Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study

BACKGROUND

Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection.

METHODS

This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI.

FINDINGS

Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease class signature that achieved an AUC of 94·1% (95% CI 90·6-97·7) in distinguishing bacterial from viral infections in the RAPIDS validation cohort. A ten-gene disease severity signature achieved an AUC of 82·2% (95% CI 76·3-88·1) in predicting organ dysfunction within 24 h of sampling in the RAPIDS validation cohort. Used in tandem, the disease class and disease severity signatures predicted organ dysfunction within 24 h of sampling with an AUC of 90·5% (95% CI 83·3-97·6) for patients with predicted bacterial infection and 94·7% (87·8-100·0) for patients with predicted viral infection. In the external EUCLIDS validation dataset (n=362), the disease class and disease severity predicted organ dysfunction at time of sampling with an AUC of 70·1% (95% CI 44·1-96·2) for patients with predicted bacterial infection and 69·6% (53·1-86·0) for patients with predicted viral infection.

INTERPRETATION

In children evaluated for sepsis, novel host transcriptomic signatures specific for bacterial and viral infection can identify dysregulated host response leading to organ dysfunction.

FUNDING

Australian Government Medical Research Future Fund Genomic Health Futures Mission, Children's Hospital Foundation Queensland, Brisbane Diamantina Health Partners, Emergency Medicine Foundation, Gold Coast Hospital Foundation, Far North Queensland Foundation, Townsville Hospital and Health Services SERTA Grant, and Australian Infectious Diseases Research Centre.

Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data

BACKGROUND

Culture-free real-time sequencing of clinical metagenomic samples promises both rapid pathogen detection and antimicrobial resistance profiling. However, this approach introduces the risk of patient DNA leakage. To mitigate this risk, we need near-comprehensive removal of human DNA sequences at the point of sequencing, typically involving the use of resource-constrained devices. Existing benchmarks have largely focused on the use of standardized databases and largely ignored the computational requirements of depletion pipelines as well as the impact of human genome diversity.

RESULTS

We benchmarked host removal pipelines on simulated and artificial real Illumina and Nanopore metagenomic samples. We found that construction of a custom kraken database containing diverse human genomes results in the best balance of accuracy and computational resource usage. In addition, we benchmarked pipelines using kraken and minimap2 for taxonomic classification of Mycobacterium reads using standard and custom databases. With a database representative of the Mycobacterium genus, both tools obtained improved specificity and sensitivity, compared to the standard databases for classification of Mycobacterium tuberculosis. Computational efficiency of these custom databases was superior to most standard approaches, allowing them to be executed on a laptop device.

CONCLUSIONS

Customized pangenome databases provide the best balance of accuracy and computational efficiency when compared to standard databases for the task of human read removal and M. tuberculosis read classification from metagenomic samples. Such databases allow for execution on a laptop, without sacrificing accuracy, an especially important consideration in low-resource settings. We make all customized databases and pipelines freely available.

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

BACKGROUND

Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h.

METHODS

In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores.

FINDINGS

376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%.

INTERPRETATION

This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics.

FUNDING

European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND

Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.

METHODS

A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.

FINDINGS

We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.

CONCLUSIONS

Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.

FUNDING

European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

BACKGROUND

To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections.

METHODS

Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39).

RESULTS

In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV.

CONCLUSIONS

MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters

BACKGROUND

Promoters are DNA regions that initiate the transcription of specific genes near the transcription start sites. In bacteria, promoters are recognized by RNA polymerases and associated sigma factors. Effective promoter recognition is essential for synthesizing the gene-encoded products by bacteria to grow and adapt to different environmental conditions. A variety of machine learning-based predictors for bacterial promoters have been developed; however, most of them were designed specifically for a particular species. To date, only a few predictors are available for identifying general bacterial promoters with limited predictive performance.

RESULTS

In this study, we developed TIMER, a Siamese neural network-based approach for identifying both general and species-specific bacterial promoters. Specifically, TIMER uses DNA sequences as the input and employs three Siamese neural networks with the attention layers to train and optimize the models for a total of 13 species-specific and general bacterial promoters. Extensive 10-fold cross-validation and independent tests demonstrated that TIMER achieves a competitive performance and outperforms several existing methods on both general and species-specific promoter prediction. As an implementation of the proposed method, the web server of TIMER is publicly accessible at http://web.unimelb-bioinfortools.cloud.edu.au/TIMER/.

ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species

A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.

Detection of Streptococcus pyogenes M1UK in Australia and characterization of the mutation driving enhanced expression of superantigen SpeA

A new variant of Streptococcus pyogenes serotype M1 (designated 'M1_UK') has been reported in the United Kingdom, linked with seasonal scarlet fever surges, marked increase in invasive infections, and exhibiting enhanced expression of the superantigen SpeA. The progenitor S. pyogenes 'M1_global' and M1_UK clones can be differentiated by 27 SNPs and 4 indels, yet the mechanism for speA upregulation is unknown. Here we investigate the previously unappreciated expansion of M1_UK in Australia, now isolated from the majority of serious infections caused by serotype M1 S. pyogenes. M1_UK sub-lineages circulating in Australia also contain a novel toxin repertoire associated with epidemic scarlet fever causing S. pyogenes in Asia. A single SNP in the 5' transcriptional leader sequence of the transfer-messenger RNA gene ssrA drives enhanced SpeA superantigen expression as a result of ssrA terminator read-through in the M1_UK lineage. This represents a previously unappreciated mechanism of toxin expression and urges enhanced international surveillance.

DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions

MOTIVATION

Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction.

RESULTS

In this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes.

