Littérature scientifique sur le sujet « Bioinformatics, RNA-Seq, lncRNA »

Abstract The pathogenesis of multiple sclerosis (MS) is significantly regulated by long noncoding RNAs (lncRNAs), the expression of which is substantially influenced by a number of MS-associated risk single nucleotide polymorphisms (SNPs). It is thus hypothesized that the dysregulation of lncRNA induced by genomic variants may be one of the key molecular mechanisms for the pathology of MS. However, due to the lack of sufficient data on lncRNA expression and SNP genotypes of the same MS patients, such molecular mechanisms underlying the pathology of MS remain elusive. In this study, a bioinformatics strategy was applied to obtain lncRNA expression and SNP genotype data simultaneously from 142 samples (51 MS patients and 91 controls) based on RNA-seq data, and an expression quantitative trait loci (eQTL) analysis was conducted. In total, 2383 differentially expressed lncRNAs were identified as specifically expressing in brain-related tissues, and 517 of them were affected by SNPs. Then, the functional characterization, secondary structure changes and tissue and disease specificity of the cis-eQTL SNPs and lncRNA were assessed. The cis-eQTL SNPs were substantially and specifically enriched in neurological disease and intergenic region, and the secondary structure was altered in 17.6% of all lncRNAs in MS. Finally, the weighted gene coexpression network and gene set enrichment analyses were used to investigate how the influence of SNPs on lncRNAs contributed to the pathogenesis of MS. As a result, the regulation of lncRNAs by SNPs was found to mainly influence the antigen processing/presentation and mitogen-activated protein kinases (MAPK) signaling pathway in MS. These results revealed the effectiveness of the strategy proposed in this study and give insight into the mechanism (SNP-mediated modulation of lncRNAs) underlying the pathology of MS.

Long non-coding RNA (lncRNA) of plant species undergoes dynamic regulation and acts in developmental and stress regulation. Presently, there is little information regarding the identification of lncRNAs in jujube (Ziziphus jujuba Mill.), and it is uncertain whether the lncRNAs could respond to heat stress (HS) or not. In our previous study, a cultivar (Hqing1-HR) of Z. jujuba were treated by HS (45°C) for 0, 1, 3, 5 and 7 days, and it was found that HS globally changed the gene expression by RNA sequencing (RNA-seq) experiments and informatics analyses. In the current study, 8260 lncRNAs were identified successfully from the previous RNA-seq data, and it indicated that lncRNAs expression was also altered globally, suggesting that the lncRNAs might play vital roles in response to HS. Furthermore, bioinformatics analyses of potential target mRNAs of lncRNAs with cis-acting mechanism were performed, and it showed that multiple differentially expressed (DE) mRNAs co-located with DElncRNAs were highly enriched in pathways associated with response to stress and regulation of metabolic process. Taken together, these findings not only provide a comprehensive identification of lncRNAs but also useful clues for molecular mechanism response to HS in jujube.

Animal growth and development are regulated by long non-coding RNAs (lncRNAs). However, the functions of lncRNAs in regulating cashmere fineness are poorly understood. To identify the key lncRNAs that are related to cashmere fineness in skin, we have collected skin samples of Liaoning cashmere goats (LCG) and Inner Mongolia cashmere goats (MCG) in the anagen phase, and have performed RNA sequencing (RNA-seq) approach on these samples. The high-throughput sequencing and bioinformatics analyses identified 437 novel lncRNAs, including 93 differentially expressed lncRNAs. We also identified 3,084 differentially expressed messenger RNAs (mRNAs) out of 27,947 mRNAs. Gene ontology (GO) analyses of lncRNAs and target genes in cis show a predominant enrichment of targets that are related to intermediate filament and intermediate filament cytoskeleton. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, sphingolipid metabolism is a significant pathway for lncRNA targets. In addition, this is the first report to reveal the possible lncRNA–mRNA regulatory network for cashmere fineness in cashmere goats. We also found that lncRNA XLOC_008679 and its target gene, KRT35, may be related to cashmere fineness in the anagen phase. The characterization and expression analyses of lncRNAs will facilitate future studies on the potential value of fiber development in LCG.

