scholarly journals Efficient and stable metabarcoding sequencing data using a DNBSEQ-G400 sequencer validated by comprehensive community analyses

Gigabyte ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaohuan Sun ◽  
Yue-Hua Hu ◽  
Jingjing Wang ◽  
Chao Fang ◽  
Jiguang Li ◽  
...  
Keyword(s):  
Microbial Communities ◽  
High Performance ◽  
Pcr Amplification ◽  
Sequencing Data ◽  
Noticeable Effect ◽  
Sequencing Platform ◽  
Taxonomic Markers ◽  
Sequencing Platforms ◽  
Mock Communities

Metabarcoding is a widely used method for fast characterization of microbial communities in complex environmental samples. However, the selction of sequencing platform can have a noticeable effect on the estimated community composition. Here, we evaluated the metabarcoding performance of a DNBSEQ-G400 sequencer developed by MGI Tech using 16S and internal transcribed spacer (ITS) markers to investigate bacterial and fungal mock communities, as well as the ITS2 marker to investigate the fungal community of 1144 soil samples, with additional technical replicates. We show that highly accurate sequencing of bacterial and fungal communities is achievable using DNBSEQ-G400. Measures of diversity and correlation from soil metabarcoding showed that the results correlated highly with those of different machines of the same model, as well as between different sequencing modes (single-end 400 bp and paired-end 200 bp). Moderate, but significant differences were observed between results produced with different sequencing platforms (DNBSEQ-G400 and MiSeq); however, the highest differences can be caused by selecting different primer pairs for PCR amplification of taxonomic markers. These differences suggested that care is needed while jointly analyzing metabarcoding data from differenet experiments. This study demonstrated the high performance and accuracy of DNBSEQ-G400 for short-read metabarcoding of microbial communities. Our study also produced datasets to allow further investigation of microbial diversity.

scholarly journals Comparative performance of the GenoLab M and NovaSeq 6000 sequencing platforms for transcriptome and LncRNA analysis

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yongfeng Liu ◽  
Ran Han ◽  
Letian Zhou ◽  
Mingjie Luo ◽  
Lidong Zeng ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Performance ◽  
Next Generation ◽  
Sequencing Data ◽  
Comparative Performance ◽  
Sequencing Platform ◽  
Next Generation Sequencing Platform ◽  
Alternatively Spliced ◽  
Sequencing Platforms ◽  
Generation Sequencing

Abstract Background GenoLab M is a recently established next-generation sequencing platform from GeneMind Biosciences. Presently, Illumina sequencers are the globally leading sequencing platform in the next-generation sequencing market. Here, we present the first report to compare the transcriptome and LncRNA sequencing data of the GenoLab M sequencer to NovaSeq 6000 platform in various types of analysis. Results We tested 16 libraries in three species using various library kits from different companies. We compared the data quality, genes expression, alternatively spliced (AS) events, single nucleotide polymorphism (SNP), and insertions–deletions (InDel) between two sequencing platforms. The data suggested that platforms have comparable sensitivity and accuracy in terms of quantification of gene expression levels with technical compatibility. Conclusions Genolab M is a promising next-generation sequencing platform for transcriptomics and LncRNA studies with high performance at low costs.

scholarly journals Comparative Performance of the Genolab M and Novaseq 6000 Sequencing Platforms for Transcriptome and LncRNA Analysis

2021 ◽  
Author(s):  
Yongfeng Liu ◽  
Ran Han ◽  
Letian Zhou ◽  
Mingjie Luo ◽  
Lidong Zeng ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Performance ◽  
Next Generation ◽  
Sequencing Data ◽  
Comparative Performance ◽  
Sequencing Platform ◽  
Genes Expression ◽  
Alternatively Spliced ◽  
Sequencing Platforms ◽  
Generation Sequencing

Abstract Background: GenoLab M is a recently established next-generation sequencing platform from GeneMind Biosciences. Presently, Illumina sequencers are the globally leading sequencing platform in the next-generation sequencing market. Here, we present the first report to compare the transcriptome and LncRNA sequencing data of the GenoLab M sequencer to NovaSeq 6000 platform in various types of analysis.Results: We tested 16 libraries in three species using various library kits from different companies. We compared the data quality, genes expression, alternatively spliced (AS) events, single nucleotide polymorphism (SNP), and insertions–deletions (InDel) between two sequencing platforms. The data suggested that platforms have comparable sensitivity and accuracy in terms of quantification of gene expression levels with technical compatibility. Conclusions: Genolab M is a promising sequencing platform for transcriptomics and LncRNA studies with high performance at low costs.

scholarly journals Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

2018 ◽  
Author(s):  
Arghavan Bahadorinejad ◽  
Ivan Ivanov ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
Robert S Chapkin ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sample Size ◽  
Microbial Communities ◽  
State Of The Art ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

scholarly journals Hi-C chromosome conformation capture sequencing of avian genomes using the BGISEQ-500 platform