AVAILABILITY AND IMPLEMENTATION

The webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Long-Read RNA Sequencing Identifies Polyadenylation Elongation and Differential Transcript Usage of Host Transcripts During SARS-CoV-2 In Vitro Infection

Better methods to interrogate host-pathogen interactions during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections are imperative to help understand and prevent this disease. Here we implemented RNA-sequencing (RNA-seq) using Oxford Nanopore Technologies (ONT) long-reads to measure differential host gene expression, transcript polyadenylation and isoform usage within various epithelial cell lines permissive and non-permissive for SARS-CoV-2 infection. SARS-CoV-2-infected and mock-infected Vero (African green monkey kidney epithelial cells), Calu-3 (human lung adenocarcinoma epithelial cells), Caco-2 (human colorectal adenocarcinoma epithelial cells) and A549 (human lung carcinoma epithelial cells) were analyzed over time (0, 2, 24, 48 hours). Differential polyadenylation was found to occur in both infected Calu-3 and Vero cells during a late time point (48 hpi), with Gene Ontology (GO) terms such as viral transcription and translation shown to be significantly enriched in Calu-3 data. Poly(A) tails showed increased lengths in the majority of the differentially polyadenylated transcripts in Calu-3 and Vero cell lines (up to ~101 nt in mean poly(A) length, padj = 0.029). Of these genes, ribosomal protein genes such as RPS4X and RPS6 also showed downregulation in expression levels, suggesting the importance of ribosomal protein genes during infection. Furthermore, differential transcript usage was identified in Caco-2, Calu-3 and Vero cells, including transcripts of genes such as GSDMB and KPNA2, which have previously been implicated in SARS-CoV-2 infections. Overall, these results highlight the potential role of differential polyadenylation and transcript usage in host immune response or viral manipulation of host mechanisms during infection, and therefore, showcase the value of long-read sequencing in identifying less-explored host responses to disease.

Whole genome deep sequencing analysis of cell-free DNA in samples with low tumour content

Background

Circulating cell-free DNA (cfDNA) in the plasma of cancer patients contains cell-free tumour DNA (ctDNA) derived from tumour cells and it has been widely recognized as a non-invasive source of tumour DNA for diagnosis and prognosis of cancer. Molecular profiling of ctDNA is often performed using targeted sequencing or low-coverage whole genome sequencing (WGS) to identify tumour specific somatic mutations or somatic copy number aberrations (sCNAs). However, these approaches cannot efficiently detect all tumour-derived genomic changes in ctDNA.

Methods

We performed WGS analysis of cfDNA from 4 breast cancer patients and 2 patients with benign tumours. We sequenced matched germline DNA for all 6 patients and tumour samples from the breast cancer patients. All samples were sequenced on Illumina HiSeqXTen sequencing platform and achieved approximately 30x, 60x and 100x coverage on germline, tumour and plasma DNA samples, respectively.

Results

The mutational burden of the plasma samples (1.44 somatic mutations/Mb of genome) was higher than the matched tumour samples. However, 90% of high confidence somatic cfDNA variants were not detected in matched tumour samples and were found to comprise two background plasma mutational signatures. In contrast, cfDNA from the di-nucleosome fraction (300 bp–350 bp) had much higher proportion (30%) of variants shared with tumour. Despite high coverage sequencing we were unable to detect sCNAs in plasma samples.

Conclusions

Deep sequencing analysis of plasma samples revealed higher fraction of unique somatic mutations in plasma samples, which were not detected in matched tumour samples. Sequencing of di-nucleosome bound cfDNA fragments may increase recovery of tumour mutations from plasma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09160-1.

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

Positive-unlabeled learning in bioinformatics and computational biology: a brief review

Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.

Porpoise: a new approach for accurate prediction of RNA pseudouridine sites

Pseudouridine is a ubiquitous RNA modification type present in eukaryotes and prokaryotes, which plays a vital role in various biological processes. Almost all kinds of RNAs are subject to this modification. However, it remains a great challenge to identify pseudouridine sites via experimental approaches, requiring expensive and time-consuming experimental research. Therefore, computational approaches that can be used to perform accurate in silico identification of pseudouridine sites from the large amount of RNA sequence data are highly desirable and can aid in the functional elucidation of this critical modification. Here, we propose a new computational approach, termed Porpoise, to accurately identify pseudouridine sites from RNA sequence data. Porpoise builds upon a comprehensive evaluation of 18 frequently used feature encoding schemes based on the selection of four types of features, including binary features, pseudo k-tuple composition, nucleotide chemical property and position-specific trinucleotide propensity based on single-strand (PSTNPss). The selected features are fed into the stacked ensemble learning framework to enable the construction of an effective stacked model. Both cross-validation tests on the benchmark dataset and independent tests show that Porpoise achieves superior predictive performance than several state-of-the-art approaches. The application of model interpretation tools demonstrates the importance of PSTNPs for the performance of the trained models. This new method is anticipated to facilitate community-wide efforts to identify putative pseudouridine sites and formulate novel testable biological hypothesis.

Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIRECT)

Background

Sepsis contributes significantly to morbidity and mortality globally. In Australia, 20,000 develop sepsis every year, resulting in 5,000 deaths, and more than AUD$846 million in expenditure. Prompt, appropriate antibiotic therapy is effective in improving outcomes in sepsis. Conventional culture-based methods to identify appropriate therapy have limited yield and take days to complete. Recently, nanopore technology has enabled rapid sequencing with real-time analysis of pathogen DNA. We set out to demonstrate the feasibility and diagnostic accuracy of pathogen sequencing direct from clinical samples, and estimate the impact of this approach on time to effective therapy when integrated with personalised software-guided antimicrobial dosing in children and adults on ICU with sepsis.

Methods

The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.

Participants and interventions

DIRECT will collect microbiological and pharmacokinetic samples from approximately 200 children and adults with sepsis admitted to one of four ICUs in Brisbane. In Phase 1, we will evaluate Oxford Nanopore Technologies MinION sequencing direct from blood in 50 blood culture-proven sepsis patients recruited from consecutive patients with suspected sepsis. In Phase 2, a further 50 consecutive patients with suspected sepsis will be recruited in whom MinION sequencing will be combined with Bayesian software-guided (ID-ODS) personalised antimicrobial dosing.

Outcome measures

The primary outcome is time to effective antimicrobial therapy, defined as trough drug concentrations above the MIC of the pathogen. Secondary outcomes are diagnostic accuracy of MinION sequencing from whole blood, time to pathogen identification and susceptibility testing using sequencing direct from whole blood and from positive blood culture broth.

