Short interspersed nuclear elements (SINEs) are a widespread type of small transposable element (TE). With increasing evidence for their impact on gene function and genome evolution in plants, accurate genome-scale SINE annotation becomes a fundamental step for studying the regulatory roles of SINEs and their relationship with other components in the genomes. Despite the overall promising progress made in TE annotation, SINE annotation remains a major challenge. Unlike some other TEs, SINEs are short and heterogeneous, and they usually lack well-conserved sequence or structural features. Thus, current SINE annotation tools have either low sensitivity or high false discovery rates. Given the demand and challenges, we aimed to provide a more accurate and efficient SINE annotation tool for plant genomes. The pipeline starts with maximizing the pool of SINE candidates via profile hidden Markov model-based homology search and de novo SINE search using structural features. Then, it excludes the false positives by integrating all known features of SINEs and the features of other types of TEs that can often be misannotated as SINEs. As a result, the pipeline substantially improves the tradeoff between sensitivity and accuracy, with both values close to or over 90%. We tested our tool in Arabidopsis thaliana and rice (Oryza sativa), and the results show that our tool competes favorably against existing SINE annotation tools. The simplicity and effectiveness of this tool would potentially be useful for generating more accurate SINE annotations for other plant species. The pipeline is freely available at https://github.com/yangli557/AnnoSINE.
Transposable elements (TEs) are ubiquitous in genomes and many remain active. TEs comprise an important fraction of the transcriptomes with potential effects on the host genome, either by generating deleterious mutations or promoting evolutionary novelties. However, their functional study is limited by the difficulty in their identification and quantification, particularly in non-model organisms.
We developed a new pipeline [explore active transposable elements (ExplorATE)] implemented in R and bash that allows the quantification of active TEs in both model and non-model organisms. ExplorATE creates TE-specific indexes and uses the Selective Alignment (SA) to filter out co-transcribed transposons within genes based on alignment scores. Moreover, our software incorporates a Wicker-like criteria to refine a set of target TEs and avoid spurious mapping. Based on simulated and real data, we show that the SA strategy adopted by ExplorATE achieved better estimates of non-co-transcribed elements than other available alignment-based or mapping-based software. ExplorATE results showed high congruence with alignment-based tools with and without a reference genome, yet ExplorATE required less execution time. Likewise, ExplorATE expands and complements most previous TE analyses by incorporating the co-transcription and multi-mapping effects during quantification, and provides a seamless integration with other downstream tools within the R environment.
Source code is available at https://github.com/FemeniasM/ExplorATEproject and https://github.com/FemeniasM/ExplorATE_shell_script. Data available on request.
Supplementary data are available at Bioinformatics online.
- Award ID(s):
- 2016372
- NSF-PAR ID:
- 10368208
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 38
- Issue:
- 13
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- p. 3361-3366
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Transposable elements (
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https://github.com/SNAnderson/maizeTE_variation ;https://mcstitzer.github.io/maize_TEs . -
Abstract Background Transposable elements (TEs) are powerful creators of genotypic and phenotypic diversity due to their inherent mutagenic capabilities and in this way they serve as a deep reservoir of sequences for genomic variation. As agents of genetic disruption, a TE’s potential to impact phenotype is partially a factor of its location in the genome. Previous research has shown TEs’ ability to impact the expression of neighboring genes, however our understanding of this trend is hampered by the exceptional amount of diversity in the TE world, and a lack of publicly available computational methods that quantify the presence of TEs relative to genes. Results Here, we have developed a tool to more easily quantify TE presence relative to genes through the use of only a gene and TE annotation, yielding a new metric we call TE Density. Briefly defined as the proportion of TE-occupied base-pairs relative to a window-size of the genome. This new pipeline reports TE density for each gene in the genome, for each type descriptor of TE (order and superfamily), and for multiple positions and distances relative to the gene (upstream, intragenic, and downstream) over sliding, user-defined windows. In this way, we overcome previous limitations to the study of TE-gene relationships by focusing on all TE types present in the genome, utilizing flexible genomic distances for measurement, and reporting a TE presence metric for every gene in the genome. Conclusions Together, this new tool opens up new avenues for studying TE-gene relationships, genome architecture, comparative genomics, and the tremendous diversity present of the TE world. TE Density is open-source and freely available at: https://github.com/sjteresi/TE_Density .more » « less
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