Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples. False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values. Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

Background: The aging phenotype in humans is very heterogeneous and can be described as a complex mosaic resulting from the interaction of a variety of environmental, stochastic and genetic-epigenetic variables. Therefore, each old person must be considered as a singleton, and consequently the definition of ‘aging phenotype’ is very difficult. Objective: We discuss the phenotype of centenarians, the best example of successful aging, as well as other models exploited to study human aging and longevity, such as families enriched in long-living subjects, twins and cohorts of unrelated subjects. Methods: A critical review of literature available until March 2008. Conclusions: No single model can be considered the gold standard for the study of aging and longevity, instead the combination of results obtained from different models must be considered in order to better understand these complex phenomena. We propose that a systems biology concept such as that of ‘bow-tie’ architecture, useful for managing informa...

Peter Donnelly and colleagues report a genome-wide association study for Barrett's esophagus, a common premalignant condition associated with stomach acid reflux and predisposing to esophageal adenocarcinoma. They identify two loci associated with susceptibility to Barrett's esophagus. Barrett's esophagus is an increasingly common disease that is strongly associated with reflux of stomach acid and usually a hiatus hernia, and it strongly predisposes to esophageal adenocarcinoma (EAC), a tumor with a very poor prognosis. We report the first genome-wide association study on Barrett's esophagus, comprising 1,852 UK cases and 5,172 UK controls in the discovery stage and 5,986 cases and 12,825 controls in the replication stage. Variants at two loci were associated with disease risk: chromosome 6p21, rs9257809 (Pcombined = 4.09 × 10−9; odds ratio (OR) = 1.21, 95% confidence interval (CI) =1.13–1.28), within the major histocompatibility complex locus, and chromosome 16q24, rs9936833 (Pcombined = 2.74 × 10−10; OR = 1.14, 95% CI = 1.10–1.19), for which the closest protein-coding gene is FOXF1, which is implicated in esophageal development and structure. We found evidence that many common variants of small effect contribute to genetic susceptibility to Barrett's esophagus and that SNP alleles predisposing to obesity also increase risk for Barrett's esophagus.

Acute lymphoblastic leukemia (ALL) is a malignant disease of the white blood cells. The etiology of ALL is believed to be multifactorial and likely to involve an interplay of environmental and genetic variables. We performed a genome-wide association study of 355 750 single-nucleotide polymorphisms (SNPs) in 474 controls and 419 childhood ALL cases characterized by a t(12;21)(p13;q22) — the most common chromosomal translocation observed in childhood ALL — which leads to an ETV6–RUNX1 gene fusion. The eight most strongly associated SNPs were followed-up in 951 ETV6-RUNX1-positive cases and 3061 controls from Germany/Austria and Italy, respectively. We identified a novel, genome-wide significant risk locus at 3q28 (TP63, rs17505102, P_CMH=8.94 × 10⁻⁹, OR=0.65). The separate analysis of the combined German/Austrian sample only, revealed additional genome-wide significant associations at 11q11 (OR8U8, rs1945213, P=9.14 × 10⁻¹¹, OR=0.69) and 8p21.3 (near INTS10, rs920590, P=6.12 × 10⁻⁹, OR=1.36). These associations and another association at 11p11.2 (PTPRJ, rs3942852, P=4.95 × 10⁻⁷, OR=0.72) remained significant in the German/Austrian replication panel after correction for multiple testing. Our findings demonstrate that germline genetic variation can specifically contribute to the risk of ETV6–RUNX1-positive childhood ALL. The identification of TP63 and PTPRJ as susceptibility genes emphasize the role of the TP53 gene family and the importance of proteins regulating cellular processes in connection with tumorigenesis.

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO₂ emissions low. The code is available at https://nf-co.re/sarek.