KrakenUniq: confident and fast metagenomics classification using unique k-mer counts.
Ontology highlight
ABSTRACT:
SUBMITTER: Breitwieser FP
PROVIDER: S-EPMC6238331 | biostudies-literature | 2018 Nov
REPOSITORIES: biostudies-literature
Publications
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts.
Breitwieser F P FP Baker D N DN Salzberg S L SL
Genome biology 20181116 1
False-positive identifications are a significant problem in metagenomics classification. We present KrakenUniq, a novel metagenomics classifier that combines the fast k-mer-based classification of Kraken with an efficient algorithm for assessing the coverage of unique k-mers found in each species in a dataset. On various test datasets, KrakenUniq gives better recall and precision than other methods and effectively classifies and distinguishes pathogens with low abundance from false positives in ...[more]
Similar Datasets
Project description:Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic k-mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows-Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current k-mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at http://kaiju.binf.ku.dk.
| S-EPMC4833860 | biostudies-other
Project description:Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP pipeline for identifying and estimating the relative sequence abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our pipeline facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.
| S-EPMC8543175 | biostudies-literature
Project description:A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single-cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multilabel classification of 7848 complex IHC images of human testis corresponding to 2794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, DeepHistoClass (DHC) Confidence Score, the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.
| S-EPMC8476775 | biostudies-literature
Project description:Metagenomics brings in new discoveries and insights into the uncultured microbial world. One fundamental task in metagenomics analysis is to determine the taxonomy of raw sequence fragments. Modern sequencing technologies produce relatively short fragments and greatly increase the number of fragments, and thus make the taxonomic classification considerably more difficult than before. Therefore, fast and accurate techniques are called to classify large-scale fragments. We propose EnSVM (Ensemble Support Vector Machine) and its advanced method called EnSVMB (EnSVM with BLAST) to accurately classify fragments. EnSVM divides fragments into a large confident (or small diffident) set, based on whether the fragments get consistent (or inconsistent) predictions from linear SVMs trained with different k-mers. Empirical study shows that sensitivity and specificity of EnSVM on confident set are higher than 90% and 97%, but on diffident set are lower than 60% and 75%. To further improve the performance on diffident set, EnSVMB takes advantage of best hits of BLAST to reclassify fragments in that set. Experimental results show EnSVM can efficiently and effectively divide fragments into confident and diffident sets, and EnSVMB achieves higher accuracy, sensitivity and more true positives than related state-of-the-art methods and holds comparable specificity with the best of them.
| S-EPMC5573435 | biostudies-literature
Project description:BackgroundThe biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case-control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue.ResultsWe used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from "empty" ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets.ConclusionsWe propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.
| S-EPMC4373121 | biostudies-literature
Project description:Adventitious agent detection during the production of vaccines and biotechnology-based medicines is of critical importance to ensure the final product is free from any possible viral contamination. Increasing the speed and accuracy of viral detection is beneficial as a means to accelerate development timelines and to ensure patient safety. Here, several rapid viral metagenomics approaches were tested on simulated next-generation sequencing (NGS) data sets and existing data sets from virus spike-in studies done in CHO-K1 and HeLa cell lines. It was observed that these rapid methods had comparable sensitivity to full-read alignment methods used for NGS viral detection for these data sets, but their specificity could be improved. A method that first filters host reads using KrakenUniq and then selects the virus classification tool based on the number of remaining reads is suggested as the preferred approach among those tested to detect nonlatent and nonendogenous viruses. Such an approach shows reasonable sensitivity and specificity for the data sets examined and requires less time and memory as full-read alignment methods.IMPORTANCE Next-generation sequencing (NGS) has been proposed as a complementary method to detect adventitious viruses in the production of biotherapeutics and vaccines to current in vivo and in vitro methods. Before NGS can be established in industry as a main viral detection technology, further investigation into the various aspects of bioinformatics analyses required to identify and classify viral NGS reads is needed. In this study, the ability of rapid metagenomics tools to detect viruses in biopharmaceutical relevant samples is tested and compared to recommend an efficient approach. The results showed that KrakenUniq can quickly and accurately filter host sequences and classify viral reads and had comparable sensitivity and specificity to slower full read alignment approaches, such as BLASTn, for the data sets examined.