Discussion

Rapid pathogen sequencing coupled with antimicrobial dosing software has great potential to overcome the limitations of conventional diagnostics which often result in prolonged inappropriate antimicrobial therapy. Reduced time to optimal antimicrobial therapy may reduce sepsis mortality and ICU length of stay. This pilot study will yield key feasibility data to inform further, urgently needed sepsis studies. Phase 2 of the trial protocol is registered with the ANZCTR (ACTRN12620001122943).

Trial registration

Registered with the Australia New Zealand Clinical Trials Registry Number ACTRN12620001122943

Real-time resolution of short-read assembly graph using ONT long reads

A streaming assembly pipeline utilising real-time Oxford Nanopore Technology (ONT) sequencing data is important for saving sequencing resources and reducing time-to-result. A previous approach implemented in npScarf provided an efficient streaming algorithm for hybrid assembly but was relatively prone to mis-assemblies compared to other graph-based methods. Here we present npGraph, a streaming hybrid assembly tool using the assembly graph instead of the separated pre-assembly contigs. It is able to produce more complete genome assembly by resolving the path finding problem on the assembly graph using long reads as the traversing guide. Application to synthetic and real data from bacterial isolate genomes show improved accuracy while still maintaining a low computational cost. npGraph also provides a graphical user interface (GUI) which provides a real-time visualisation of the progress of assembly. The tool and source code is available at https://github.com/hsnguyen/assembly.

Comparison of long-read methods for sequencing and assembly of a plant genome

BACKGROUND

Sequencing technologies have advanced to the point where it is possible to generate high-accuracy, haplotype-resolved, chromosome-scale assemblies. Several long-read sequencing technologies are available, and a growing number of algorithms have been developed to assemble the reads generated by those technologies. When starting a new genome project, it is therefore challenging to select the most cost-effective sequencing technology, as well as the most appropriate software for assembly and polishing. It is thus important to benchmark different approaches applied to the same sample.

RESULTS

Here, we report a comparison of 3 long-read sequencing technologies applied to the de novo assembly of a plant genome, Macadamia jansenii. We have generated sequencing data using Pacific Biosciences (Sequel I), Oxford Nanopore Technologies (PromethION), and BGI (single-tube Long Fragment Read) technologies for the same sample. Several assemblers were benchmarked in the assembly of Pacific Biosciences and Nanopore reads. Results obtained from combining long-read technologies or short-read and long-read technologies are also presented. The assemblies were compared for contiguity, base accuracy, and completeness, as well as sequencing costs and DNA material requirements.

CONCLUSIONS

The 3 long-read technologies produced highly contiguous and complete genome assemblies of M. jansenii. At the time of sequencing, the cost associated with each method was significantly different, but continuous improvements in technologies have resulted in greater accuracy, increased throughput, and reduced costs. We propose updating this comparison regularly with reports on significant iterations of the sequencing technologies.

Comparison of long-read methods for sequencing and assembly of a plant genome

BACKGROUND

Sequencing technologies have advanced to the point where it is possible to generate high-accuracy, haplotype-resolved, chromosome-scale assemblies. Several long-read sequencing technologies are available, and a growing number of algorithms have been developed to assemble the reads generated by those technologies. When starting a new genome project, it is therefore challenging to select the most cost-effective sequencing technology, as well as the most appropriate software for assembly and polishing. It is thus important to benchmark different approaches applied to the same sample.

RESULTS

Here, we report a comparison of 3 long-read sequencing technologies applied to the de novo assembly of a plant genome, Macadamia jansenii. We have generated sequencing data using Pacific Biosciences (Sequel I), Oxford Nanopore Technologies (PromethION), and BGI (single-tube Long Fragment Read) technologies for the same sample. Several assemblers were benchmarked in the assembly of Pacific Biosciences and Nanopore reads. Results obtained from combining long-read technologies or short-read and long-read technologies are also presented. The assemblies were compared for contiguity, base accuracy, and completeness, as well as sequencing costs and DNA material requirements.

CONCLUSIONS

The 3 long-read technologies produced highly contiguous and complete genome assemblies of M. jansenii. At the time of sequencing, the cost associated with each method was significantly different, but continuous improvements in technologies have resulted in greater accuracy, increased throughput, and reduced costs. We propose updating this comparison regularly with reports on significant iterations of the sequencing technologies.

Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations

Despite advances in sequencing technologies, assembly of complex plant genomes remains elusive due to polyploidy and high repeat content. Here we report PolyGembler for grouping and ordering contigs into pseudomolecules by genetic linkage analysis. Our approach also provides an accurate method with which to detect and fix assembly errors. Using simulated data, we demonstrate that our approach is of high accuracy and outperforms three existing state-of-the-art genetic mapping tools. Particularly, our approach is more robust to the presence of missing genotype data and genotyping errors. We used our method to construct pseudomolecules for allotetraploid lawn grass utilizing PacBio long reads in combination with restriction site-associated DNA sequencing, and for diploid Ipomoea trifida and autotetraploid potato utilizing contigs assembled from Illumina reads in combination with genotype data generated by single-nucleotide polymorphism arrays and genotyping by sequencing, respectively. We resolved 13 assembly errors for a published I. trifida genome assembly and anchored eight unplaced scaffolds in the published potato genome.

Insights into population structure of East African sweetpotato cultivars from hybrid assembly of chloroplast genomes

Background: The chloroplast (cp) genome is an important resource for studying plant diversity and phylogeny. Assembly of the cp genomes from next-generation sequencing data is complicated by the presence of two large inverted repeats contained in the cp DNA. Methods: We constructed a complete circular cp genome assembly for the hexaploid sweetpotato using extremely low coverage (<1×) Oxford Nanopore whole-genome sequencing (WGS) data coupled with Illumina sequencing data for polishing. Results: The sweetpotato cp genome of 161,274 bp contains 152 genes, of which there are 96 protein coding genes, 8 rRNA genes and 48 tRNA genes. Using the cp genome assembly as a reference, we constructed complete cp genome assemblies for a further 17 sweetpotato cultivars from East Africa and an I. triloba line using Illumina WGS data. Analysis of the sweetpotato cp genomes demonstrated the presence of two distinct subpopulations in East Africa. Phylogenetic analysis of the cp genomes of the species from the Convolvulaceae Ipomoea section Batatas revealed that the most closely related diploid wild species of the hexaploid sweetpotato is I. trifida. Conclusions: Nanopore long reads are helpful in construction of cp genome assemblies, especially in solving the two long inverted repeats. We are generally able to extract cp sequences from WGS data of sufficiently high coverage for assembly of cp genomes. The cp genomes can be used to investigate the population structure and the phylogenetic relationship for the sweetpotato.

