasingly common technique through which minute amounts of DNA can be multiplied to generate quantities suitable for genetic testing and analysis. Questions of amplification-induced error and template bias generated by these methods have previously been addressed through either small scale (SNPs) or large scale (CGH array, FISH) methodologies. Here we utilized whole genome sequencing to assess amplification-induced bias in both coding and non-coding regions of two bacterial genomes. Halobacterium species NRC-1 DNA and Campylobacter jejuni were amplified by several common, commercially available protocols: multiple displacement amplification, primer extension pre-amplification and degenerate oligonucleotide primed PCR. The amplification-induced bias of each method was assessed by sequencing both genomes in their entirety using the 454 Sequencing System technology and comparing the results with those obtained from unamplified controls. Results All amplification methodologies induced statistically significant bias relative to the unamplified control. For the Halobacterium species NRC-1 genome, assessed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 119 times greater than those from unamplified material, 164.0 times greater for Repli-G, 165.0 times greater for PEP-PCR and 252.0 times greater than the unamplified controls for DOP-PCR. For Campylobacter jejuni, also analyzed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 15 times greater than those from unamplified material, 19.8 times greater for Repli-G, 61.8 times greater for PEP-PCR and 220.5 times greater than the unamplified controls for DOP-PCR. Conclusion Of the amplification methodologies examined in this paper, the multiple displacement amplification products generated the least bias, and produced significantly higher yields of amplified DNA.

The improvements in technology which would make it possible to generate error-free sequencing reads over 500 bases in length for genomes from microbes to humans is reviewed.

Author(s): Marcel Margulies; Michael Egholm; William E. Altman; Said Attiya; Joel S. Bader; Lisa A. Bemben; Jan Berka; Michael S. Braverman; Yi-Ju Chen; Zhoutao Chen; Scott B. Dewell; Alex de [...]

Background: Whole genome amplification is an increasingly common technique through which minute amounts of DNA can be multiplied to generate quantities suitable for genetic testing and analysis. Questions of amplification-induced error and template bias generated by these methods have previously been addressed through either small scale (SNPs) or large scale (CGH array, FISH) methodologies. Here we utilized whole genome sequencing to assess amplification-induced bias in both coding and non-coding regions of two bacterial genomes. Halobacterium species NRC-1 DNA and Campylobacter jejuni were amplified by several common, commercially available protocols: multiple displacement amplification, primer extension pre-amplification and degenerate oligonucleotide primed PCR. The amplification-induced bias of each method was assessed by sequencing both genomes in their entirety using the 454 Sequencing System technology and comparing the results with those obtained from unamplified controls. Results: All amplification methodologies induced statistically significant bias relative to the unamplified control. For the Halobacterium species NRC-1 genome, assessed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 119 times greater than those from unamplified material, 164.0 times greater for Repli-G, 165.0 times greater for PEP-PCR and 252.0 times greater than the unamplified controls for DOP-PCR. For Campylobacter jejuni, also analyzed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 15 times greater than those from unamplified material, 19.8 times greater for Repli-G, 61.8 times greater for PEP-PCR and 220.5 times greater than the unamplified controls for DOP-PCR. Conclusion: Of the amplification methodologies examined in this paper, the multiple displacement amplification products generated the least bias, and produced significantly higher yields of amplified DNA. [ABSTRACT FROM AUTHOR]

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans. [ABSTRACT FROM AUTHOR]

Examines the implications of oncogenomics on cancer research and clinical oncology. Use of strategies applied by the academic and biotechnology sector; Potential of oncogenomics to intervene at two levels.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.

We demonstrate successful, simultaneous polymerase chain reaction (PCR) amplification of up to 300 000 discrete reactions in a novel platform, the PicoTiterPlate. In addition to elevated throughput, the PicoTiterPlate based amplifications (PTPCR) can be performed in extremely small volumes: individual reactions volumes are as low as 39.5 pL, with a total 15.3 microL reaction volume for the entire PicoTiterPlate. The bulk PTPCR product can be recovered and assayed with real-time PCR, or discrete PTPCR products can be driven to solid supports, enabling downstream applications such as translation/transcription or sequencing.