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Non-coding RNAs (ncRNAs) are regulatory elements present in a wide range of organisms, including trypanosomatids. ncRNAs transcribed from the untranslated regions (UTRs) of coding genes have been described in the transcriptomes of several eukaryotes, including Trypanosoma brucei. To uncover novel putative ncRNAs in two Leishmania species, we examined a L. major cDNA library and a L. donovani non-polysomal RNA library. Using a combination of computational analysis and experimental approaches, we classified 26 putative ncRNA in L. major, of these, 5 arising from intergenic regions and 21 from untranslated regions. In L. donovani, we classified 37 putative ncRNAs, of these, 7 arising from intergenic regions, and 30 from UTRs. Our results suggest, for the first time, that UTR-transcripts may be a common feature in the eukaryote Leishmania similarly to those previously shown in T. brucei and other eukaryotes.

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BACKGROUND: Transcriptome sequences provide a complement to structural genomic information and provide snapshots of an organism's transcriptional profile. Such sequences also represent an alternative method for characterizing neglected species that are not expected to undergo whole-genome sequencing. One difficulty for transcriptome sequencing of these organisms is the low quality of reads and incomplete coverage of transcripts, both of which compromise further bioinformatics analyses. Another complicating factor is the lack of known protein homologs, which frustrates searches against established protein databases. This lack of homologs may be caused by divergence from well-characterized and over-represented model organisms. Another explanation is that non-coding RNAs (ncRNAs) may be caught during sequencing. NcRNAs are RNA sequences that, unlike messenger RNAs, do not code for protein products and instead perform unique functions by folding into higher order structural conformations. There is ncRNA screening software available that is specific for transcriptome sequences, but their analyses are optimized for those transcriptomes that are well represented in protein databases, and also assume that input ESTs are full-length and high quality. RESULTS: We propose an algorithm called PORTRAIT, which is suitable for ncRNA analysis of transcriptomes from poorly characterized species. Sequences are translated by software that is resistant to sequencing errors, and the predicted putative proteins, along with their source transcripts, are evaluated for coding potential by a support vector machine (SVM). Either of two SVM models may be employed: if a putative protein is found, a protein-dependent SVM model is used; if it is not found, a protein-independent SVM model is used instead. Only ab initio features are extracted, so that no homology information is needed. We illustrate the use of PORTRAIT by predicting ncRNAs from the transcriptome of the pathogenic fungus Paracoccidoides brasiliensis and five other related fungi. CONCLUSION: PORTRAIT can be integrated into pipelines, and provides a low computational cost solution for ncRNA detection in transcriptome sequencing projects.

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O carcinoma de célula renal (RCC) subtipo célula clara é o câncer mais letal e prevalente do sistema urinário. O diagnóstico deste tipo de câncer frequentemente é tardio em conseqüência da falta de sintomas perceptíveis aos pacientes. Um dos objetivos deste trabalho é a identificação de novos marcadores moleculares para diagnóstico precoce, o que ajudaria a diminuir a mortalidade em função de complicações resultantes do avanço da doença. Outro objetivo é a identificação de um conjunto de marcadores moleculares de prognóstico, de modo à prever com acurácia a evolução clínica da doença e, por conseqüência, o tempo de sobrevida do paciente. As modificações transcricionais associadas à carcinogênese e à progressão do câncer de rim ainda não foram completamente elucidadas. Além dos oncogenes e genes supressores de tumor, RNAs não-codificadores (ncRNAs) recentemente foram apontados como importantes reguladores da expressão gênica em humanos, e podem ter um papel importante na transformação maligna do câncer de rim. Para analisar a expressão gênica de ncRNAs e de genes codificadores para proteína foram utilizados dois microarranjos desenvolvidos por nosso grupo, enriquecidos em sondas para ncRNAs. Uma das plataformas possui 4 mil sondas de cDNA, das quais 822 sondas são para ncRNAs mapeando em regiões intrônicas. Outra possui 44 mil elementos e combina sondas de oligonucleotídeos (60-mer) intrônicas e exônicas de um mesmo locus genômico. Análises estatísticas foram feitas com a ferramenta Significance Analysis of Microarrays (q ≤ 0,05) combinadas ou com a técnica de "patient leave-one-out" (genes com presença em 8 100% dos subconjuntos), ou alternativamente com o teste discriminante de Golub (p ≤ 0,01 ou p < 0,05). Com a plataforma de 4 mil sondas foram estudadas 30 amostras de tecido renal de 18 pacientes com RCC subtipo célula clara. Um conjunto de 36 ncRNAs foi identificado como diferencialmente expresso entre amostras tumorais e não-tumorais...

