Diabetes remains one of the most prevalent non-communicable diseases in the world, affecting over 400 million of people worldwide, causing serious complications leading to amputations and even death. Over the years, researchers have found that, in addition to genomic mutations, epigenetic mechanisms also play a role in the development of diabetes-specifically type-2 diabetes. Long noncoding RNAs (lncRNAs) have been linked to mediate epigenetic mechanisms, including those in late-stage diabetes. This study attempts to assess the unexplored topic of how lncRNAs could be used to assess the epigenetic mechanisms present in diabetic peripheral neuropathy (DPN); a serious complication of the disease often leading to amputation. Differential lncRNA expression analysis was done with a dataset containing DPN and healthy patients. Standard and corrected t test, and also LIMMA was applied. Results of this study indicates the usefulness of lncRNAs as an exploratory tool to elucidate the complexity of the epigenetic mechanisms of human DPN.

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how in-silico approaches can minimize their impact. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. Using the novel Removing Unwanted Variations-III-Negative Binomial (RUV-III-NB), we identified several known experimental factors, specifically storage conditions and freeze–thaw cycles, as likely major sources of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performances of different correction methods, including ComBat, ComBat-seq, RUVg, RUVs, and RUV-III-NB. While RUV-III-NB performed consistently robust across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally. Our analyses suggest that a careful consideration of possible technical confounders is critical during experimental design of microbiome studies, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how a state-of-the art approach can minimize their impact on downstream analyses. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. We identify several known experimental factors, specifically storage conditions and freeze-thaw cycles, as a likely major source of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performance of a novel batch correcting tool used in this study, RUV-III-NB (https://github.com/limfuxing/ruvIIInb/), to other popular batch correction methods, including ComBat, ComBat-seq, RUVg, and RUVs. While RUV-III-NB performed consistently robustly across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally, with RUVg even overcorrecting and removing some of the true biological signals from the samples. Our analyses suggests that a careful consideration of possible technical confounders is critical in the experimental design of microbiome studies to ensure accurate biological reading of microbial taxa of interest, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.