Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures

MRT-ModSeq - Rapid Detection of RNA Modifications with MarathonRT

Chemical modifications are essential regulatory elements that modulate the behavior and function of cellular RNAs. Despite recent advances in sequencing-based RNA modification mapping, methods combining accuracy and speed are still lacking. Here, we introduce MRT-ModSeq for rapid, simultaneous detection of multiple RNA modifications using MarathonRT. MRT-ModSeq employs distinct divalent cofactors to generate 2-D mutational profiles that are highly dependent on nucleotide identity and modification type. As a proof of concept, we use the MRT fingerprints of well-studied rRNAs to implement a general workflow for detecting RNA modifications. MRT-ModSeq rapidly detects positions of diverse modifications across a RNA transcript, enabling assignment of m1acp3Y, m1A, m3U, m7G and 2'-OMe locations through mutation-rate filtering and machine learning. m1A sites in sparsely modified targets, such as MALAT1 and PRUNE1 could also be detected. MRT-ModSeq can be trained on natural and synthetic transcripts to expedite detection of diverse RNA modification subtypes across targets of interest.

MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT

Chemical modifications are essential regulatory elements that modulate the behavior and function of cellular RNAs. Despite recent advances in sequencing-based RNA modification mapping, methods combining accuracy and speed are still lacking. Here, we introduce MRT- ModSeq for rapid, simultaneous detection of multiple RNA modifications using MarathonRT. MRT-ModSeq employs distinct divalent cofactors to generate 2-D mutational profiles that are highly dependent on nucleotide identity and modification type. As a proof of concept, we use the MRT fingerprints of well-studied rRNAs to implement a general workflow for detecting RNA modifications. MRT-ModSeq rapidly detects positions of diverse modifications across a RNA transcript, enabling assignment of m1acp3Y, m1A, m3U, m7G and 2'-OMe locations through mutation-rate filtering and machine learning. m1A sites in sparsely modified targets, such as MALAT1 and PRUNE1 could also be detected. MRT-ModSeq can be trained on natural and synthetic transcripts to expedite detection of diverse RNA modification subtypes across targets of interest.

Changes of the tRNA Modification Pattern during the Development of Dictyostelium discoideum

Dictyostelium discoideum is a social amoeba, which on starvation develops from a single-cell state to a multicellular fruiting body. This developmental process is accompanied by massive changes in gene expression, which also affect non-coding RNAs. Here, we investigate how tRNAs as key regulators of the translation process are affected by this transition. To this end, we used LOTTE-seq to sequence the tRNA pool of D. discoideum at different developmental time points and analyzed both tRNA composition and tRNA modification patterns. We developed a workflow for the specific detection of modifications from reverse transcriptase signatures in chemically untreated RNA-seq data at single-nucleotide resolution. It avoids the comparison of treated and untreated RNA-seq data using reverse transcription arrest patterns at nucleotides in the neighborhood of a putative modification site as internal control. We find that nucleotide modification sites in D. discoideum tRNAs largely conform to the modification patterns observed throughout the eukaroytes. However, there are also previously undescribed modification sites. We observe substantial dynamic changes of both expression levels and modification patterns of certain tRNA types during fruiting body development. Beyond the specific application to D. discoideum our results demonstrate that the developmental variability of tRNA expression and modification can be traced efficiently with LOTTE-seq.

Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N⁶-methyladenosine (m⁶A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.

Deep learning in next-generation sequencing

Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addressing questions such as variant calling, metagenomic classification and quantification, genomic feature detection, or downstream analysis in larger biological or medical contexts. In addition to classical algorithmic approaches, machine-learning (ML) techniques are often used for such tasks. In particular, deep learning (DL) methods that use multilayered artificial neural networks (ANNs) for supervised, semisupervised, and unsupervised learning have gained significant traction for such applications. Here, we highlight important network architectures, application areas, and DL frameworks in a NGS context.

Manganese Ions Individually Alter the Reverse Transcription Signature of Modified Ribonucleosides

Reverse transcription of RNA templates containing modified ribonucleosides transfers modification-related information as misincorporations, arrest or nucleotide skipping events to the newly synthesized cDNA strand. The frequency and proportion of these events, merged from all sequenced cDNAs, yield a so-called RT signature, characteristic for the respective RNA modification and reverse transcriptase (RT). While known for DNA polymerases in so-called error-prone PCR, testing of four different RTs by replacing Mg²⁺ with Mn²⁺ in reaction buffer revealed the immense influence of manganese chloride on derived RT signatures, with arrest rates on m¹A positions dropping from 82% down to 24%. Additionally, we observed a vast increase in nucleotide skipping events, with single positions rising from 4% to 49%, thus implying an enhanced read-through capability as an effect of Mn²⁺ on the reverse transcriptase, by promoting nucleotide skipping over synthesis abortion. While modifications such as m¹A, m²₂G, m¹G and m³C showed a clear influence of manganese ions on their RT signature, this effect was individual to the polymerase used. In summary, the results imply a supporting effect of Mn²⁺ on reverse transcription, thus overcoming blockades in the Watson-Crick face of modified ribonucleosides and improving both read-through rate and signal intensity in RT signature analysis.

Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics

Post-transcriptional RNA modification occurs on all types of RNA and plays a vital role in regulating every aspect of RNA function. Thanks to the development of high-throughput sequencing technologies, transcriptome-wide profiling of RNA modifications has been made possible. With the accumulation of a large number of high-throughput datasets, bioinformatics approaches have become increasing critical for unraveling the epitranscriptome. We review here the recent progress in bioinformatics approaches for deciphering the epitranscriptomes, including epitranscriptome data analysis techniques, RNA modification databases, disease-association inference, general functional annotation, and studies on RNA modification site prediction. We also discuss the limitations of existing approaches and offer some future perspectives.

Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes

Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to train a random forest model as a machine learning regimen for prediction of modifications, we found strongly variegated success rates for the prediction of methylated purines, as exemplified with N1-methyladenosine (m1A). Among the 13 enzymes, a correlation was found between read length, misincorporation, and prediction success. Inversely, low average read length was correlated to high arrest rate and lower prediction success. The three most successful polymerases were then applied to the characterization of RT-signatures of other methylated purines. Guanosines featuring methyl groups on the Watson-Crick face were identified with high confidence, but discrimination between m1G and m22G was only partially successful. In summary, the results suggest that, given sufficient coverage and a set of specifically optimized reaction conditions for reverse transcription, all RNA modifications that impede Watson-Crick bonds can be distinguished by their RT-signature.

m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human

Motivation

Recent progress in N7-methylguanosine (m7G) RNA methylation studies has focused on its internal (rather than capped) presence within mRNAs. Tens of thousands of internal mRNA m7G sites have been identified within mammalian transcriptomes, and a single resource to best share, annotate and analyze the massive m7G data generated recently are sorely needed.

Results

We report here m7GHub, a comprehensive online platform for deciphering the location, regulation and pathogenesis of internal mRNA m7G. The m7GHub consists of four main components, including: the first internal mRNA m7G database containing 44 058 experimentally validated internal mRNA m7G sites, a sequence-based high-accuracy predictor, the first web server for assessing the impact of mutations on m7G status, and the first database recording 1218 disease-associated genetic mutations that may function through regulation of m7G methylation. Together, m7GHub will serve as a useful resource for research on internal mRNA m7G modification.

Availability and implementation

m7GHub is freely accessible online at www.xjtlu.edu.cn/biologicalsciences/m7ghub.

Contact

kunqi.chen@liverpool.ac.uk

Supplementary information

Supplementary data are available at Bioinformatics online.