Two Color Single Molecule Sequencing on GenoCare 1600 Platform to Facilitate Clinical Applications

With the rapid development of precision medicine industry, DNA sequencing becomes increasingly important as a research and diagnosis tool. For clinical applications, medical professionals require a platform which is fast, easy to use, and presents clear information relevant to definitive diagnosis. We have developed a single molecule desktop sequencing platform, GenoCare 1600. Fast library preparation (without amplification) and simple instrument operation make it friendlier for clinical use. Here we presented sequencing data of E. coli sample from GenoCare 1600 with consensus accuracy reaches 99.99%. We also demonstrated sequencing of microbial mixtures and COVID-19 samples from throat swabs. Our data show accurate quantitation of microbial, sensitive identification of SARS-CoV-2 virus and detection of variants confirmed by Sanger sequencing.

Download Full-text

Genome assembly of the maize inbred line A188 provides a new reference genome for functional genomics

10.1101/2021.03.15.435372 ◽

2021 ◽

Author(s):

Fei Ge ◽

Jingtao Qu ◽

Peng Liu ◽

Lang Pan ◽

Chaoying Zou ◽

...

Keyword(s):

Single Molecule ◽

Inbred Line ◽

Genome Mapping ◽

Maize Inbred Line ◽

Sequencing Data ◽

Structural Variations ◽

Single Molecule Sequencing ◽

Maize Genetic ◽

Induction Ratio ◽

Phenotypic Variations

Heretofore, little is known about the mechanism underlying the genotype-dependence of embryonic callus (EC) induction, which has severely inhibited the development of maize genetic engineering. Here, we report the genome sequence and annotation of a maize inbred line with high EC induction ratio, A188, which is assembled from single-molecule sequencing and optical genome mapping. We assembled a 2,210 Mb genome with a scaffold N50 size of 11.61 million bases (Mb), compared to those of 9.73 Mb for B73 and 10.2 Mb for Mo17. Comparative analysis revealed that ~30% of the predicted A188 genes had large structural variations to B73, Mo17 and W22 genomes, which caused considerable protein divergence and might lead to phenotypic variations between the four inbred lines. Combining our new A188 genome, previously reported QTLs and RNA sequencing data, we reveal 8 large structural variation genes and 4 differentially expressed genes playing potential roles in EC induction.

Download Full-text

Parallel and scalable workflow for the analysis of Oxford Nanopore direct RNA sequencing datasets

10.1101/818336 ◽

2019 ◽

Author(s):

Luca Cozzuto ◽

Huanle Liu ◽

Leszek P. Pryszcz ◽

Toni Hermoso Pulido ◽

Julia Ponomarenko ◽

...

Keyword(s):

Rna Sequencing ◽

Single Molecule ◽

Tail Length ◽

Rna Modification ◽

Sequencing Data ◽

Polya Tail ◽

Sequencing Platform ◽

Rna Molecules ◽

Oxford Nanopore ◽

Quality Filtering

ABSTRACTThe direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline converts raw current intensities into multiple types of processed data, providing metrics of the quality of the run, quality-filtering, base-calling and mapping. The output of the pipeline can in turn be used to compute per-gene counts, RNA modifications, and prediction of polyA tail length and RNA isoforms. The software is written using the NextFlow framework for parallelization and portability, and relies on Linux containers such as Docker and Singularity for achieving better reproducibility. The MasterOfPores workflow can be executed on any Unix-compatible OS on a computer, cluster or cloud without the need of installing any additional software or dependencies, and is freely available in Github (https://github.com/biocorecrg/master_of_pores). This workflow will significantly simplify the analysis of nanopore direct RNA sequencing data by non-bioinformatics experts, thus boosting the understanding of the (epi)transcriptome with single molecule resolution.

Download Full-text

Bottom-up fabrication of a multi-component nanopore sensor that unfolds, processes and recognizes single proteins

10.1101/2020.12.04.411884 ◽

2020 ◽

Author(s):

Shengli Zhang ◽

Gang Huang ◽

Roderick Versloot ◽

Bart Marlon Herwig ◽

Paulo Cesar Telles de Souza ◽

...

