MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data

Fast and space-efficient taxonomic classification of long reads with hierarchical interleaved XOR filters

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Because of the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memory-efficient querying of long reads. Here, we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches, such as syncmers, for pseudoalignment to classify reads and an expectation-maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms state-of-the-art tools regarding precision while having a similar recall for long-read taxonomic classification. Most notably, Taxor reduces the memory requirements and index size by >50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field.

Next Generation Sequencing in orthopaedic infections - Where is the road headed?

Next-generation sequencing (NGS) has emerged as a game changer in the field of orthopaedic diagnostics, notably in the detection and management of infections associated with prosthetic joints and implants. This paper conducts an exhaustive examination of the pivotal role, outcomes, and prospective future uses of NGS in diagnosing orthopaedic infections. In comparison to conventional culture-based methods, NGS offers a marked improvement in sensitivity thereby facilitating prompt and comprehensive identification of pathogens. This encompasses the ability to detect polymicrobial infections, antibiotic-resistant strains, and previously imperceptible microorganisms. Furthermore, this article delves into the technology's contribution to advancing personalized medicine and promoting judicious antibiotic use. Nonetheless, the seamless integration of NGS into routine clinical practice is impeded by challenges such as substantial financial outlays, the requisite for specialized equipment and expertise, and the intricacy associated with data analysis. Notwithstanding these impediments, the potential for NGS to revolutionize orthopaedic diagnostics remains substantial, with ongoing advancements poised to address current limitations and broaden its scope within clinical applications.

Application of high-throughput sequencing technologies and analytical tools for pathogen detection in urban water systems: Progress and future perspectives

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences

Learning low-dimensional continuous vector representation for short k-mers divided from long DNA sequences is key to DNA sequence modeling that can be utilized in many bioinformatics investigations, such as DNA sequence retrieval and classification. DNA2Vec is the most widely used method for DNA sequence embedding. However, it poorly scales to large data sets due to its extremely long training time in kmer embedding. In this paper, we propose a novel efficient graph-based kmer embedding method, named Kmer-Node2Vec, to tackle this concern. Our method converts the large DNA corpus into one kmer co-occurrence graph, and extracts kmer relation on the graph by random walks to learn fast and high-quality kmer embedding. Extensive experiments show that our method is faster than DNA2Vec by 29 times for training on a 4GB data set, and on par with DNA2Vec in terms of task-specific accuracy of sequence retrieval and classification.

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping

MOTIVATION

The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities.

RESULTS

We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth.

AVAILABILITY AND IMPLEMENTATION

The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive virome analysis of the viral spectrum in paediatric patients diagnosed with Mycoplasma pneumoniae pneumonia

Background

Among hospitalized children suffering from community-acquired pneumonia, Mycoplasma pneumoniae (MP) is one of the most common pathogens. MP often exists as a co-infection with bacteria or viruses, which can exacerbate the clinical symptoms. We investigated the pathogen spectrum in MP-positive and MP-negative samples from hospitalized children with respiratory tract infections in Beijing, China.

Method

This study included 1038 samples of nasopharyngeal aspirates obtained between April, 2017 and March, 2018 from hospitalized children under 6 years of age with respiratory tract infections. To explore the impact of MP infection on the composition of the pathogen spectrum, 185 nasopharyngeal aspirates (83 MP-positive/102 MP-negative) were randomly selected for next-generation sequencing and comprehensive metagenomics analysis. Real-time PCR was used to detect and verify common respiratory viruses.

Results

Of the 1038 samples, 454 (43.7%) were infected with MP. In children < 6 years of age, the MP infection rate gradually increased with age, with the highest rate of 74.2% in 5–6-year-olds. The results of metagenomics analysis revealed 11 human, animal and plant virus families, and bacteriophages, including common respiratory viruses, enteroviruses and anelloviruses. The virus family with the highest number of reads in both MP-positive and MP-negative samples was the Pneumoviridae, and the number of reads for human respiratory syncytial virus (HRSV) in MP-positive samples was higher than that in MP-negative samples. Among the 83 MP-positive samples, 47 (56.63%) were co-infected with viruses, the most common of which was influenza virus (IFV). The durations of hospitalization and fever were higher in patients with MP co-infection than MP single infection, but the difference was not statistically significant.

Conclusion

The viral family with the highest number of reads in both groups was Pneumoviridae, and the number of reads matched to HRSV in MP-positive samples was much higher than MP-negative samples. Co-infection of MP and IFV infection were the most cases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01914-y.