Energy entropy vector: a novel approach for efficient microbial genomic sequence analysis and classification

In this paper we propose a large-scale high-dimensional indexing algorithm based on sparse approximation and inverted indexing. Our goal was to devise a method that smoothly scales to handle databases with over 100 million descriptors on a single machine. To meet this goal, we implemented an inverted indexed based on a sparsifying dictionary with l0 regression to assign documents to buckets. The sparsifying dictionary is optimized to reduce the data dimensionality, by concentrating the energy of the original vector on a few coefficients of a higher dimensional representation. These descriptors are added to an inverted index explores the locality of the coefficients of sparse representations to enable efficient pruned search. Evaluation on four large-scale datasets with multiple types of features showed that our method compares favorably to state-of-the-art techniques. On a 100 million dataset of SIFT descriptors, our method achieved 47.6 % precision at 50, by inspecting only 1 % of the full dataset, and by using only 1/20 of the time of a linear search.

A comprehensive molecular epidemiological study was conducted on a vancomycin-resistant Enterococcus faecium (VR-Efm) outbreak in Guangdong, China, with the aim of analyzing transmission routes and antimicrobial resistance patterns, developing specific diagnostic tools for early detection, and elucidating genetic relationships with previously reported strains. Strain identification was performed using matrix-assisted laser desorption/ionization mass spectrometry, whereas whole-genome sequencing was performed for bacterial genomic characterization. Comprehensive bioinformatic analyses, including multilocus sequence typing, phylogenetic tree construction, principal component analysis, minimum spanning tree, and comparative genomic analyses, were performed. Our genomic investigation identified as predominant the sequence type 80 strain of VR-Efm, which exhibited a high level of resistance to a range of antibiotics, particularly vancomycin, amoxicillin, and teicoplanin. Through genome sequencing, we established genetic proximity between the outbreak strain and those previously identified in India and Japan. Furthermore, functional genomic analyses have elucidated genetic variations within critical genes, such as rsmH, which may be associated with the acquisition of antibiotic resistance. The identification of unique molecular markers within the outbreak strain facilitated the development of specific PCR assays, thereby significantly enhancing our capacity for early and precise detection of VR-Efm. Our in-depth genomic analysis of the VR-Efm outbreak in Guangdong, China, identified a predominant ST80 strain that exhibited multidrug resistance, especially to vancomycin. This finding underscores the need for enhanced global public health surveillance to address this emerging threat.

Rubella is listed as a disease that needs to be eliminated worldwide by the World Health Organization. This study aimed to investigate rubella epidemiology and genetic characteristics based on data from 12 years of laboratory-based surveillance (2009-2020) in Tianjin and to provide baseline genotype data for monitoring future rubella control efforts. We collected RV-positive throat swab samples from confirmed rubella cases during 2009-2020 in Tianjin to isolate RV, amplify and sequence target gene fragments, construct phylogenetic trees, and analyze nucleotide homologies. Four rubella isolates were chosen for whole genome sequencing. The epidemiological data of rubella cases were collected to describe and analyze the epidemiological characteristics of the rubella outbreak. Most cases (87.6%) occurred between March and June, and the peak incidence was observed in May. The age of the reported rubella cases ranged from almost 1 month to 82 years, most were between 10 and 29 years old (83.3%). Almost half (48.3%) of the confirmed rubella cases were from just four districts (Beichen District, Binhai New Area, Hebei District, and Nankai District). A total of 211 rubella virus strains were obtained during 2009-2020, phylogenetic analyses identified four lineages, including 1E-L1, 1E-L2, 2B-L1, and 2B-L2c, with high homologies of nucleotide sequences compared with RV strains from other provinces of China. Recombination analysis indicated that strain RVi/Tianjin.CHN/37.19/1 a possible recombination strain by 7 analysis methods, with p-values of 4.993 × 10-28-2.922 × 10-4. Our study provided comprehensive data on rubella epidemiology and the first information on rubella genotypes in Tianjin. Clear evidence of recombination was found, indicating that RV has the potential to continually mutate, so close monitoring of the genetic variations of wild-type rubella virus strains is necessary. Rubella viruses were highly conserved at the genomic level, the incidence of rubella in Tianjin, as well as in China, has sustains at a low level, which gives us the idea that the rubella control and elimination goal could be achieved in the near future if strengthened RV surveillance continues and vaccine immunization coverage maintaining at the high level.