Ischemic cardiomyopathy (ICM), which increases along with aging, is the leading cause of heart failure. Currently, immune response is believed to be critical in ICM whereas the roles of immune-related lncRNAs remain vague. In this study, we aimed to systematically analyze immune-related lncRNAs in the aging-related disease ICM. Here, we downloaded publicly available RNA-seq data from ischemic cardiomyopathy patients and non-failing controls (GSE116250). Weighted gene co-expression network analysis (WGCNA) was performed to identify key ICM-related modules. The immune-related lncRNAs of key modules were screened by co-expression analysis of immune-related mRNAs. Then, a competing endogenous RNA (ceRNA) network, including 5 lncRNAs and 13 mRNAs, was constructed using lncRNA–mRNA pairs which share regulatory miRNAs and have significant correlation. Among the lncRNA–mRNA pairs, one pair (AC011483.1-CCR7) was verified in another publicly available ICM dataset (GSE46224) and ischemic cell model. Further, the immune cell infiltration analysis of the GSE116250 dataset revealed that the proportions of monocytes and CD8+ T cells were negatively correlated with the expression of AC011483.1-CCR7, while plasma cells were positively correlated, indicating that AC011483.1-CCR7 may participate in the occurrence and development of ICM through immune cell infiltration. Together, our findings revealed that lncRNA–mRNA pair AC011483.1-CCR7 may be a novel biomarker and therapeutic target for ICM.

Background: Renal ischemia-reperfusion (IR) injury is one of the major causes of acute renal failure which seriously endangers the health and life of patients. Currently, there is still lack of comprehensive knowledge of the molecular mechanism of renal IR injury, and the regulatory role of long noncoding RNA (lncRNA) in renal IR damage remains poorly understood. Aim: The aim of this study was to analyze the expression spectrum of lncRNA in renal IR damage in mice and to explore specific lncRNA that may be involved in regulating the development of human renal IR injury. Methods: RNA-Seq was used to investigate the lncRNA profile of renal IR injury in a mouse model, and conservation analysis was performed on mouse lncRNAs with differential expression (fragments per kilobase of transcript per million mapped reads ≥2) by BLASTN. The potential functions and associated pathways of the differentially expressed lncRNA were explored by bioinformatics analysis. The cell hypoxia model was used to detect the expression of the candidate lncRNA. Results: Of the 45,923 lncRNA transcripts detected in the samples, and 5,868 lncRNAs were found to be significantly differentially expressed (p < 0.05 and fold change ≥ 2) in 24-h IR kidney tissue compared to the expression in the control group. It was found that 56 differently expressed mouse lncRNA transcripts have human homology by analyzing the conserved sequences. We also found that lncRNA-NONHSAT183385.1 expression significantly increased in HK2 cells after 24 h of hypoxia and increased further 6 h after reoxygenation, and after 24 h of reoxygenation it was dramatically downregulated, indicating that NONHSAT183385.1 may be involved in the pathophysiological process of renal tubular epithelial cells in response to ischemia in human renal IR. Conclusion: Our study revealed differentially expressed lncRNAs in renal IR damage in mice and identified a set of conserved lncRNAs, which would help to explore lncRNAs that may play important regulatory roles in human renal IR injury.

ObjectiveSjögren’s disease (SjD) is an autoimmune disease characterised by inflammatory destruction of exocrine glands. Patients with autoantibodies to Ro/SSA (SjDRo+) exhibit more severe disease. Long non-coding RNAs (lncRNAs) are a functionally diverse class of non-protein-coding RNAs whose role in autoimmune disease pathology has not been well characterised.MethodsWhole blood RNA-sequencing (RNA-seq) was performed on SjD cases (n=23 Ro/SSA negative (SjDRo−); n=27 Ro/SSA positive (SjDRo+) and healthy controls (HCs; n=27). Bioinformatics and pathway analyses of differentially expressed (DE) transcripts (log2fold change ≥2 or ≤0.5; padj<0.05) were used to predict lncRNA function.LINC01871was characterised by RNA-seq analyses of HSB-2 cells with CRISPR-targetedLINC01871deletion (LINC01871−/−) and in vitro stimulation assays.ResultsWhole blood RNA-seq revealed autoantibody-specific transcription profiles and disproportionate downregulation of DE transcripts in SjD cases relative to HCs. Sixteen DE lncRNAs exhibited correlated expression with the interferon (IFN)-regulated gene,RSAD2, in SjDRo+(r≥0.65 or ≤−0.6); four antisense lncRNAs exhibited IFN-regulated expression in immune cell lines.LINC01871was upregulated in all SjD cases. RNA-seq and pathway analyses ofLINC01871−/−cells implicated roles in cytotoxic function, differentiation and IFNγ induction.LINC01871was induced by IFNγ in a myeloid cell line and regulated by calcineurin/NFAT pathway and T cell receptor (TCR) signalling in primary human T cells.ConclusionLINC01871influences expression of many immune cell genes and growth factors, is IFNγ inducible, and regulated by calcineurin signalling and TCR ligand engagement. AlteredLINC01871expression may influence the dysregulated T cell inflammatory pathways implicated in SjD.