Octapeptin C4 and polymyxin resistance occur via distinct pathways in an epidemic XDR Klebsiella pneumoniae ST258 isolate

BACKGROUND

Polymyxin B and E (colistin) have been pivotal in the treatment of XDR Gram-negative bacterial infections; however, resistance has emerged. A structurally related lipopeptide, octapeptin C4, has shown significant potency against XDR bacteria, including polymyxin-resistant strains, but its mode of action remains undefined.

OBJECTIVES

We sought to compare and contrast the acquisition of resistance in an XDR Klebsiella pneumoniae (ST258) clinical isolate in vitro with all three lipopeptides to potentially unveil variations in their mode of action.

METHODS

The isolate was exposed to increasing concentrations of polymyxins and octapeptin C4 over 20 days. Day 20 strains underwent WGS, complementation assays, antimicrobial susceptibility testing and lipid A analysis.

RESULTS

Twenty days of exposure to the polymyxins resulted in a 1000-fold increase in the MIC, whereas for octapeptin C4 a 4-fold increase was observed. There was no cross-resistance observed between the polymyxin- and octapeptin-resistant strains. Sequencing of polymyxin-resistant isolates revealed mutations in previously known resistance-associated genes, including crrB, mgrB, pmrB, phoPQ and yciM, along with novel mutations in qseC. Octapeptin C4-resistant isolates had mutations in mlaDF and pqiB, genes related to phospholipid transport. These genetic variations were reflected in distinct phenotypic changes to lipid A. Polymyxin-resistant isolates increased 4-amino-4-deoxyarabinose fortification of lipid A phosphate groups, whereas the lipid A of octapeptin C4-resistant strains harboured a higher abundance of hydroxymyristate and palmitoylate.

CONCLUSIONS

Octapeptin C4 has a distinct mode of action compared with the polymyxins, highlighting its potential as a future therapeutic agent to combat the increasing threat of XDR bacteria.

Evaluating the genome and resistome of extensively drug-resistant Klebsiella pneumoniae using native DNA and RNA Nanopore sequencing

BACKGROUND

Klebsiella pneumoniae frequently harbours multidrug resistance, and current diagnostics struggle to rapidly identify appropriate antibiotics to treat these bacterial infections. The MinION device can sequence native DNA and RNA in real time, providing an opportunity to compare the utility of DNA and RNA for prediction of antibiotic susceptibility. However, the effectiveness of bacterial direct RNA sequencing and base-calling has not previously been investigated. This study interrogated the genome and transcriptome of 4 extensively drug-resistant (XDR) K. pneumoniae clinical isolates; however, further antimicrobial susceptibility testing identified 3 isolates as pandrug-resistant (PDR).

RESULTS

The majority of acquired resistance (≥75%) resided on plasmids including several megaplasmids (≥100 kb). DNA sequencing detected most resistance genes (≥70%) within 2 hours of sequencing. Neural network-based base-calling of direct RNA achieved up to 86% identity rate, although ≤23% of reads could be aligned. Direct RNA sequencing (with ∼6 times slower pore translocation) was able to identify (within 10 hours) ≥35% of resistance genes, including those associated with resistance to aminoglycosides, β-lactams, trimethoprim, and sulphonamide and also quinolones, rifampicin, fosfomycin, and phenicol in some isolates. Direct RNA sequencing also identified the presence of operons containing up to 3 resistance genes. Polymyxin-resistant isolates showed a heightened transcription of phoPQ (≥2-fold) and the pmrHFIJKLM operon (≥8-fold). Expression levels estimated from direct RNA sequencing displayed strong correlation (Pearson: 0.86) compared to quantitative real-time PCR across 11 resistance genes.

CONCLUSION

Overall, MinION sequencing rapidly detected the XDR/PDR K. pneumoniae resistome, and direct RNA sequencing provided accurate estimation of expression levels of these genes.

Quantitative trait loci and differential gene expression analyses reveal the genetic basis for negatively associated β-carotene and starch content in hexaploid sweetpotato [Ipomoea batatas (L.) Lam.]

KEY MESSAGE

β-Carotene content in sweetpotato is associated with the Orange and phytoene synthase genes; due to physical linkage of phytoene synthase with sucrose synthase, β-carotene and starch content are negatively correlated. In populations depending on sweetpotato for food security, starch is an important source of calories, while β-carotene is an important source of provitamin A. The negative association between the two traits contributes to the low nutritional quality of sweetpotato consumed, especially in sub-Saharan Africa. Using a biparental mapping population of 315 F1 progeny generated from a cross between an orange-fleshed and a non-orange-fleshed sweetpotato variety, we identified two major quantitative trait loci (QTL) on linkage group (LG) three (LG3) and twelve (LG12) affecting starch, β-carotene, and their correlated traits, dry matter and flesh color. Analysis of parental haplotypes indicated that these two regions acted pleiotropically to reduce starch content and increase β-carotene in genotypes carrying the orange-fleshed parental haplotype at the LG3 locus. Phytoene synthase and sucrose synthase, the rate-limiting and linked genes located within the QTL on LG3 involved in the carotenoid and starch biosynthesis, respectively, were differentially expressed in Beauregard versus Tanzania storage roots. The Orange gene, the molecular switch for chromoplast biogenesis, located within the QTL on LG12 while not differentially expressed was expressed in developing roots of the parental genotypes. We conclude that these two QTL regions act together in a cis and trans manner to inhibit starch biosynthesis in amyloplasts and enhance chromoplast biogenesis, carotenoid biosynthesis, and accumulation in orange-fleshed sweetpotato. Understanding the genetic basis of this negative association between starch and β-carotene will inform future sweetpotato breeding strategies targeting sweetpotato for food and nutritional security.