The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.

Genetic algorithms are a set of stochastic algorithms for efficient and robust search. The problem of aligning biomolecular sequences can be converted into a one of searching for optimal points in a problem space. Thus a genetic algorithm can be developed for sequence alignment. In this paper, a genetic algorithm for aligning two sequences is described. The experimental results indicate that genetic algorithms may provide an efficient and accurate approach for biomolecular sequence comparison and analysis. >

Unlike classical systems based on the use of morphological data, modern phylogenetic analyses use genetic information to construct phylogenetic trees. Ongoing research in the field of phylogenetics is evaluating the accuracy of phylogenetic estimation results and the reliability of phylogenetic trees to explain evolutionary relationships. Recently, the probability of stochastic errors in large-scale phylogenetic datasets has decreased, while the probability of systematic errors has increased. Therefore, before constructing a phylogenetic tree, it is necessary to assess the causes of systematic bias to improve the accuracy of phylogenetic estimates. We performed analyses of three datasets (Terebelliformia, Daphniid, and Glires clades) using bioinformatics software to assess systematic error and improve phylogenetic tree accuracy. Then, we proposed a combination of systematic biases capable of discerning the most suitable gene markers within a series of taxa and generating conflicting phylogenetic topologies. Our findings will help improve the reliability of phylogenetic software to estimate phylogenies more accurately by exploiting systematic bias.

RDP4 is the latest version of recombination detection program (RDP), a Windows computer program that implements an extensive array of methods for detecting and visualising recombination in, and stripping evidence of recombination from, virus genome sequence alignments. RDP4 is capable of analysing twice as many sequences (up to 2,500) that are up to three times longer (up to 10 Mb) than those that could be analysed by older versions of the program. RDP4 is therefore also applicable to the analysis of bacterial full-genome sequence datasets. Other novelties in RDP4 include (1) the capacity to differentiate between recombination and genome segment reassortment, (2) the estimation of recombination breakpoint confidence intervals, (3) a variety of ‘recombination aware’ phylogenetic tree construction and comparison tools, (4) new matrix-based visualisation tools for examining both individual recombination events and the overall phylogenetic impacts of multiple recombination events and (5) new tests to detect the influences of gene arrangements, encoded protein structure, nucleic acid secondary structure, nucleotide composition, and nucleotide diversity on recombination breakpoint patterns. The key feature of RDP4 that differentiates it from other recombination detection tools is its flexibility. It can be run either in fully automated mode from the command line interface or with a graphically rich user interface that enables detailed exploration of both individual recombination events and overall recombination patterns.

Aiming at the problem that the high computational complexity of calculating information entropy and mutual information of a neighborhood rough set, a fast calculation method based on data sorting was proposed to estimate neighborhood mutual information speedily. This method can reduce the computational complexity of neighborhood entropy from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(nlogn). Under this premise, the method can calculate the approximation of the joint neighborhood entropy by infinite-norm-calculated neighborhood relation, thus to estimate the neighborhood mutual information quickly. For the reason that the method is based on neighborhood entropy, it is also effective for mixed data. Experimental results show that this method can significantly shorten the computational time of neighborhood mutual information and ensure high approximation quality when using large-scale data sets.

Objective To study the epidemiology and the etiology characteristics of first imported severe fever with thrombocytopenia syndrome (SFTS) case reported in Shenzhen city in 2017. Methods Data on descriptive epidemiology was collected to study the characteristics to the epidemic. The serum sample collected from the suspect SFTS case was detected for IgM, IgG by ELISA and severe fever with thrombocytopenia syndrome bunyavirus (SFTSV) nucleic acid by real-time RT-PCR. The samples were further inoculated in Vero cell for virus isolation. The partial fragements of L and S gene were amplified by RT-PCR and sequenced to construct homology comparison and phylogenetic tree with the strains isolated from other areas. Results The case was laboratory confirmed imported SFTS case in Shenzhen on May 2017. IgM antibody and RNA of SFTSV were detected in the serum sample. SFTSV named GDSZ01/2017/China was successfully isolated from the serum sample. The high nucleotide homology of L and S genome segments were found at 95.3%-98.2% and 93.8%-98.8% with other representative strains from the popular provinces, respectively. The phylogenetic tree indicated that GDSZ01 was most close to SDTA_3 strain, next to strains in Hubei procince. The isolated SFTSV belonged to genotype C3 with HB29, HB154. Conclusions The virological, serological and molecular features showed that the imported case of SFTS in 2017 was caused by SFTSV C3 genotype. Key words: Severe fever with thrombocytopenia syndrome; Bunyavirus; Epidemiology; Etiology

