Data supporting development and validation of liquid chromatography tandem mass spectrometry method for the quantitative determination of bile acids in feces

Measuring bile acids in feces has an important role in disease prevention, diagnosis, treatment, and can be considered a measure of health status. Therefore, the primary aim was to develop a sensitive, robust, and high throughput liquid chromatography tandem mass spectrometry method with minimal sample preparation for quantitative determination of bile acids in human feces applicable to large cohorts. Due to the chemical diversity of bile acids, their wide concentration range in feces, and the complexity of feces itself, developing a sensitive and selective analytical method for bile acids is challenging. A simple extraction method using methanol suitable for subsequent quantification by liquid chromatography tandem mass spectrometry has been reported in, “Extraction and quantitative determination of bile acids in feces” [1]. The data highlight the importance of optimization of the extraction procedure and the stability of the bile acids in feces post-extraction and prior to analysis and after several freeze-thaw cycles

Public perceptions of environmental friendliness of renewable energy power plants

Demanding EU targets for renewables create challenges for governmental decisions regarding energy sources and plant sitting. In this study we explore perceptions of the Portuguese general population regarding renewable energy power plants. In particular we study how these are affected by dimensions such as home distance to the power plant and its visibility, familiarity with the different energy sources, involvement in terms of employment, and socioeconomic characteristics. We find considerable differences in perception depending on familiarity and involvement with energy sources, environmental friendliness, and specific environmental impacts. Assessment of public perceptions of renewables should thus include these different dimensions.The authors gratefully acknowledge the financial support from FCT Fundacao para a Ciencia e Tecnologia with Grant Number PTDC/EGE-ECO/122402/2010

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy