DROPA: DRIP-seq optimized peak annotator

Human DNA topoisomerase I poisoning causes R loop-mediated genome instability attenuated by transcription factor IIS

DNA topoisomerase I can contribute to cancer genome instability. During catalytic activity, topoisomerase I forms a transient intermediate, topoisomerase I-DNA cleavage complex (Top1cc) to allow strand rotation and duplex relaxation, which can lead to elevated levels of DNA-RNA hybrids and micronuclei. To comprehend the underlying mechanisms, we have integrated genomic data of Top1cc-triggered hybrids and DNA double-strand breaks (DSBs) shortly after Top1cc induction, revealing that Top1ccs increase hybrid levels with different mechanisms. DSBs are at highly transcribed genes in early replicating initiation zones and overlap with hybrids downstream of accumulated RNA polymerase II (RNAPII) at gene 5'-ends. A transcription factor IIS mutant impairing transcription elongation further increased RNAPII accumulation likely due to backtracking. Moreover, Top1ccs can trigger micronuclei when occurring during late G1 or early/mid S, but not during late S. As micronuclei and transcription-replication conflicts are attenuated by transcription factor IIS, our results support a role of RNAPII arrest in Top1cc-induced transcription-replication conflicts leading to DSBs and micronuclei.

RLSuite: An Integrative R-Loop Bioinformatics Framework

We recently described the development of a database of 810 R-loop mapping datasets and used this data to conduct a meta-analysis of R-loops. R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids and we were able to verify that 30% of expressed genes have an associated R-loop in a location conserved manner.. Moreover, intergenic R-loops map to enhancers, super enhancers and with TAD domain boundaries. This work demonstrated that R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, however the analysis and quality control of these data is a non-trivial task for which few bioinformatic tools exist. Herein we describe RLSuite, an integrative R-loop bioinformatics framework for pre-processing, quality control, and downstream analysis of R-loop mapping data. RLSuite enables users to compare their data to hundreds of public datasets and generate a user-friendly analysis report for sharing with non-bioinformatician colleagues. Taken together, RLSuite is a novel analysis framework that should greatly benefit the emerging R-loop bioinformatics community in a rapidly expanding aspect of epigenetic control that is still poorly understood.

R-Loop Tracker: Web Access-Based Tool for R-Loop Detection and Analysis in Genomic DNA Sequences

R-loops are common non-B nucleic acid structures formed by a three-stranded nucleic acid composed of an RNA–DNA hybrid and a displaced single-stranded DNA (ssDNA) loop. Because the aberrant R-loop formation leads to increased mutagenesis, hyper-recombination, rearrangements, and transcription-replication collisions, it is regarded as important in human diseases. Therefore, its prevalence and distribution in genomes are studied intensively. However, in silico tools for R-loop prediction are limited, and therefore, we have developed the R-loop tracker tool, which was implemented as a part of the DNA Analyser web server. This new tool is focused upon (1) prediction of R-loops in genomic DNA without length and sequence limitations; (2) integration of R-loop tracker results with other tools for nucleic acids analyses, including Genome Browser; (3) internal cross-evaluation of in silico results with experimental data, where available; (4) easy export and correlation analyses with other genome features and markers; and (5) enhanced visualization outputs. Our new R-loop tracker tool is freely accessible on the web pages of DNA Analyser tools, and its implementation on the web-based server allows effective analyses not only for DNA segments but also for full chromosomes and genomes.

Unraveling the structure and biological functions of RNA triple helices

It has been nearly 63 years since the first characterization of an RNA triple helix in vitro by Gary Felsenfeld, David Davies, and Alexander Rich. An RNA triple helix consists of three strands: A Watson–Crick RNA double helix whose major‐groove establishes hydrogen bonds with the so‐called “third strand”. In the past 15 years, it has been recognized that these major‐groove RNA triple helices, like single‐stranded and double‐stranded RNA, also mediate prominent biological roles inside cells. Thus far, these triple helices are known to mediate catalysis during telomere synthesis and RNA splicing, bind to ligands and ions so that metabolite‐sensing riboswitches can regulate gene expression, and provide a clever strategy to protect the 3′ end of RNA from degradation. Because RNA triple helices play important roles in biology, there is a renewed interest in better understanding the fundamental properties of RNA triple helices and developing methods for their high‐throughput discovery. This review provides an overview of the fundamental biochemical and structural properties of major‐groove RNA triple helices, summarizes the structure and function of naturally occurring RNA triple helices, and describes prospective strategies to isolate RNA triple helices as a means to establish the “triplexome”.

This article is categorized under:

RNA Structure and Dynamics > RNA Structure and Dynamics

RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry

RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems

Seq'ing identity and function in a repeat-derived noncoding RNA world

Innovations in high-throughout sequencing approaches are being marshaled to both reveal the composition of the abundant and heterogeneous noncoding RNAs that populate cell nuclei and lend insight to the mechanisms by which noncoding RNAs influence chromosome biology and gene expression. This review focuses on some of the recent technological developments that have enabled the isolation of nascent transcripts and chromatin-associated and DNA-interacting RNAs. Coupled with emerging genome assembly and analytical approaches, the field is poised to achieve a comprehensive catalog of nuclear noncoding RNAs, including those derived from repetitive regions within eukaryotic genomes. Herein, particular attention is paid to the challenges and advances in the sequence analyses of repeat and transposable element-derived noncoding RNAs and in ascribing specific function(s) to such RNAs.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.