Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of ‘big data’, including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

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Recently, we showed a natural reprogramming process during infection with Mycobacterium leprae (ML), the causative organism of human leprosy. ML hijacks the notable plasticity of adult Schwann cells in the peripheral nervous system (PNS), bacteria's preferred nonimmune niche, to reprogram infected cells to progenitor/stem cell-like cells (pSLCs). Whereas ML appear to use this reprogramming process as a sophisticated bacterial strategy to spread infection to other tissues, understanding the mechanisms may shed new insights into the basic biology of cellular reprogramming and the development of new approaches for generating pSLC for therapeutic purposes as well as targeting bacterial infectious diseases at an early stage. Toward these goals, we extended our studies to identify other players that might be involved in this complex host cell reprogramming. Here we show that ML activates numerous immune-related genes mainly involved in innate immune responses and inflammation during early infection before downregulating Schwann cell lineage genes and reactivating developmental transcription factors. We validated these findings by demonstrating the ability of infected cells to secrete soluble immune factor proteins at early time points and their continued release during the course of reprogramming. By using time-lapse microscopy and a migration assay with reprogrammed Schwann cells (pSLCs) cultured with macrophages, we show that reprogrammed cells possess the ability to attract macrophages, providing evidence for a functional role of immune gene products during reprogramming. These findings suggest a potential role of innate immune response and the related signaling pathways in cellular reprogramming and the initiation of neuropathogenesis during ML infection.

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.