Retooling phage display with electrohydrodynamic nanomixing and nanopore sequencing

Phage display methodologies offer a versatile platform for the isolation of single-chain Fv (scFv) molecules which may be rebuilt into monoclonal antibodies. Herein, we report on a complete workflow termed PhageXpress, for rapid selection of single-chain Fv sequences by leveraging electrohydrodynamic-manipulation of a solution containing phage library particles to enhance target binding whilst minimizing non-specific interactions. Our PhageXpress technique is combined with Oxford Nanopore Technologies' MinION sequencer and custom bioinformatics to achieve high-throughput screening of phage libraries. We performed 4 rounds of biopanning against Dengue virus (DENV) non-structural protein 1 (NS1) using traditional methods (4 week turnaround), which resulted in the isolation of 19 unique scFv clones. We validated the feasibility and efficiency of the PhageXpress method utilizing the same phage library and antigen target. Notably, we successfully mapped 14 of the 19 anti-NS1 scFv sequences (∼74%) with our new method, despite using ∼30-fold less particles during screening and conducting only a single round of biopanning. We believe this approach supersedes traditional methods for the discovery of bio-recognition molecules such as antibodies by speeding up the process for the development of therapeutic and diagnostic biologics.

GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

HLA-C variants associated with amino acid substitutions in the peptide binding groove influence susceptibility to Kawasaki disease

Kawasaki disease (KD) is a pediatric vasculitis caused by an unknown trigger in genetically susceptible children. The incidence varies widely across genetically diverse populations. Several associations with HLA Class I alleles have been reported in single cohort studies. Using a genetic approach, from the nine single nucleotide variants (SNVs) associated with KD susceptibility in children of European descent, we identified SNVs near the HLA-C (rs6906846) and HLA-B genes (rs2254556) whose association was replicated in a Japanese descent cohort (rs6906846 p = 0.01, rs2254556 p = 0.005). The risk allele (A at rs6906846) was also associated with HLA-C*07:02 and HLA-C*04:01 in both US multi-ethnic and Japanese cohorts and HLA-C*12:02 only in the Japanese cohort. The risk A-allele was associated with eight non-conservative amino acid substitutions (amino acid positions); Asp or Ser (9), Arg (14), Ala (49), Ala (73), Ala (90), Arg (97), Phe or Ser (99), and Phe or Ser (116) in the HLA-C peptide binding groove that binds peptides for presentation to cytotoxic T cells (CTL). This raises the possibility of increased affinity to a "KD peptide" that contributes to the vasculitis of KD in genetically susceptible children.

Cross-Border Movement of Highly Drug-Resistant Mycobacterium tuberculosis from Papua New Guinea to Australia through Torres Strait Protected Zone, 2010-2015

In this retrospective study, we used whole-genome sequencing (WGS) to delineate transmission dynamics, characterize drug-resistance markers, and identify risk factors of transmission among Papua New Guinea residents of the Torres Strait Protected Zone (TSPZ) who had tuberculosis diagnoses during 2010-2015. Of 117 isolates collected, we could acquire WGS data for 100; 79 were Beijing sublineage 2.2.1.1, which was associated with active transmission (odds ratio 6.190, 95% CI 2.221-18.077). Strains were distributed widely throughout the TSPZ. Clustering occurred more often within than between villages (p = 0.0013). Including 4 multidrug-resistant tuberculosis isolates from Australia citizens epidemiologically linked to the TSPZ into the transmission network analysis revealed 2 probable cross-border transmission events. All multidrug-resistant isolates (33/104) belonged to Beijing sublineage 2.2.1.1 and had high-level isoniazid and ethionamide co-resistance; 2 isolates were extensively drug resistant. Including WGS in regional surveillance could improve tuberculosis transmission tracking and control strategies within the TSPZ.

Rapid diagnosis of Capnocytophaga canimorsus septic shock in an immunocompetent individual using real-time Nanopore sequencing: a case report

BACKGROUND

Rapid diagnosis and appropriate treatment is imperative in bacterial sepsis due increasing risk of mortality with every hour without appropriate antibiotic therapy. Atypical infections with fastidious organisms may take more than 4 days to diagnose leading to calls for improved methods for rapidly diagnosing sepsis. Capnocytophaga canimorsus is a slow-growing, fastidious gram-negative bacillus which is a common commensal within the mouths of dogs, but rarely cause infections in humans. C. canimorsus sepsis risk factors include immunosuppression, alcoholism and elderly age. Here we report on the application of emerging nanopore sequencing methods to rapidly diagnose an atypical case of C. canimorsus septic shock.

CASE PRESENTATION

A 62 year-old female patient was admitted to an intensive care unit with septic shock and multi-organ failure six days after a reported dog bite. Blood cultures were unable to detect a pathogen after 3 days despite observed intracellular bacilli on blood smears. Real-time nanopore sequencing was subsequently employed on whole blood to detect Capnocytophaga canimorsus in 19 h. The patient was not immunocompromised and did not have any other known risk factors. Whole-genome sequencing of clinical sample and of the offending dog's oral swabs showed near-identical C. canimorsus genomes. The patient responded to antibiotic treatment and was discharged from hospital 31 days after admission.

CONCLUSIONS

Use of real-time nanopore sequencing reduced the time-to-diagnosis of Capnocytophaga canimorsus in this case from 6.25 days to 19 h. Capnocytophaga canimorsus should be considered in cases of suspected sepsis involving cat or dog contact, irrespective of the patient's known risk factors.

Simulating the dynamics of targeted capture sequencing with CapSim

Motivation

Targeted sequencing using capture probes has become increasingly popular in clinical applications due to its scalability and cost-effectiveness. The approach also allows for higher sequencing coverage of the targeted regions resulting in better analysis statistical power. However, because of the dynamics of the hybridization process, it is difficult to evaluate the efficiency of the probe design prior to the experiments which are time consuming and costly.

