Short Abstract: Dendritic cells are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they have been used as targets for immunotherapy. Monocytes can differentiate into dendritic cells in vitro in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB, and PI3K). However, the transcriptional regulation responsible for dendritic cell differentiation from monocytes (moDCs) remains unknown. By curating scientific literature on moDCs differentiation, we established a preliminary logical model that helped us identify missing information for the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active and inactive promoters, together with differentially expressed genes in monocytes, moDCs, and macrophages (which correspond to an alternative cell fate). We then used this functional genomic information to predict novel targets for the identified TFs. We established a second logical model integrating this information, which enabled us to recapitulate the main established facts regarding moDCs differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies based on moDCs regulatory network.

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Short Abstract: Leishmania spp. is the causal agent of several diseases called leishmaniasis, which is one of the neglected diseases that seek to be eradicated in the coming years. We aimed to study the genomic structure and function of ncRNAs (non-coding RNAs) from Leishmania spp. and to get some insights into its RNAome. We studied 26 strains corresponding to 16 different species of the genera. The analysis of RNAome revealed the presence of several ncRNAs that are shared through different species and were differentially expressed in the same developmental stage of the parasite, that coexpressed to several other coding genes involved in chromatin structure and host interaction. This work constitutes the first effort to characterize the Leishmania RNAome, supporting further approaches to better understand the role of ncRNAs in the gene regulation, infective process and host-parasite interaction.

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Short Abstract: The study of long non-coding RNAs (lncRNAs), > 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Hence, current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based Machine Learning method called FGGA-lnc for the automatic Gene Ontology (GO) annotation of lncRNAs across the three GO sub-domains. We build upon FGGA (Factor Graph GO Annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. The set of likely inconsistent GO annotations is then polished by the message passing machinery embodied in the factor graph model of the target ontology. Evaluations of FGGA-lnc on zebrafish lncRNA data showed promising results suggesting it as a candidate to satisfy the huge demand of functional annotations arising from high-throughput sequencing technologies.

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Short Abstract: Genomic and transcriptomic variation in health and disease have been extensively studied. Post-transcriptional regulation is less explored due to its higher complexity which requires more sophisticated experimental technology and analytical methods. Ribosome profiling (RP) offers a way to quantify translation efficiency across the whole transcriptome. We have developed Ribolog, a ribosome profiling data analysis tool set which has several distinct advantages over other existing methods: (i) It detects and eliminates the translation stalling bias, (ii) It does not require estimation of dispersion parameter (does not rely on negative binomial distribution), (iii) It has more statistical power and is more robust, (iv) It can work with few replicates and yield reliable results even from low-coverage libraries, (v) It is easily adaptable for experiments with synthetic spike-in standards, (vi) It introduces new RP-specific QC measures, (vii) It can accommodate complex experimental designs involving multiple samples and covariates in one model, and is not limited to pairwise comparisons. Our preliminary results applying Ribolog to a dataset comparing two poorly metastatic and two highly metastatic cell lines (CN34 and MDA vs. LM1a and LM2) indicated that this method is indeed highly sensitive and 80-90% reproducible among biological replicates. It also revealed the roles of codon optimality, tRNA abundance and RNA-binding protein binding sites on translation dynamics. Translation rate of more transcripts was influenced by metastatic state compared to genetic background. We observed patterns of co-translational regulation in downstream targets of post-transcriptional master modulators such as HNRNPC. In addition to providing a sensitive bias-corrected test of translation efficiency, Ribolog contains a method for empirical null testing to further reduce false positives, a meta-analysis tool, and an experimental design module for power analysis and sample size calculation of RP studies. Ribolog allows seamless integration of various omic data types e.g. tRNA abundances, and offers a feature selection module to identify the set of factors influencing translation efficiency of individual transcripts. Another Ribolog module quantifies the usage of upstream ORFs from annotated and unannotated translation initiation sites, and measure their regulatory effects on translation rate from the main ORF. Ribolog is available on github, and is being regularly updated, improving its performance and adding more functionalities.

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Short Abstract: Motivation: The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. Results: We developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Availability: Fly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.io

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Short Abstract: Trans-acting expression quantitative trait loci (trans-eQTLs) are SNPs regulating the expression of distant target genes (>1Mb). Trans-eQTLs account for =70% heritability of gene expression levels and have great potential to uncover the underlying biological mechanisms of complex diseases. However, trans-eQTLs are more challenging to identify than locally-acting cis-eQTLs because of their small effect sizes and the severe multiple-testing burden. Furthermore, strong gene expression correlations entails strong correlations among SNP-gene association P-values. Our method Tejaas can discover trans-eQTLs by performing L2-regularized ‘reverse’ multiple regression of each SNP on all gene expression levels, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Tejaas, coupled with a novel k-nearest neighbors confounder correction, predicts 18851 unique trans-eQTLs across 49 tissues from the GTEx(v8) data. They are enriched in various functional regions of the genome, including DHS sites (1.32x enrichment), open chromatin (1.19x), enhancer (1.31x) and promoter (1.10x) regions. They overlap with known cis-eQTLs (1.24x enrichment) from the GTEx analysis and are 1.09x enriched to be located within 100kb of known transcription factors. They are also enriched in reporter assay QTL regions of K562 (1.79x) and HepG2 (3.29x) cells indicating regulatory activity. Many trans-eQTLs overlap with disease-associated SNPs from GWAS, revealing tissue-specific transcriptional regulation mechanisms that drive disease etiology. For example, trans-eQTLs predicted in whole blood show enrichment in GWAS for blood traits, while heart and aorta are enriched in cardiometabolic traits. Thyroid and pancreas trans-eQTLs show enrichments for endocrine system diseases. Tejaas is open-source and available online (https://github.com/soedinglab/tejaas).

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