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Abstract Background When using Illumina high throughput short read data, sometimes the genotype inferred from the positive strand and negative strand are significantly different, with one homozygous and the other heterozygous. This phenomenon is known as strand bias. In this study, we used Illumina short-read sequencing data to evaluate the effect of strand bias on genotyping quality, and to explore the possible causes of strand bias. Result We collected 22 breast cancer samples from 22 patients and sequenced their exome using the Illumina GAIIx machine. By comparing the consistency between the genotypes inferred from this sequencing data with the genotypes inferred from SNP chip data, we found that, when using sequencing data, SNPs with extreme strand bias did not have significantly lower consistency rates compared to SNPs with low or no strand bias. However, this result may be limited by the small subset of SNPs present in both the exome sequencing and the SNP chip data. We further compared the transition and transversion ratio and the number of novel non-synonymous SNPs between the SNPs with low or no strand bias and those with extreme strand bias, and found that SNPs with low or no strand bias have better overall quality. We also discovered that the strand bias occurs randomly at genomic positions across these samples, and observed no consistent pattern of strand bias location across samples. By comparing results from two different aligners, BWA and Bowtie, we found very consistent strand bias patterns. Thus strand bias is unlikely to be caused by alignment artifacts. We successfully replicated our results using two additional independent datasets with different capturing methods and Illumina sequencers. Conclusion Extreme strand bias indicates a potential high false-positive rate for SNPs.

Abstract Background Exome sequencing using next-generation sequencing technologies is a cost efficient approach to selectively sequencing coding regions of human genome for detection of disease variants. A significant amount of DNA fragments from the capture process fall outside target regions, and sequence data for positions outside target regions have been mostly ignored after alignment. Result We performed whole exome sequencing on 22 subjects using Agilent SureSelect capture reagent and 6 subjects using Illumina TrueSeq capture reagent. We also downloaded sequencing data for 6 subjects from the 1000 Genomes Project Pilot 3 study. Using these data, we examined the quality of SNPs detected outside target regions by computing consistency rate with genotypes obtained from SNP chips or the Hapmap database, transition-transversion (Ti/Tv) ratio, and percentage of SNPs inside dbSNP. For all three platforms, we obtained high-quality SNPs outside target regions, and some far from target regions. In our Agilent SureSelect data, we obtained 84,049 high-quality SNPs outside target regions compared to 65,231 SNPs inside target regions (a 129% increase). For our Illumina TrueSeq data, we obtained 222,171 high-quality SNPs outside target regions compared to 95,818 SNPs inside target regions (a 232% increase). For the data from the 1000 Genomes Project, we obtained 7,139 high-quality SNPs outside target regions compared to 1,548 SNPs inside target regions (a 461% increase). Conclusions These results demonstrate that a significant amount of high quality genotypes outside target regions can be obtained from exome sequencing data. These data should not be ignored in genetic epidemiology studies.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type. [ABSTRACT FROM AUTHOR]

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMCp, RNMCpm and RNMCpu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMCp and NMCpm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions. [ABSTRACT FROM AUTHOR]

Recent studies have emphasized a role of adaptive immunity, and particularly T cells, in the genesis of hypertension. We sought to determine the T-cell subtypes that contribute to hypertension and renal inflammation in angiotensin II-induced hypertension. Using T-cell receptor spectratyping to examine T-cell receptor usage, we demonstrated that CD8(+) cells, but not CD4(+) cells, in the kidney exhibited altered T-cell receptor transcript lengths in Vβ3, 8.1, and 17 families in response to angiotensin II-induced hypertension. Clonality was not observed in other organs. The hypertension caused by angiotensin II in CD4(-/-) and MHCII(-/-) mice was similar to that observed in wild-type mice, whereas CD8(-/-) mice and OT1xRAG-1(-/-) mice, which have only 1 T-cell receptor, exhibited a blunted hypertensive response to angiotensin II. Adoptive transfer of pan T cells and CD8(+) T cells but not CD4(+)/CD25(-) cells conferred hypertension to RAG-1(-/-) mice. In contrast, transfer of CD4(+)/CD25(+) cells to wild-type mice receiving angiotensin II decreased blood pressure. Mice treated with angiotensin II exhibited increased numbers of kidney CD4(+) and CD8(+) T cells. In response to a sodium/volume challenge, wild-type and CD4(-/-) mice infused with angiotensin II retained water and sodium, whereas CD8(-/-) mice did not. CD8(-/-) mice were also protected against angiotensin-induced endothelial dysfunction and vascular remodeling in the kidney. These data suggest that in the development of hypertension, an oligoclonal population of CD8(+) cells accumulates in the kidney and likely contributes to hypertension by contributing to sodium and volume retention and vascular rarefaction. [ABSTRACT FROM AUTHOR]

RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. Among the many difficulties of RNAseq analysis, correctly adjusting for batch effect is a pivotal one for large-scale RNAseq based studies. The batch effect of RNAseq data is most obvious in microRNA (miRNA) sequencing studies. Using real miRNA sequencing (miRNAseq) data, we evaluated several batch removal techniques and discussed their effectiveness. We illustrate that by adjusting for batch effect, more reliable differentially expressed genes can be identified. Our study on batch effect in miRNAseq data can serve as a guideline for future miRNAseq studies that might contain batch effect.