Results

We developed CapSim, a software package for simulation of targeted sequencing. Given a genome sequence and a set of probes, CapSim simulates the fragmentation, the dynamics of probe hybridization and the sequencing of the captured fragments on Illumina and PacBio sequencing platforms. The simulated data can be used for evaluating the performance of the analysis pipeline, as well as the efficiency of the probe design. Parameters of the various stages in the sequencing process can also be evaluated in order to optimize the experiments.

Availability and implementation

CapSim is publicly available under BSD license at https://github.com/Devika1/capsim.

Supplementary information

Supplementary data are available at Bioinformatics online.

Ongoing human chromosome end extension revealed by analysis of BioNano and nanopore data

The majority of human chromosome ends remain incompletely assembled due to their highly repetitive structure. In this study, we use BioNano data to anchor and extend chromosome ends from two European trios as well as two unrelated Asian genomes. At least 11 BioNano assembled chromosome ends are structurally divergent from the reference genome, including both missing sequence and extensions. These extensions are heritable and in some cases divergent between Asian and European samples. Six out of nine predicted extension sequences from NA12878 can be confirmed and filled by nanopore data. We identify two multi-kilobase sequence families both enriched more than 100-fold in extension sequence (p-values < 1e-5) whose origins can be traced to interstitial sequence on ancestral primate chromosome 7. Extensive sub-telomeric duplication of these families has occurred in the human lineage subsequent to divergence from chimpanzees.

Genome sequences of two diploid wild relatives of cultivated sweetpotato reveal targets for genetic improvement

Sweetpotato [Ipomoea batatas (L.) Lam.] is a globally important staple food crop, especially for sub-Saharan Africa. Agronomic improvement of sweetpotato has lagged behind other major food crops due to a lack of genomic and genetic resources and inherent challenges in breeding a heterozygous, clonally propagated polyploid. Here, we report the genome sequences of its two diploid relatives, I. trifida and I. triloba, and show that these high-quality genome assemblies are robust references for hexaploid sweetpotato. Comparative and phylogenetic analyses reveal insights into the ancient whole-genome triplication history of Ipomoea and evolutionary relationships within the Batatas complex. Using resequencing data from 16 genotypes widely used in African breeding programs, genes and alleles associated with carotenoid biosynthesis in storage roots are identified, which may enable efficient breeding of varieties with high provitamin A content. These resources will facilitate genome-enabled breeding in this important food security crop.

Diagnosis of Kawasaki Disease Using a Minimal Whole-Blood Gene Expression Signature

IMPORTANCE

To date, there is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms.

OBJECTIVE

To identify a whole-blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions.

DESIGN, SETTING, AND PARTICIPANTS

The case-control study comprised a discovery group that included a training and test set and a validation group of children with KD or comparator febrile illness. The setting was pediatric centers in the United Kingdom, Spain, the Netherlands, and the United States. The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, and 242 bacterial or viral infections) and 55 healthy controls. The independent validation group comprised 102 patients with KD, including 72 in the first 7 days of illness, and 130 febrile controls. The study dates were March 1, 2009, to November 14, 2013, and data analysis took place from January 1, 2015, to December 31, 2017.

MAIN OUTCOMES AND MEASURES

Whole-blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (parallel regularized regression model search). The ability of transcript signatures (implemented as disease risk scores) to discriminate KD cases from controls was assessed by area under the curve (AUC), sensitivity, and specificity at the optimal cut point according to the Youden index.

RESULTS

Among 404 patients in the discovery set, there were 78 with KD (median age, 27 months; 55.1% male) and 326 febrile controls (median age, 37 months; 56.4% male). Among 202 patients in the validation set, there were 72 with KD (median age, 34 months; 62.5% male) and 130 febrile controls (median age, 17 months; 56.9% male). A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set, with AUC of 96.2% (95% CI, 92.5%-99.9%), sensitivity of 81.7% (95% CI, 60.0%-94.8%), and specificity of 92.1% (95% CI, 84.0%-97.0%). In the validation set, the signature distinguished KD from febrile controls, with AUC of 94.6% (95% CI, 91.3%-98.0%), sensitivity of 85.9% (95% CI, 76.8%-92.6%), and specificity of 89.1% (95% CI, 83.0%-93.7%). The signature was applied to clinically defined categories of definite, highly probable, and possible KD, resulting in AUCs of 98.1% (95% CI, 94.5%-100%), 96.3% (95% CI, 93.3%-99.4%), and 70.0% (95% CI, 53.4%-86.6%), respectively, mirroring certainty of clinical diagnosis.

CONCLUSIONS AND RELEVANCE

In this study, a 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript disease risk score may enable earlier diagnosis and treatment of KD and reduce inappropriate treatment in those with other diagnoses.

Insights into population structure of East African sweetpotato cultivars from hybrid assembly of chloroplast genomes

Background: The chloroplast (cp) genome is an important resource for studying plant diversity and phylogeny. Assembly of the cp genomes from next-generation sequencing data is complicated by the presence of two large inverted repeats contained in the cp DNA. Methods: We constructed a complete circular cp genome assembly for the hexaploid sweetpotato using extremely low coverage (<1×) Oxford Nanopore whole-genome sequencing (WGS) data coupled with Illumina sequencing data for polishing. Results: The sweetpotato cp genome of 161,274 bp contains 152 genes, of which there are 96 protein coding genes, 8 rRNA genes and 48 tRNA genes. Using the cp genome assembly as a reference, we constructed complete cp genome assemblies for a further 17 sweetpotato cultivars from East Africa and an I. triloba line using Illumina WGS data. Analysis of the sweetpotato cp genomes demonstrated the presence of two distinct subpopulations in East Africa. Phylogenetic analysis of the cp genomes of the species from the Convolvulaceae Ipomoea section Batatas revealed that the most closely related diploid wild species of the hexaploid sweetpotato is I. trifida. Conclusions: Nanopore long reads are helpful in construction of cp genome assemblies, especially in solving the two long inverted repeats. We are generally able to extract cp sequences from WGS data of sufficiently high coverage for assembly of cp genomes. The cp genomes can be used to investigate the population structure and the phylogenetic relationship for the sweetpotato.

