scholarly journals Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
F. A. Bastiaan von Meijenfeldt ◽  
Ksenia Arkhipova ◽  
Diego D. Cambuy ◽  
Felipe H. Coutinho ◽  
Bas E. Dutilh
Keyword(s):  
Dna Sequences ◽  
De Novo ◽  
Taxonomic Classification ◽  
Classification Method ◽  
Reference Database ◽  
Annotation Tool ◽  
Multiple Signals

Abstract Current-day metagenomics analyses increasingly involve de novo taxonomic classification of long DNA sequences and metagenome-assembled genomes. Here, we show that the conventional best-hit approach often leads to classifications that are too specific, especially when the sequences represent novel deep lineages. We present a classification method that integrates multiple signals to classify sequences (Contig Annotation Tool, CAT) and metagenome-assembled genomes (Bin Annotation Tool, BAT). Classifications are automatically made at low taxonomic ranks if closely related organisms are present in the reference database and at higher ranks otherwise. The result is a high classification precision even for sequences from considerably unknown organisms.

scholarly journals Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT

2019 ◽  
Author(s):  
F.A. Bastiaan von Meijenfeldt ◽  
Ksenia Arkhipova ◽  
Diego D. Cambuy ◽  
Felipe H. Coutinho ◽  
Bas E. Dutilh
Keyword(s):  
Dna Sequences ◽  
Real World ◽  
Taxonomic Classification ◽  
Reference Database ◽  
Annotation Tool ◽  
High Quality

ABSTRACTCurrent-day metagenomics increasingly requires taxonomic classification of long DNA sequences and metagenome-assembled genomes (MAGs) of unknown microorganisms. We show that the standard best-hit approach often leads to classifications that are too specific. We present tools to classify high-quality metagenomic contigs (Contig Annotation Tool, CAT) and MAGs (Bin Annotation Tool, BAT) and thoroughly benchmark them with simulated metagenomic sequences that are classified against a reference database where related sequences are increasingly removed, thereby simulating increasingly unknown queries. We find that the query sequences are correctly classified at low taxonomic ranks if closely related organisms are present in the reference database, while classifications are made higher in the taxonomy when closely related organisms are absent, thus avoiding spurious classification specificity. In a real-world challenge, we apply BAT to over 900 MAGs from a recent rumen metagenomics study and classified 97% consistently with prior phylogeny-based classifications, but in a fully automated fashion.

scholarly journals Contig annotation tool CAT robustly classifies assembled metagenomic contigs and long sequences

2016 ◽  
Author(s):  
Diego D. Cambuy ◽  
Felipe H. Coutinho ◽  
Bas E. Dutilh
Keyword(s):  
Single Molecule ◽  
Dna Sequences ◽  
Taxonomic Classification ◽  
Annotation Tool ◽  
Single Molecule Sequencing ◽  
Short Read ◽  
Long Read ◽  
Micro Organisms ◽  
Taxonomic Annotation

AbstractIn modern-day metagenomics, there is an increasing need for robust taxonomic annotation of long DNA sequences from unknown micro-organisms. Long metagenomic sequences may be derived from assembly of short-read metagenomes, or from long-read single molecule sequencing. Here we introduce CAT, a pipeline for robust taxonomic classification of long DNA sequences. We show that CAT correctly classifies contigs at different taxonomic levels, even in simulated metagenomic datasets that are very distantly related from the sequences in the database. CAT is implemented in Python and the required scripts can be freely downloaded from Github.

scholarly journals Field-based species identification in eukaryotes using real-time nanopore sequencing

2017 ◽  
Author(s):  
Joe Parker ◽  
Andrew J. Helmstetter ◽  
Dion Devey ◽  
Alexander S.T. Papadopulos
Keyword(s):  
Real Time ◽  
Species Identification ◽  
Dna Sequences ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Reference Database ◽  
Nanopore Sequencing ◽  
Technological Advances ◽  
A Genome ◽  
Hybrid Genome

Advances in DNA sequencing and informatics have revolutionised biology over the past four decades, but technological limitations have left many applications unexplored1,2. Recently, portable, real-time, nanopore sequencing (RTnS) has become available. This offers opportunities to rapidly collect and analyse genomic data anywhere3–5. However, the generation of datasets from large, complex genomes has been constrained to laboratories6,7. The portability and long DNA sequences of RTnS offer great potential for field-based species identification, but the feasibility and accuracy of these technologies for this purpose have not been assessed. Here, we show that a field-based RTnS analysis of closely-related plant species (Arabidopsis spp.)8 has many advantages over laboratory-based high-throughput sequencing (HTS) methods for species level identification-by-sequencing and de novo phylogenomics. Samples were collected and sequenced in a single day by RTnS using a portable, “al fresco” laboratory. Our analyses demonstrate that correctly identifying unknown reads from matches to a reference database with RTnS reads enables rapid and confident species identification. Individually annotated RTnS reads can be used to infer the evolutionary relationships of A. thaliana. Furthermore, hybrid genome assembly with RTnS and HTS reads substantially improved upon a genome assembled from HTS reads alone. Field-based RTnS makes real-time, rapid specimen identification and genome wide analyses possible. These technological advances are set to revolutionise research in the biological sciences9 and have broad implications for conservation, taxonomy, border agencies and citizen science.

