Biology subjects

Soltysiak, M. P. M.

Publications and source records attributed to Soltysiak, M. P. M..

2 recordsLinked to original sources

bioRxiv2020

Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses

As 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 Spearmans 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.

bioinformatics↗

bioRxiv2019

Rapid method for generating designer algal mitochondrial genomes

With synthetic biology, we can turn algae into bio-factories that produce high-value molecules (e.g. medicines or biofuels) or tackle global challenges (e.g. malnutrition and climate change). This realization has provoked rapid progress towards the creation of genetic tools for multiple algal species, notably Phaeodactylum tricornutum. The power of synthetic biology to generate more useful or productive organisms is contingent on the ability to produce diverse DNA molecules and rapidly screen them for beneficial variants. However, it is still relatively expensive to synthesize DNA, and delivering large DNA (>50 kbp) to eukaryotic cellular compartments remains challenging. In this study, we establish a robust system for building algal mitochondrial genomes as a practical alternative to DNA synthesis. Our approach permits an inexpensive and rapid generation of mitochondrial derivatives designed for testing targeted DNA delivery. First, we cloned the mitochondrial genome of P. tricornutum into the eukaryotic host organism S. cerevisiae using two different techniques: transformation-associated recombination; and PCR-based cloning. Next, we analyzed the cloned genomes by multiplex PCR, and correct genomes were transferred to prokaryotic host E. coli. Then these genomes were again analyzed by multiplex PCR, followed by diagnostic digest and complete-plasmid sequencing to evaluate the fidelity of each cloning method. Finally, we assessed the burden on eukaryotic and prokaryotic hosts to propagate the cloned genomes. We conclude that our system can reliably generate variants for genome-level engineering of algal mitochondria. HIGHLIGHTSO_LITAR-cloned mitochondrial genome of P. tricornutum in yeast C_LIO_LIDeveloped PCR-based cloning method to create designer algal mitochondrial genomes C_LIO_LIStably propagated algal mitochondrial genomes in S. cerevisiae and E. coli hosts C_LI

synthetic biology↗