Background Sickle Cell Disease (SCD) is a genetic disorder caused by a single amino acid substitution in the ß-hemoglobin chain. Clinical manifestations of SCD are multisystemic and heterogeneous, with a wide range of organ damage between patients despite identical genetic mutations. The sub-phenotypes of SCD reflect damage to different organs arising from the pathophysiology of the disease. Biomarkers, such as N-terminal-pro-Brain natriuretic peptide (NT-proBNP), tricuspid regurgitant velocity (TRV), and hemolytic parameters provide readouts of the degree of different organ damage and are known risk markers. Dissecting its pathophysiology along known biological pathways has led to development of therapeutic targets, but we still do not fully understand the pathogenesis for much of the organ damage. Given the complexity of the pathophysiology of SCD that is likely to involve multiple overlapping and interacting biological pathways, an agnostic, network-based approach using gene expression data with annotated risk markers provides an attractive alternative. We assessed the plausibility of using such an approach with bilirubin levels as a phenotype. Bilirubin is a breakdown product of red blood cells (RBCs) that is measured in routine labs and a validated hemolytic and disease severity marker of SCD. Methods Using RNA-Seq data from 224 patients with SCD (80% HbSS and HbSBeta0 thalassemia combined, 15% HbSC, 5% HbSBeta+ thalassemia, and 0.4% HbSD), we performed a differential gene expression (DGE) analysis and weighted gene co-expression network analysis (WGCNA) of 12,450 genes. For the DGE analysis we used total bilirubin levels as a continuous variable for the main phenotype of interest, adjusting for age, sex, and hemoglobin genotype. We then conducted WGCNA to identify modules of genes with similar coexpression patterns, which we functionally annotated (via the ClusterProfiler R package) for interesting biological themes. Results Of the 12,450 genes, 10,604 were protein coding, and 1,560 were lncRNAs, and 286 were other non-coding transcripts. 3,509 (2,975 coding, 447 lncRNA, and 87 other non-coding) were considered differentially expressed (unadjusted p-value &lt; 0.05) according to bilirubin level. Through the WCGNA, the genes clustered into 11 co-expression modules, representing a range of biological pathways. Four of these modules had representative eigengenes that were significantly correlated with bilirubin (p &lt; 0.05), and we focused on 2 of these that had biologically interesting functions. The first module of interest (turquoise) consisted of 3,636 genes (3,371 protein-coding and 210 lncRNA) (25% differentially expressed, and 62% upregulated). Genes in this module have been associated with cellular functioning of younger RBCs, validating the presence of the significant Gene Ontology (GO) biological pathways listed in the bar graph (mitochondria gene expression, ribonucleotide processes, etc.), and aligning with expected processes as the young RBCs replace the degraded RBCs that had caused the high bilirubin. Notably, this module contained the biliverdin reductase A (BLVRA) gene (differentially expressed at p &lt; 0.008), which reduces biliverdin to bilirubin. The other highlighted module (red) consisted of 914 genes (846 protein-coding and 57 lncRNA) (42% differentially expressed, and 97% upregulated). Multiple pathways in the red module were related to catabolic processes. As previously stated, bilirubin is the byproduct of hemoprotein catabolism, suggesting an interesting correlation. Additionally, this module contained 2 other pathways associated with porphyrin, which is a component of RBCs that contains the heme group and is involved in heme biosynthesis. Conclusion Taken together, these results provide interesting insights into the biological pathways driving one facet of sickle cell disease's many clinical manifestations. Figure 1 Disclosures No relevant conflicts of interest to declare.