Keyword(s):

Single Molecule ◽

Design Principle ◽

Protein Sequencing ◽

Transmembrane Helices ◽

Single Molecule Sequencing ◽

E Coli ◽

Transmembrane Channels ◽

Single Molecule Analysis ◽

Nanopore Sensors ◽

Substrate Proteins

AbstractTransmembrane channels and pores have many biotechnological applications, notably in the single-molecule sequencing of DNA. Small synthetic nanopores have been designed using amphipathic peptides, or by assembling computationally designed transmembrane helices. The fabrication of more complex transmembrane devices has yet to be reported. In this work, we fabricated in two steps a multi-protein transmembrane device that addresses some of the main challenges in nanopore protein sequencing. In the first step, artificial nanopores are created from soluble proteins with toroid shapes. This design principle will allow fabricating a variety of nanopores for single-molecule analysis. In the second step one α-subuinit of the 20S proteasome from Thermoplasma acidophilum is genetically integrated into the artificial nanopore, and a 28-component nanopore-proteasome is co-assembled in E. coli cells. This multi-component molecular machine opens the door to two new approaches in protein sequencing, in which selected substrate proteins are unfolded, fed to into the proteasomal chamber and then identified by the nanopore sensor either as intact or fragmented polypeptides. The ability to integrate molecular devices directly onto a nanopore sensors allows creating next-generation protein sequencing devices, and will shed new lights on the fundamental processes of biological nanomachines.

Download Full-text

Assembly of chromosome-scale contigs by efficiently resolving repetitive sequences with long reads

10.1101/345983 ◽

2018 ◽

Cited By ~ 2

Author(s):

Huilong Du ◽

Chengzhi Liang

Keyword(s):

Single Molecule ◽

High Efficiency ◽

Reference Genome ◽

Repetitive Sequences ◽

Sequencing Data ◽

High Quality ◽

Single Molecule Sequencing ◽

Genome Maps ◽

Long Reads ◽

Novel Method

AbstractDue to the large number of repetitive sequences in complex eukaryotic genomes, fragmented and incompletely assembled genomes lose value as reference sequences, often due to short contigs that cannot be anchored or mispositioned onto chromosomes. Here we report a novel method Highly Efficient Repeat Assembly (HERA), which includes a new concept called a connection graph as well as algorithms for constructing the graph. HERA resolves repeats at high efficiency with single-molecule sequencing data, and enables the assembly of chromosome-scale contigs by further integrating genome maps and Hi-C data. We tested HERA with the genomes of rice R498, maize B73, human HX1 and Tartary buckwheat Pinku1. HERA can correctly assemble most of the tandemly repetitive sequences in rice using single-molecule sequencing data only. Using the same maize and human sequencing data published by Jiao et al. (2017) and Shi et al. (2016), respectively, we dramatically improved on the sequence contiguity compared with the published assemblies, increasing the contig N50 from 1.3 Mb to 61.2 Mb in maize B73 assembly and from 8.3 Mb to 54.4 Mb in human HX1 assembly with HERA. We provided a high-quality maize reference genome with 96.9% of the gaps filled (only 76 gaps left) and several incorrectly positioned sequences fixed compared with the B73 RefGen_v4 assembly. Comparisons between the HERA assembly of HX1 and the human GRCh38 reference genome showed that many gaps in GRCh38 could be filled, and that GRCh38 contained some potential errors that could be fixed. We assembled the Pinku1 genome into 12 scaffolds with a contig N50 size of 27.85 Mb. HERA serves as a new genome assembly/phasing method to generate high quality sequences for complex genomes and as a curation tool to improve the contiguity and completeness of existing reference genomes, including the correction of assembly errors in repetitive regions.

Download Full-text

Critical assessment of bioinformatics methods for the characterization of pathological repeat expansions with single-molecule sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbz099 ◽

2019 ◽

Vol 21 (6) ◽

pp. 1971-1986 ◽

Cited By ~ 1

Author(s):

Matteo Chiara ◽

Federico Zambelli ◽

Ernesto Picardi ◽

David S Horner ◽

Graziano Pesole

Keyword(s):

Single Molecule ◽

Tandem Repeats ◽

Simulated Data ◽

Detailed Comparison ◽

Sequencing Data ◽

Single Molecule Sequencing ◽

Sequencing Technologies ◽

Repeat Expansions

Abstract A number of studies have reported the successful application of single-molecule sequencing technologies to the determination of the size and sequence of pathological expanded microsatellite repeats over the last 5 years. However, different custom bioinformatics pipelines were employed in each study, preventing meaningful comparisons and somewhat limiting the reproducibility of the results. In this review, we provide a brief summary of state-of-the-art methods for the characterization of expanded repeats alleles, along with a detailed comparison of bioinformatics tools for the determination of repeat length and sequence, using both real and simulated data. Our reanalysis of publicly available human genome sequencing data suggests a modest, but statistically significant, increase of the error rate of single-molecule sequencing technologies at genomic regions containing short tandem repeats. However, we observe that all the methods herein tested, irrespective of the strategy used for the analysis of the data (either based on the alignment or assembly of the reads), show high levels of sensitivity in both the detection of expanded tandem repeats and the estimation of the expansion size, suggesting that approaches based on single-molecule sequencing technologies are highly effective for the detection and quantification of tandem repeat expansions and contractions.

