SUMMARY: Transposable elements (TEs) play key roles in crucial biological pathways. Therefore, several tools enabling the quantification of their expression were recently developed. However, many of the existing tools lack the capability to distinguish between the transcription of autonomously expressed TEs and TE fragments embedded in canonical coding/non-coding non-TE transcripts. Consequently, an apparent change in the expression of a given TE may simply reflect the variation in the expression of the transcripts containing TE-derived sequences. To overcome this issue, we have developed TEspeX, a pipeline for the quantification of TE expression at the consensus level. TEspeX uses Illumina RNA-seq short reads to quantify TE expression avoiding counting reads deriving from inactive TE fragments embedded in canonical transcripts. AVAILABILITY AND IMPLEMENTATION: The tool is implemented in python3, distributed under the GNU General Public License (GPL) and available on Github at https://github.com/fansalon/TEspeX (Zenodo URL: https://doi.org/10.5281/zenodo.6800331). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Transposable Elements (TE) are mobile sequences that make up large portions of eukaryote genomes. The functions they play within the complex cellular architecture are still not clearly understood, but it is becoming evident that TE have a role in several physiological and pathological processes. In particular, it has been shown that TE transcription is necessary for the correct development of mice embryos and that their expression is able to finely modulate transcription of coding and non-coding genes. Moreover, their activity in the central nervous system (CNS) and other tissues has been correlated with the creation of somatic mosaicisms and with pathologies such as neurodevelopmental and neurodegenerative diseases as well as cancers. RESULTS: We analyzed TE expression among different cell types of the Caenorhabditis elegans (C. elegans) early embryo asking if, where and when TE are expressed and whether their expression is correlated with genes playing a role in early embryo development. To answer these questions, we took advantage of a public C. elegans embryonic single-cell RNA-seq (sc-RNAseq) dataset and developed a bioinformatics pipeline able to quantify reads mapping specifically against TE, avoiding counting reads mapping on TE fragments embedded in coding/non-coding transcripts. Our results suggest that i) canonical TE expression analysis tools, which do not discard reads mapping on TE fragments embedded in annotated transcripts, may over-estimate TE expression levels, ii) Long Terminal Repeats (LTR) elements are mostly expressed in undifferentiated cells and might play a role in pluripotency maintenance and activation of the innate immune response, iii) non-LTR are expressed in differentiated cells, in particular in neurons and nervous system-associated tissues, and iv) DNA TE are homogenously expressed throughout the C. elegans early embryo development. CONCLUSIONS: TE expression appears finely modulated in the C. elegans early embryo and different TE classes are expressed in different cell types and stages, suggesting that TE might play diverse functions during early embryo development.

BACKGROUND: Transposable elements (TEs) are DNA sequences able to mobilize themselves and to increase their copy-number in the host genome. In the past, they have been considered mainly selfish DNA without evident functions. Nevertheless, currently they are believed to have been extensively involved in the evolution of primate genomes, especially from a regulatory perspective. Due to their recent activity they are also one of the primary sources of structural variants (SVs) in the human genome. By taking advantage of sequencing technologies and bioinformatics tools, recent surveys uncovered specific TE structural variants (TEVs) that gave rise to polymorphisms in human populations. When combined with RNA-seq data this information provides the opportunity to study the potential impact of TEs on gene expression in human. RESULTS: In this work, we assessed the effects of the presence of specific TEs in cis on the expression of flanking genes by producing associations between polymorphic TEs and flanking gene expression levels in human lymphoblastoid cell lines. By using public data from the 1000 Genome Project and the Geuvadis consortium, we exploited an expression quantitative trait loci (eQTL) approach integrated with additional bioinformatics data mining analyses. We uncovered human loci enriched for common, less common and rare TEVs and identified 323 significant TEV-cis-eQTL associations. SINE-R/VNTR/Alus (SVAs) resulted the TE class with the strongest effects on gene expression. We also unveiled differential functional enrichments on genes associated to TEVs, genes associated to TEV-cis-eQTLs and genes associated to the genomic regions mostly enriched in TEV-cis-eQTLs highlighting, at multiple levels, the impact of TEVs on the host genome. Finally, we also identified polymorphic TEs putatively embedded in transcriptional units, proposing a novel mechanism in which TEVs may mediate individual-specific traits. CONCLUSION: We contributed to unveiling the effect of polymorphic TEs on transcription in lymphoblastoid cell lines.

Summary: Studies on gene clusters proved to be an excellent source of information to understand genomes evolution and identifying specific metabolic pathways or gene families. Improvements in sequencing methods have resulted in a large increase of sequenced genomes for which cluster annotation could be performed and standardized. Currently available programs are developed to search for specific cluster types and none of them is suitable for a broad range of user-based choices. We have developed ClusterScan which allows identifying clusters of any kind of feature simply based on their genomic coordinates and user-defined categorical annotations. Availability and implementation: The tool is written in Python, distributed under the GNU General Public License (GPL) and available on Github at http://bit.ly/ClusterScan or as Docker image at sangeslab/clusterscan: latest. It is supported through a mailing-list on http://bit.ly/ClusterScanSupport. Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: The eukaryotic transcriptome is composed of thousands of coding and long non-coding RNAs (lncRNAs). However, we lack a software platform to identify both RNA classes in a given transcriptome. Here we introduce Annocript, a pipeline that combines the annotation of protein coding transcripts with the prediction of putative lncRNAs in whole transcriptomes. It downloads and indexes the needed databases, runs the analysis and produces human readable and standard outputs together with summary statistics of the whole analysis. AVAILABILITY AND IMPLEMENTATION: Annocript is distributed under the GNU General Public License (version 3 or later) and is freely available at https://github.com/frankMusacchia/Annocript. CONTACT: [email protected].

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.

UNLABELLED: OneChannelGUI is an add-on Bioconductor package providing a new set of functions extending the capability of the affylmGUI package. This library provides a graphical interface (GUI) for Bioconductor libraries to be used for quality control, normalization, filtering, statistical validation and data mining for single channel microarrays. Affymetrix 3' expression (IVT) arrays as well as the new whole transcript expression arrays, i.e. gene/exon 1.0 ST, are actually implemented. oneChannelGUI is available for most platforms on which R runs, i.e. Windows and Unix-like machines. AVAILABILITY: http://www.bioconductor.org/packages/2.0/bioc/html/oneChannelGUI.html