Microbial Diversity of Ticks And A Novel Tyhpus Group Rickettsia Species ( R. bacterium Ac37b) Detected In Inner Mongolia, China

Abstract Background: Ticks are arthropods that can carry multiple pathogens and parasitize on livestock and mammals as well as on humans. Animal husbandry in Inner Mongolia, China, provides a suitable tick habitat. In this study, PacBio full-length 16S rDNA third-generation sequencing was used to analyze the diversity of microbial communities carried by ticks in different regions of Inner Mongolia. The aim of the study is to characterize the microbiome carried by ticks in different geographical locations and to provide theoretical support for regional prevention and control of pathogen populations in the future. Methods: In this study, a total of 905 Dermacentor nuttalli and 36 Ixodes persuleatus were collected from the surface of sheep in four main pasture areas in Inner Mongolia. Pooled DNA samples were prepared from three samples from each region and from each tick species. In total the microbial diversity of 12 samples was analyzed by PacBio full-length 16S rRNA third-generation sequencing, and the α and β diversity were determined. Results: The main bacterial genera we found were Rickettsia (35.27%), Ac37b (19.33%), Arsenophonus (11.21%), Candidatus Lariskella (10.84%), and Acinetobacter (7.17%). There were significant differences in the microbial composition of ticks from different regions and in different tick species. Rickettsia bellii was found in the I. persuleatus group. In addition, Anaplasma and a novel tyhpus group Rickettsia species (R. bacterium Ac37b) were found in the sample group of D. nuttalli in the city of Ordos.Conclusions: In this study, Rickettsia bellii was first found in I. persuleatus in Inner Mongolia, and a novel tyhpus group Rickettsia species (R. bacterium Ac37b) was found in D. nuttalli from the city of Ordos. Our study provides a basis for the prevention and control of tick-borne diseases through the analysis of tick microbial diversity in different regions of Inner Mongolia. Furthermore, we were able to detect a new tick-borne pathogen in D. nutalli.

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GENE-18. TRANSCRIPTOME-WIDE ANALYSIS USING NANOPORE THIRD GENERATION SEQUENCING IN A RAT GLIOBLASTOMA MODEL: PROOF OF PRINCIPLE

Neuro-Oncology ◽

10.1093/neuonc/noz175.420 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi101-vi101

Author(s):

Piroon Jejaroenpun ◽

Thidathip Wongsurawat ◽

Annick DeLoose ◽

David Ussery ◽

Intawat Nookaew ◽

...

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Full Length ◽

Third Generation ◽

Fusion Genes ◽

Rna Seq ◽

Cdna Sequencing ◽

Third Generation Sequencing ◽

Differential Gene ◽

Generation Sequencing

Abstract The RNA sequencing (RNA-Seq) technique is now routinely used to quantitatively explore genome-wide expression by various research fields including cancer research. The most common RNA-seq methodology produce billions of short-read sequencing in the range of 100–600 base pairs, from which it is occasionally difficult to reconstruct isoform-level transcriptome and fusion genes. The limitations of the short-reads can be overcome by using third-generation sequencing technologies, such as Oxford Nanopore Technologies (ONT). This study aims to perform full-length cDNA sequencing using ONT platform and investigate the abilities of ONT in (1) identifying differential gene expression, (2) detecting differential transcript isoform usage, and (3) detecting fusion genes. To do these methods, CNS-1 cells were implanted into the frontal lobes of three Lewis rats. The CNS-1 model is a histocompatible astrocytoma cell line with an invasive pattern mimicking glioblastoma (GBM). After two weeks of transplantation, the transplanted tumors and the normal brain on the other side were collected as matched normal-tumor pairs. Total RNA extracted from the samples were subjected to the full-length cDNA sequencing on a portable MinION sequencer. In tumors samples, 615 genes involved in cell cycle were upregulated, whereas 1067 genes involved in neurological functions were downregulated. Finally, we could identify differential transcript isoform expression and fusion genes from the matched normal-tumor pairs. Overall, full-length sequencing of the cDNA molecules permitted a detailed characterization of the differential gene expression, the isoform complexity, and fusion genes. In the near future, we will use these methods on human samples.

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BUILDING CATALOGUE OF LIFE: ULTRAHIGH THROUGHPUT DNA BARCODING USING THIRD GENERATION SEQUENCING

MOLECULAR PHYLOGENETICS ◽

10.30826/molphy2018-05 ◽

2018 ◽

Author(s):

P.D.N. HEBERT ◽

◽

T.W.A. BRAUKMANN ◽

S.W.J. PROSSER ◽

S. RATNASINGHAM ◽

...

Keyword(s):

Dna Barcoding ◽

Third Generation ◽

Third Generation Sequencing ◽

Generation Sequencing

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IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

Current Bioinformatics ◽

10.2174/1574893615666200316101205 ◽

2020 ◽

Vol 15 ◽

Author(s):

Hongdong Li ◽

Wenjing Zhang ◽

Yuwen Luo ◽

Jianxin Wang

Keyword(s):

Sequence Similarity ◽

Detection Methods ◽

Sequence Information ◽

Third Generation ◽

Sequencing Data ◽

Splice Isoforms ◽

Third Generation Sequencing ◽

Long Reads ◽

Feature Sequence ◽

Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

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Comparative and comprehensive analysis on bacterial communities of two full-scale wastewater treatment plants by second and third-generation sequencing

Bioresource Technology Reports ◽

10.1016/j.biteb.2020.100450 ◽

2020 ◽

Vol 11 ◽

pp. 100450

Author(s):

Bin Ji ◽

Shulian Wang ◽

Dabin Guo ◽

Heliang Pang

Keyword(s):

Wastewater Treatment ◽

Bacterial Communities ◽

Wastewater Treatment Plants ◽

Comprehensive Analysis ◽

Full Scale ◽

Third Generation ◽

Third Generation Sequencing ◽

Generation Sequencing

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Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

Bioinformatics ◽

10.1093/bioinformatics/btaa179 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3669-3679 ◽

Cited By ~ 3

Author(s):

Can Firtina ◽

Jeremie S Kim ◽

Mohammed Alser ◽

Damla Senol Cali ◽

A Ercument Cicek ◽

...

Keyword(s):

Genome Analysis ◽

Supplementary Information ◽

Third Generation ◽

Sequencing Technology ◽

Base Pairs ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Generation Sequencing ◽

Large Genomes

Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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