A Novel Competing Endogenous RNA Network Associated With the Pathogenesis of Graves’ Ophthalmopathy

Background: Growing evidence has recently revealed the characteristics of long noncoding (lncRNA)/circular RNA (circRNA)-microRNA (miRNA)-mRNA networks in numerous human diseases. However, a scientific lncRNA/circRNA-miRNA-mRNA network related to Graves’ ophthalmopathy (GO) remains lacking.Materials and methods: The expression levels of RNAs in GO patients were measured through high-throughput sequencing technology, and the results were proven by quantitative real-time PCR (qPCR). We constructed a protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes (STRING) database and identified hub genes by the Cytoscape plug-in CytoHubba. Then, the miRNAs related to differentially expressed lncRNAs/circRNAs and mRNAs were predicted through seed sequence matching analysis. Correlation coefficient analysis was performed on the interesting RNAs to construct a novel competing endogenous RNA (ceRNA) network.Results: In total, 361 mRNAs, 355 circRNAs, and 242 lncRNAs were differentially expressed in GO patients compared with control patients, 166 pairs were identified, and ceRNA networks were constructed. The qPCR results showed that 4 mRNAs (THBS2, CHRM3, CXCL1, FPR2) and 2 lncRNAs (LINC01820:13, ENST00000499452) were differentially expressed between the GO patients and control patients.Conclusion: An innovative lncRNA/circRNA-miRNA-mRNA ceRNA network between GO patients and control patients was constructed, and two important ceRNA pathways were identified, the LINC01820:13-hsa-miR-27b-3p-FPR2 ceRNA pathway and the ENST00000499452-hsa-miR-27a-3p-CXCL1 pathway, which probably affect the autoimmune response and inflammation in GO patients.

miR-221-5p-Mediated Downregulation of JNK2 Aggravates Acute Lung Injury

Sepsis and acute lung injury (ALI) are linked to mitochondrial dysfunction; however, the underlying mechanism remains elusive. We previously reported that c-Jun N-terminal protein kinase 2 (JNK2) promotes stress-induced mitophagy by targeting small mitochondrial alternative reading frame (smARF) for ubiquitin-mediated proteasomal degradation, thereby preventing mitochondrial dysfunction and restraining inflammasome activation. Here we report that loss of JNK2 exacerbates lung inflammation and injury during sepsis and ALI in mice. JNK2 is downregulated in mice with endotoxic shock or ALI, concomitantly correlated inversely with disease severity. Small RNA sequencing revealed that miR-221-5p, which contains seed sequence matching to JNK2 mRNA 3’ untranslated region (3’UTR), is upregulated in response to lipopolysaccharide, with dynamically inverse correlation with JNK2 mRNA levels. miR-221-5p targets the 3’UTR of JNK2 mRNA leading to its downregulation. Accordingly, miR-221-5p exacerbates lung inflammation and injury during sepsis in mice by targeting JNK2. Importantly, in patients with pneumonia in medical intensive care unit, JNK2 mRNA levels in alveolar macrophages flow sorted from non-bronchoscopic broncholaveolar lavage (BAL) fluid were inversely correlated strongly and significantly with the percentage of neutrophils, neutrophil and white blood cell counts in BAL fluid. Our data suggest that miR-221-5p targets JNK2 and thereby aggravates lung inflammation and injury during sepsis.

CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching

Adaptive immune receptor (AIR) repertoires (AIRRs) record past immune encounters with exquisite specificity. Therefore, identifying identical or similar AIR sequences across individuals is a key step in AIRR analysis for revealing convergent immune response patterns that may be exploited for diagnostics and therapy. Existing methods for quantifying AIRR overlap do not scale with increasing dataset numbers and sizes. To address this limitation, we developed CompAIRR, which enables ultra-fast computation of AIRR overlap, based on either exact or approximate sequence matching. CompAIRR improves computational speed 1000-fold relative to the state of the art and uses only one-third of the memory: on the same machine, the exact pairwise AIRR overlap of 104 AIRRs with 105 sequences is found in ~17 minutes, while the fastest alternative tool requires 10 days. CompAIRR has been integrated with the machine learning ecosystem immuneML to speed up various commonly used AIRR-based machine learning applications.

Design of Automatic Scoring System for Oral English Test Based on Sequence Matching and Big Data Analysis

With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking.

IMU-Based Hand Gesture Interface Implementing a Sequence-Matching Algorithm for the Control of Assistive Technologies

Assistive technologies (ATs) often have a high-dimensionality of possible movements (e.g., assistive robot with several degrees of freedom or a computer), but the users have to control them with low-dimensionality sensors and interfaces (e.g., switches). This paper presents the development of an open-source interface based on a sequence-matching algorithm for the control of ATs. Sequence matching allows the user to input several different commands with low-dimensionality sensors by not only recognizing their output, but also their sequential pattern through time, similarly to Morse code. In this paper, the algorithm is applied to the recognition of hand gestures, inputted using an inertial measurement unit worn by the user. An SVM-based algorithm, that is aimed to be robust, with small training sets (e.g., five examples per class) is developed to recognize gestures in real-time. Finally, the interface is applied to control a computer’s mouse and keyboard. The interface was compared against (and combined with) the head movement-based AssystMouse software. The hand gesture interface showed encouraging results for this application but could also be used with other body parts (e.g., head and feet) and could control various ATs (e.g., assistive robotic arm and prosthesis).

Effective sequence similarity detection with strobemers

k-mer-based methods are widely used in bioinformatics for various types of sequence comparisons. However, a single mutation will mutate k consecutive k-mers and make most k-mer-based applications for sequence comparison sensitive to variable mutation rates. Many techniques have been studied to overcome this sensitivity, for example, spaced k-mers and k-mer permutation techniques, but these techniques do not handle indels well. For indels, pairs or groups of small k-mers are commonly used, but these methods first produce k-mer matches, and only in a second step, a pairing or grouping of k-mers is performed. Such techniques produce many redundant k-mer matches owing to the size of k. Here, we propose strobemers as an alternative to k-mers for sequence comparison. Intuitively, strobemers consist of two or more linked shorter k-mers, where the combination of linked k-mers is decided by a hash function. We use simulated data to show that strobemers provide more evenly distributed sequence matches and are less sensitive to different mutation rates than k-mers and spaced k-mers. Strobemers also produce higher match coverage across sequences. We further implement a proof-of-concept sequence-matching tool StrobeMap and use synthetic and biological Oxford Nanopore sequencing data to show the utility of using strobemers for sequence comparison in different contexts such as sequence clustering and alignment scenarios.

Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.