Long non-coding RNAs (lncRNAs) are often defined as transcripts >200nts that have no discernable protein-coding ability (Quinn and Chang, 2016). Although relatively little is understood about the molecular mechanisms of lncRNA function, they have established roles in regulation of gene expression during development, cell differentiation and pluripotency (Fatica and Bozzoni, 2014; Luo et al., 2016; Quinn and Chang, 2016; Rinn and Chang, 2012) across vastly diverse organisms ranging from plants to humans (Ulitsky and Bartel, 2013). LncRNAs have also been associated with numerous pathological conditions, such as cancers (Brunner et al., 2012), cardiovascular disease and neurodegeneration (Chen et al., 2013). Investigations into lncRNAs in wide ranging organisms, have revealed that many influence gene activity by forming ribonucleoprotein complexes that affect the conformational state of chromatin (Rinn and Chang, 2012). A genomic region that has revealed several functional lncRNAs in diverse organisms is the Hox complex (Pauli et al., 2011; Pettini, 2012; Rinn et al., 2007). The Hox complex encodes a set of transcription factors (TFs), physically clustered in the genome, which provide morphological identity along the anterior to posterior axis of developing embryos (Mallo and Alonso, 2013), throughout the majority of bilatarian animals (Moreno et al., 2011). Misexpression or mutation of Hox genes causes morphological and pathophysiological defects (Quinonez and Innis, 2014). We investigated clustering of lncRNAs throughout the D. melanogaster genome using available annotations and carried out RNA-seq in D. virilis to expand the repertoire of lncRNAs and identify clusters of lncRNAs. We found the Hox complex to be heavily enriched with lncRNAs in both organisms, and syntenic transcripts from D. melanogaster could be identified in D. pseudoobscura and D. virilis. Several lncRNAs aligned with polycomb response elements (PREs); transcription of PREs has previously been linked to a switch in their activity (Herzog et al., 2014). However, we found that transcribed PREs in D. melanogaster move positions relative to the protein-coding genes in other drosophilids, whilst the transcriptional units remain in the same syntenic region. Conservation of syntenic transcripts without evidence of remaining a PRE suggest that the transcription is not linked to PRE function, agreeing with recent findings that transcription of PREs does not affect their function (Kassis and Muller, 2015). We investigated functions of a novel lncRNA and adjacent PRE in the Hox complex by ectopic expression and utilization of other genetic manipulation tools. Overexpression of either the lncRNA or PRE and partial duplication of the lncRNA caused phenotypes such as missing halteres and/or T3 legs, misshaped T3 legs or malformed abdominal segments. The observations that ectopic expression of this lncRNA and an adjacent regulatory element from the Hox complex causes phenotypes that can be linked to adjacent Hox gene misregulation, Antp and Ubx, suggest that they are likely to have roles in the regulation of at least one of these Hox genes.