Non-antibiotic antimicrobial triclosan induces multiple antibiotic resistance through genetic mutation

Antibiotic resistance poses a major threat to public health. Overuse and misuse of antibiotics are generally recognized as the key factors contributing to antibiotic resistance. However, whether non-antibiotic, anti-microbial (NAAM) chemicals can directly induce antibiotic resistance is unclear. We aim to investigate whether the exposure to a NAAM chemical triclosan (TCS) has an impact on inducing antibiotic resistance on Escherichia coli. Here, we report that at a concentration of 0.2 mg/L TCS induces multi-drug resistance in wild-type Escherichia coli after 30-day TCS exposure. The oxidative stress induced by TCS caused genetic mutations in genes such as fabI, frdD, marR, acrR and soxR, and subsequent up-regulation of the transcription of genes encoding beta-lactamases and multi-drug efflux pumps, together with down-regulation of genes related to membrane permeability. The findings advance our understanding of the potential role of NAAM chemicals in the dissemination of antibiotic resistance in microbes, and highlight the need for controlling biocide applications.

Real-time demultiplexing Nanopore barcoded sequencing data with npBarcode

Motivation

The recent introduction of a barcoding protocol for Oxford Nanopore sequencing has increased the versatility of the technology. Several bioinformatics tools have been developed to demultiplex barcoded reads, but none of them supports streaming analysis. This limits the use of multiplexed sequencing in real-time applications, which is one of the main advantages of the technology.

Results

We introduced npBarcode, an open source and cross-platform tool for barcode demultiplexing in streaming fashion that can be used to pipe data to further real-time analyses. The tool also provides a friendly graphical user interface by integrating the module into npReader, making possible to monitor the progress concurrently when the sequencing is still in progress. We show that our algorithm achieves accuracies at least as good as competing tools.

Availability and implementation

npBarcode is bundled in Japsa-a Java tools kit for genome analysis, and is freely available at https://github.com/mdcao/japsa.

Contact

s.nguyen@uq.edu.au or l.coin@imb.uq.edu.au.

Supplementary information

Supplementary data are available at Bioinformatics online.

npInv: accurate detection and genotyping of inversions using long read sub-alignment

Background

Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored.

Result

We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats.

Conclusion

The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2252-9) contains supplementary material, which is available to authorized users.

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

Multifactorial chromosomal variants regulate polymyxin resistance in extensively drug-resistant Klebsiella pneumoniae

Extensively drug-resistant Klebsiella pneumoniae (XDR-KP) infections cause high mortality and are disseminating globally. Identifying the genetic basis underpinning resistance allows for rapid diagnosis and treatment. XDR isolates sourced from Greece and Brazil, including 19 polymyxin-resistant and five polymyxin-susceptible strains, were subjected to whole genome sequencing. Seventeen of the 19 polymyxin-resistant isolates harboured variations upstream or within mgrB. The most common mutation identified was an insertion at nucleotide position 75 in mgrB via an ISKpn26-like element in the ST258 lineage and ISKpn13 in one ST11 isolate. Three strains acquired an IS1 element upstream of mgrB and another strain had an ISKpn25 insertion at 133 bp. Other isolates had truncations (C28STOP, Q30STOP) or a missense mutation (D29E) affecting mgrB. Complementation assays revealed all mgrB perturbations contributed to resistance. Missense mutations in phoQ (T281M, G385C) were also found to facilitate resistance. Several variants in phoPQ co-segregating with the ISKpn26-like insertion were identified as potential partial suppressor mutations. Three ST258 samples were found to contain subpopulations with different resistance-conferring mutations, including the ISKpn26-like insertion colonizing with a novel mutation in pmrB (P158R), both confirmed via complementation assays. These findings highlight the broad spectrum of chromosomal modifications which can facilitate and regulate resistance against polymyxins in K. pneumoniae.

sCNAphase: using haplotype resolved read depth to genotype somatic copy number alterations from low cellularity aneuploid tumors

Accurate identification of copy number alterations is an essential step in understanding the events driving tumor progression. While a variety of algorithms have been developed to use high-throughput sequencing data to profile copy number changes, no tool is able to reliably characterize ploidy and genotype absolute copy number from tumor samples that contain less than 40% tumor cells. To increase our power to resolve the copy number profile from low-cellularity tumor samples, we developed a novel approach that pre-phases heterozygote germline single nucleotide polymorphisms (SNPs) in order to replace the commonly used ‘B-allele frequency’ with a more powerful ‘parental-haplotype frequency’. We apply our tool—sCNAphase—to characterize the copy number and loss-of-heterozygosity profiles of four publicly available breast cancer cell-lines. Comparisons to previous spectral karyotyping and microarray studies revealed that sCNAphase reliably identified overall ploidy as well as the individual copy number mutations from each cell-line. Analysis of artificial cell-line mixtures demonstrated the capacity of this method to determine the level of tumor cellularity, consistently identify sCNAs and characterize ploidy in samples with as little as 10% tumor cells. This novel methodology has the potential to bring sCNA profiling to low-cellularity tumors, a form of cancer unable to be accurately studied by current methods.

Scaffolding and completing genome assemblies in real-time with nanopore sequencing

Third generation sequencing technologies provide the opportunity to improve genome assemblies by generating long reads spanning most repeat sequences. However, current analysis methods require substantial amounts of sequence data and computational resources to overcome the high error rates. Furthermore, they can only perform analysis after sequencing has completed, resulting in either over-sequencing, or in a low quality assembly due to under-sequencing. Here we present npScarf, which can scaffold and complete short read assemblies while the long read sequencing run is in progress. It reports assembly metrics in real-time so the sequencing run can be terminated once an assembly of sufficient quality is obtained. In assembling four bacterial and one eukaryotic genomes, we show that npScarf can construct more complete and accurate assemblies while requiring less sequencing data and computational resources than existing methods. Our approach offers a time- and resource-effective strategy for completing short read assemblies.