Genome analysis of microbial pathogens has provided unique insights into their virulence, host adaptation and evolution. Common themes have emerged, including lateral gene transfer among enteric pathogens, genome decay among obligate intracellular pathogens and antigenic variation among mucosal pathogens. The advent of post-genomic approaches and the sequencing of the human genome will enable scientists to investigate the complex and dynamic interplay between host and pathogen. This wealth of information will catalyse the development of new intervention strategies to reduce the burden of microbial-related disease.

Dendarus foraminosus Mulsant and Rey, 1855 is a darkling beetle in the family Tenebrionidae and one of the many Dendarus species endemic to the island of Crete. Dendarus foraminosus is a commonly found species and is widespread in the lowland and montane phrygana and maquis of central Crete. The species is classified as Least Concern (LC) by the IUCN Red List. The reference genome of Dendarus foraminosus will enable phylogenetic, population, and evolutionary research regarding this endemic species and its close relatives. A total of 11 contiguous chromosomal pseudomolecules (sex chromosomes included) were assembled from the genome sequence. This chromosome-level assembly encompasses 0.59 Gb, composed of 430 contigs and 415 scaffolds, with contig and scaffold N50 values of 24.4 Mb and 51.9 Mb, respectively.

Tibetan antelope (Pantholops hodgsonii), a wild ruminant endemic to the Qinghai-Tibetan Plateau (QTP) in China, has evolved a series of genetic and physiological adaptation strategies to thrive in the harsh plateau environments. However, limited research on the genome of this species exists. Here, we established a high-quality chromosome-level reference genome assembly of the Tibetan antelope using PacBio HiFi, DNBSEQ, and Hi-C sequencing data. The assembly, totaling 3.13 GB, consists of 31 chromosomes (29 + X + partial Y), with a Scaffold N50 length of 92.23 Mb. The quality value (QV) and Benchmarking Universal Single-Copy Ortholog (BUSCO) score were 70.14 and 98.20%, respectively, indicating that our genome sequence is of high quality and completeness. Our genome not only contribute to the genetic conservation of Tibetan antelope but also provides a valuable resource for genetic, ecological, and evolutionary research within the sub-family Caprinae.

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

ObjectivesThis study analyzed Protease-PR and Reverse Transcriptase-RT HIV-1 genomic information entropy metrics among patients under antiretroviral virologic failure, according to the numbers of virologic failures or resistance mutations. MethodsFor this purpose, we used genomic sequences from PR and RT of HIV-1 from a cohort of chronic patients followed up at São Paulo Hospital. ResultsInformational entropy proportionally increases with the number of antiretroviral virologic failures in PR and RT (p < .001). Affected regions of PR were related to catalytic and structural functions, such as Fulcrum (K20) Flap (M46) and Cantilever (A71). In RT, this occurred at Fingers (E44) and Palm (K219). Informational entropy increases according to the number of resistance mutations in PR and RT (p < .001). Higher PR entropy was proportional to the resistance mutation numbers in Fulcrum (L10), Active site (L24) Flap (M46), Cantilever (L63) and near Interface (L90). In RT, they related to regions responsible for protein stability such as Fingers (T39) and Palm (L100). ConclusionsThe antiretroviral selective pressure affects HIV genomic informational entropy at the PR and RT regions, leading to the emergence of more unstable virions. Mapping the three-dimensional structure in these HIV-1 proteins is relevant to designing new antiretroviral targeting resistant strains.

Under this award, numerous genome sequences were generated, analyzed, published and made publicly available.