Whole genome transcriptome analysis has revealed that a large proportion of the genome in higher metazoa is transcribed, yet only a small proportion of this transcription is protein-coding. One possible function of non-coding transcription is that it enables complex and diverse body plans to evolve through variation in deployment of a relatively common set of protein-coding genes. Functional studies suggest that long non-coding RNAs (lncRNAs) regulate gene expression via diverse mechanisms, operating in both cis and trans to activate or repress target genes. An emerging theme common to lncRNA function is interaction with proteins that modify chromatin and mediate epigenetic regulation. The Hox gene complexes are particularly rich in lncRNAs and require precise and fine-tuned expression to deploy Hox transcription factors throughout development. Here we identify and functionally characterize two novel lncRNAs within the D. melanogaster Hox complex, in the interval between Scr and Antp. We use nascent transcript fluorescent in-situ hybridization (ntFISH) to characterize the embryonic expression patterns of each lncRNA with respect to flanking Hox genes, and to analyze co-transcription within individual nuclei. We find that the transcription of one lncRNA, ncX, is an initial response to early transcription factors and may activate Scr expression, while transcription of the other lncRNA, ncPRE is consistent with activation and/or maintenance of Scr expression. ntFISH performed in D.virilis embryos revealed the presence of a lncRNA ortholog with highly similar expression to ncX, indicating functional conservation of lncRNA transcription across ~60 million years of evolution. We identify the ncPRE lncRNA locus as a binding site for multiple proteins associated with Polycomb/Trithorax response elements (PREs/TREs) and show that DNA encoding the ncPRE lncRNA functions as a bona fide PRE, mediating trans-interactions between chromosomes and silencing of nearby genes. We find that transcription through the ncPRE DNA relieves silencing, suggesting a role for endogenous transcription of the ncPRE lncRNA in relieving Polycomb-silencing and enabling Scr activation. We demonstrate that both lncRNA transcripts are required for proper Scr expression, and over-expression of either lncRNAs from ectopic genomic loci has no effect on Scr expression, but ectopic expression at the endogenous locus is associated with ectopic Scr activation, indicating that the lncRNA-mediated regulation functions locally at the site of transcription on the chromosome. ncX may mediate transvection effects previously observed at the Scr locus, independent of the protein Zeste. Together our results support a model of competing mechanisms in the regulation of Scr expression - a background of Polycomb repression acting from the ncPRE locus, which in the first thoracic segment is counteracted by lncRNA transcription and Trithorax binding to ncPRE, enabling activation and maintenance of Scr expression. This work provides a functional insight into the complex regulatory interactions between lncRNAs and epigenetic mechanisms, essential to establish and maintain the precise expression pattern of Hox genes through development.

Le virus de l'hépatite B (VHB) reste un problème de santé majeur dans le monde malgré la disponibilité du vaccin. Le VHB n’est pas éradiqué par les thérapies actuelles et 240 millions de personnes infectées chroniquement restent à risque de développer une cirrhose du foie et un carcinome hépatocellulaire (CHC).Le VHB est un petit virus hépatotrope doté d'un génome à ADN double brin partiel (ADNrc). Après infection l'ADNrc est converti en ADN épisomal (ADNccc) qui est ensuite organisé en minichromosome viral, qui est le modèle pour la transcription et qui initie la réplication. La protéine de l'hépatite B x (HBx) est recrutée sur l'ADNccc pour initier et maintenir la transcription de l'ADN ccc. HBx cible aussi directement des gènes cellulaires impliqué dans le développement du CHC.Nous avons utilisé une approche ChIP-Seq pour identifier toutes les cibles génomiques de HBx dans les cellules qui répliquent le VHB. Les cibles HBx sont à la fois des gènes codant les protéines et des ARNnc (75 miARN et 34 lncRNA). Nous avons montré que HBx réprimait un sous-ensemble de miARNs qui réguleraient négativement la réplication virale (ex : miR-24) et des miARNs impliqués dans le développement du CHC (ex : miR-21). Parmi les lncARNs ciblés pour HBx, nous avons étudié DLEU2, qui est fortement surexprimé dans l’infection par le VHB et le CHC. Nous avons en outre montré que DLEU2 lie à la fois HBx et l’histone méthyltransférase