Download Full-text

Evaluation of Single-Molecule Sequencing Technologies for Structural Variant Detection in Two Swedish Human Genomes

Genes ◽

10.3390/genes11121444 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1444

Author(s):

Nazeefa Fatima ◽

Anna Petri ◽

Ulf Gyllensten ◽

Lars Feuk ◽

Adam Ameur

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Single Molecule ◽

Large Scale ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Structural Variations ◽

Single Molecule Sequencing ◽

Human Samples

Long-read single molecule sequencing is increasingly used in human genomics research, as it allows to accurately detect large-scale DNA rearrangements such as structural variations (SVs) at high resolution. However, few studies have evaluated the performance of different single molecule sequencing platforms for SV detection in human samples. Here we performed Oxford Nanopore Technologies (ONT) whole-genome sequencing of two Swedish human samples (average 32× coverage) and compared the results to previously generated Pacific Biosciences (PacBio) data for the same individuals (average 66× coverage). Our analysis inferred an average of 17k and 23k SVs from the ONT and PacBio data, respectively, with a majority of them overlapping with an available multi-platform SV dataset. When comparing the SV calls in the two Swedish individuals, we find a higher concordance between ONT and PacBio SVs detected in the same individual as compared to SVs detected by the same technology in different individuals. Downsampling of PacBio reads, performed to obtain similar coverage levels for all datasets, resulted in 17k SVs per individual and improved overlap with the ONT SVs. Our results suggest that ONT and PacBio have a similar performance for SV detection in human whole genome sequencing data, and that both technologies are feasible for population-scale studies.

Download Full-text

NanoSim: nanopore sequence read simulator based on statistical characterization

10.1101/044545 ◽

2016 ◽

Cited By ~ 7

Author(s):

Chen Yang ◽

Justin Chu ◽

Ren&eacute L Warren ◽

Inanç Birol

Keyword(s):

Single Molecule ◽

Sequencing Technology ◽

Statistical Characterization ◽

Single Molecule Sequencing ◽

Sequencing Platform ◽

Early Access ◽

Oxford Nanopore ◽

Commercial Technology ◽

Read Simulator ◽

Oxford Nanopore Technologies

Motivation: In 2014, Oxford Nanopore Technologies (ONT) announced a new sequencing platform called MinION. The particular features of MinION reads, longer read lengths and single-molecule sequencing in particular, show potential for genome characterization. As of yet, the pre-commercial technology is exclusively available through early-access, and only a few datasets are publically available for testing. Further, no software exists that simulates MinION platform reads with genuine ONT characteristics. Results: In this article, we introduce NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data, and allows for adjustments upon improvement of nanopore sequencing technology. Availability: NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

Download Full-text

Penguin: A Tool for Predicting Pseudouridine Sites in Direct RNA Nanopore Sequencing Data

10.1101/2021.03.31.437901 ◽

2021 ◽

Author(s):

Doaa Hassan ◽

Daniel Acevedo ◽

Swapna Vidhur Daulatabad ◽

Quoseena Mir ◽

Sarath Chandra Janga

Keyword(s):

Rna Sequencing ◽

Single Molecule ◽

Direct Detection ◽

Support Vector ◽

Sequencing Data ◽

Rna Modifications ◽

Sequencing Platform ◽

Independent Validation ◽

Oxford Nanopore ◽

Validation Testing

AbstractPseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and also has an importance in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies enable direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge this technology has not been used to identify RNA Pseudouridine sites. To this end, in this paper, we address this limitation by introducing a tool called Penguin that integrates several developed machine learning (ML) models (i.e., predictors) to identify RNA Pseudouridine sites in Nanopore direct RNA sequencing reads. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, is able to predict whether the signal is modified by the presence of Pseudouridine sites. We have included various predictors in Penguin including Support vector machine (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets show that Penguin is able to identify Pseudouridine sites with a high accuracy of 93.38% and 92.61% using SVM in random split testing and independent validation testing respectively. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature that achieved an accuracy of 76.0 at most with an independent validation testing. A GitHub of the tool is accessible at https://github.com/Janga-Lab/Penguin.

Download Full-text