NOVEL COMPUTATIONAL METHODS FOR SEQUENCING DATA ANALYSIS: MAPPING, QUERY, AND CLASSIFICATION

Over the past decade, the evolution of next-generation sequencing technology has considerably advanced the genomics research. As a consequence, fast and accurate computational methods are needed for analyzing the large data in different applications. The research presented in this dissertation focuses on three areas: RNA-seq read mapping, large-scale data query, and metagenomics sequence classification. A critical step of RNA-seq data analysis is to map the RNA-seq reads onto a reference genome. This dissertation presents a novel splice alignment tool, MapSplice3. It achieves high read alignment and base mapping yields and is able to detect splice junctions, gene fusions, and circular RNAs comprehensively at the same time. Based on MapSplice3, we further extend a novel lightweight approach called iMapSplice that enables personalized mRNA transcriptional profiling. As huge amount of RNA-seq has been shared through public datasets, it provides invaluable resources for researchers to test hypotheses by reusing existing datasets. To meet the needs of efficiently querying large-scale sequencing data, a novel method, called SeqOthello, has been developed. It is able to efficiently query sequence k-mers against large-scale datasets and finally determines the existence of the given sequence. Metagenomics studies often generate tens of millions of reads to capture the presence of microbial organisms. Thus efficient and accurate algorithms are in high demand. In this dissertation, we introduce MetaOthello, a probabilistic hashing classifier for metagenomic sequences. It supports efficient query of a taxon using its k-mer signatures

iMapSplice: Alleviating Reference Bias Through Personalized RNA-seq Alignment

Genomic variants in both coding and non-coding sequences can have functionally important and sometimes deleterious effects on exon splicing of gene transcripts. For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The algorithm makes use of personal genomic information and performs an unbiased alignment towards genome indices carrying both reference and alternative bases. Importantly, this breaks the dependency on reference genome splice site dinucleotide motifs and enables iMapSplice to discover personal splice junctions created through splice site polymorphisms. We report comparative analyses using a number of simulated and real datasets. Besides general improvements in read alignment and splice junction discovery, iMapSplice greatly alleviates allelic ratio biases and unravels many previously uncharacterized splice junctions created by splice site polymorphisms, with minimal overhead in computation time and storage. Software download URL: https://github.com/LiuBioinfo/iMapSplice

Antibody Immunity Induced by H7N9 Avian Influenza Vaccines: Evaluation Criteria, Affecting Factors, and Implications for Rational Vaccine Design

Severe H7N9 avian influenza virus (AIV) infections in humans have public health authorities around the world on high alert for the potential development of a human influenza pandemic. Currently, the newly-emerged highly pathogenic avian influenza A (H7N9) virus poses a dual challenge for public health and poultry industry. Numerous H7N9 vaccine candidates have been generated using various platforms. Immunization trials in animals and humans showed that H7N9 vaccines are apparently poorly immunogenic because they induced low hemagglutination inhibition and virus neutralizing antibody titers. However, H7N9 vaccines elicit comparable levels of total hemagglutinin (HA)-reactive IgG antibody as the seasonal influenza vaccines, suggesting H7N9 vaccines are as immunogenic as their seasonal counterparts. A large fraction of overall IgG antibody is non-neutralizing antibody and they target unrecognized epitopes outside of the traditional antigenic sites in HA. Further, the Treg epitope identified in H7 HA may at least partially contribute to regulation of antibody immunity. Here, we review the latest advances for the development of H7N9 vaccines and discuss the influence of serological criteria on evaluation of immunogenicity of H7N9 vaccines. Next, we discuss factors affecting antibody immunity induced by H7N9 vaccines, including the change in antigenic epitopes in HA and the presence of the Treg epitope. Last, we present our perspectives for the unique features of antibody immunity of H7N9 vaccines and propose some future directions to improve or modify antibody response induced by H7N9 vaccines. This perspective would provide critical implications for rational design of H7N9 vaccines for human and veterinary use

Positive solutions of higher order fractional integral boundary value problem with a parameter

In this paper, we study a higher-order fractional differential equation with integral boundary conditions and a parameter. Under different conditions of nonlinearity, existence and nonexistence results for positive solutions are derived in terms of different intervals of parameter. Our approach relies on the Guo–Krasnoselskii fixed point theorem on cones

Discerning Novel Splice Junctions Derived from RNA-Seq Alignment: A Deep Learning Approach

