Genome-wide measurement of RNA dissociation from chromatin classifies transcripts by their dynamics and reveals rapid dissociation of enhancer lncRNAs.

Long non-coding RNAs (lncRNAs) are involved in gene expression regulation in cis. Although enriched in the cell chromatin fraction, to what degree this defines their regulatory potential remains unclear. Furthermore, the factors underlying lncRNA chromatin tethering, as well as the molecular basis of efficient lncRNA chromatin dissociation and its impact on enhancer activity and target gene expression, remain to be resolved. Here, we developed chrTT-seq, which combines the pulse-chase metabolic labeling of nascent RNA with chromatin fractionation and transient transcriptome sequencing to follow nascent RNA transcripts from their transcription on chromatin to release and allows the quantification of dissociation dynamics. By incorporating genomic, transcriptomic, and epigenetic metrics, as well as RNA-binding protein propensities, in machine learning models, we identify features that define transcript groups of different chromatin dissociation dynamics. Notably, lncRNAs transcribed from enhancers display reduced chromatin retention, suggesting that, in addition to splicing, their chromatin dissociation may shape enhancer activity.

Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading.

Motivation: Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (single instruction multiple data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we (a) distribute many independent alignments on multiple threads and (b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal. Results: We evaluated our alignment vectorization and parallelization on different processors, including the newest Intel® Xeon® (Skylake) and Intel® Xeon PhiTM (KNL) processors, and use cases. The instruction set AVX512-BW (Byte and Word), available on Skylake processors, can genuinely improve the performance of vectorized alignments. We could run single alignments 1600 times faster on the Xeon PhiTM and 1400 times faster on the Xeon® than executing them with our previous sequential alignment module. Availability and implementation: The module is programmed in C++ using the SeqAn (Reinert et al., 2017) library and distributed with version 2.4 under the BSD license. We support SSE4, AVX2, AVX512 instructions and included UME: SIMD, a SIMD-instruction wrapper library, to extend our module for further instruction sets. We thoroughly test all alignment components with all major C++ compilers on various platforms. Supplementary information: Supplementary data are available at Bioinformatics online.

pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.

Summary: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily, we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment. The package runs seamlessly on CPU and GPU and provides a simple interface to train and evaluate a network with a handful lines of code. Using an RNA A-to-I editing dataset and cross-linking immunoprecipitation (CLIP)-seq binding site sequences, we demonstrate that pysster classifies sequences with higher accuracy than previous methods, such as GraphProt or ssHMM, and is able to recover known sequence and structure motifs. Availability and implementation: pysster is freely available at https://github.com/budach/pysster. Supplementary information: Supplementary data are available at Bioinformatics online.

Principles of microRNA Regulation Revealed Through Modeling microRNA Expression Quantitative Trait Loci.

Extensive work has been dedicated to study mechanisms of microRNA-mediated gene regulation. However, the transcriptional regulation of microRNAs themselves is far less well understood, due to difficulties determining the transcription start sites of transient primary transcripts. This challenge can be addressed using expression quantitative trait loci (eQTLs) whose regulatory effects represent a natural source of perturbation of cis-regulatory elements. Here we used previously published cis-microRNA-eQTL data for the human GM12878 cell line, promoter predictions, and other functional annotations to determine the relationship between functional elements and microRNA regulation. We built a logistic regression model that classifies microRNA/SNP pairs into eQTLs or non-eQTLs with 85% accuracy; shows microRNA-eQTL enrichment for microRNA precursors, promoters, enhancers, and transcription factor binding sites; and depletion for repressed chromatin. Interestingly, although there is a large overlap between microRNA eQTLs and messenger RNA eQTLs of host genes, 74% of these shared eQTLs affect microRNA and host expression independently. Considering microRNA-only eQTLs we find a significant enrichment for intronic promoters, validating the existence of alternative promoters for intragenic microRNAs. Finally, in line with the GM12878 cell line derived from B cells, we find genome-wide association (GWA) variants associated to blood-related traits more likely to be microRNA eQTLs than random GWA and non-GWA variants, aiding the interpretation of GWA results.