Molecular barcoding of native RNAs using nanopore sequencing and deep learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Smith MA
PROVIDER: S-EPMC7545146 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Biodiversity research is becoming increasingly dependent on genomics, which allows the unprecedented digitization and understanding of the planet's biological heritage. The use of genetic markers i.e. DNA barcoding, has proved to be a powerful tool in species identification. However, full exploitation of this approach is hampered by the high sequencing costs and the absence of equipped facilities in biodiversity-rich countries. In the present work, we developed a portable sequencing laboratory based on the portable DNA sequencer from Oxford Nanopore Technologies, the MinION. Complementary laboratory equipment and reagents were selected to be used in remote and tough environmental conditions. The performance of the MinION sequencer and the portable laboratory was tested for DNA barcoding in a mimicking tropical environment, as well as in a remote rainforest of Tanzania lacking electricity. Despite the relatively high sequencing error-rate of the MinION, the development of a suitable pipeline for data analysis allowed the accurate identification of different species of vertebrates including amphibians, reptiles and mammals. In situ sequencing of a wild frog allowed us to rapidly identify the species captured, thus confirming that effective DNA barcoding in the field is possible. These results open new perspectives for real-time-on-site DNA sequencing thus potentially increasing opportunities for the understanding of biodiversity in areas lacking conventional laboratory facilities.
| S-EPMC5627904 | biostudies-literature
Project description:Purpose: The majority of bladder cancer patients present with localized disease and are managed by transurethral resection. However, the high rate of recurrence necessitates lifetime cystoscopic surveillance. Developing a sensitive and specific urine-based test would significantly improve bladder cancer screening, detection, and surveillance.Experimental Design: RNA-seq was used for biomarker discovery to directly assess the gene expression profile of exfoliated urothelial cells in urine derived from bladder cancer patients (n = 13) and controls (n = 10). Eight bladder cancer specific and 3 reference genes identified by RNA-seq were quantitated by qPCR in a training cohort of 102 urine samples. A diagnostic model based on the training cohort was constructed using multiple logistic regression. The model was further validated in an independent cohort of 101 urines.Results: A total of 418 genes were found to be differentially expressed between bladder cancer and controls. Validation of a subset of these genes was used to construct an equation for computing a probability of bladder cancer score (PBC) based on expression of three markers (ROBO1, WNT5A, and CDC42BPB). Setting PBC = 0.45 as the cutoff for a positive test, urine testing using the three-marker panel had overall 88% sensitivity and 92% specificity in the training cohort. The accuracy of the three-marker panel in the independent validation cohort yielded an AUC of 0.87 and overall 83% sensitivity and 89% specificity.Conclusions: Urine-based molecular diagnostics using this three-marker signature could provide a valuable adjunct to cystoscopy and may lead to a reduction of unnecessary procedures for bladder cancer diagnosis. Clin Cancer Res; 23(14); 3700-10. ©2017 AACR.
| S-EPMC5873297 | biostudies-other
Project description:BackgroundThe Oxford Nanopore Technologies MinION(TM) is a mobile DNA sequencer that can produce long read sequences with a short turn-around time. Here we report the first demonstration of single contig genome assembly using Oxford Nanopore native barcoding when applied to a multiplexed library of 12 samples and combined with existing Illumina short read data. This paves the way for the closure of multiple bacterial genomes from a single MinION(TM) sequencing run, given the availability of existing short read data. The strain we used, MHO_001, represents the important community-acquired methicillin-resistant Staphylococcus aureus lineage USA300.FindingsUsing a hybrid assembly of existing short read and barcoded long read sequences from multiplexed data, we completed a genome of the S. aureus USA300 strain MHO_001. The long read data represented only ∼5% to 10% of an average MinION(TM) run (∼7x genomic coverage), but, using standard tools, this was sufficient to complete the circular chromosome of S. aureus strain MHO_001 (2.86 Mb) and two complete plasmids (27 Kb and 3 Kb). Minor differences were noted when compared to USA300 reference genome, USA300_FPR3757, including the translocation, loss, and gain of mobile genetic elements.ConclusionHere we demonstrate that MinION(TM) reads, multiplexed using native barcoding, can be used in combination with short read data to fully complete a bacterial genome. The ability to complete multiple genomes, for which short read data is already available, from a single MinION(TM) run is set to impact our understanding of accessory genome content, plasmid diversity, and genome rearrangements.
| S-EPMC5467021 | biostudies-literature
Project description:Single-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo assembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order alignment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 90× dataset of E. coli and a 37× human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results.
| S-EPMC7229283 | biostudies-literature
Project description:BackgroundMeasuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software.ResultsThis paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent .ConclusionsThis paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.
