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Background. After a decade of microarray technology dominating the field of high-throughput gene expression profiling, the introduction of RNAseq has revolutionized gene expression research. While RNAseq provides more abundant information than microarray, its analysis has proved considerably more complicated. To date, no consensus has been reached on the best approach for RNAseq-based differential expression analysis. Not surprisingly, different studies have drawn different conclusions as to the best approach to identify differentially expressed genes based upon their own criteria and scenarios considered. Furthermore, the lack of effective quality control may lead to misleading results interpretation and erroneous conclusions. To solve these aforementioned problems, we propose a simple yet safe and practical rank-sum approach for RNAseq-based differential gene expression analysis named MultiRankSeq. MultiRankSeq first performs quality control assessment. For data meeting the quality control criteria, MultiRankSeq compares the study groups using several of the most commonly applied analytical methods and combines their results to generate a new rank-sum interpretation. MultiRankSeq provides a unique analysis approach to RNAseq differential expression analysis. MultiRankSeq is written in R, and it is easily applicable. Detailed graphical and tabular analysis reports can be generated with a single command line.

Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N=16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens.Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data.Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection.

The most popular RNA library used for RNA sequencing is the poly(A) captured RNA library. This library captures RNA based on the presence of poly(A) tails at the 3′ end. Another type of RNA library for RNA sequencing is the total RNA library which differs from the poly(A) library by capture method and price. The total RNA library costs more and its capture of RNA is not dependent on the presence of poly(A) tails. In practice, only ribosomal RNAs and small RNAs are washed out in the total RNA library preparation. To evaluate the ability of detecting RNA for both RNA libraries we designed a study using RNA sequencing data of the same two breast cancer cell lines from both RNA libraries. We found that the RNA expression values captured by both RNA libraries were highly correlated. However, the number of RNAs captured was significantly higher for the total RNA library. Furthermore, we identify several subsets of protein coding RNAs that were not captured efficiently by the poly(A) library. One of the most noticeable is the histone-encode genes, which lack the poly(A) tail.

Proteolytic18O-labeling has been widely used in quantitative proteomics since it can uniformly label all peptides from different kinds of proteins. There have been multiple algorithms and tools developed over the last few years to analyze high-resolution proteolytic16O/18O labeled mass spectra. We have developed a software package, O18Quant, which addresses two major issues in the previously developed algorithms. First, O18Quant uses a robust linear model (RLM) for peptide-to-protein ratio estimation. RLM can minimize the effect of outliers instead of iteratively removing them which is a common practice in other approaches. Second, the existing algorithms lack applicable implementation. We address this by implementing O18Quant using C# under Microsoft.net framework and R. O18Quant automatically calculates the peptide/protein relative ratio and provides a friendly graphical user interface (GUI) which allows the user to manually validate the quantification results at scan, peptide, and protein levels. The intuitive GUI of O18Quant can greatly enhance the user’s visualization and understanding of the data analysis. O18Quant can be downloaded for free as part of the software suite ProteomicsTools.