TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets

Jin, Ying; Tam, Oliver H.; Paniagua, Eric; Hammell, Molly

doi:10.1093/bioinformatics/btv422

Cited by 451 publications

(455 citation statements)

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“…Gene expression levels were quantified as read counts mapped to NCBI RefSeq gene annotations (Pruitt et al, 2012). TE expression levels—for Alu, L1 and SVA elements—were quantified using reads mapped to RepeatMasker annotations, which were subsequently analyzed with the TEtranscripts package (Jin et al, 2015). The TEtranscripts program uses an expectation maximization (EM) algorithm to choose optimal unique TE locations for multi-mapped reads, thereby allowing for accurate expression level measurements for active TE families.…”

Section: Methodsmentioning

confidence: 99%

“…The Cancer Genome Atlas (TCGA) provides access to both transcriptome sequence data (RNA-seq) and whole genome sequence data (DNA-seq) for a number of matched normal and primary tumor sample pairs from individual patients (Weinstein et al, 2013). In addition, recently developed bioinformatics algorithms allow for the detection of TE transcripts directly from RNA-seq data (Jin et al, 2015) as well as for the characterization of novel TE insertions from DNA-seq data (Thung et al, 2014; Sudmant et al, 2015). We took advantage of these developments in order to evaluate the patterns of both TE expression and insertional activity in three cancer types: breast invasive carcinoma, head, and neck squamous cell carcinoma, and lung adenocarcinoma (Figure 1 and Supplementary Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Patterns of Transposable Element Expression and Insertion in Cancer

Clayton

Wang

Rishishwar

et al. 2016

Front. Mol. Biosci.

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Human transposable element (TE) activity in somatic tissues causes mutations that can contribute to tumorigenesis. Indeed, TE insertion mutations have been implicated in the etiology of a number of different cancer types. Nevertheless, the full extent of somatic TE activity, along with its relationship to tumorigenesis, have yet to be fully explored. Recent developments in bioinformatics software make it possible to analyze TE expression levels and TE insertional activity directly from transcriptome (RNA-seq) and whole genome (DNA-seq) next-generation sequence data. We applied these new sequence analysis techniques to matched normal and primary tumor patient samples from the Cancer Genome Atlas (TCGA) in order to analyze the patterns of TE expression and insertion for three cancer types: breast invasive carcinoma, head and neck squamous cell carcinoma, and lung adenocarcinoma. Our analysis focused on the three most abundant families of active human TEs: Alu, SVA, and L1. We found evidence for high levels of somatic TE activity for these three families in normal and cancer samples across diverse tissue types. Abundant transcripts for all three TE families were detected in both normal and cancer tissues along with an average of ~80 unique TE insertions per individual patient/tissue. We observed an increase in L1 transcript expression and L1 insertional activity in primary tumor samples for all three cancer types. Tumor-specific TE insertions are enriched for private mutations, consistent with a potentially causal role in tumorigenesis. We used genome feature analysis to investigate two specific cases of putative cancer-causing TE mutations in further detail. An Alu insertion in an upstream enhancer of the CBL tumor suppressor gene is associated with down-regulation of the gene in a single breast cancer patient, and an L1 insertion in the first exon of the BAALC gene also disrupts its expression in head and neck squamous cell carcinoma. Our results are consistent with widespread somatic activity of human TEs leading to numerous insertion mutations that can contribute to tumorigenesis in a variety of tissues.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Patterns of Transposable Element Expression and Insertion in Cancer

Clayton

Wang

Rishishwar

et al. 2016

Front. Mol. Biosci.

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“…6 We compared the performance in terms of running time and quantification accuracy between our proposed pipeline and other tools, including TEtranscripts, HTSeq-count, Cuffdiff and RepEnrich. 14,[32][33][34] In the second dataset, we seek to identify new TEs that are associated with Amyotrophic Lateral Sclerosis (ALS). We applied our pipeline to a K562 cell-line RNA-seq dataset from ENCODE (Encyclopedia of DNA Elements, http://encodeproject.org) Consortium (accession ID: ENCBS555BYH).…”

Section: Datasetsmentioning

confidence: 99%

“…[6][7][8][9] Toward this end, several algorithms and pipelines were proposed to analyze reads files from TE studies. [10][11][12][13][14][15][16] However, most of the tools share some common limitations: 1) discordant read mapping due to increased chance of multiple mapping in repetitive elements from TEs in the same clade, 2) limited scalability for large-scale analysis, and 3) small coverage for the entire TEs defined in the human genome, i.e., a tool used in [16] only considered LINE 1 (Long Interspersed Nuclear Element 1) elements. 17 Among the existing tools, TEtranscripts has performed well on various datasets.…”

Section: Introductionmentioning

confidence: 99%

An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data

et al. 2017

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Transposable elements (TEs) are DNA sequences which are capable of moving from one location to another and represent a large proportion (45%) of the human genome. TEs have functional roles in a variety of biological phenomena such as cancer, neurodegenerative disease, and aging. Rapid development in RNA-sequencing technology has enabled us, for the first time, to study the activity of TE at the systems level.However, efficient TE analysis tools are not yet developed. In this work, we developed SalmonTE, a fast and reliable pipeline for the quantification of TEs from RNA-seq data. We benchmarked our tool against TEtranscripts, a widely used TE quantification method, and three other quantification methods using several RNA-seq datasets from Drosophila melanogaster and human cell-line. We achieved 20 times faster execution speed without compromising the accuracy. This pipeline will enable the biomedical research community to quantify and analyze TEs from large amounts of data and lead to novel TE centric discoveries.

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“…We limited our list of potential TEs to those included in TEtranscripts (Jin et al 2015) and RepEnrich 438 (Criscione et al 2014) to enable comparisons between these different programs. Using the selected TE 439 coordinates we generated a BED file using Clean and obtained Fasta sequences using Seek.…”

mentioning

confidence: 99%

SQuIRE: Software for Quantifying Interspersed Repeat Elements

Ardeljan

et al. 2018

Preprint

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22Transposable elements are interspersed repeat sequences that make up much of the human 23 genome. Conventional approaches to RNA-seq analysis often exclude these sequences, fail to 24 optimally adjudicate read alignments, or align reads to interspersed repeat consensus sequences 25 without considering these transcripts in their genomic contexts. As a result, repetitive sequence 26 contributions to transcriptomes are not well understood. Here, we present Software for 27Quantifying Interspersed Repeat Expression (SQuIRE), an RNA-seq analysis pipeline that 28 integrates repeat and genome annotation (RepeatMasker), read alignment (STAR), gene 29 expression (StringTie) and differential expression (DESeq2). SQuIRE uniquely provides a locus-30 specific picture of interspersed repeat-encoded RNA expression. SQuIRE can be downloaded at 31 (github.com/wyang17/SQuIRE). 32

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TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 451 publications

References 58 publications

Patterns of Transposable Element Expression and Insertion in Cancer

Patterns of Transposable Element Expression and Insertion in Cancer

An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data

SQuIRE: Software for Quantifying Interspersed Repeat Elements

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