Methods to study splicing from high-throughput RNA Sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

Practical Data Processing Approach for RNA Sequencing of Microorganisms

The rapid evolvement of sequencing technology has generated huge amounts of DNA/RNA sequences, even with the continuous performance acceleration. Due to the wide variety of basic studies and applications derived from the huge number of species and the microorganism diversity, the targets to be sequenced are also expanding. The huge amounts of data generated by recently developed high-throughput sequencers have required highly efficient data analysis algorithms using recently developed high-performance computers. We have developed a highly accurate and cost-effective mapping strategy that includes the exclusion of unreliable base calls and correction of the reference sequence through provisional mapping of RNA sequencing reads. The use of mapping software tools, such as HISAT and STAR, precisely aligned RNA-Seq reads to the genome of a filamentous fungus considering exon-intron boundaries. The accuracy of the expression analysis through the refinement of gene models was achieved by the results of mapped RNA-Seq reads in combination with ab initio gene finding tools using generalized hidden Markov models (GHMMs). Visualization of the mapping results greatly helps evaluate and improve the entire analysis in terms of both wet experiment and data processing. We believe that at least a portion of our approach is useful and applicable to the analysis of any microorganism

Bioinformatics for RNA‐Seq Data Analysis

While RNA sequencing (RNA‐seq) has become increasingly popular for transcriptome profiling, the analysis of the massive amount of data generated by large‐scale RNA‐seq still remains a challenge. RNA‐seq data analyses typically consist of (1) accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events; (2) quantifying expression levels of genes, transcripts, and exons; (3) differential analysis of gene expression among different biological conditions; and (4) biological interpretation of differentially expressed genes. Despite the fact that multiple algorithms pertinent to basic analyses have been developed, there are still a variety of unresolved questions. In this chapter, we review the main tools and algorithms currently available for RNA‐seq data analyses, and our goal is to help RNA‐seq data analysts to make an informed choice of tools in practical RNA‐seq data analysis. In the meantime, RNA‐seq is evolving rapidly, and newer sequencing technologies are briefly introduced, including stranded RNA‐seq, targeted RNA‐seq, and single‐cell RNA‐seq

Modeling and analysis of RNA-seq data: a review from a statistical perspective

Background: Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date. Results: We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. Conclusion: The development of statistical and computational methods for analyzing RNA- seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development

Machine Learning Algorithms For Molecular Signature Identification with High-throughput Genome Sequencing Data

Powered by the high-throughput genomic technologies, the RNA sequencing (RNA-Seq) method is capable of measuring transcriptome-wide mRNA expressions and molecular activities in cells. Elucidation of gene expressions at the isoform resolution enables the detection of better molecular signatures for phenotype prediction, and the identified biomarkers may provide insights into the functional consequences of disease. This dissertation research focuses on developing advanced machine learning algorithms for mining large-scale RNA-Seq data in cancer transcriptome analysis. A platform-integrated model for transcript quantification (IntMTQ) is developed to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ provides more precise RNA-Seq-based isoform quantification, and the gene expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses. In light of recent challenges posted by the COVID-19 pandemic, computational methods are developed and applied to RNA-Seq data of lung cancer cell lines to detect novel molecular signatures that are highly correlated with SARS-CoV-2 pathogenesis and prognosis for COVID-19 studies. The results from the data analyses demonstrate that post-transcriptional gene regulations provide additional molecular signatures for COVID-19 therapeutic targets compared to the transcriptional signatures. To further investigate post-transcriptional regulations, a pan-cancer analysis is performed to reveal discrete intronic polyadenylation in human cancer transcriptome. The identified intronic APA profile can add additional prognostic and predictive power beyond conventional gene expression profiles in cancer survival analysis and phenotype prediction. In view of this, a biological pathway encoded transformer model is proposed to maximize the use of RNA-Seq data for cancer phenotype prediction

SUFFIX TREE, MINWISE HASHING AND STREAMING ALGORITHMS FOR BIG DATA ANALYSIS IN BIOINFORMATICS

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) sux-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (Kmer-Estimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use sux tree, a trie data structure, for alignment-free, non-pairwise algorithms for a conserved non-coding sequence (CNS) identification problem. We provided two different algorithms: STAG-CNS to identify exact-matched CNSs and DiCE to identify CNSs with mismatches. Using our algorithms, CNSs among various grass species were identified. A different approach was employed for identification of longer CNSs ( 100 bp, mostly found in animals). In our new method (MinCNE), the minhash approach was used to estimate the Jaccard similarity. Using also LSH, k-mers extracted from genomic sequences were clustered and CNSs were identified. Another new algorithm (MinIsoClust) that also uses minhash and LSH techniques was developed for an isoform clustering problem. Isoforms are generated from the same gene but by alternative splicing. As the isoform sequences share some exons but in different combinations, regular sequencing clustering methods do not work well. Our algorithm generates clusters for isoform sequences based on their shared minhash signatures. Finally, we discuss de novo transcriptome assembly algorithms and how to improve the assembly accuracy using ensemble approaches. First, we did a comprehensive performance analysis on different transcriptome assemblers using simulated benchmark datasets. Then, we developed a new ensemble approach (Minsemble) for the de novo transcriptome assembly problem that integrates isoform-clustering using minhash technique to identify potentially correct transcripts from various de novo transcriptome assemblers. Minsemble identified more correctly assembled transcripts as well as genes compared to other de novo and ensemble methods. Adviser: Jitender S. Deogu