Abstract Background Glioblastoma multiforme (GBM) are the major death contributor in primary brain tumour. Despite having an improved diagnostic criterion by integrating both histological and molecular features such as Isocitrate Dehydrogenase (IDH) detection, the prognosis of GBM patients still remain poor. Lipid metabolism is an essential pathway that fuel GBM aggressiveness. IDH1 one of the key enzyme that regulates it. Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) in tumour initiation and progression. In parallel, miRNA-mediate ceRNA crosstalk between lncRNAs and mRNAs. In this study, we aim to investigate the IDH1 subgroup lncRNA associated ceRNA network hub gene responsible in the coordination of glioblastoma multiforme lipid metabolism using bioinformatics approach. Methods TCGA-GBM dataset consist of 168 GBM RNA-seq (159 IDH1 wt and 9 IDH1 mutation) were downloaded. Differentially expressed genes (DEG) were then obtained using Limma. Gene sets related with lipid metabolism from GSEA-MSigDB were overlapped with DEG using Venn diagram to identify the DEmRNA that are related with lipid metabolism. Construction of mRNA-miRNA and lncRNA-miRNA interaction networks were performed using miRNet. The ceRNA interaction network were later combined in the Cytoscape software. Potential lncRNA hub genes were identified by CytoHubba analysis. Results From 1389 DEG, 67 genes were identified to be significant in the regulation of lipid metabolism. By analysing the lncRNA-miRNA-mRNA interaction network, candidate hub lncRNAs consists of three genes with highest connective nodes; CYTOR, LOXL1-AS1 and HOTAIR. These genes are significantly upregulated in glioma. LOXL1-AS1 serve as an excellent prognostic biomarker for both glioma and glioblastoma as the effect of high and low LOXL1-AS1 expression on patients’ survival is significant (p&lt;0.05). Conclusions Data mining and bioinformatics approach guided the identification of the potential hub lncRNAs associated ceRNA network in GBM lipid metabolism. This allows us to uncover the novel role of lncRNA in GBM tumorigenesis.

The alteration in skeletal muscle fiber is a critical factor affecting livestock meat quality traits and human metabolic diseases. Long non-coding RNAs (lncRNAs) are a diverse class of non-coding RNAs with a length of more than 200 nucleotides. However, the mechanisms underlying the regulation of lncRNAs in skeletal muscle fibers remain elusive. To understand the genetic basis of lncRNA-regulated skeletal muscle fiber development, we performed a transcriptome analysis to identify the key lncRNAs affecting skeletal muscle fiber and meat quality traits on a pig model. We generated the lncRNA expression profiles of fast-twitch Biceps femoris (Bf) and slow-twitch Soleus (Sol) muscles and identified the differentially expressed (DE) lncRNAs using RNA-seq and performed bioinformatics analyses. This allowed us to identify 4581 lncRNA genes among six RNA libraries and 92 DE lncRNAs between Bf and Sol which are the key candidates for the conversion of skeletal muscle fiber types. Moreover, we detected the expression patterns of lncRNA MSTRG.42019 in different tissues and skeletal muscles of various development stages. In addition, we performed a correlation analyses between the expression of DE lncRNA MSTRG.42019 and meat quality traits. Notably, we found that DE lncRNA MSTRG.42019 was highly expressed in skeletal muscle and its expression was significantly higher in Sol than in Bf, with a positive correlation with the expression of Myosin heavy chain 7 (MYH7) (r = 0.6597, p = 0.0016) and a negative correlation with meat quality traits glycolytic potential (r = −0.5447, p = 0.0130), as well as drip loss (r = −0.5085, p = 0.0221). Moreover, we constructed the lncRNA MSTRG.42019–mRNAs regulatory network for a better understanding of a possible mechanism regulating skeletal muscle fiber formation. Our data provide the groundwork for studying the lncRNA regulatory mechanisms of skeletal muscle fiber conversion, and given the importance of skeletal muscle fiber types in muscle-related diseases, our data may provide insight into the treatment of muscular diseases in humans.