Ezh2, la sous-unité catalytique du complexe répressif PRC2. L'interaction avec DLEU2 et HBx relie les fonctions Ezh2/PRC2 conduisant à l'activation constitutive d'un sous-ensemble de gènes cibles d'Ezh2 qui sont normalement conservés dans un état réprimé. Nous avons également montré que l’interaction de HBx avec DLEU2 se produisait sur le minichromosome de l’ADNccc où elle stimulait la transcription/réplication du virus. Enfin, nous avons caractérisé par ATAC-Seq les changements d'accessibilité de la chromatine imposés par HBV dans les hépatocytes humains primaires / Hepatitis B virus (HBV) remains a major health problem worldwide despite the availability of the vaccine. No cure is available for the 240 million peoples chronically infected with HBV that are at risk to develop liver cirrhosis and hepatocellular carcinoma (HCC). Viral suppression, achieved by long term treatment with nucleotides analogues (NUCs), impacts on liver fibrosis and prevents liver decompensation but HCC risk is not reduced in the first 5 years of treatment. HBV is a small hepatotropic virus with a partially double strand DNA (rcDNA) genome. After hepatocyte infection the rcDNA is converted into the cccDNA episome that is then organized into a viral minichromosome that is the template for all viral transcripts and initiates replication. The hepatitis B x protein (HBx) is recruited on the cccDNA and is required to launch and maintain cccDNA transcription. HBx has also been shown to directly target cellular genes and this has been related to HCC development.We used a ChIP-Seq approach to determine the full repertoire of HBx genomic targets in HBV replicating cells. HBx targets include both protein coding genes and ncRNA (75 miRNAs and 34 lncRNAs). We showed that HBx represses a subset of miRNAs that would negatively regulate viral replication (i.e. miR-24) and miRNAs involved in HCC development (i.e. miR-21). Among the HBx targeted lncRNAs we focused DLEU2, which is strongly upregulated in HBV infection and HCC. We further showed that DLEU2 binds both HBx the Ezh2 histone methyltransferase, the catalytic subunit of the repressive PRC2 complex. The interaction with DLEU2 and HBx re-wires Ezh2/PRC2 functions leading to the constitutive activation of a subset of Ezh2 target genes that are normally kept in a repressed state. We also showed that HBx interaction with DLEU2 occurs on the cccDNA minichromosome where it boosts HBV transcription/replication. Finally, we characterized by ATAC-Seq HBV imposed changes of chromatin accessibility in primary human hepatocytes

Nespas is the non-coding antisense transcript of the imprinted Gnas cluster; it is expressed from the paternal allele and is located on mouse distal chromosome 2. In this thesis new transcripts of >10 kb and 0.8 kb have been identified. The 0.8 kb transcript is a spliced variant that is retained in the nucleus and its 3’ end lies approximately 30 kb from the start site. Transcription from the Nespas promoter does not proceed beyond this point. A collection of previously known splice variants have also been detected and are exported to the cytoplasm. Nespas is expressed in the embryo during the second half of gestation and peaks at 13.5 dpc. Nespas is imprinted in the placenta at 11.5, 15.5 and 17.5 dpc. The Nespastm4Jop allele, to truncate the Nespas transcript 10.5 kb from the start site, has been transmitted through the germline and a breeding colony established. Preliminary analysis shows Nespas has a regulatory function. A second targeting construct to truncate Nespas 12.5 kb from the start site has been designed and assembled to investigate whether the 3’ end of the Nespas transcript that is transcribed upstream of the Nesp promoter is required for Nespas-mediated silencing of Nesp.

BACKGROUND: Breast cancer is the most commonly diagnosed cancer and second leading cancer death cause among females in the U.S.A. About 1 in 8 women in U.S will develop invasive breast cancer over the course of her lifetime. In 2013, 234,580 new invasive breast cancer cases are expected to occur in women within the US and approximately 64,640 non-invasive carcinomas in situ were diagnosed in 2013, most of which were ductal carcinoma in situ (DCIS). Along with technological advances, a wide variety of candidate biomarkers have been proposed for cancer diagnosis and prognosis, including DNA content and non-coding RNA. Current techniques for detecting DNA content abnormalities in formalin-fixed, paraffin-embedded (FFPE) tissue samples by flow cytometric analysis have used cells recovered from ≥50µm whole tissue sections. Here, in our first study, a novel core