Renal cell carcinoma (RCC) is the most common malignancy of the adult kidney, and the clear cell subtype is the most prevalent and lethal cancer of the urinary system. Late diagnosis for this type of cancer is frequent, usually as a consequence of the lack of symptoms. One of the objectives of the present work is the identification of new molecular markers for the early diagnosis, which would help decrease mortality that develops as a function of disease progression. Another objective is the identification of a set of prognosis molecular markers, so as to accurately predict the clinical outcome of the disease, and consequently, patient survival. Transcriptional changes associated to carcinogenesis and to kidney cancer progression have not been entirely elucidated. Besides oncogenes and tumor suppressor genes, non-coding RNAs (ncRNAs) have been recently indicated as important regulators of gene expression in humans, and could have an important role in the malignant transformation in renal cancer. In order to measure ncRNA and protein-coding gene expression we have used two microarray platforms developed by our group, which are enriched in ncRNA probes. One of the platforms has 4 thousand cDNA probes, of which 822 are for ncRNAs that map to intronic regions. Another has 44 thousand elements and combines 60-mer oligonucleotide probes for intronic and exonic regions from the same genomic locus. Statistical analyses have been performed with the Significance Analysis of Microarrays tool (q ≤ 0.05) combined with a patient leave-one-out approach (genes present in 100% of the sub-sets), or alternatively with Golubs discriminant test (p ≤ 0.01 or p < 0.05). 11 With the 4-thousand probes platform we studied 30 samples from renal tissue of 18 RCC patients with clear cell subtype. A set of 36 ncRNAs has been identified as differentially expressed between tumor and non-tumor tissue...

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BACKGROUND: Small non-coding RNAs (sRNAs) have emerged as ubiquitous regulatory elements in bacteria and other life domains. However, few sRNAs have been identified outside several well-studied species of gamma-proteobacteria and thus relatively little is known about the role of RNA-mediated regulation in most other bacterial genera. Here we have conducted a computational prediction of putative sRNA genes in intergenic regions (IgRs) of the symbiotic alpha-proteobacterium S. meliloti 1021 and experimentally confirmed the expression of dozens of these candidate loci in the closely related strain S. meliloti 2011. RESULTS: Our first sRNA candidate compilation was based mainly on the output of the sRNAPredictHT algorithm. A thorough manual sequence analysis of the curated list rendered an initial set of 18 IgRs of interest, from which 14 candidates were detected in strain 2011 by Northern blot and/or microarray analysis. Interestingly, the intracellular transcript levels varied in response to various stress conditions. We developed an alternative computational method to more sensitively predict sRNA-encoding genes and score these predicted genes based on several features to allow identification of the strongest candidates. With this novel strategy, we predicted 60 chromosomal independent transcriptional units that, according to our annotation, represent strong candidates for sRNA-encoding genes, including most of the sRNAs experimentally verified in this work and in two other contemporary studies. Additionally, we predicted numerous candidate sRNA genes encoded in megaplasmids pSymA and pSymB. A significant proportion of the chromosomal- and megaplasmid-borne putative sRNA genes were validated by microarray analysis in strain 2011. CONCLUSION: Our data extend the number of experimentally detected S. meliloti sRNAs and significantly expand the list of putative sRNA-encoding IgRs in this and closely related alpha-proteobacteria. In addition, we have developed a computational method that proved useful to predict sRNA-encoding genes in S. meliloti. We anticipate that this predictive approach can be flexibly implemented in many other bacterial species.

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