scholarly journals Taxonomic identification from metagenomic and metabarcoding data using any genetic marker

2018 ◽  
Author(s):  
Johan Bengtsson-Palme ◽  
Rodney T. Richardson ◽  
Marco Meola ◽  
Christian Wurzbacher ◽  
Émilie D. Tremblay ◽  
...  
Keyword(s):  
Genetic Marker ◽  
Dna Sequences ◽  
Sequence Data ◽  
Taxonomic Diversity ◽  
Taxonomic Classification ◽  
Taxonomic Identification ◽  
Link Type

Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, there is no genetic marker that gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. We here present a major update to Metaxa2 (http://microbiology.se/software/metaxa2/) that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data.

scholarly journals Assessing alignment-based taxonomic classification of ancient microbial DNA

2018 ◽  
Author(s):  
Raphael Eisenhofer ◽  
Laura Susan Weyrich
Keyword(s):  
Ancient Dna ◽  
Dna Sequences ◽  
Random Sequence ◽  
Taxonomic Classification ◽  
Metagenomic Data ◽  
Data Sets ◽  
Protein Alignments ◽  
Microbial Dna ◽  
Dna Characteristics

The field of paleomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ~100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future paleomicrobiological studies.

scholarly journals BERTax: taxonomic classification of DNA sequences with Deep Neural Networks

2021 ◽  
Author(s):  
Florian Mock ◽  
Fleming Kretschmer ◽  
Anton Kriese ◽  
Sebastian Böcker ◽  
Manja Marz
Keyword(s):  
Language Processing ◽  
Dna Sequences ◽  
Information Gain ◽  
Taxonomic Classification ◽  
Training Data ◽  
Misclassification Rate ◽  
Genomic Sequences ◽  
Similar Species ◽  
Common Task

Taxonomic classification, i.e., the identification and assignment to groups of biological organisms with the same origin and characteristics, is a common task in genetics. Nowadays, taxonomic classification is mainly based on genome similarity search to large genome databases. In this process, the classification quality depends heavily on the database since representative relatives have to be known already. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a program that uses a deep neural network to precisely classify the superkingdom, phylum, and genus of DNA sequences taxonomically without the need for a known representative relative from a database. For this, BERTax uses the natural language processing model BERT trained to represent DNA. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. In case of an entirely novel organism, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality. Since BERTax is not based on homologous entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences. This leads to a higher number of correctly classified sequences and thus increases the overall information gain.

scholarly journals Assessing alignment-based taxonomic classification of ancient microbial DNA

2018 ◽  
Author(s):  
Raphael Eisenhofer ◽  
Laura Susan Weyrich
Keyword(s):  
Ancient Dna ◽  
Dna Sequences ◽  
Random Sequence ◽  
Taxonomic Classification ◽  
Metagenomic Data ◽  
Data Sets ◽  
Protein Alignments ◽  
Microbial Dna ◽  
Dna Characteristics

The field of paleomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ~100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future paleomicrobiological studies.

scholarly journals Assessing alignment-based taxonomic classification of ancient microbial DNA

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6594 ◽  
Author(s):  
Raphael Eisenhofer ◽  
Laura Susan Weyrich
Keyword(s):  
Ancient Dna ◽  
Dna Sequences ◽  
Random Sequence ◽  
Taxonomic Classification ◽  
Metagenomic Data ◽  
Data Sets ◽  
Protein Alignments ◽  
Microbial Dna ◽  
Dna Characteristics

The field of palaeomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to short DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ∼100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future palaeomicrobiological studies.

scholarly journals TERL: Classification of Transposable Elements by Convolutional Neural Networks

2020 ◽  
Author(s):  
Murilo Horacio Pereira da Cruz ◽  
Douglas Silva Domingues ◽  
Priscila Tiemi Maeda Saito ◽  
Alexandre Rossi Paschoal ◽  
Pedro Henrique Bugatti
Keyword(s):  
Neural Networks ◽  
Transposable Elements ◽  
Convolutional Neural Networks ◽  
Dna Sequences ◽  
Dimensional Space ◽  
Regulation Of Gene Expression ◽  
Classification Method ◽  
Hierarchical Level ◽  
Space Data

AbstractTransposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. They are capable of transpose and generate multiple copies of themselves throughout genomes. These sequences can produce a variety of effects on organisms, such as regulation of gene expression. There are several types of these elements, which are classified in a hierarchical way into classes, subclasses, orders and superfamilies. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology based search, which could be inefficient for classifying non-homologous sequences. Here we propose a pipeline, transposable elements representation learner (TERL), that use four preprocessing steps, a transformation of one-dimensional nucleic acid sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks (CNNs). CNN is used to classify TE sequences because it is a very flexible classification method, given it can be easily retrained to classify different categories and any other DNA sequences. This classification method tries to learn the best representation of the input data to correctly classify it. CNNs can also be accelerated via GPUs to provide fast results. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for the superfamily sequences from RepBase and 95.7% and 91.5% for the order sequences from RepBase respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. We also show a way to preprocess sequences and prepare train and test sets. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system, is on average 162 times and four orders of magnitude faster than TEclass and PASTEC respectively and on a real-world scenario obtained better accuracy, recall, and specificity than the other methods.

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