Bedshift: perturbation of genomic interval sets

TEENA: an integrated web server for transposable element enrichment analysis in various model and non-model organisms

Transposable elements (TEs) are abundant in the genomes of various eukaryote organisms. Increasing evidence suggests that TEs can play crucial regulatory roles-usually by creating cis-elements (e.g. enhancers and promoters) bound by distinct transcription factors (TFs). TE-derived cis-elements have gained unprecedented attentions recently, and one key step toward their understanding is to identify the enriched TEs in distinct genomic intervals (e.g. a set of enhancers or TF binding sites) as candidates for further study. Nevertheless, such analysis remains challenging for researchers unfamiliar with TEs or lack strong bioinformatic skills. Here, we present TEENA (Transposable Element ENrichment Analyzer) to streamline TE enrichment analysis in various organisms. It implements an optimized pipeline, hosts the genome/gene/TE annotations of almost one hundred species, and provides multiple parameters to enable its flexibility. Taking genomic interval data as the only user-supplied file, it can automatically retrieve the corresponding annotations and finish a routine analysis in a couple minutes. Multiple case studies demonstrate that it can produce highly reliable results matching previous knowledge. TEENA can be freely accessed at: https://sun-lab.yzu.edu.cn/TEENA. Due to its easy-to-use design, we expect it to facilitate the studies of the regulatory function of TEs in various model and non-model organisms.

RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity

Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic elements. However, none of them explicitly considered the transcriptome heterogeneity and isoform ambiguity, making them less appropriate for analyzing transcriptome elements. Here, we developed RgnTX, an R/Bioconductor tool for the colocalization analysis of transcriptome elements with permutation tests. Different from existing approaches, RgnTX directly takes advantage of transcriptome annotation, and offers high flexibility in the null model to simulate realistic transcriptome-wide background, such as the complex alternative splicing patterns. Importantly, it supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. Proposed package offers a wide selection of pre-defined functions, easy to be utilized by users for visualizing permutation results, calculating shifted z-scores and conducting multiple hypothesis testing under Benjamini-Hochberg correction. Moreover, with synthetic and real datasets, we show that RgnTX novel testing modes return distinct and more significant results compared to existing genome-based methods. We believe RgnTX should make a useful tool to characterize the randomness of the transcriptome, and for conducting statistical association analysis for genomic region sets within the heterogeneous transcriptome. The package now has been accepted by Bioconductor and is freely available at: https://bioconductor.org/packages/RgnTX.

OUP accepted manuscript

Motivation

Genome annotations are a common way to represent genomic features such as genes, regulatory elements or epigenetic modifications. The amount of overlap between two annotations is often used to ascertain if there is an underlying biological connection between them. In order to distinguish between true biological association and overlap by pure chance, a robust measure of significance is required. One common way to do this is to determine if the number of intervals in the reference annotation that intersect the query annotation is statistically significant. However, currently employed statistical frameworks are often either inefficient or inaccurate when computing P-values on the scale of the whole human genome.

Results

We show that finding the P-values under the typically used ‘gold’ null hypothesis is NP-hard. This motivates us to reformulate the null hypothesis using Markov chains. To be able to measure the fidelity of our Markovian null hypothesis, we develop a fast direct sampling algorithm to estimate the P-value under the gold null hypothesis. We then present an open-source software tool MCDP that computes the P-values under the Markovian null hypothesis in O(m2+n) time and O(m) memory, where m and n are the numbers of intervals in the reference and query annotations, respectively. Notably, MCDP runtime and memory usage are independent from the genome length, allowing it to outperform previous approaches in runtime and memory usage by orders of magnitude on human genome annotations, while maintaining the same level of accuracy.

Availability and implementation

The software is available at https://github.com/fmfi-compbio/mc-overlaps. All data for reproducibility are available at https://github.com/fmfi-compbio/mc-overlaps-reproducibility.

Supplementary information

Supplementary data are available at Bioinformatics online.

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

Background

Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software.

Results

To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata.

Conclusions

The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/.