| S-EPMC8546726 | biostudies-literature
Project description:Motivation:Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Although many algorithms have been developed to date, they suffer significant memory and/or computational costs. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. Results:We introduce MetaOthello, a probabilistic hashing classifier for metagenomic sequencing reads. The algorithm employs a novel data structure, called l-Othello, to support efficient querying of a taxon using its k-mer signatures. MetaOthello is an order-of-magnitude faster than the current state-of-the-art algorithms Kraken and Clark, and requires only one-third of the RAM. In comparison to Kaiju, a metagenomic classification tool using protein sequences instead of genomic sequences, MetaOthello is three times faster and exhibits 20-30% higher classification sensitivity. We report comparative analyses of both scalability and accuracy using a number of simulated and empirical datasets. Availability and implementation:MetaOthello is a stand-alone program implemented in C?++. The current version (1.0) is accessible via https://doi.org/10.5281/zenodo.808941. Contact:liuj@cs.uky.edu. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5870563 | biostudies-literature
Project description:Accurately identifying and eliminating allergens from biotechnology-derived products are important for human health. From a biomedical research perspective, it is also important to identify allergens in sequenced genomes. Many allergen prediction tools have been developed during the past years. Although these tools have achieved certain levels of specificity, when applied to large-scale allergen discovery (e.g. at a whole-genome scale), they still yield many false positives and thus low precision (even at low recall) due to the extreme skewness of the data (allergens are rare). Moreover, the most accurate tools are relatively slow because they use protein sequence alignment to build feature vectors for allergen classifiers. Additionally, only web server implementations of the current allergen prediction tools are publicly available and are without the capability of large batch submission. These weaknesses make large-scale allergen discovery ineffective and inefficient in the public domain.We developed Allerdictor, a fast and accurate sequence-based allergen prediction tool that models protein sequences as text documents and uses support vector machine in text classification for allergen prediction. Test results on multiple highly skewed datasets demonstrated that Allerdictor predicted allergens with high precision over high recall at fast speed. For example, Allerdictor only took ?6 min on a single core PC to scan a whole Swiss-Prot database of ?540 000 sequences and identified <1% of them as allergens.Allerdictor is implemented in Python and available as standalone and web server versions at http://allerdictor.vbi.vt.edu CONTACT: lawrence@vbi.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online.
| S-EPMC3982160 | biostudies-literature
Project description:Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantification estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis compared to exon-level analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing a variety of RNA-seq analysis directly on equivalence class counts (ECCs). Methods: Here we demonstrate that ECCs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECCs have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform differential transcript usage analysis.
| S-EPMC6524746 | biostudies-literature
Project description:MotivationConverting nucleotide sequences into short overlapping fragments of uniform length, k-mers, is a common step in many bioinformatics applications. While existing software packages count k-mers, few are optimized for speed, offer an application programming interface (API), a graphical interface or contain features that make it extensible and maintainable. We designed KAnalyze to compete with the fastest k-mer counters, to produce reliable output and to support future development efforts through well-architected, documented and testable code. Currently, KAnalyze can output k-mer counts in a sorted tab-delimited file or stream k-mers as they are read. KAnalyze can process large datasets with 2 GB of memory. This project is implemented in Java 7, and the command line interface (CLI) is designed to integrate into pipelines written in any language.ResultsAs a k-mer counter, KAnalyze outperforms Jellyfish, DSK and a pipeline built on Perl and Linux utilities. Through extensive unit and system testing, we have verified that KAnalyze produces the correct k-mer counts over multiple datasets and k-mer sizes.Availability and implementationKAnalyze is available on SourceForge: https://sourceforge.net/projects/kanalyze/.
| S-EPMC4080738 | biostudies-literature