punch sampling method was investigated for assessing DNA content abnormalities and intratumoral heterogeneity in FFPE specimens. Secondly, long non-coding RNAs (lncRNAs) has been examined. LncRNA participates in a broad spectrum of biological activities by diverse mechanisms and its dysregulation is associated with tumorgenesis. Some lncRNAs may function as oncogenes (O) and others as tumor suppressor genes (TSG). To date, lncRNA has been investigated primarily by qRT-PCR and RNA sequencing. This study has examined the relationship of lncRNA expression patterns to breast tumor pathology by chromogenic in situ hybridization (CISH). METHODS: Firstly, FFPE breast carcinoma specimens were selectively targeted using 1.0 mm diameter punch needles. Extracted cores were assayed by flow cytometry using a modified-Headley method. Secondly, the lncRNA expression levels of 6 lncRNAs: HOTAIR, H19, KCNQ1OT1, MEG3, MALAT11 and Zfas1, was examined by RNAscope® CISH using FFPE breast tissue microarrays (TMAs) comprising normal adjacent epithelia (NA), DCIS, and invasive carcinoma (IC) from 46 patients. LncRNA associate polycomb complex protein EZH2 was evaluated by immunohistochemistry (IHC). LncRNA data was also compared to standard breast tumor data including ER, PR, Her2 and Ki67 IHC. SYSTAT version 11 statistical package was used to perform for all the tests. RESULTS: Following optimization experiments of the core punch flow cytometric approach, DNA index and percent S-phase fraction intratumoral heterogeneities were detected in 10/23 (44%) and 11/23 (47%) specimens respectively. The lncRNA CISH study utilized a TMA that contained 36 spots of NA breast tissues, 34 DCIS spots and 43 IC spots. HOTAIR CISH staining was significantly stronger in IC than DCIS (p CONCLUSION: Core-punching is an effective alternative to whole specimen sectioning and shows that macro-level genomic heterogeneity is common even within a single FFPE block. The interrelationship of DNA content heterogeneity to other forms of heterogeneity requires further study. RNAscope CISH supports bright-field microscopy investigations of lncRNA expression in FFPE tissue specimens. HOTAIR, H19 and KCNQ1OT1 may be potential breast cancer biomarkers, both HOTAIR and H19 may be a marker for DCIS at increased risk of progression to invasive cancer. HOTAIR, in particular, may be a predictor for invasive cancer grade.

PART1, TITLE: A p53-inducible long non-coding RNA PICART1 mediating cancer cell proliferation and migration. Long non-coding RNAs (lncRNAs) function in the development and progression of cancer, but only a small portion of lncRNAs are characterized thus far. A novel lncRNA transcript with 2.53 kb in length was identified by a transcriptome sequencing analysis, named p53-inducible cancer-associated RNA transcript 1 (PICART1). This PICART1 is upregulated by p53 through a p53-binding site at -1808 to -1783bp. In breast and colorectal cancer cells and tissues, PICART1 expression was decreased. Ectopic expression of the PICART1 suppressed growth, proliferation, migration, and invasion of MCF7, MDA-MB-231 and HCT116 cells whereas silencing of PICART1 stimulated the cell growth and migration. In these cells, the expression of PICART1 lowered down the levels of p-AKT (Thr308 & Ser473) and p-GSK3β (Ser9), and accordingly, β-catenin, cyclin D1 and c-Myc expression were decreased, but p21cip1/Waf1 expression was increased. Together these data suggest that PICART1 is a novel p53-inducible tumor suppressor lncRNA, functioning through the AKT/GSK3β/β-catenin signaling cascade. PART2, TITLE: The novel long non-coding RNA PANCR is a tumor suppressor gene in breast cancer. Long non-coding RNAs (lncRNAs) function as oncogenes or tumor suppressors in development and progression of cancer. Chromosome 16q22.1 region is frequently deleted in breast cancer, which may contribute to breast carcinogenesis by inactivation of tumor suppressor genes. This study characterized a new lncRNA tumor suppressor, named p53 activating non-coding RNA (PANCR), located in this Chromosome 16q22.1 region. This PANCR lncRNA consists of 1.5kb in length. Our data showed that PANCR was downregulated in breast cancer tissues and cell lines. In the breast cancer cell lines, PANCR expression appeared reversely correlated with cell malignancy, and in breast cancer tissues, PANCR was downregulated over 2 times in 31 (62.0%) of 50 cases when compared to adjacent normal breast tissues. In breast cancer cells MCF7 cells, ectopic expression of PANCR suppressed cell proliferation in culture, but in contrast, shRNA–mediated silencing of PANCR promoted cell growth and proliferation.