GigaScience ◽  
2020 ◽  
Vol 9 (8) ◽  
Author(s):  
Marcela Sandoval-Velasco ◽  
Juan Antonio Rodríguez ◽  
Cynthia Perez Estrada ◽  
Guojie Zhang ◽  
Erez Lieberman Aiden ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Data Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Yield Data ◽  
Chromosome Conformation ◽  
Sequencing Platform ◽  
Sequencing Platforms ◽  
Generation Sequencing

Abstract Background Hi-C experiments couple DNA-DNA proximity with next-generation sequencing to yield an unbiased description of genome-wide interactions. Previous methods describing Hi-C experiments have focused on the industry-standard Illumina sequencing. With new next-generation sequencing platforms such as BGISEQ-500 becoming more widely available, protocol adaptations to fit platform-specific requirements are useful to give increased choice to researchers who routinely generate sequencing data. Results We describe an in situ Hi-C protocol adapted to be compatible with the BGISEQ-500 high-throughput sequencing platform. Using zebra finch (Taeniopygia guttata) as a biological sample, we demonstrate how Hi-C libraries can be constructed to generate informative data using the BGISEQ-500 platform, following circularization and DNA nanoball generation. Our protocol is a modification of an Illumina-compatible method, based around blunt-end ligations in library construction, using un-barcoded, distally overhanging double-stranded adapters, followed by amplification using indexed primers. The resulting libraries are ready for circularization and subsequent sequencing on the BGISEQ series of platforms and yield data similar to what can be expected using Illumina-compatible approaches. Conclusions Our straightforward modification to an Illumina-compatible in situHi-C protocol enables data generation on the BGISEQ series of platforms, thus expanding the options available for researchers who wish to utilize the powerful Hi-C techniques in their research.

scholarly journals Taxon Disappearance from Microbiome Analysis Reinforces the Value of Mock Communities as a Standard in Every Sequencing Run

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Yi-Chun Yeh ◽  
David M. Needham ◽  
Ella T. Sieradzki ◽  
Jed A. Fuhrman
Keyword(s):  
Method Development ◽  
Pcr Amplification ◽  
Unknown Origin ◽  
Community Analysis ◽  
Sequencing Analysis ◽  
Mock Community ◽  
Microbiome Analysis ◽  
Biological Studies ◽  
Sequencing Platforms ◽  
Mock Communities

ABSTRACT Mock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification and sequencing and to optimize pipeline outputs. Nevertheless, the strong value of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed >2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded “normal” results. Although obvious from the strange mock community results, we could have easily missed the problem had we not used the mock communities because of natural variation of microbiomes at our site. The “normal” results were validated over four MiSeqPE300 runs and three HiSeqPE250 runs, and run-to-run variation was usually low. While validating these “normal” results, we also discovered that some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We strongly advise the use of mock communities in every sequencing run to distinguish potentially serious aberrations from natural variations. The mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed to detect problems that show up only in some taxa and also to help validate clustering. IMPORTANCE Despite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be detected by the use of mock communities.

scholarly journals Taxon disappearance from microbiome analysis indicates need for mock communities as a standard in every sequencing run

2017 ◽  
Author(s):  
Yi-Chun Yeh ◽  
David M. Needham ◽  
Ella T. Sieradzki ◽  
Jed A. Fuhrman
Keyword(s):  
Method Development ◽  
Pcr Amplification ◽  
Unknown Origin ◽  
Community Analysis ◽  
Sequencing Analysis ◽  
Mock Community ◽  
Microbiome Analysis ◽  
Biological Studies ◽  
Sequencing Platforms ◽  
Mock Communities

AbstractMock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification, sequencing, and to optimize pipeline outputs. Nevertheless, the necessity of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed >2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded “normal” results. Although obvious from the strange mock community results, due to natural variation of microbiomes at our site, we easily could have missed the problem had we not used the mock communities. The “normal” results were validated over 4 MiSeqPE300 runs and 3 HiSeqPE250 runs, and run-to-run variation was usually low (Bray-Curtis distance was 0.12±0.04). While validating these “normal” results, we also discovered some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We suggest that using mock communities in every sequencing run is essential to distinguish potentially serious aberrations from natural variations. Such mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed, to detect problems that show up only in some taxa, as we observed.ImportanceDespite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs, and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be avoided by the use of suitable mock communities.

scholarly journals Characterization and Simulation of Metagenomic Nanopore Sequencing Data with Meta-NanoSim

2021 ◽  
Author(s):  
Chen Yang ◽  
Theodora Lo ◽  
Ka Ming Nip ◽  
Saber Hafezqorani ◽  
René L Warren ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Simulated Data ◽  
Ground Truth ◽  
Read Length ◽  
Abundance Estimation ◽  
Nanopore Sequencing ◽  
Microbial Abundance ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Metagenome Assembly

