Genome Survey Sequencing of Betula platyphylla

Research Highlights: A rigorous genome survey helped us to estimate the genomic characteristics, remove the DNA contamination, and determine the sequencing scheme of Betula platyphylla. Background and Objectives: B. platyphylla is a common tree species in northern China that has high economic and medicinal value. However, there is a lack of complete genomic information for this species, which severely constrains the progress of relevant research. The objective of this study was to survey the genome of B. platyphylla and determine the large-scale sequencing scheme of this species. Materials and Methods: Next-generation sequencing was used to survey the genome. The genome size, heterozygosity rate, and repetitive sequences were estimated by k-mer analysis. After preliminary genome assembly, sequence contamination was identified and filtered by sequence alignment. Finally, we obtained sterilized plantlets of B. platyphylla by plant tissue culture, which can be used for third-generation sequencing. Results: We estimated the genome size to be 432.9 Mb and the heterozygosity rate to be 1.22%, with repetitive sequences accounting for 62.2%. Bacterial contamination was observed in the leaves taken from the field, and most of the contaminants may be from the genus Mycobacterium. A total of 249,784 simple sequence repeat (SSR) loci were also identified in the B. platyphylla genome. Among the SSRs, only 11,326 can be used as candidates to distinguish the three Betula species. Conclusions: The B. platyphylla genome is complex and highly heterozygous and repetitive. Higher-depth third-generation sequencing may yield better assembly results. Sterilized plantlets can be used for sequencing to avoid contamination.

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TOGGLe, a flexible framework for easily building complex workflows and performing robust large-scale NGS analyses

10.1101/245480 ◽

2018 ◽

Cited By ~ 4

Author(s):

Christine Tranchant-Dubreuil ◽

Sébastien Ravel ◽

Cécile Monat ◽

Gautier Sarah ◽

Abdoulaye Diallo ◽

...

Keyword(s):

Population Structure ◽

High Performance ◽

Large Scale ◽

Genotyping By Sequencing ◽

Whole Genome ◽

Third Generation ◽

Third Generation Sequencing ◽

Sequencing Method ◽

Flexible Framework ◽

Generation Sequencing

ABSTRACTThe advent of NGS has intensified the need for robust pipelines to perform high-performance automated analyses. The required softwares depend on the sequencing method used to produce raw data (e.g. Whole genome sequencing, Genotyping By Sequencing, RNASeq) as well as the kind of analyses to carry on (GWAS, population structure, differential expression). These tools have to be generic and scalable, and should meet the biologists needs.Here, we present the new version of TOGGLe (Toolbox for Generic NGS Analyses), a simple and highly flexible framework to easily and quickly generate pipelines for large-scale second- and third-generation sequencing analyses, including multi-sample and multi-threading support. TOGGLe is a workflow manager designed to be as effortless as possible to use for biologists, so the focus can remain on the analyses. Pipelines are easily customizable and supported analyses are reproducible and shareable. TOGGLe is designed as a generic, adaptable and fast evolutive solution, and has been tested and used in large-scale projects on various organisms. It is freely available at http://toggle.southgreen.fr/, under the GNU GPLv3/CeCill-C licenses) and can be deployed onto HPC clusters as well as on local machines.

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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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Combined genomic, transcriptomic, and metabolomic analyses provide insights into chayote (Sechium edule) evolution and fruit development

Horticulture Research ◽

10.1038/s41438-021-00487-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Anzhen Fu ◽

Qing Wang ◽

Jianlou Mu ◽

Lili Ma ◽

Changlong Wen ◽

...

Keyword(s):

Fruit Development ◽

Repetitive Sequences ◽

Genetic Research ◽

Future Research ◽

Agricultural Crop ◽

Protein Coding ◽

Third Generation Sequencing ◽

Sechium Edule ◽

Generation Sequencing ◽

Cucurbitaceae Family

AbstractChayote (Sechium edule) is an agricultural crop in the Cucurbitaceae family that is rich in bioactive components. To enhance genetic research on chayote, we used Nanopore third-generation sequencing combined with Hi–C data to assemble a draft chayote genome. A chromosome-level assembly anchored on 14 chromosomes (N50 contig and scaffold sizes of 8.40 and 46.56 Mb, respectively) estimated the genome size as 606.42 Mb, which is large for the Cucurbitaceae, with 65.94% (401.08 Mb) of the genome comprising repetitive sequences; 28,237 protein-coding genes were predicted. Comparative genome analysis indicated that chayote and snake gourd diverged from sponge gourd and that a whole-genome duplication (WGD) event occurred in chayote at 25 ± 4 Mya. Transcriptional and metabolic analysis revealed genes involved in fruit texture, pigment, flavor, flavonoids, antioxidants, and plant hormones during chayote fruit development. The analysis of the genome, transcriptome, and metabolome provides insights into chayote evolution and lays the groundwork for future research on fruit and tuber development and genetic improvements in chayote.

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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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