[EN] Transcriptomics is one of the most important and relevant areas of bioinformatics. It allows detecting the genes that are expressed at a particular moment in time to explore the relation between genotype and phenotype. Transcriptomic analysis has been historically performed using microarrays until 2008 when high-throughput RNA sequencing (RNA-Seq) was launched on the market, replacing the old technique. However, despite the clear advantages over microarrays, it was necessary to understand factors such as the quality of the data, reproducibility and replicability of the analyses and potential biases. The first section of the thesis covers these studies. First, an R package called NOISeq was developed and published in the public repository "Bioconductor", which includes a set of tools to better understand the quality of RNA-Seq data, minimise the impact of noise in any posterior analyses and implements two new methodologies (NOISeq and NOISeqBio) to overcome the difficulties of comparing two different groups of samples (differential expression). Second, I show our contribution to the Sequencing Quality Control (SEQC) project, a continuation of the Microarray Quality Control (MAQC) project led by the US Food and Drug Administration (FDA, United States) that aims to assess the reproducibility and replicability of any RNA-Seq analysis. One of the most effective approaches to understand the different factors that influence the regulation of gene expression, such as the synergic effect of transcription factors, methylation events and chromatin accessibility, is the integration of transcriptomic with other omics data. To this aim, a file that contains the chromosomal position where the events take place is required. For this reason, in the second chapter, we present a new and easy to customise tool (RGmatch) to associate chromosomal positions to the exons, transcripts or genes that could regulate the events. Another aspect of great interest is the study of non-coding genes, especially long non-coding RNAs (lncRNAs). Not long ago, these regions were thought not to play a relevant role and were only considered as transcriptional noise. However, they represent a high percentage of the human genes and it was recently shown that they actually play an important role in gene regulation. Due to these motivations, in the last chapter we focus, first, in trying to find a methodology to find out the generic functions of every lncRNA using publicly available data and, second, we develop a new tool (spongeScan) to predict the lncRNAs that could be involved in the sequestration of micro-RNAs (miRNAs) and therefore altering their regulation task. / [ES] La transcriptómica es una de las áreas más importantes y destacadas en bioinformática, ya que permite ver qué genes están expresados en un momento dado para poder explorar la relación existente entre genotipo y fenotipo. El análisis transcriptómico se ha realizado históricamente mediante el uso de microarrays hasta que, en el año 2008, la secuenciación masiva de ARN (RNA-Seq) fue lanzada al mercado y comenzó a desplazar poco a poco su uso. Sin embargo, a pesar de las ventajas evidentes frente a los microarrays, resultaba necesario entender factores como la calidad de los datos, reproducibilidad y replicabilidad de los análisis así como los potenciales sesgos. La primera parte de la tesis aborda precisamente estos estudios. En primer lugar, se desarrolla un paquete de R llamado NOISeq, publicado en el repositorio público "Bioconductor", el cual incluye un conjunto de herramientas para entender la calidad de datos de RNA-Seq, herramientas de procesado para minimizar el impacto del ruido en posteriores análisis y dos nuevas metodologías (NOISeq y NOISeqBio) para abordar la problemática de la comparación entre dos grupos (expresión diferencial). Por otro lado, presento nuestra contribución al proyecto Sequencing Quality Control (SEQC), una continuación del proyecto Microarray Quality Control (MAQC) liderado por la US Food and Drug Administration (FDA) que pretende evaluar precisamente la reproducibilidad y replicabilidad de los análisis realizados sobre datos de RNA-Seq. Una de las estrategias más efectivas para entender los diferentes factores que influyen en la regulación de la expresión génica, como puede ser el efecto sinérgico de los factores de transcripción, eventos de metilación y accesibilidad de la cromatina, es la integración de la transcriptómica con otros datos ómicos. Para ello se necesita generar un fichero que indique las posiciones cromosómicas donde se producen estos