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Motivation Long-read sequencing methods have considerable advantages for characterizing RNA isoforms. Oxford Nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilizing matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages. Results We developed ‘NanoSplicer’ to identify splice junctions using raw nanopore signal (squiggles). For each splice junction, the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using (i) synthetic mRNAs with known splice junctions and (ii) biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. Availability and implementation NanoSplicer is available at https://github.com/shimlab/NanoSplicer and archived at https://doi.org/10.5281/zenodo.6403849. Data is available from ENA: ERS7273757 and ERS7273453. Supplementary information Supplementary data are available at Bioinformatics online.

Background: Splice junctions are the key to going from pre-messenger RNA to mature messenger RNA in many multi-exon genes due to alternative splicing. Since the percentage of multi-exon genes that undergo alternative splicing is very high, identifying splice junctions is an attractive research topic with important implications. Objective: The aim is to develop a deep learning model capable of identifying splice junctions in RNA sequences using 13,666 unique sequences of primate RNA. Method: A Long Short-Term Memory (LSTM) Neural Network model is developed that classifies a given sequence as EI (Exon-Intron splice), IE (Intron-Exon splice), or N (No splice). The model is trained with groups of trinucleotides and its performance is tested using validation and test data to prevent bias. Results: Model performance was measured using accuracy and f-score in test data. The finalized model achieved an average accuracy of 91.34% with an average f-score of 91.36% over 50 runs. Conclusion: Comparisons show a highly competitive model to recent Convolutional Neural Network structures. The proposed LSTM model achieves the highest accuracy and f-score among published alternative LSTM structures.

Most of the current alternative splicing (AS) analysis tools are powerless to analyse complex splicing. To address this, we developed SUVA (Splice sites Usage Variation Analysis) that decomposes complex splicing events into five types of splice junction pairs. By analysing real and simulated data, SUVA showed higher sensitivity and accuracy in detecting AS events than the compared methods. Notably, SUVA detected extensive complex AS events and screened out 69 highly conserved and dominant AS events associated with cancer. The cancer-associated complex AS events in FN1 and the co-regulated RNA-binding proteins were significantly correlated with patient survival.

A method based on mRNA-templated ligation of splice-junction anchored DNA probes followed by PCR amplification of the ligated product has been developed for multiplexed detection of mRNA splice variants with high sensitivity and specificity. The proposed assay can detect as low as 10 aM mRNA splicing variants and has been successfully applied to detect real samples.

Background The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants. Results A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427–433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20–28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5′ and 3′ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage. Conclusion A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN is a general method that can be applied to bulk or single-cell data, but has particular utility for single-cell analysis due to that data’s unique challenges and opportunities for discovery. SICILIAN’s precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, and increases agreement between biological replicates. SICILIAN detects unannotated splicing in single cells, enabling the discovery of novel splicing regulation through single-cell analysis workflows. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02434-8.

Most of the current computational models for splice junction prediction are based on the identification of canonical splice junctions. However, it is observed that the junctions lacking the consensus dimers GT and AG also undergo splicing. Identification of such splice junctions, called the non-canonical splice junctions, is also essentially important for a comprehensive understanding of the splicing phenomenon. This work focuses on the identification of non-canonical splice junctions through the application of a bidirectional long short-term memory (BLSTM) network. Furthermore, we apply a back-propagation based (integrated gradient) and a perturbation based (occlusion) visualization techniques to extract the non-canonical splicing features learned by the model. The features obtained are validated with the existing knowledge from the literature. Integrated gradient extracts features that comprise contiguous nucleotides, whereas occlusion extracts features that are individual nucleotides distributed across the sequence.

Circular RNA (circRNA) has the potential to be applied to disease diagnosis and therapy. However, the currently available circRNA detection techniques are limited. This work proposes a sensitive and selective approach for circRNA detection based on gold nanoparticle-modified screen-printed magnetic electrodes (AuNPs-SPME). Magnetic beads (MBs) with capture probes based on specific back-splice junction (BSJ) sites were employed to identify and selectively isolate the target circRNA, which could be directly adsorbed onto the AuNPs-SPME. Then, the circRNA attached to the surface was detected by changes in the methylene blue redox signal. The simple and time-saving AuNPs-SPME is highly sensitive (LOD = 1.0 pM) to circCDYL, one of the biomarkers of hepatocellular cancer (HCC). The analytical performance of the method presented has also been verified in human serum samples, holding great promise for clinical diagnosis.

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN’s precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, increases agreement between biological replicates, and reliably detects un-annotated splicing in single cells, enabling the discovery of novel splicing regulation.

Significance Naturally split inteins are important tools in chemical biology and protein engineering, as they provide a rapid and bioorthogonal means to link two polypeptides, termed exteins, together in a near-traceless manner. However, their use is currently limited by sequence constraints imposed by these extein residues. The engineered split inteins reported in this work, Npu GEP and Cfa GEP , demonstrate a marked enhancement in extein tolerance, offering greater promiscuity for splicing nonnative sequences. As such, they are shown to improve two important applications of naturally split inteins: protein cyclization and the chemical tailoring of native chromatin. We expect these promiscuous inteins to find broad use in other applications of split inteins that involve the construction of proteins with well-defined sequences.

