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Background: Although advances in sequencing technologies have popularized the use of microRNA (miRNA) sequencing (miRNA-seq) for the quantification of miRNA expression, questions remain concerning the optimal methodologies for analysis and utilization of the data. The construction of a miRNA sequencing library selects RNA by length rather than type. However, as we have previously described, miRNAs represent only a subset of the species obtained by size selection. Consequently, the libraries obtained for miRNA sequencing also contain a variety of additional species of small RNAs. This study looks at the prevalence of these other species obtained from bone marrow aspirate specimens and explores the predictive value of these small RNAs in the determination of response to therapy in myelodysplastic syndromes (MDS)., Methods: Paired pre and post treatment bone marrow aspirate specimens were obtained from patients with MDS who were treated with either azacytidine or decitabine (24 pre-treatment specimens, 23 post-treatment specimens) with 22 additional non-MDS control specimens. Total RNA was extracted from these specimens and submitted for next generation sequencing after an additional size exclusion step to enrich for small RNAs. The species of small RNAs were enumerated, single nucleotide variants (SNVs) identified, and finally the differential expression of tRNA-derived species (tDRs) in the specimens correlated with diseasestatus and response to therapy., Results: Using miRNA sequencing data generated from bone marrow aspirate samples of patients with known MDS (N = 47) and controls (N = 23), we demonstrated that transfer RNA (tRNA) fragments (specifically tRNA halves, tRHs) are one of the most common species of small RNA isolated from size selection. Using tRNA expression values extracted from miRNA sequencing data, we identified six tRNA fragments that are differentially expressed between MDS and normal samples. Using the elastic net method, we identified four tRNAs-derived small RNAs (tDRs) that together can explain 67 % of the variation in treatment response for MDS patients. Similar analysis of specifically mitochondrial tDRs (mt-tDRs) identified 13 mt-tDRs which distinguished disease status in the samples and a single mt-tDR which predited response. Finally, 14 SNVs within the tDRs were found in at least 20 % of the MDS samples and were not observed in any of the control specimens., Discussion: This study highlights the prevalence of tDRs in RNA-seq studies focused on small RNAs. The potential etiologies of these species, both technical and biologic, are discussed as well as important challenges in the interpretation of tDR data., Conclusions: Our analysis results suggest that tRNA fragments can be accurately detected through miRNA sequencing data and that the expression of these species may be useful in the diagnosis of MDS and the prediction of response to therapy.

Background: Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size., Results: We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data., Conclusions: The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.

Background: 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. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis., Results: In this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity., Conclusions: Based on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.

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.

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.