eventos. Por este motivo, en el segundo capítulo de la tesis presentamos una nueva herramienta (RGmatch) altamente customizable que permite asociar estas posiciones cromosómicas a los posibles genes, transcritos o exones a los que podría estar regulando cada uno de estos eventos. Otro de los aspectos de gran interés en este campo es el estudio de los genes no codificantes, especialmente los ARN largos no codificantes (lncRNAs). Hasta no hace mucho, se pensaba que estos genes no jugaban ningún papel fundamental y se consideraban como simple ruido transcripcional. Sin embargo, suponen un alto porcentaje de los genes del ser humano y se ha demostrado que juegan un papel crucial en la regulación de otros genes. Por este motivo, en el último capítulo nos centramos, en un primer lugar, en intentar obtener una metodología que permita averiguar las funciones generales de cada lncRNA haciendo uso de datos ya publicados y, en segundo lugar, generamos una nueva herramienta (spongeScan) que permite predecir qué lncRNAs podrían estar secuestrando determinados micro-RNAs (miRNAs), alterando así la regulación llevada a cabo por estos últimos. / [CA] La transcriptòmica és una de les àrees més importants i destacades en bioinformàtica, ja que permet veure quins gens s'expressen en un moment donat per a poder explorar la relació existent entre genotip i fenotip. L'anàlisi transcriptòmic s'ha fet històricament per mitjà de l'ús de microarrays fins l'any 2008 quan la tècnica de seqüenciació massiva d'ARN (RNA-Seq) es va fer pública i va començar a desplaçar a poc a poc el seu ús. No obstant això, a pesar dels avantatges evidents enfront dels microarrays, resultava necessari entendre factors com la qualitat de les dades, reproducibilitat i replicabilitat dels anàlisis, així com els possibles caires introduïts. La primera part de la tesi aborda precisament estos estudis. En primer lloc, es va programar un paquet de R anomenat NOISeq publicat al repositori públic "Bioconductor", el qual inclou un conjunt d'eines per a entendre la qualitat de les dades de RNA-Seq, eines de processat per a minimitzar l'impact del soroll en anàlisis posteriors i dos noves metodologies (NOISeq i NOISeqBio) per a abordar la problemàtica de la comparació entre dos grups (expressió diferencial). D'altra banda, presente la nostra contribució al projecte Sequencing Quality Control (SEQC), una continuació del projecte Microarray Quality Control (MAQC) liderat per la US Food and Drug Administration (FDA) que pretén avaluar precisament la reproducibilitat i replicabilitat dels anàlisis realitzats sobre dades de RNA-Seq. Una de les estratègies més efectives per a entendre els diferents factors que influïxen a la regulació de l'expressió gènica, com pot ser l'efecte sinèrgic dels factors de transcripció, esdeveniments de metilació i accessibilitat de la cromatina, és la integració de la transcriptómica amb altres dades ómiques. Per això es necessita generar un fitxer que indique les posicions cromosòmiques on es produïxen aquests esdeveniments. Per aquest motiu, en el segon capítol de la tesi presentem una nova eina (RGmatch) altament customizable que permet associar aquestes posicions cromosòmiques als possibles gens, transcrits o exons als que podria estar regulant cada un d'aquests esdeveniments regulatoris. Altre dels aspectes de gran interés en aquest camp és l'estudi dels genes no codificants, especialment dels ARN llargs no codificants (lncRNAs). Fins no fa molt, encara es pensava que aquests gens no jugaven cap paper fonamental i es consideraven com a simple soroll transcripcional. No obstant això, suposen un alt percentatge dels gens de l'ésser humà i s'ha demostrat que juguen un paper crucial en la regulació d'altres gens. Per aquest motiu, en l'últim capítol ens centrem, en un primer lloc, en intentar obtenir una metodologia que permeta esbrinar les funcions generals de cada lncRNA fent ús de dades ja publicades i, en segon lloc, presentem una nova eina (spongeScan) que permet predeir quins lncRNAs podríen estar segrestant determinats micro-RNAs (miRNAs), alterant així la regulació duta a terme per aquests últims. / Furió Tarí, P. (2020). Development of bioinformatic tools for massive sequencing analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/152485 / TESIS