Realtime analysis and visualization of MinION sequencing data with npReader

MOTIVATION

The recently released Oxford Nanopore MinION sequencing platform presents many innovative features opening up potential for a range of applications not previously possible. Among these features, the ability to sequence in real-time provides a unique opportunity for many time-critical applications. While many software packages have been developed to analyze its data, there is still a lack of toolkits that support the streaming and real-time analysis of MinION sequencing data.

RESULTS

We developed npReader, an open-source software package to facilitate real-time analysis of MinION sequencing data. npReader can simultaneously extract sequence reads and stream them to downstream analysis pipelines while the samples are being sequenced on the MinION device. It provides a command line interface for easy integration into a bioinformatics work flow, as well as a graphical user interface which concurrently displays the statistics of the run. It also provides an application programming interface for development of streaming algorithms in order to fully utilize the extent of nanopore sequencing potential.

AVAILABILITY AND IMPLEMENTATION

npReader is written in Java and is freely available at https://github.com/mdcao/npReader

CONTACT

m.cao1@uq.edu.au or l.coin@imb.uq.edu.au.

cnvOffSeq: detecting intergenic copy number variation using off-target exome sequencing data

MOTIVATION

Exome sequencing technologies have transformed the field of Mendelian genetics and allowed for efficient detection of genomic variants in protein-coding regions. The target enrichment process that is intrinsic to exome sequencing is inherently imperfect, generating large amounts of unintended off-target sequence. Off-target data are characterized by very low and highly heterogeneous coverage and are usually discarded by exome analysis pipelines. We posit that off-target read depth is a rich, but overlooked, source of information that could be mined to detect intergenic copy number variation (CNV). We propose cnvOffseq, a novel normalization framework for off-target read depth that is based on local adaptive singular value decomposition (SVD). This method is designed to address the heterogeneity of the underlying data and allows for accurate and precise CNV detection and genotyping in off-target regions.

RESULTS

cnvOffSeq was benchmarked on whole-exome sequencing samples from the 1000 Genomes Project. In a set of 104 gold standard intergenic deletions, our method achieved a sensitivity of 57.5% and a specificity of 99.2%, while maintaining a low FDR of 5%. For gold standard deletions longer than 5 kb, cnvOffSeq achieves a sensitivity of 90.4% without increasing the FDR. cnvOffSeq outperforms both whole-genome and whole-exome CNV detection methods considerably and is shown to offer a substantial improvement over naïve local SVD.

AVAILABILITY AND IMPLEMENTATION

cnvOffSeq is available at http://sourceforge.net/p/cnvoffseq/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

cnvCapSeq: detecting copy number variation in long-range targeted resequencing data

Targeted resequencing technologies have allowed for efficient and cost-effective detection of genomic variants in specific regions of interest. Although capture sequencing has been primarily used for investigating single nucleotide variants and indels, it has the potential to elucidate a broader spectrum of genetic variation, including copy number variants (CNVs). Various methods exist for detecting CNV in whole-genome and exome sequencing datasets. However, no algorithms have been specifically designed for contiguous target sequencing, despite its increasing importance in clinical and research applications. We have developed cnvCapSeq, a novel method for accurate and sensitive CNV discovery and genotyping in long-range targeted resequencing. cnvCapSeq was benchmarked using a simulated contiguous capture sequencing dataset comprising 21 genomic loci of various lengths. cnvCapSeq was shown to outperform the best existing exome CNV method by a wide margin both in terms of sensitivity (92.0 versus 48.3%) and specificity (99.8 versus 70.5%). We also applied cnvCapSeq to a real capture sequencing cohort comprising a contiguous 358 kb region that contains the Complement Factor H gene cluster. In this dataset, cnvCapSeq identified 41 samples with CNV, including two with duplications, with a genotyping accuracy of 99%, as ascertained by quantitative real-time PCR.

YHap: a population model for probabilistic assignment of Y haplogroups from re-sequencing data

BACKGROUND

Y haplogroup analyses are an important component of genealogical reconstruction, population genetic analyses, medical genetics and forensics. These fields are increasingly moving towards use of low-coverage, high throughput sequencing. While there have been methods recently proposed for assignment of Y haplogroups on the basis of high-coverage sequence data, assignment on the basis of low-coverage data remains challenging.

RESULTS

We developed a new algorithm, YHap, which uses an imputation framework to jointly predict Y chromosome genotypes and assign Y haplogroups using low coverage population sequence data. We use data from the 1000 genomes project to demonstrate that YHap provides accurate Y haplogroup assignment with less than 2x coverage.

CONCLUSIONS

Borrowing information across multiple samples within a population using an imputation framework enables accurate Y haplogroup assignment.

Dysregulation of complement system and CD4+ T cell activation pathways implicated in allergic response

Allergy is a complex disease that is likely to involve dysregulated CD4+ T cell activation. Here we propose a novel methodology to gain insight into how coordinated behaviour emerges between disease-dysregulated pathways in response to pathophysiological stimuli. Using peripheral blood mononuclear cells of allergic rhinitis patients and controls cultured with and without pollen allergens, we integrate CD4+ T cell gene expression from microarray data and genetic markers of allergic sensitisation from GWAS data at the pathway level using enrichment analysis; implicating the complement system in both cellular and systemic response to pollen allergens. We delineate a novel disease network linking T cell activation to the complement system that is significantly enriched for genes exhibiting correlated gene expression and protein-protein interactions, suggesting a tight biological coordination that is dysregulated in the disease state in response to pollen allergen but not to diluent. This novel disease network has high predictive power for the gene and protein expression of the Th2 cytokine profile (IL-4, IL-5, IL-10, IL-13) and of the Th2 master regulator (GATA3), suggesting its involvement in the early stages of CD4+ T cell differentiation. Dissection of the complement system gene expression identifies 7 genes specifically associated with atopic response to pollen, including C1QR1, CFD, CFP, ITGB2, ITGAX and confirms the role of C3AR1 and C5AR1. Two of these genes (ITGB2 and C3AR1) are also implicated in the network linking complement system to T cell activation, which comprises 6 differentially expressed genes. C3AR1 is also significantly associated with allergic sensitisation in GWAS data.