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

Plunging Breakers - Part 1. Analysis of an Ensemble of Wave Profiles

An experimental study of the dynamics and droplet production in three mechanically generated plunging breaking waves is presented in this two-part paper. In the present paper (Part 1), the dynamics of the three breakers are studied through measurements of the evolution of their free surface profiles during 10 repeated breaking events for each wave. The waves are created from dispersively focused wave packets which are generated by a highly accurate programmable wave maker. The wave maker motions that create the three breakers differ primarily only by small changes in their overall amplitude. Breaker profiles are measured with a cinematic laser induced fluorescence technique covering a streamwise region of approximately one breaker wavelength and over a time of 2.6 breaker periods. The 10 repeated sets of breaker profiles are spatially and temporally aligned to the location and time of jet impact. The aligned profile data is used to create spatio-temporal maps of the ensemble average surface height and the standard deviation of both the local normal distance and the local arc length relative to the instantaneous mean profile. It is found that the mean and standard deviation maps contain strongly correlated localized features and indicate that the transition from laminar to turbulent flow is a highly repeatable process. Regions of high standard deviation include the splash created by the plunging jet impact and subsequent splash impacts at the front of the breaking region as well as the site where the air pocket entrained under the plunging jet at the moment of jet tip impact comes to the surface and pops on the back face of the wave. In Part 2, these features are used to interpret various features of the distributions of droplet number, diameter and velocity

SeqOthello: Querying RNA-Seq Experiments at Scale

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs

Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits

The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits. However, analyzing functional imaging data of addiction and detecting functional addiction circuits still have challenges. We have developed a data-driven and end-to-end generative artificial intelligence(AI) framework to address these difficulties. The framework integrates dynamic brain network modeling and novel network architecture networks architecture, including temporal graph Transformer and contrastive learning modules. A complete workflow is formed by our generative AI framework: the functional imaging data, from neurobiological experiments, and computational modeling, to end-to-end neural networks, is transformed into dynamic nicotine addiction-related circuits. It enables the detection of addiction-related brain circuits with dynamic properties and reveals the underlying mechanisms of addiction

Roles of the spiA gene from Salmonella enteritidis in biofilm formation and virulence

Salmonella enteritidis has emerged as one of the most important food-borne pathogens for humans, and the formation of biofilms by this species may improve its resistance to disadvantageous conditions. The spiA gene of Salmonella typhimurium is essential for its virulence in host cells. However, the roles of the spiA gene in biofilm formation and virulence of S. enteritidis remain unclear. In this study we constructed a spiA gene mutant with a suicide plasmid. Phenotypic and biological analysis revealed that the mutant was similar to the wild-type strain in growth rate, morphology, and adherence to and invasion of epithelial cells. However, the mutant showed reduced biofilm formation in a quantitative microtitre assay and by scanning electron microscopy, and significantly decreased curli production and intracellular proliferation of macrophages during the biofilm phase. In addition, the spiA mutant was attenuated in a mouse model in both the exponential growth and biofilm phases. These data indicate that the spiA gene is involved in both biofilm formation and virulence of S. enteritidis

Control of LED Emission with Functional Dielectric Metasurfaces

The improvement of light-emitting diodes (LEDs) is one of the major goals of optoelectronics and photonics research. While emission rate enhancement is certainly one of the targets, in this regard, for LED integration to complex photonic devices, one would require to have, additionally, precise control of the wavefront of the emitted light. Metasurfaces are spatial arrangements of engineered scatters that may enable this light manipulation capability with unprecedented resolution. Most of these devices, however, are only able to function properly under irradiation of light with a large spatial coherence, typically normally incident lasers. LEDs, on the other hand, have angularly broad, Lambertian-like emission patterns characterized by a low spatial coherence, which makes the integration of metasurface devices on LED architectures extremely challenging. A novel concept for metasurface integration on LED is proposed, using a cavity to increase the LED spatial coherence through an angular collimation. Due to the resonant character of the cavity, extending the spatial coherence of the emitted light does not come at the price of any reduction in the total emitted power. The experimental demonstration of the proposed concept is implemented on a GaP LED architecture including a hybrid metallic-Bragg cavity. By integrating a silicon metasurface on top we demonstrate two different functionalities of these compact devices: directional LED emission at a desired angle and LED emission of a vortex beam with an orbital angular momentum. The presented concept is general, being applicable to other incoherent light sources and enabling metasurfaces designed for plane waves to work with incoherent light emitters.Comment: 29 pages, 6 figure