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Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements.

ABSTRACT: BACKGROUND:Long non-coding RNAs (lncRNA) are a major class of non-coding RNAs. They are involved in diverse intra-cellular mechanisms like molecular scaffolding, splicing and DNA methylation. Through these mechanisms they are reported to play a role in cellular differentiation and development. They show an enriched expression in the brain where they are implicated in maintaining cellular identity, homeostasis, stress responses and plasticity. Low sequence conservation and lack of functional annotations make it difficult to identify homologs of mammalian lncRNAs in other vertebrates. A computational evaluation of the lncRNAs through systematic conservation analyses of both sequences as well as their genomic architecture is required. RESULTS:Our results show that a subset of mouse candidate lncRNAs could be distinguished from random sequences based on their alignment with zebrafish phastCons elements. Using ROC analyses we were able to define a measure to select significantly conserved lncRNAs. Indeed, starting from ~2,800 mouse lncRNAs we could predict that between 4 and 11% present conserved sequence fragments in fish genomes. Gene ontology (GO) enrichment analyses of protein coding genes, proximal to the region of conservation, in both organisms highlighted similar GO classes like regulation of transcription and central nervous system development. The proximal coding genes in both the species show enrichment of their expression in brain. In summary, we show that interesting genomic regions in zebrafish could be marked based on their sequence homology to a mouse lncRNA, overlap with ESTs and proximity to genes involved in nervous system development. CONCLUSIONS:Conservation at the sequence level can identify a subset of putative lncRNA orthologs. The similar protein-coding neighborhood and transcriptional information about the conserved candidates provide support to the hypothesis that they share functional homology. The pipeline herein presented represents a proof of principle showing that a portion between 4 and 11% of lncRNAs retains region of conservation between mammals and fishes. We believe this study will result useful as a reference to analyze the conservation of lncRNAs in newly sequenced genomes and transcriptomes.

SUBMITTER: Basu S

PROVIDER: S-EPMC3633045 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements.

Basu Swaraj S Müller Ferenc F Sanges Remo R

BMC bioinformatics 20130422

<h4>Background</h4>Long non-coding RNAs (lncRNA) are a major class of non-coding RNAs. They are involved in diverse intra-cellular mechanisms like molecular scaffolding, splicing and DNA methylation. Through these mechanisms they are reported to play a role in cellular differentiation and development. They show an enriched expression in the brain where they are implicated in maintaining cellular identity, homeostasis, stress responses and plasticity. Low sequence conservation and lack of functio ...[more]

PMID: 23815359

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Project description:BackgroundA considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns.MethodsWe use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply.ResultsThe empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints.ConclusionsOur study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

Project description:BackgroundTransposable elements (TEs) are mobile sequences found in nearly all eukaryotic genomes. They have the ability to move and replicate within a genome, often influencing genome evolution and gene expression. The identification of TEs is an important part of every genome project. The number of sequenced genomes is rapidly rising, and the need to identify TEs within them is also growing. The ability to do this automatically and effectively in a manner similar to the methods used for genes is of increasing importance. There exist many difficulties in identifying TEs, including their tendency to degrade over time and that many do not adhere to a conserved structure. In this work, we describe a homology-based approach for the automatic identification of high-quality consensus TEs, aimed for use in the analysis of newly sequenced genomes.ResultsWe describe a homology-based approach for the automatic identification of TEs in genomes. Our modular approach is dependent on a thorough and high-quality library of representative TEs. The implementation of the approach, named TESeeker, is BLAST-based, but also makes use of the CAP3 assembly program and the ClustalW2 multiple sequence alignment tool, as well as numerous BioPerl scripts. We apply our approach to newly sequenced genomes and successfully identify consensus TEs that are up to 99% identical to manually annotated TEs.ConclusionsWhile TEs are known to be a major force in the evolution of genomes, the automatic identification of TEs in genomes is far from mature. In particular, there is a lack of automated homology-based approaches that produce high-quality TEs. Our approach is able to generate high-quality consensus TE sequences automatically, requiring the user to only provide a few basic parameters. This approach is intentionally modular, allowing researchers to use components separately or iteratively. Our approach is most effective for TEs with intact reading frames. The implementation, TESeeker, is available for download as a virtual appliance, while the library of representative TEs is available as a separate download.

Dataset Information

Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements.

Publications

Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements.

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