Abstract Background: Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, sequencing platform-specific challenges, including high base-call error rate, non-uniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical tools, such as microbial abundance estimation and metagenome assembly algorithms. When developing and testing bioinformatics tools and pipelines, the use of simulated datasets with characteristics that are true to the sequencing platform under evaluation is a cost-effective way to provide a ground truth and assess the performance in a controlled environment. Results: Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. It improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. Meta-NanoSim can simulate complex microbial communities composed of both linear and circular genomes, and can stream reference genomes from online servers directly. Simulated datasets showed high congruence with experimental data in terms of read length, error profiles, and abundance levels. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenome assembly benchmarking task. Conclusions: The Meta-NanoSim characterization module investigates read features including chimeric information and abundance levels, while the simulation module simulates large and complex multi-sample microbial communities with different abundance profiles. All trained models and the software are freely accessible at Github: https://github.com/bcgsc/NanoSim .

scholarly journals Highly comparable metabarcoding results from MGI-Tech and Illumina sequencing platforms

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12254
Author(s):  
Sten Anslan ◽  
Vladimir Mikryukov ◽  
Kęstutis Armolaitis ◽  
Jelena Ankuda ◽  
Dagnija Lazdina ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Coi Gene ◽  
Synthesis Methods ◽  
Sequence Variant ◽  
Sequencing Data ◽  
Data Set ◽  
Soil Dna ◽  
Sequencing Platform ◽  
Sequencing By Synthesis ◽  
Sequencing Platforms

With the developments in DNA nanoball sequencing technologies and the emergence of new platforms, there is an increasing interest in their performance in comparison with the widely used sequencing-by-synthesis methods. Here, we test the consistency of metabarcoding results from DNBSEQ-G400RS (DNA nanoball sequencing platform by MGI-Tech) and NovaSeq 6000 (sequencing-by-synthesis platform by Illumina) platforms using technical replicates of DNA libraries that consist of COI gene amplicons from 120 soil DNA samples. By subjecting raw sequencing data from both platforms to a uniform bioinformatics processing, we found that the proportion of high-quality reads passing through the filtering steps was similar in both datasets. Per-sample operational taxonomic unit (OTU) and amplicon sequence variant (ASV) richness patterns were highly correlated, but sequencing data from DNBSEQ-G400RS harbored a higher number of OTUs. This may be related to the lower dominance of most common OTUs in DNBSEQ data set (thus revealing higher richness by detecting rare taxa) and/or to a lower effective read quality leading to generation of spurious OTUs. However, there was no statistical difference in the ASV and post-clustered ASV richness between platforms, suggesting that additional denoising step in the ASV workflow had effectively removed the ‘noisy’ reads. Both OTU-based and ASV-based composition were strongly correlated between the sequencing platforms, with essentially interchangeable results. Therefore, we conclude that DNBSEQ-G400RS and NovaSeq 6000 are both equally efficient high-throughput sequencing platforms to be utilized in studies aiming to apply the metabarcoding approach, but the main benefit of the former is related to lower sequencing cost.

scholarly journals NanoCLUST: a species-level analysis of 16S rRNA nanopore sequencing data

2020 ◽  
Author(s):  
Héctor Rodríguez-Pérez ◽  
Laura Ciuffreda ◽  
Carlos Flores
Keyword(s):  
16S Rrna ◽  
Species Level ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Abundance Profile ◽  
Profile Estimation ◽  
Mock Communities ◽  
Better Than

Abstract Summary NanoCLUST is an analysis pipeline for the classification of amplicon-based full-length 16S rRNA nanopore reads. It is characterized by an unsupervised read clustering step, based on Uniform Manifold Approximation and Projection (UMAP), followed by the construction of a polished read and subsequent Blast classification. Here, we demonstrate that NanoCLUST performs better than other state-of-the-art software in the characterization of two commercial mock communities, enabling accurate bacterial identification and abundance profile estimation at species-level resolution. Availability and implementation Source code, test data and documentation of NanoCLUST are freely available at https://github.com/genomicsITER/NanoCLUST under MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals NanoCLUST: a species-level analysis of 16S rRNA nanopore sequencing data

2020 ◽  
Author(s):  
Héctor Rodríguez-Pérez ◽  
Laura Ciuffreda ◽  
Carlos Flores
Keyword(s):  
16S Rrna ◽  
Species Level ◽  
Sequencing Data ◽  
Abundance Profile ◽  
Profile Estimation ◽  
Level Analysis ◽  
Mock Communities ◽  
Better Than

AbstractSummaryNanoCLUST is an analysis pipeline for classification of amplicon-based full-length 16S rRNA nanopore reads. It is characterized by an unsupervised read clustering step, based on Uniform Manifold Approximation and Projection (UMAP), followed by the construction of a polished read and subsequent Blast classification. Here we demonstrate that NanoCLUST performs better than other state-of-the-art software in the characterization of two commercial mock communities, enabling accurate bacterial identification and abundance profile estimation at species level resolution.Availability and implementationSource code, test data and documentation of NanoCLUST is freely available at https://github.com/genomicsITER/NanoCLUST under MIT [email protected]

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