Mapping the Space of Genomic Signatures

Kari, Lila; Hill, Kathleen A.; Sayem, Abu Sadat Md.; Karamichalis, Rallis; Bryans, Nathaniel; Davis, Katelyn; Dattani, Nikesh S.

doi:10.1371/journal.pone.0119815

Cited by 34 publications

(44 citation statements)

References 48 publications

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“…Moreover, the alignment 62 demands the sequences to be continuously homologous which is not always the case. 63 Alignment-free methods [48][49][50][51][52] have been proposed in the past as an alternative to 64 address the limitations of the alignment-based methods. Comparative genomics beyond 65 alignment-based approaches have benefited from the computational power of machine 66 learning.…”

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confidence: 99%

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

Randhawa

Soltysiak

Roz

et al. 2020

Preprint

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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.

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confidence: 99%

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

Randhawa

Soltysiak

Roz

et al. 2020

Preprint

Self Cite

153

151

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“…The third limitation is the computational power required. 31, 32 Future whole-genome sequences will need to be analyzed when NGS analysis is expanded into non-coding regions of the genome, and this will require “super computers”. Currently, these are the barriers, but it is also expected that these issues may be resolved with the advancement and widespread use of the technology, just like personal computers have become cheaper and faster.…”

Section: Discussionmentioning

confidence: 99%

Formalin-fixed paraffin-embedded sample conditions for deep next generation sequencing

Nagahashi

Shimada

Ichikawa

et al. 2017

Journal of Surgical Research

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Introduction Precision medicine is only possible in oncology practice if targetable genes in fragmented DNA, such as DNA from formalin-fixed paraffin-embedded (FFPE) samples, can be sequenced using next generation sequencing (NGS). The aim of this study is to examine the quality and quantity of DNA from FFPE cancerous tissue samples from surgically resected and biopsy specimens. Methods DNA was extracted from unstained FFPE tissue sections prepared from surgically resected specimens of breast, colorectal and gastric cancer, and biopsy specimens of breast cancer. A total quantity of DNA ≥60 ng from a sample was considered adequate for NGS. The DNA quality was assessed by Q-ratios, with a Q-ratio >0.1 considered sufficient for NGS. Results The Q-ratio for DNA from FFPE tissue processed with neutral-buffered formalin was significantly better than that processed with unbuffered formalin. All Q-ratios for DNA from breast, colorectal and gastric cancer samples indicated DNA levels sufficient for NGS. DNA extracted from gastric cancer FFPE samples prepared within the last seven years is suitable for NGS analysis, whereas those older than seven years may not be suitable. Our data suggested that adequate amounts of DNA can be extracted from FFPE samples, not only of surgically resected tissue but also of biopsy specimens. Conclusion The type of formalin used for fixation and the time since FFPE sample preparation affect DNA quality. Sufficient amounts of DNA can be extracted from FFPE samples of both surgically resected and biopsy tissue, thus expanding the potential diagnostic uses of NGS in a clinical setting.

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“…In addition, with next-generation sequencing (NGS) playing an increasingly important role, it may not always be possible to align many short reads coming from different parts of genomes [9]. To address situations where alignment-based methods fail or are insufficient, alignment-free methods have been proposed [10], including approaches based on Chaos Game Representation of DNA sequences [11,12,13], random walk [14], graph theory [15], iterated maps [16], information theory [17], category-position-frequency [18], spaced-words frequencies [19], Markov-model [20], thermal melting profiles [21], word analysis [22], among others. Software implementations of alignment-free methods also exist, among them COMET [23], CASTOR [24], SCUEAL [25], REGA [26], KAMERIS [27], and FFP (Feature Frequency Profile) [28].…”

Section: Introductionmentioning

confidence: 99%

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

Randhawa

Hill

Kari

2018

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Background: Although 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. Results: We 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. Conclusions: We 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.

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Mapping the Space of Genomic Signatures

Cited by 34 publications

References 48 publications

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

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

Formalin-fixed paraffin-embedded sample conditions for deep next generation sequencing

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

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