scholarly journals ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

2018 ◽  
Author(s):  
Gurjit S. Randhawa ◽  
Kathleen A. Hill ◽  
Lila Kari
Keyword(s):  
Software Tool ◽  
Digital Signal ◽  
Taxonomic Classification ◽  
Genomic Sequences ◽  
Mitochondrial Genomes ◽  
Genomic Signatures ◽  
Alignment Free ◽  
Benchmark Datasets ◽  
Similar Accuracy

AbstractBackgroundAlthough methods and software tools abound for the comparison, analysis, identification, and taxonomic classification of the enormous amount of genomic sequences that are continuously being produced, taxonomic classification remains challenging. The difficulty lies within both the magnitude of the dataset and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods.ResultsWe combine supervised Machine Learning with Digital Signal Processing to design ML-DSP, an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels.We test ML-DSP by classifying 7,396 full mitochondrial genomes from the kingdom to genus levels, with 98% classification accuracy. Compared with the alignment-based classification tool MEGA7 (with sequences aligned with either MUSCLE, or CLUSTALW), ML-DSP has similar accuracy scores while being significantly faster on two small benchmark datasets (2,250 to 67,600 times faster for 41 mammalian mitochondrial genomes). ML-DSP also successfully scales to accurately classify a large dataset of 4,322 complete vertebrate mtDNA genomes, a task which MEGA7 with MUSCLE or CLUSTALW did not complete after several hours, and had to be terminated. ML-DSP also outperforms the alignment-free tool FFP (Feature Frequency Profiles) in terms of both accuracy and time, being three times faster for the vertebrate mtDNA genomes dataset.ConclusionsWe provide empirical evidence that ML-DSP distinguishes complete genome sequences at all taxonomic levels. Ultrafast and accurate taxonomic classification of genomic sequences is predicted to be highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures, in identifying mechanistic determinants of genomic signatures, and in evaluating genome integrity.

scholarly journals Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

2020 ◽  
Author(s):  
Gurjit S. Randhawa ◽  
Maximillian P.M. Soltysiak ◽  
Hadi El Roz ◽  
Camila P.E. de Souza ◽  
Kathleen A. Hill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Death Rate ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Rank Correlation ◽  
Taxonomic Classification ◽  
Supervised Machine Learning ◽  
Biological Knowledge ◽  
Alignment Free

AbstractAs of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

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 MLDSP-GUI: an alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

2019 ◽  
Vol 36 (7) ◽  
pp. 2258-2259 ◽  
Author(s):  
Gurjit S Randhawa ◽  
Kathleen A Hill ◽  
Lila Kari
Keyword(s):  
User Interface ◽  
Open Source ◽  
Graphical User Interface ◽  
Dna Sequences ◽  
Software Tool ◽  
Digital Signal ◽  
Disease Classification ◽  
Supplementary Information ◽  
Alignment Free ◽  
Comparison And Analysis

Abstract Summary Machine Learning with Digital Signal Processing and Graphical User Interface (MLDSP-GUI) is an open-source, alignment-free, ultrafast, computationally lightweight, and standalone software tool with an interactive GUI for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others. Availability and implementation MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/. Supplementary information Supplementary data are available at Bioinformatics online.

Exploring the Lethality of Human-Adapted Coronavirus through Alignment-Free Machine Learning Approaches Using Genomic Sequences

2021 ◽  
Vol 23 ◽  
Author(s):  
Rui Yin ◽  
Zihan Luo ◽  
Chee Keong Kwoh
Keyword(s):  
Machine Learning ◽  
Time Estimation ◽  
Digital Signal ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Genomic Sequences ◽  
Biological Knowledge ◽  
Learning Approaches ◽  
Human Coronavirus ◽  
Alignment Free

Background: A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe, on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspectives for treatment. Methods: We developed an alignment-free framework that utilizes machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of human-adapted coronavirus using genomic sequences. We performed extensive experiments through six different feature transformation and machine learning algorithms combining digital signal processing to identify the lethality of possible future novel coronaviruses using existing strains. Results: The results tested on SARS-CoV, MERS-CoV and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our framework achieves high levels of prediction performance that is alignment-free and based on RNA sequences alone without genome annotations and specialized biological knowledge. Conclusion: The results demonstrate that, for any novel human coronavirus strains, this study can offer a reliable real-time estimation for its viral lethality.

scholarly journals Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Congyu Lu ◽  
Zheng Zhang ◽  
Zena Cai ◽  
Zhaozhong Zhu ◽  
Ye Qiu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Functional Characterization ◽  
Gaussian Model ◽  
Software Tool ◽  
Biological Properties ◽  
Rapid Identification ◽  
Taxonomic Assignment ◽  
Genus Level ◽  
Alignment Free ◽  
Archaeal Viruses

