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Abstract Background Polyandrous social insects such as the honey bee are prime candidates for parental manipulation of gene expression in offspring. Although there is good evidence for parent-of-origin effects in honey bees the epigenetic mechanisms that underlie these effects remain a mystery. Small RNA molecules such as miRNAs, piRNAs and siRNAs play important roles in transgenerational epigenetic inheritance and in the regulation of gene expression during development. Results Here we present the first characterisation of small RNAs present in honey bee reproductive tissues: ovaries, spermatheca, semen, fertilised and unfertilised eggs, and testes. We show that semen contains fewer piRNAs relative to eggs and ovaries, and that piRNAs and miRNAs which map antisense to genes involved in DNA regulation and developmental processes are differentially expressed between tissues. tRNA fragments are highly abundant in semen and have a similar profile to those seen in the semen of other animals. Intriguingly we also find abundant piRNAs that target the sex determination locus, suggesting that piRNAs may play a role in honey bee sex determination. Conclusions We conclude that small RNAs may play a fundamental role in honey bee gametogenesis and reproduction and provide a plausible mechanism for parent-of-origin effects on gene expression and reproductive physiology.

Abstract Background Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Results Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. Conclusions scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify