PyLiger: Scalable single-cell multi-omic data integration in Python

Motivation: LIGER is a widely-used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly-optimized scientific computing libraries for increased efficiency. Results: We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5× speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability, and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.

Gene Ontology Enrichment Analysis of Renal Agenesis: Improving Prenatal Molecular Diagnosis

Uni- or bilateral renal agenesis (RA) is a commonly occurring major congenital anomaly impacting fetal and neonatal outcomes. Since the etiology is highly heterogeneous, our aim was to provide a logically structured approach by highlighting the genes in which variants have been identified to be associated with RA and to define the pathways involved in this type of abnormal kidney development. We used Phenolyzer to collect a list of all the genes known as causative for RA. Using ClueGO gene enrichment analysis, we classified the relationship between these genes and the biological processes defined by gene ontology. We identified 287 genes and 69 groups of enriched biological processes. About 50% included pathways directly related to the development of urogenital organ tissues. Several ciliary, axis specification, hindgut development, and endocrine pathways were enriched, which may relate to different clinical presentations of RA. Our gene ontology enrichment analysis shows that genes representing distinct biological pathways are significantly enriched. This knowledge will lead to an improved molecular diagnosis in clinical care when applying genome-wide sequencing approaches. The findings will also allow to further study the biological pathways involved in RA and to identify novel candidate genes and pathways.

A multi-omics study on cutaneous and uveal melanoma

AIM: To present the multi-omics landscape of cutaneous melanoma (CM) and uveal melanoma (UM) from The Cancer Genome Atlas (TCGA). METHODS: The differentially expressed genes (DEGs) between CM and UM were found and integrated into a gene ontology enrichment analysis. Besides, the differentially expressed miRNAs were also identified. We also compared the methylation level of CM with UM and identified the differentially methylated regions to integrate with the DEGs to display the relationship between the gene expression and DNA methylation. The differentially expressed transcription factors (TFs) were identified. RESULTS: Though CM had more mutational burden than UM, they shared several similarities such as the same rankings in diverse variant types. Except GNAQ and GNA11, the other top 18 mutated genes of the combined group were mostly detected in CM instead of UM. On the transcriptomic level, 4610 DEGs were found and integrated into a gene ontology enrichment analysis. We also identified 485 differentially expressed miRNAs. The methylation analysis showed that UM had a significantly higher methylation level than CM. The integration of differentially methylated regions and DEGs demonstrated that most DEGs were downregulated in UM and the hypo- and hypermethylation presented no obvious difference within these DEGs. Finally, 116 hypermethylated TFs and 114 hypomethylated TFs were identified as differentially expressed TFs in CM when compared with UM. CONCLUSION: This multi-omics study on comparing CM with UM confirms that they differ in all analyzed levels. Of notice, the results also offer new insights with implications for elucidating certain unclear problems such as the distinct role of epithelial mesenchymal transition in two melanomas, the different metastatic routes of CM and UM and the liver tropism of metastatic UM.

Molecular signatures of the rediae, cercariae and adult stages in the complex life cycles of parasitic flatworms (Digenea: Psilostomatidae)

Background Parasitic flatworms (Trematoda: Digenea) represent one of the most remarkable examples of drastic morphological diversity among the stages within a life cycle. Which genes are responsible for extreme differences in anatomy, physiology, behavior, and ecology among the stages? Here we report a comparative transcriptomic analysis of parthenogenetic and amphimictic generations in two evolutionary informative species of Digenea belonging to the family Psilostomatidae. Methods In this study the transcriptomes of rediae, cercariae and adult worm stages of Psilotrema simillimum and Sphaeridiotrema pseudoglobulus, were sequenced and analyzed. High-quality transcriptomes were generated, and the reference sets of protein-coding genes were used for differential expression analysis in order to identify stage-specific genes. Comparative analysis of gene sets, their expression dynamics and Gene Ontology enrichment analysis were performed for three life stages within each species and between the two species. Results Reference transcriptomes for P. simillimum and S. pseudoglobulus include 21,433 and 46,424 sequences, respectively. Among 14,051 orthologous groups (OGs), 1354 are common and specific for two analyzed psilostomatid species, whereas 13 and 43 OGs were unique for P. simillimum and S. pseudoglobulus, respectively. In contrast to P. simillimum, where more than 60% of analyzed genes were active in the redia, cercaria and adult worm stages, in S. pseudoglobulus less than 40% of genes had such a ubiquitous expression pattern. In general, 7805 (36.41%) and 30,622 (65.96%) of genes were preferentially expressed in one of the analyzed stages of P. simillimum and S. pseudoglobulus, respectively. In both species 12 clusters of co-expressed genes were identified, and more than a half of the genes belonging to the reference sets were included into these clusters. Functional specialization of the life cycle stages was clearly supported by Gene Ontology enrichment analysis. Conclusions During the life cycles of the two species studied, most of the genes change their expression levels considerably, consequently the molecular signature of a stage is not only a unique set of expressed genes, but also the specific levels of their expression. Our results indicate unexpectedly high level of plasticity in gene regulation between closely related species. Transcriptomes of P. simillimum and S. pseudoglobulus provide high quality reference resource for future evolutionary studies and comparative analyses.

Identification of infectious disease-associated host genes using machine learning techniques

Background With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets. Results We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases. Conclusions To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.