|1.||Multiplexed array-based and in-solution genomic enrichment for flexible and cost-effective targeted next-generation sequencing.|
|Harakalova M, Mokry M, Hrdlickova B, Renkens I, Duran K, van Roekel H, Lansu N, van Roosmalen M, de Bruijn E, Nijman IJ, Kloosterman WP, Cuppen E.|
|Nat Protoc. 2011 Nov 3;6(12):1870-86. doi: 10.1038/nprot.2011.396.|
|PMID: 22051800 [PubMed - in process]|
AbstractThe unprecedented increase in the throughput of DNA sequencing driven by next-generation technologies now allows efficient analysis of the complete protein-coding regions of genomes (exomes) for multiple samples in a single sequencing run. However, sample preparation and targeted enrichment of multiple samples has become a rate-limiting and costly step in high-throughput genetic analysis. Here we present an efficient protocol for parallel library preparation and targeted enrichment of pooled multiplexed bar-coded samples. The procedure is compatible with microarray-based and solution-based capture approaches. The high flexibility of this method allows multiplexing of 3-5 samples for whole-exome experiments, 20 samples for targeted footprints of 5 Mb and 96 samples for targeted footprints of 0.4 Mb. From library preparation to post-enrichment amplification, including hybridization time, the protocol takes 5-6 d for array-based enrichment and 3-4 d for solution-based enrichment. Our method provides a cost-effective approach for a broad range of applications, including targeted resequencing of large sample collections (e.g., follow-up genome-wide association studies), and whole-exome or custom mini-genome sequencing projects. This protocol gives details for a single-tube procedure, but scaling to a manual or automated 96-well plate format is possible and discussed.
|2.||A computational index derived from whole-genome copy number analysis is a novel tool for prognosis in early stage lung squamous cell carcinoma.|
|Belvedere O, Berri S, Chalkley R, Conway C, Barbone F, Pisa F, Maclennan K, Daly C, Alsop M, Morgan J, Menis J, Tcherveniakov P, Papagiannopoulos K, Rabbitts P, Wood HM.|
|Genomics. 2011 Oct 25. [Epub ahead of print]|
|PMID: 22050995 [PubMed - as supplied by publisher]|
AbstractSquamous cell carcinoma of the lung is remarkable for the extent to which the same chromosomal abnormalities are detected in individual tumours. We have used next generation sequencing at low coverage to produce high resolution copy number karyograms of a series of 89 non-small cell lung tumours specifically of the squamous cell subtype. Because this methodology is able to create karyograms from formalin-fixed paraffin-embedded material, we were able to use archival stored samples for which survival data were available and correlate frequently occurring copy number changes with disease outcome. No single region of genomic change showed significant correlation with survival. However, adopting a whole-genome approach, we devised an algorithm that relates to total genomic damage, specifically the relative ratios of copy number states across the genome. This algorithm generated a novel index, which is an independent prognostic indicator in early stage squamous cell carcinoma of the lung.
|3.||The utility of gene expression in blood cells for diagnosing neuropsychiatric disorders.|
|Woelk CH, Singhania A, Pérez-Santiago J, Glatt SJ, Tsuang MT.|
|Int Rev Neurobiol. 2011;101:41-63.|
|PMID: 22050848 [PubMed - in process]|
AbstractObjective diagnostic tools are required for neuropsychiatric disorders. Gene expression in blood cells may provide such a tool and has already been used to construct classifiers capable of diagnosing many human diseases. This chapter discusses the use of microarray gene expression data to construct diagnostic classifiers for neuropsychiatric disorders. The potential pitfalls of microarray gene expression analysis and the experimental design and methods suitable for classifier construction are described in detail. A review of studies that have analyzed gene expression in blood cells from patients with neuropsychiatric disorders is presented with an emphasis on the feasibility of generating a diagnostic classifier for schizophrenia. Finally, the future directions of the field are discussed with respect to using blood gene expression to tailor antipsychotic medications to individual patients, applying microRNA expression for diagnostic purposes, as well as the implications of next-generation sequencing technologies for gene expression analysis.