Enterovirus 71 (EV71) is the main pathogen of severe hand-foot-mouth disease (HFMD). Long non-coding RNAs (lncRNAs) are recognized as pivotal factors during the pathogenesis of viral infection. However, the critical functions of lncRNAs in EV71–host interactions have not been characterized. Here, for the first time, we performed global transcriptome analysis of lncRNA and mRNA expression profiles in EV71-infected human rhabdomyosarcoma (RD) cells and skeletal muscle of mice using second-generation sequencing. In our study, a total of 3801 novel lncRNAs were identified. In addition, 23 lncRNAs and 372 mRNAs exhibited remarkable differences in expression levels between infected and uninfected RD cells, while 104 lncRNAs and 2647 mRNAs were differentially expressed in infected skeletal muscle from neonatal mice. Comprehensive bioinformatics analysis included target gene prediction, lncRNA‑mRNA co-expression network construction, as well as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis mainly focused on differentially-expressed genes (DEGs). Our results suggest that lncRNAs may participate in EV71 infection-induced pathogenesis through regulating immune responses, protein binding, cellular component biogenesis and metabolism. The present study provides novel insights into the functions of lncRNAs and the possible pathogenic mechanism following EV71 infection.

I linfociti T CD4+ orchestrano la risposta immunitaria differenziandosi in vari sottogruppi di cellule T effettrici. Nella prima parte del lavoro, abbiamo studiato il ruolo dei long non-coding RNAs nel processo differenziativo delle cellule T mediante un'analisi completa del trascrittoma con tecnologia RNA-Seq di tredici sottopopolazioni linfocitarie altamente purificate da cellule primarie umane e abbiamo identificato oltre 500 nuovi lincRNAs. Dai nostri risultati si evince che i lincRNAs sono preferenzialmente espressi in specifiche sottopopolazioni linfocitarie e che i loro pattern di espressione cambiano durante il differenziamento delle cellule T. Abbiamo inoltre caratterizzato funzionalmente Linc-MAF-4, un lincRNA 'signature' delle cellule CD4+ Th1, e abbiamo dimostrato che la downregolazione di Linc-MAF-4 aumenta i livelli di espressione del fattore di trascrizione MAF e spinge il processo differenziativo delle cellule CD4+ verso un profilo di espressione TH2. Dopo aver evidenziato la centralitá del ruolo dei lncRNAs in cellule T primarie da donatori sani, cerchiamo ora di caratterizzare il loro coinvolgimento nei processi regolatori dei linfociti infiltranti i tumori (TIL). Una caratterizzazione approfondita dei meccanismi molecolari alla base delle caratteristiche funzionali dei TIL possono condurre ad una comprensione del loro ruolo nei meccanismi di fuga del tumore dalla risposta immunitaria e consentire l'identificazione di nuovi bersagli terapeutici per una efficace modulazione di queste cellule nel cancro. Dal momento che si sa molto poco riguardo all'espressione dei lncRNAs nei TIL, abbiamo isolato cellule CD4+ Th1, Th17 e Tregs infiltranti sia tumore che tessuto sano, e linfociti da organi linfoidi secondari e sangue periferico di pazienti affetti da Non-small cell lung cancer (cancro al polmone) e cancro del colon-retto. Abbiamo analizzato il trascrittoma di queste cellule con tecnologia RNA-Seq e definito una serie di lncRNAs che sono specificamente espressi in linfociti infiltranti il tumore.
CD4+ T lymphocytes orchestrate immune responses by differentiating into various subsets of effector T cells. We addressed the role of regulatory long non-coding RNAs in T-cell differentiation and plasticity performing a comprehensive transcriptome analysis by RNA-seq of thirteen highly purified human primary lymphocyte subsets and identified more than 500 new lincRNAs. We found that lincRNAs are preferentially expressed in specific lymphocyte subsets and that their expression patterns change during T-cell differentiation. Furthermore, we functionally characterized linc-MAF-4, a Th1 CD4+ signature lincRNA, and found that linc-MAF-4 down regulation increases the expression levels of transcription factor MAF and skews CD4+ differentiating cells towards a Th2 like expression profile. After assessing the role of lncRNAs in primary T cells from healthy donors, we seek to characterize their involvement in regulatory processes for tumor infiltrating lymphocytes (TIL). An in-depth characterization and understanding of the molecular mechanisms underlying the functional features of TIL may lead to a comprehension of their role in tumor immune escape and allow the identification of new therapeutic targets for the effective modulation of these cells in cancer. Since very little is known on the expression of lncRNAs in TILs, we isolated CD4+ Th1, Th17 and Tregs cells infiltrating both tumor and healthy tissue as well as lymphocytes from lymphoid Issues and peripheral blood of Non-Small- Cell-Lung and Colorectal cancer patients. We analysed these cells by RNA-seq and defined a set of lncRNAs that are specifically expressed in TIL subsets.