Journal Article

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

Kari, Lila, Randhawa, Gurjit S., Hill, Kathleen A.

2019

Overview

Background Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets 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. Results We propose a novel combination of supervised M achine L earning with D igital S ignal P rocessing, resulting in ML-DSP : an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97 % . A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. Conclusions Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Alignment / Alignment-free sequence analysis / Animal Genetics and Genomics / Animals / Artificial intelligence / Bacterial genetics / Benchmarking / Bioinformatics / Biomedical and Life Sciences / Classification / Comparative and evolutionary genomics / Computer programs / Computer Simulation / Data processing / Datasets / Dengue fever / Dengue virus / Dengue Virus - genetics / Deoxyribonucleic acid / Digital signal processing / Digital signal processors / DNA / DNA sequencing / Fourier transforms / Gene expression / Gene sequencing / Genome, Bacterial / Genome, Mitochondrial / Genome, Viral / Genomes / Genomic signature / Genomics / Genomics - methods / Humans / Learning algorithms / Life Sciences / Machine Learning / Methods / Microarrays / Microbial Genetics and Genomics / Mitochondria / Mitochondrial DNA / Muscles / Novels / Nucleotide sequence / Numerical representation of DNA sequences / Plant Genetics and Genomics / Principal components analysis / Proteomics / Representations / Signal processing / Signal Processing, Computer-Assisted / Software / Software Article / Software development tools / Taxonomic classification / Taxonomy / Technology application / Vector-borne diseases / Vertebrates / Vertebrates - classification / Vertebrates - genetics / Viral diseases / Viruses / Whole genome analysis