Abstract Background Viruses are ubiquitous biological entities, estimated to be the largest reservoirs of unexplored genetic diversity on Earth. Full functional characterization and annotation of newly discovered viruses requires tools to enable taxonomic assignment, the range of hosts, and biological properties of the virus. Here we focus on prokaryotic viruses, which include phages and archaeal viruses, and for which identifying the viral host is an essential step in characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus, which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts, which needs improvements at both accuracy and usability. Here we develop a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. Results We present here Prokaryotic virus Host Predictor (PHP), a software tool using a Gaussian model, to predict hosts for prokaryotic viruses using the differences of k-mer frequencies between viral and host genomic sequences as features. PHP gave a host prediction accuracy of 34% (genus level) on the VirHostMatcher benchmark dataset and a host prediction accuracy of 35% (genus level) on a new dataset containing 671 viruses and 60,105 prokaryotic genomes. The prediction accuracy exceeded that of two alignment-free methods (VirHostMatcher and WIsH, 28–34%, genus level). PHP also outperformed these two alignment-free methods much (24–38% vs 18–20%, genus level) when predicting hosts for prokaryotic viruses which cannot be predicted by the BLAST-based or the CRISPR-spacer-based methods alone. Requiring a minimal score for making predictions (thresholding) and taking the consensus of the top 30 predictions further improved the host prediction accuracy of PHP. Conclusions The Prokaryotic virus Host Predictor software tool provides an intuitive and user-friendly API for the Gaussian model described herein. This work will facilitate the rapid identification of hosts for newly identified prokaryotic viruses in metagenomic studies.

scholarly journals Fast lightweight accurate xenograft sorting

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jens Zentgraf ◽  
Sven Rahmann
Keyword(s):  
State Of The Art ◽  
Hash Table ◽  
Human Tumor ◽  
Surrounding Tissue ◽  
Cpu Time ◽  
Alignment Free ◽  
Time Usage ◽  
The One ◽  
Similar Accuracy ◽  
Software Prefetching

Abstract Motivation With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species’ (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results. Results We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art sorting by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy. Several engineering steps (e.g., shortcuts for unsuccessful lookups, software prefetching) improve the performance even further. Availability Our software xengsort is available under the MIT license at http://gitlab.com/genomeinformatics/xengsort. It is written in numba-compiled Python and comes with sample Snakemake workflows for hash table construction and dataset processing.

Genetic characteristics and taxonomic classification of Fimic Anthrosols in China

Geoderma ◽  
2003 ◽  
Vol 115 (1-2) ◽  
pp. 31-44 ◽  
Author(s):  
Min Zhang ◽  
Li Ma ◽  
Wenqing Li ◽  
Baocheng Chen ◽  
Jiwen Jia
Keyword(s):  
Taxonomic Classification ◽  
Genetic Characteristics

scholarly journals A singular value decomposition approach for improved taxonomic classification of biological sequences

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 4) ◽  
pp. S11 ◽  
Author(s):  
Anderson R Santos ◽  
Marcos A Santos ◽  
Jan Baumbach ◽  
John A McCulloch ◽  
Guilherme C Oliveira ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Taxonomic Classification ◽  
Biological Sequences ◽  
Decomposition Approach ◽  
Value Decomposition

scholarly journals Extreme diversification driven by parallel events of massive loss of heterozygosity in the hybrid lineage of Candida albicans

Genetics ◽  
2020 ◽  
Vol 217 (2) ◽  
Author(s):  
Verónica Mixão ◽  
Ester Saus ◽  
Teun Boekhout ◽  
Toni Gabaldón
Keyword(s):  
Candida Albicans ◽  
Loss Of Heterozygosity ◽  
Type Strain ◽  
Taxonomic Classification ◽  
Hybrid Origin ◽  
Genomic Analyses ◽  
Candida Africana

Abstract Candida albicans is the most commonly reported species causing candidiasis. The taxonomic classification of C. albicans and related lineages is controversial, with Candida africana (syn. C. albicans var. africana) and Candida stellatoidea (syn. C. albicans var. stellatoidea) being considered different species or C. albicans varieties depending on the authors. Moreover, recent genomic analyses have suggested a shared hybrid origin of C. albicans and C. africana, but the potential parental lineages remain unidentified. Although the genomes of C. albicans and C. africana have been extensively studied, the genome of C. stellatoidea has not been sequenced so far. In order to get a better understanding of the evolution of the C. albicans clade, and to assess whether C. stellatoidea could represent one of the unknown C. albicans parental lineages, we sequenced C. stellatoidea type strain (CBS 1905). This genome was compared to that of C. albicans and of the closely related lineage C. africana. Our results show that, similarly to C. africana, C. stellatoidea descends from the same hybrid ancestor as other C. albicans strains and that it has undergone a parallel massive loss of heterozygosity.

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