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Large-scale sequencing of transcripts via RNA-Seq has been changing paradigms by demonstrating that transcription is prevalent throughout the eukaryotic genome. In these organisms, the vast majority of transcripts are non-coding (ncRNA). One type of RNA that has aroused great interest, given its prevalence, is long non-coding RNAs (lncRNAs), which are ncRNA with more than 200 nucleotides. However, little is known about the role and prevalence of these lncRNAs in plant genomes, even in model species such as Arabidopsis thaliana (L.) Heynh. The objective of this work was to identify lncRNAs in the Arabidopsis genome and to characterize their size, structure and nucleotide diversity. The sequences were obtained from previous work that sequenced total RNA from A. thaliana, grown under different light regimes, using Illumina Hiseq 2000 platform. These sequences were mapped into the reference genome with TopHat and assembled with Cufflinks. The assembled transcripts were compared with the genome annotation with Cuffcompare, to identify non-annotated transcripts. A total of 4,305 long putative RNAs were obtained, with 314 (7%) sense in relation to coding transcripts (mRNAs), 392 (9%) intergenic, 2,216 intronic (52%) and 1,383 (32%) antisense mRNAs. The lncRNAs obtained were filtered to eliminate those with coding potential, as well as those related to rRNA, tRNA and miRNA synthesis. A total of 3,710 high-confidence lncRNAs (HC-lncRNA) were obtained, of which 58.6% were not previously annotated. These HC-lncRNA emcompass a low proportion (~ 1%) lncRNAs in the genome of Arabidopsis thaliana. A functional enrichment analysis of Gene Ontology (GO) categories demonstrated that among genes containing lncRNAs there is a high proportion of categories linked to the localization and transport of proteins within the cell, as well as to nucleic acid binding. A gene expression analyses identified only 22 differentially expressed lncRNAs under the different light conditions in which samples were exposed. Using the SNP data from the 1001 genomes project, identified high nucleotide diversity within lncRNAs regions, indicating low conservation of the primary structure of these transcripts. The nucleotide diversity in regions of long noncoding RNAs is lower than in coding regions, but less than a diversity observed in neutral regions such as pseudogenes.
O sequenciamento em larga escala de transcritos, via RNA-Seq, vêm mudando paradigmas ao demonstrar que a transcrição é prevalente por todo o genoma dos eucariotos. Nesses organismos, a grande maioria dos transcritos não codificam proteínas (ncRNA). Um tipo de RNA que vêm despertando grande interesse, dado sua prevalência, são os RNAs longos não codificantes (lncRNAs), que são ncRNA com mais de 200 nucleotídeos. No entanto, pouco se sabe sobre o seu papel e prevalência nos genomas de plantas, mesmo em espécies modelo como Arabidopsis thaliana (L.) Heynh. O objetivo desse trabalho foi identificar lncRNAs no genoma de Arabidopsis e caracterizar seus tamanhos, estruturas e diversidade genética. As sequências utilizadas foram obtidas de um trabalho que sequenciou RNA total de A. thaliana, sob diferentes regimes de luminosidade, utilizando a plataforma Illumina HiSeq 2000. Estas sequências foram mapeadas no genoma referência com o programa TopHat e montadas com o Cufflinks. Os transcritos montados foram comparados com a anotação do genoma com o Cuffcompare, afim de identificar transcritos ainda não anotados. Um total de 4.305 RNAs longos putativos foi obtido, sendo 314 (7%) senso em relação a transcritos codantes (mRNAs), 392 (9%) intergênicos, 2.216 intrônicos (52%) e 1.383 (32%) antisenso de mRNAs. Os lncRNAs obtidos foram filtrados para eliminar aqueles com potencial de codificação, bem como aqueles relacionados com a síntese rRNA, tRNA e miRNA. Após essa filtragem, foram obtidos 3.710 lncRNAs de alta cofiança (HC-lncRNA), sendo que desses 58,6% ainda não foram previamente anotados. Esses HC-lncRNA representam uma baixa proporção (~1%) do genoma de Arabidopsis thaliana. Uma análise de enriquecimento funcional de categorias do Gene Ontology (GO) demonstrou que os genes que contém lncRNAs apresentam enriquecimento para processos ligados à localização e transporte de proteínas dentro da célula, bem como para ligação a ácidos nucléicos. Uma análise de expressão gênica identificou apenas 22 lncRNAs diferencialmente expressos entre as diferentes condições de luminosidade em que as amostras foram expostas. Utilizando os SNPs do projeto 1001 genomes, identificou-se alta diversidade nucleotídica em regiões de lncRNAs, indicando baixa conservação da estrutura primária destes transcritos. A diversidade nucleotídica em regiões de RNAs longos não codificantes é menor do que em regiões codantes, mas menor do que a diversidade observada em regiões neutras como os pseudogenes.

