Biology subjects

Miller, T. L. A.

Publications and source records attributed to Miller, T. L. A..

3 recordsLinked to original sources

bioRxiv2024

FREDDIE: A comprehensive tool for detecting exonization of retrotransposable elements in short and long RNA sequencing data

BackgroundTransposable elements (TEs) constitute a significant portion of mammalian genomes, accounting for about 50% of the total DNA. Intragenic TEs are of particular interest as they are co-transcribed with their host genes in pre-mRNA, potentially leading to the formation of novel chimeric transcripts and the exonization of TEs. The abundance of RNA sequencing data currently available offers a unique opportunity to explore transcriptomic variations. However, a significant limitation is the capability of existing computational tools. Here, we introduce FREDDIE, an innovative algorithm designed to detect the exonization of retrotransposable elements using RNA-seq data. FREDDIE can process short and long RNA sequencing data, assemble and quantify transcripts, evaluate coding potential, and identify protein domains in chimeric transcripts involving exonized TEs and retrocopies. ResultsTo demonstrate the efficacy of FREDDIE, we analyzed and validated TE exonization in two human cancer cell lines, K562 and U251. We have identified 322 chimeric transcripts, of which 126 were from K562, and 196 were from U251. Among these chimeric transcripts, there were 35 that showed similar exonization patterns and host genes. These transcripts involve protein-coding genes of the host and exonization of LINE-1 (L1), Alu elements, and retrocopies of coding genes. We have selected some candidates and validated them experimentally through RT-PCR. The validation rate for these candidates was 70%, later confirmed by long-read sequencing. Additionally, we applied FREDDIE to analyze TE exonization across 157 glioblastoma samples, identifying 1,010 chimeric transcripts. The majority of these transcripts involved the exonization of Alu elements (69.8%), followed by L1 (20.6%) and retrocopies (9.6%). Notably, we discovered a highly expressed L1 exonization within the ROS gene, resulting in a truncated open reading frame (ORF) with the deletion of two protein domains. ConclusionsFREDDIE is an efficient and user-friendly tool for identifying chimeric transcripts that involve exonization of intragenic TEs. Overall, FREDDIE enables comprehensive investigations into the contributions of TEs to transcriptome evolution, variation, and disease-associated abnormalities, and it operates effectively on standard computing systems. FREDDIE is publicly available: https://github.com/galantelab/freddie

bioinformatics↗

bioRxiv2023

Sandy: A user-friendly and versatile NGS simulator to facilitate sequencing assay design and optimization

Next-generation sequencing (NGS) is currently the gold standard technique for large-scale genome and transcriptome studies. However, the downstream processing of NGS data is a critical bottleneck that requires difficult decisions regarding data analysis methods and parameters. Simulated or synthetic NGS datasets are practical and cost-effective alternatives for overcoming these difficulties. Simulated NGS datasets have known true values and provide a standardized scenario for driving the development of data analysis methodologies and tuning cut-off values. Although tools for simulating NGS data are available, they have limitations in terms of their overall usability and documentation. Here, we present Sandy, an open-source simulator that generates synthetic reads that mimic DNA or RNA next-generation sequencing on the Illumina, Oxford Nanopore, and Pacific Bioscience platforms. Sandy is designed to be user-friendly, computationally efficient, and capable of simulating data resembling a wide range of features of real NGS assays, including sequencing quality, genomic variations, and gene expression profiles per tissue. To demonstrate Sandys versatility, we used it to address two critical questions in designing an NGS assay: (i) How many reads should be sequenced to ensure unbiased analysis of gene expression in an RNA sequencing run? (ii) What is the lowest genome coverage required to identify most (90%) of the single nucleotide variants and structural variations in whole-genome sequencing? In summary, Sandy is an ideal tool for assessing and validating pipelines for processing, optimizing results, and defining the costs of NGS assays. Sandy runs on Linux, MacOS, and Microsoft Windows and can provide feasible results, even on personal computers. Availability: Sandy is freely available at https://galantelab.github.io/sandy.

bioinformatics↗

bioRxiv2020

sideRETRO: a pipeline for identifying somatic and dimorphic insertions of processed pseudogenes or retrocopies

Retrocopies or processed pseudogenes are gene copies resulting from mRNA retrotransposition. These gene duplicates can be fixed, somatically inserted or dimorphic in the genome. However, knowledge regarding unfixed retrocopies (retroCNVs) is still limited, and the development of computational tools for effectively identifying and genotyping them is an urgent need. Here, we present sideRETRO, a pipeline dedicated not only to detecting retroCNVs in whole-genome or whole-exome sequencing data but also to revealing their insertion sites, zygosity, and genomic context and classifying them as somatic or dimorphic events. We show that sideRETRO can identify novel retroCNVs and genotype them (93.2% accuracy), in addition to identifying dimorphic retroCNVs in whole-genome and whole-exome data. Therefore, sideRETRO fills a gap in the literature and presents an efficient and straightforward algorithm to accelerate the study of retroCNVs. AvailabilitysideRETRO is available at https://github.com/galantelab/sideRETRO

bioinformatics↗