Background Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation. Results In this work, we present a deep learning based splice junction sequence classifier, named DeepSplice, which employs convolutional neural networks to classify candidate splice junctions. We show (I) DeepSplice outperforms state-of-the-art methods for splice site classification when applied to the popular benchmark dataset HS3D, (II) DeepSplice shows high accuracy for splice junction classification with GENCODE annotation, and (III) the application of DeepSplice to classify putative splice junctions generated by Rail-RNA alignment of 21,504 human RNA-seq data significantly reduces 43 million candidates into around 3 million highly confident novel splice junctions. Conclusions A model inferred from the sequences of annotated exon junctions that can then classify splice junctions derived from primary RNA-seq data has been implemented. The performance of the model was evaluated and compared through comprehensive benchmarking and testing, indicating a reliable performance and gross usability for classifying novel splice junctions derived from RNA-seq alignment. Electronic supplementary material The online version of this article (10.1186/s12864-018-5350-1) contains supplementary material, which is available to authorized users.

Next-generation sequencing technologies enable rapid and cheap genome-wide transcriptome analysis, providing vital information about gene structure, transcript expression, and alternative splicing. Key to this is the accurate identification of exon-exon junctions from RNA sequenced (RNA-seq) reads. A number of RNA-seq aligners capable of splitting reads across these splice junctions (SJs) have been developed; however, it has been shown that while they correctly identify most genuine SJs available in a given sample, they also often produce large numbers of incorrect SJs. Here, we describe the extent of this problem using popular RNA-seq mapping tools and present a new method, called Portcullis, to rapidly filter false SJs derived from spliced alignments. We show that Portcullis distinguishes between genuine and false-positive junctions to a high degree of accuracy across different species, samples, expression levels, error profiles, and read lengths. Portcullis is portable, efficient, and, to our knowledge, currently the only SJ prediction tool that reliably scales for use with large RNA-seq datasets and large, highly fragmented genomes, while delivering accurate SJs.

Neural models have been able to obtain state-of-the-art performances on several genome sequence-based prediction tasks. Such models take only nucleotide sequences as input and learn relevant features on its own. However, extracting the interpretable motifs from the model remains a challenge. This work explores various existing visualization techniques in their ability to infer relevant sequence information learned by a recurrent neural network (RNN) on the task of splice junction identification. The visualization techniques have been modulated to suit the genome sequences as input. The visualizations inspect genomic regions at the level of a single nucleotide as well as a span of consecutive nucleotides. This inspection is performed based on modification of input sequences (perturbation-based) or the embedding space (back-propagation based). We infer features pertaining to both canonical and non-canonical splicing from a single neural model. Results indicate that the visualization techniques produce comparable performance for branchpoint detection. However, in case of canonical donor and acceptor junction motifs, perturbation based visualizations perform better than back-propagation based visualizations and vice-versa for non-canonical motifs.

Motivation: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions. Results: Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events. Contact: arul@med.umich.edu, nesvi@med.umich.edu Availability and implementation: Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2. Supplementary information: Supplementary data are available at Bioinformatics online.

Covalently closed circular RNA (circRNA) has potential implications for disease diagnosis and therapy. However, current methods for circRNA detection are infrequent, and require RNase R pretreatment in general, which is deviation-oriented and labor-intensive. Herein, we have developed a simple electrochemical method for direct detection of circRNA. Firstly, the hairpin probes are designed to identify circRNA based on back-splice junction (BSJ) sites. Then, the selection of substrate length by duplex-specific nuclease (DSN) is utilized to trigger DSN-assisted amplification, resulting in significantly amplified signal. Therefore, the limit of detection of the proposed method for circRNA can reach as low as 3.47 fM, which may contribute to a better selectivity, reproducibility, and stability. This method has also been employed to assay circRNA in human serum samples, which may hold great potential in the clinical diagnosis applications.

Comparison of observed exon–exon junction counts to those predicted from estimated transcript abundances can identify genes with misannotated or misquantified transcripts., Most methods for statistical analysis of RNA-seq data take a matrix of abundance estimates for some type of genomic features as their input, and consequently the quality of any obtained results is directly dependent on the quality of these abundances. Here, we present the junction coverage compatibility score, which provides a way to evaluate the reliability of transcript-level abundance estimates and the accuracy of transcript annotation catalogs. It works by comparing the observed number of reads spanning each annotated splice junction in a genomic region to the predicted number of junction-spanning reads, inferred from the estimated transcript abundances and the genomic coordinates of the corresponding annotated transcripts. We show that although most genes show good agreement between the observed and predicted junction coverages, there is a small set of genes that do not. Genes with poor agreement are found regardless of the method used to estimate transcript abundances, and the corresponding transcript abundances should be treated with care in any downstream analyses.

IVS-I-2 (T>C) (HBB: c.92 ± 2T>C) is a point mutation that affect the splice junction between the exon and intron of the HBB gene. The aberrant splicing site generates an abnormal mRNA. It is a rare...

Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.

In order to boost the theatrical performance of a TUX-E-DO Pipeline, we concentrated on the programs (tools) running within a pipeline to optimize their processing time. Initially we figured out the programs executing in the central part of the tuxedo pipeline which consume time more critically. We processed multiple raw RNA-Seq datasets on a tuxedo pipeline and recorded the time consumed by each tool to achieve this task. Therefore, we identified tophat as the maximum time consuming program (tool). Anyhow, tophat is a fast and efficient spliced aligner, as aligning RNA-Seq reads to a reference genome comparatively it consumes more time than the other programs. To find the logic behind the lengthy processing of tophat we executed multiple independent raw RNA-Seq data-sets by tophat used different number of threads and the execution-time of a data-set is recorded. As we know that, increasing the number of threads reduces the processing time. Contrarily, the results show that the processing time increases with increasing the number of threads. After the analysis and comprehensive simulations of the data processing-time of all data-sets, we found that between the threads there is a lack of communication and synchronization. To increase number of threads requires increase resolution of communication and synchronization. There is an enormous increase in alignment time resulting in processing time elongation.