[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

Hypoxia pathways are associated with the pathogenesis of both ischaemic and neoplastic diseases. In response to hypoxia the transcription factor hypoxia‐inducible factor (HIF) induces the expression of hundreds of genes with diverse functions. These enable cells to adapt to low oxygen availability. To date, pan-genomic analyses of these transcriptional responses have focussed on protein-coding genes and microRNAs. However, the role of other classes of non-coding RNAs, in particular lncRNAs, in the hypoxia response is largely uncharacterised. My thesis aimed at improving understanding of the transcriptional regulation of the non-coding transcriptome in hypoxia. I performed an integrated genomic analysis of both non-coding and coding transcripts by massively parallel sequencing. This was interfaced with pan-genomic analyses of DNAse hypersensitivity and HIF, H3k4me3 and RNApol2 binding in hypoxic cells. These analyses have revealed that hypoxia profoundly regulated all RNA classes. snRNAs and tRNAs are globally downregulated in hypoxia, whilst miRNAs, mRNAs and lncRNAs are both up- and downregulated with an overall trend towards slight upregulation. In addition, a significant number of previously non-annotated (and largely hypoxia upregulated) transcripts were identified, including novel intergenic transcripts and natural antisense transcripts. HIF bound close to genes for mRNAs, miRNAs and lncRNAs that were upregulated by hypoxia, but was excluded from binding at genes for RNA classes that showed global downregulation. This suggests that HIF acts as a transcriptional activator (but not repressor), of lncRNAs as well as mRNAs and miRNAs. Consistent with direct regulation by HIF, many of these hypoxia-inducible, HIF-binding lncRNAs were downregulated following HIF knockdown. Analysis of RNApol2 binding and DNAse HSS signals at HIF transcriptional target genes indicated that HIF-dependent transcriptional activation occurs through release of RNApol2 that is pre-bound to open promoters of lncRNAs as well as mRNAs. In these datasets, NEAT1 was the most hypoxia-upregulated, HIF-targeted lncRNA in MCF-7 cells and, despite binding of both HIF-1 and HIF-2 isoforms at its promoter, was selectively regulated by HIF-2 alone. Furthermore, NEAT1 was induced by hypoxia in a wide range of breast cancer cell lines and in hypoxic xenograft models. Functionally, NEAT1 is required for the assembly of nuclear paraspeckle structures. Increased nuclear paraspeckle formation was observed in hypoxia and was dependent on both NEAT1 and HIF-2. Knockdown of hypoxia-induced NEAT1 significantly reduced cell proliferation and survival and induced apoptosis. Finally, high expression of NEAT1 correlated with poor clinical outcome in a large cohort of breast cancer patients. These findings extend the role of the hypoxic transcriptional response in cancer into the spectrum of non-coding transcripts and provide new insights into molecular roles of hypoxia-regulated lncRNAs, which may provide the basis for novel therapeutic targets in the future.