Bayesian nonparametric discovery of isoforms and individual specific quantification

Discovery of Novel Protein-Coding and Long Non-coding Transcripts in Distinct Regions of the Human Brain

Recent improvements in the accuracy of long-read sequencing (LRS) technologies have expanded the scope for novel transcriptional isoform discovery. Additionally, these advancements have improved the precision of transcript quantification, enabling a more accurate reconstruction of complex splicing patterns and transcriptomes. Thus, this project aims to take advantage of these analytical developments for the discovery and analysis of RNA isoforms in the human brain. A set of novel transcript isoforms was compiled using three bioinformatic tools, quantifying their expression across eight replicates of the cerebellar hemisphere, five replicates of the frontal cortex, and six replicates of the putamen. By taking a subset of the novel isoforms consistent across all discovery methods, a set of 170 highly confident novel RNA isoforms was curated for downstream analysis. This set consisted of 104 messenger RNAs (mRNAs) and 66 long non-coding RNAs (lncRNAs) isoforms. The detailed structure, expression, and potential encoded proteins of novel mRNA isoform BambuTx321 have been further described as an exemplary representative. Additionally, the tissue-specific expression [mean counts per million (CPM) of 5.979] of novel lncRNA, BambuTx1299, in the cerebellar hemisphere was observed. Overall, this project has identified and annotated several novel RNA isoforms across diverse tissues of the human brain, providing insights into their expression patterns and investigating their potential functional roles. Thus, this project has contributed to a more comprehensive understanding of the brain's transcriptomic landscape for applications in basic research.

Alternative splicing landscape in mouse skeletal muscle and adipose tissue: Effects of intermittent fasting and exercise

Alternative splicing contributes to diversify the cellular protein landscape, but aberrant splicing is implicated in many diseases. To which extent mis-splicing contributes to insulin resistance as the causal defect of type 2 diabetes and whether this can be reversed by lifestyle interventions is largely unknown. Therefore, RNA sequencing data from skeletal muscle and adipose tissue of diabetes-susceptible NZO mice treated with or without intermittent fasting and of healthy C57BL/6J mice subjected to exercise were analyzed for alternative splicing differences using Whippet and rMATS. Diet and exercise interventions triggered comparable levels of splicing changes, although the splicing profile of skeletal muscle appeared to be more flexible than that of adipose tissue, with 72-114 differential splicing events in muscle and less than 25 in adipose tissue. Splicing changes induced by time-restricted feeding, alternate-day fasting and exercise were generally mild, with a maximal percent spliced in (PSI) difference of 67%, indicating that alternative splicing plays a rather minor role in lifestyle-induced adaptations of muscle and adipose tissue in mice. However, intron retention contributed to the regulation of gene expression, influencing genes whose expression was directly linked to phenotypic parameters (e.g. Eno2 and Pan2). Alternate-day fasting promoted skipping of exon 7 in Mlxipl (coding for ChREBP), thereby affecting the glucose sensing module of this carbohydrate-responsive transcription factor. Both intermittent fasting and exercise training led to alternative splicing of known diabetes-related GWAS genes (e.g. Abcc8, Ifnar2, Smarcad1), highlighting the potential metabolic relevance of these changes.

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches

Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.

Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures

MOTIVATION

Non-canonical (or non-B) DNA are genomic regions whose three-dimensional conformation deviates from the canonical double helix. Non-B DNA play an important role in basic cellular processes and are associated with genomic instability, gene regulation, and oncogenesis. Experimental methods are low-throughput and can detect only a limited set of non-B DNA structures, while computational methods rely on non-B DNA base motifs, which are necessary but not sufficient indicators of non-B structures. Oxford Nanopore sequencing is an efficient and low-cost platform, but it is currently unknown whether nanopore reads can be used for identifying non-B structures.

RESULTS

We build the first computational pipeline to predict non-B DNA structures from nanopore sequencing. We formalize non-B detection as a novelty detection problem and develop the GoFAE-DND, an autoencoder that uses goodness-of-fit (GoF) tests as a regularizer. A discriminative loss encourages non-B DNA to be poorly reconstructed and optimizing Gaussian GoF tests allows for the computation of P-values that indicate non-B structures. Based on whole genome nanopore sequencing of NA12878, we show that there exist significant differences between the timing of DNA translocation for non-B DNA bases compared with B-DNA. We demonstrate the efficacy of our approach through comparisons with novelty detection methods using experimental data and data synthesized from a new translocation time simulator. Experimental validations suggest that reliable detection of non-B DNA from nanopore sequencing is achievable.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/bayesomicslab/ONT-nonb-GoFAE-DND.

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles

MOTIVATION

High-throughput RNA sequencing has revolutionized the scope and depth of transcriptome analysis. Accurate reconstruction of a phenotype-specific transcriptome is challenging due to the noise and variability of RNA-seq data. This requires computational identification of transcripts from multiple samples of the same phenotype, given the underlying consensus transcript structure.

RESULTS

We present a Bayesian method, integrated assembly of phenotype-specific transcripts (IntAPT), that identifies phenotype-specific isoforms from multiple RNA-seq profiles. IntAPT features a novel two-layer Bayesian model to capture the presence of isoforms at the group layer and to quantify the abundance of isoforms at the sample layer. A spike-and-slab prior is used to model the isoform expression and to enforce the sparsity of expressed isoforms. Dependencies between the existence of isoforms and their expression are modeled explicitly to facilitate parameter estimation. Model parameters are estimated iteratively using Gibbs sampling to infer the joint posterior distribution, from which the presence and abundance of isoforms can reliably be determined. Studies using both simulations and real datasets show that IntAPT consistently outperforms existing methods for the IntAPT. Experimental results demonstrate that, despite sequencing errors, IntAPT exhibits a robust performance among multiple samples, resulting in notably improved identification of expressed isoforms of low abundance.

AVAILABILITY AND IMPLEMENTATION

The IntAPT package is available at http://github.com/henryxushi/IntAPT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional and structural features of proteins associated with alternative splicing

The alternative splicing is a mechanism increasing the number of expressed proteins and a variety of these functions. We uncovered the protein domains most frequently lacked or occurred in the splice variants. Proteins presented by several isoforms participate in such processes as transcription regulation, immune response, etc. Our results displayed the association of alternative splicing with branched regulatory pathways. By considering the published data on the protein proteins encoded by the 18th human chromosome, we noted that alternative products display the differences in several functional features, such as phosphorylation, subcellular location, ligand specificity, protein-protein interactions, etc. The investigation of alternative variants referred to the protein kinase domain was performed by comparing the alternative sequences with 3D structures. It was shown that large enough insertions/deletions could be compatible with the kinase fold if they match between the conserved secondary structures. Using the 3D data on human proteins, we showed that conformational flexibility could accommodate fold alterations in splice variants. The investigations of structural and functional differences in splice isoforms are required to understand how to distinguish the isoforms expressed as functioning proteins from the non-realized transcripts. These studies allow filling the gap between genomic and proteomic data.