HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads
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PROVIDER: S-EPMC10873905 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC11293544 | biostudies-literature
Project description:Most current approach to metagenomic classification employ short next generation sequencing (NGS) reads that are present in metagenomic samples to identify unique genomic regions. NGS reads, however, might not be long enough to differentiate similar genomes. This suggests a potential for using longer reads to improve classification performance. Presently, longer reads tend to have a higher rate of sequencing errors. Thus, given the pros and cons, it remains unclear which types of reads is better for metagenomic classification. We compared two taxonomic classification protocols: a traditional assembly-free protocol and a novel assembly-based protocol. The novel assembly-based protocol consists of assembling short-reads into longer reads, which will be subsequently classified by a traditional taxonomic classifier. We discovered that most classifiers made fewer predictions with longer reads and that they achieved higher classification performance on synthetic metagenomic data. Generally, we observed a significant increase in precision, while having similar recall rates. On real data, we observed similar characteristics that suggest that the classifiers might have similar performance of higher precision with similar recall with longer reads. We have shown a noticeable difference in performance between assembly-based and assembly-free taxonomic classification. This finding strongly suggests that classifying species in metagenomic environments can be achieved with higher overall performance simply by assembling short reads. Further, it also suggests that long-read technologies might be better for species classification.
| S-EPMC7465921 | biostudies-literature
Project description:Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ∼8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.
| S-EPMC3538547 | biostudies-literature
Project description:MotivationMetagenomics is a recent field of biology that studies microbial communities by analyzing their genomic content directly sequenced from the environment. A metagenomic dataset consists of many short DNA or RNA fragments called reads. One interesting problem in metagenomic data analysis is the discovery of the taxonomic composition of a given dataset. A simple method for this task, called the Lowest Common Ancestor (LCA), is employed in state-of-the-art computational tools for metagenomic data analysis of very short reads (about 100 bp). However LCA has two main drawbacks: it possibly assigns many reads to high taxonomic ranks and it discards a high number of reads.ResultsWe present MTR, a new method for tackling these drawbacks using clustering at Multiple Taxonomic Ranks. Unlike LCA, which processes the reads one-by-one, MTR exploits information shared by reads. Specifically, MTR consists of two main phases. First, for each taxonomic rank, a collection of potential clusters of reads is generated, and each potential cluster is associated to a taxon at that rank. Next, a small number of clusters is selected at each rank using a combinatorial optimization algorithm. The effectiveness of the resulting method is tested on a large number of simulated and real-life metagenomes. Results of experiments show that MTR improves on LCA by discarding a significantly smaller number of reads and by assigning much more reads at lower taxonomic ranks. Moreover, MTR provides a more faithful taxonomic characterization of the metagenome population distribution.AvailabilityMatlab and C++ source codes of the method available at http://cs.ru.nl/gori/software/MTR.tar.gz.
| S-EPMC3018814 | biostudies-literature
Project description:The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.
| S-EPMC3462201 | biostudies-literature
Project description:SummarySequence comparison of genetic material between known and unknown organisms plays a crucial role in genomics, metagenomics and phylogenetic analysis. The emerging long-read sequencing technologies can now produce reads of tens of kilobases in length that promise a more accurate assessment of their origin. To facilitate the classification of long and short DNA sequences, we have developed a Python package that implements a new sequence classification model that we have demonstrated to improve the classification accuracy when compared with other state of the art classification methods. For the purpose of validation, and to demonstrate its usefulness, we test the combined sequence similarity score classifier (CSSSCL) using three different datasets, including a metagenomic dataset composed of short reads.Availability and implementationPackage's source code and test datasets are available under the GPLv3 license at https://github.com/oicr-ibc/cssscl.Contactivan.borozan@oicr.on.caSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4734043 | biostudies-literature
Project description:The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp.
| S-EPMC3577917 | biostudies-literature
Project description:The development of high-throughput sequencing technologies has provided microbial ecologists with an efficient approach to assess bacterial diversity at an unseen depth, particularly with the recent advances in the Illumina MiSeq sequencing platform. However, analyzing such high-throughput data is posing important computational challenges, requiring specialized bioinformatics solutions at different stages during the processing pipeline, such as assembly of paired-end reads, chimera removal, correction of sequencing errors, and clustering of those sequences into Operational Taxonomic Units (OTUs). Individual algorithms grappling with each of those challenges have been combined into various bioinformatics pipelines, such as mothur, QIIME, LotuS, and USEARCH. Using a set of well-described bacterial mock communities, state-of-the-art pipelines for Illumina MiSeq amplicon sequencing data are benchmarked at the level of the amount of sequences retained, computational cost, error rate, and quality of the OTUs. In addition, a new pipeline called OCToPUS is introduced, which is making an optimal combination of different algorithms. Huge variability is observed between the different pipelines in respect to the monitored performance parameters, where in general the amount of retained reads is found to be inversely proportional to the quality of the reads. By contrast, OCToPUS achieves the lowest error rate, minimum number of spurious OTUs, and the closest correspondence to the existing community, while retaining the uppermost amount of reads when compared to other pipelines. The newly introduced pipeline translates Illumina MiSeq amplicon sequencing data into high-quality and reliable OTUs, with improved performance and accuracy compared to the currently existing pipelines.
| S-EPMC5466709 | biostudies-literature
Project description:It has now become clear that in silico prediction of ADME (absorption, distribution, metabolism, and elimination) characteristics is an important component of the drug discovery process. Therefore, there has been considerable interest in the development of in silico modeling of ADME prediction in recent years. Despite the advances in this field, there remains challenges when facing the unbalanced and high dimensionality problems simultaneously. In this work, we introduce a novel adaptive ensemble classification framework named as AECF to deal with the above issues. AECF includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble. We considered five sampling methods, seven base modeling techniques, and ten ensemble rules to build a choice pool. The proper route of constructing predictive models was determined automatically according to the imbalance ratio (IR). With the adaptive characteristics of AECF, it can be used to work on the different kinds of ADME data, and the balanced data is a special case in AECF. We evaluated the performance of our approach using five extensive ADME datasets concerning Caco-2 cell permeability (CacoP), human intestinal absorption (HIA), oral bioavailability (OB), and P-glycoprotein (P-gp) binders (substrates/inhibitors, PS/PI). The performance of AECF was evaluated on two independent datasets, and the average AUC values were 0.8574-0.8602, 0.8968-0.9182, 0.7821-0.7981, 0.8139-0.8311, and 0.8874-0.8898 for CacoP, HIA, OB, PS and PI, respectively. Our results show that AECF can provide better performance and generality compared with individual models and two representative ensemble methods bagging and boosting. Furthermore, the degree of complementarity among the AECF ensemble members was investigated for the purpose of elucidating the potential advantages of our framework. We found that AECF can effectively select complementary members to construct predictive models by our auto-adaptive optimization approach, and the additional diversity in both sample and feature space mainly contribute to the complementarity of ensemble members.
| S-EPMC9079056 | biostudies-literature
Project description:The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub:https://github.com/KennthShang/CHEER.
| S-EPMC7255349 | biostudies-literature