Polycomb Proteins (PcG) are chromatin proteins that control the maintenance of “transcriptional memory” and cell identity by fixing the repressed state of developmentally regulated genes. This function has been linked to interaction with RNA moieties, in particular long non-coding RNAs (lncRNAs). However, specificity of PcG-RNA interactions has been controversial (Beltran et al., 2016; Chen Davidovich, Leon Zheng, Karen J. Goodrich, & Thomas R. Cech, 2013). In this study we took advantage of recent work published from our lab reporting about a novel and reversible mechanism regulating genome wide Ezh1-PRC2 activation in mouse skeletal muscle cells in response to atrophic stress (Bodega et al., 2017). Using this physiological, in vivo tool we could identify a functional dynamic crosstalk between Malat1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) and PRC2-Ezh1 complex. By combining immuno-fluorescence, biochemistry, epigenomics, ChIRP, DNA and RNA immunoprecipitation we identified a novel pathway in which Malat1 plays a role in compartmentalization, assembly and activity of PRC2 in chromatin, allowing epigenetic plastic response to atrophic stress and recovery. We conclude that Malat1 is an essential partner for PRC2-Ezh1 adaptive function in skeletal muscle cells.

Regulation and function of protein-coding genes are increasingly well-understood, but no comparable evidence exists for non-coding RNA (ncRNA) genes, which appear to be more numerous than protein-coding genes. We developed a novel machine-learning model to distinguish promoters of long ncRNA (lncRNA) genes from those of protein-coding genes. This represents the first attempt to make this distinction based on properties of the associated gene promoters. From our analyses, several transcription factors (TFs), which are known to be regulated by lncRNAs, also emerged as potential global regulators of lncRNAs, suggesting that lncRNAs and TFs may participate in bidirectional feedback regulatory network. Our results also raise the possibility that, due to the historical dependence on protein-coding gene in defining the chromatin states of active promoters, an adjustment of these chromatin signature profiles to incorporate lncRNAs is warranted in the future. Secondly, we developed a novel method to infer functions for lncRNA and microRNA (miRNA) transcripts based on their transcriptional regulatory networks in 119 tissues and 177 primary cells of human. This method for the first time combines information of cell/tissueVspecific expression of a transcript and the TFs and transcription coVfactors (TcoFs) that control activation of that transcript. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues and associated knowledgebase (FARNA) is developed. FARNA, having the most comprehensive function annotation of considered ncRNAs across the widest spectrum of cells/tissues, has a potential to contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. Thirdly, we developed a novel machine-learning model to identify LD motif (a protein interaction motif) of paxillin, a ncRNA target that is involved in cell motility and cancer metastasis. Our recognition model identified new proteins not previously known to harbor LD motifs and we experimentally confirmed some of our predicted motifs. This novel discovery will expand our knowledge of cancer metastasis and will facilitate therapeutic targeting linking specific ncRNAs via paxillin proteins to diseases. Finally, through bioinformatics approaches, we identified lncRNAs as markers that distinguish classical from alternative activation of macrophage. This result may have good use in the diagnosis of infectious diseases.

Promoter region of protein-coding genes are gradually being well understood, yet no comparable studies exist for the promoter of long non-coding RNA (lncRNA) genes which has emerged as a global potential regulator in multiple cellular process and different diseases for human. To understand the difference in the transcriptional regulation pattern of these genes, previously, we proposed a machine learning based model to classify the promoter of protein-coding genes and lncRNA genes. In this study, we are presenting DeepCNPP (deep coding non-coding promoter predictor), an improved model based on deep learning (DL) framework to classify the promoter of lncRNA genes and protein-coding genes. We used convolution neural network (CNN) based deep network to classify the promoter of these two broad categories of human genes. Our computational model, built upon the sequence information only, was able to classify these two groups of promoters from human at a rate of 83.34% accuracy and outperformed the existing model. Further analysis and interpretation of the output from DeepCNPP architecture will enable us to understand the difference in transcription regulatory pattern for these two groups of genes.