| S-EPMC8191041 | biostudies-literature
Project description:Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data
| PRJEB31789 | ENA
Project description:Background: The ability to obtain long read lengths during DNA sequencing has several potentially important practical applications. Especially long read lengths have been reported using the Nanopore sequencing method, currently commercially available from Oxford Nanopore Technologies (ONT). However, early reports have demonstrated only limited levels of combined throughput and sequence accuracy. Recently, ONT released a new CsgG pore sequencing system as well as a 250b/s translocation chemistry with potential for improvements. Methods: We made use of such components on ONTs miniature 'MinION' device and sequenced native genomic DNA obtained from the near haploid cancer cell line HAP1. Analysis of our data was performed utilising recently described computational tools tailored for nanopore/long-read sequencing outputs, and here we present our key findings. Results: From a single sequencing run, we obtained ~240,000 high-quality mapped reads, comprising a total of ~2.3 billion bases. A mean read length of 9.6kb and an N50 of ~17kb was achieved, while sequences mapped to reference with a mean identity of 85%. Notably, we obtained ~68X coverage of the mitochondrial genome and were able to achieve a mean consensus identity of 99.8% for sequenced mtDNA reads. Conclusions: With improved sequencing chemistries already released and higher-throughput instruments in the pipeline, this early study suggests that ONT CsgG-based sequencing may be a useful option for potential practical long-read applications.
| S-EPMC5426553 | biostudies-literature
Project description:PurposeTo evaluate the utility of nanopore sequencing for identifying potential causative pathogens in endophthalmitis, comparing culture results against full-length 16S rRNA nanopore sequencing (16S Nanopore), whole genome nanopore sequencing (Nanopore WGS), and Illumina (Illumina WGS).DesignCross-sectional diagnostic comparison.MethodsPatients with clinically suspected endophthalmitis underwent intraocular vitreous biopsy as per standard care. Clinical samples were cultured by conventional methods, together with full-length 16S rRNA and WGS using nanopore and Illumina sequencing platforms.ResultsOf 23 patients (median age 68.5 years [range 47-88]; 14 males [61%]), 18 cases were culture-positive. Nanopore sequencing identified the same cultured organism in all of the culture-positive cases and identified potential pathogens in two culture-negative cases (40%). Nanopore WGS was able to additionally detect the presence of bacteriophages in three samples. The agreements at genus level between culture and 16S Nanopore, Nanopore WGS, and Illumina WGS were 75%, 100%, and 78%, respectively.ConclusionsWhole genome sequencing has higher sensitivity and provides a viable alternative to culture and 16S sequencing for detecting potential pathogens in endophthalmitis. Moreover, WGS has the ability to detect other potential pathogens in culture-negative cases. Whilst Nanopore and Illumina WGS provide comparable data, nanopore sequencing provides potential for cost-effective point-of-care diagnostics.
| S-EPMC9850836 | biostudies-literature
Project description:Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.
| S-EPMC8482760 | biostudies-literature
Project description:Nanopore sequencing is regarded as one of the most promising third-generation sequencing (TGS) technologies. Since 2014, Oxford Nanopore Technologies (ONT) has developed a series of devices based on nanopore sequencing to produce very long reads, with an expected impact on genomics. However, the nanopore sequencing reads are susceptible to a fairly high error rate owing to the difficulty in identifying the DNA bases from the complex electrical signals. Although several basecalling tools have been developed for nanopore sequencing over the past years, it is still challenging to correct the sequences after applying the basecalling procedure. In this study, we developed an open-source DNA basecalling reviser, NanoReviser, based on a deep learning algorithm to correct the basecalling errors introduced by current basecallers provided by default. In our module, we re-segmented the raw electrical signals based on the basecalled sequences provided by the default basecallers. By employing convolution neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks, we took advantage of the information from the raw electrical signals and the basecalled sequences from the basecallers. Our results showed NanoReviser, as a post-basecalling reviser, significantly improving the basecalling quality. After being trained on standard ONT sequencing reads from public E. coli and human NA12878 datasets, NanoReviser reduced the sequencing error rate by over 5% for both the E. coli dataset and the human dataset. The performance of NanoReviser was found to be better than those of all current basecalling tools. Furthermore, we analyzed the modified bases of the E. coli dataset and added the methylation information to train our module. With the methylation annotation, NanoReviser reduced the error rate by 7% for the E. coli dataset and specifically reduced the error rate by over 10% for the regions of the sequence rich in methylated bases. To the best of our knowledge, NanoReviser is the first post-processing tool after basecalling to accurately correct the nanopore sequences without the time-consuming procedure of building the consensus sequence. The NanoReviser package is freely available at https://github.com/pkubioinformatics/NanoReviser.
| S-EPMC7434944 | biostudies-literature