Bias-invariant RNA-sequencing metadata annotation.

BACKGROUND: Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs. FINDINGS: Here, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning-based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression-based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm can learn biases from datasets with few samples. CONCLUSION: Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable.

SEAweb: the small RNA Expression Atlas web application.

We present the Small RNA Expression Atlas (SEAweb), a web application that allows for the interactive querying, visualization and analysis of known and novel small RNAs across 10 organisms. It contains sRNA and pathogen expression information for over 4200 published samples with standardized search terms and ontologies. In addition, SEAweb allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAweb's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEAweb's fidelity by (i) generating a set of 591 tissue specific miRNAs across 29 tissues, (ii) finding known and novel bacterial and viral infections across diseases and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEAweb's simple semantic search interface, the flexible interactive reports and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases and organisms.

Krüppel-like factor 9 is a circadian transcription factor in human epidermis that controls proliferation of keratinocytes.

Circadian clocks govern a wide range of cellular and physiological functions in various organisms. Recent evidence suggests distinct functions of local clocks in peripheral mammalian tissues such as immune responses and cell cycle control. However, studying circadian action in peripheral tissues has been limited so far to mouse models, leaving the implication for human systems widely elusive. In particular, circadian rhythms in human skin, which is naturally exposed to strong daytime-dependent changes in the environment, have not been investigated to date on a molecular level. Here, we present a comprehensive analysis of circadian gene expression in human epidermis. Whole-genome microarray analysis of suction-blister epidermis obtained throughout the day revealed a functional circadian clock in epidermal keratinocytes with hundreds of transcripts regulated in a daytime-dependent manner. Among those, we identified a circadian transcription factor, Krüppel-like factor 9 (Klf9), that is substantially up-regulated in a cortisol and differentiation-state-dependent manner. Gain- and loss-of-function experiments showed strong antiproliferative effects of Klf9. Putative Klf9 target genes include proliferation/differentiation markers that also show circadian expression in vivo, suggesting that Klf9 affects keratinocyte proliferation/differentiation by controlling the expression of target genes in a daytime-dependent manner.