Search

Abstract Background The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. Results We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. Conclusions This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

The optimized geometry, energetics, and vibrational properties of Al(D2O)n3+ clusters, with n=1,2,4, and 6, have been studied using plane waves, different local basis sets, different methodologies [density-functional theory, MP2, CCSD(T)], and different functionals (BLYP, PBE). Moreover, Car-Parrinello molecular-dynamics (MD) simulations using the BLYP functional, plane waves, and the Vanderbilt ultrasoft pseudopotentials have been performed for an aqueous Al3+ solution with 1 ion and 32 D2O molecules in a periodic box at room temperature, studied for 10 ps. The cluster calculations were performed to pinpoint possible shortcomings of the electronic structure description used in the Car-Parinello MD (CPMD) simulation. For the clusters, the hydration structure and interaction energies calculated with the ‘BLYP/plane-wave’ approach agree well with high-level ab initio methods but the exchange-correlation functional introduces errors in the OD stretching frequencies (both in the absolute values and in the ion-induced shifts). For the aqueous solution, the CPMD simulation yields structural properties in good agreement with experimental data. The CPMD-simulated OD stretching vibrational band for the first-shell water molecules around Al3+ is strongly downshifted by the influence of the ion and is compared with experimental data from the literature. To make such a comparison meaningful, the influences of a number of systematic effects have been addressed, such as the exchange-correlation functional, the fictitious electron mass, anharmonicity effects, and the small box size in the simulation. Each of these factors (except the last one) is found to affect the OD frequency by 100 cm-1 or more. The final “corrected” frequencies agree with experiment within ∼30 cm-1 for bulk water but are too little downshifted for the first-shell Al3+(aq) water molecules (by ∼200 cm-1). [ABSTRACT FROM AUTHOR]

A Car-Parrinello simulation of a single Fe(super 3+) ion in periodic box of 32 water molecules is presented. The aim is to investigate the hydration shell structure and water vibrations for the hydrated ferric ion.

The structure and dynamics of water molecules around the Fe(super 2+) and Fe(super 3+) ions, as well as the ionic diffusion is discussed, using a new flexible water model (SPC+CCL) combined with ion-water potential. It is concluded that the flexible-water model SPC+CCL describes the structural properties and the diffusion equally well or better than the SPC/E water model for the Fe(super 2+)(ag) and Fe(super 3+)(ag) solutions.

The optimized geometry and energetic properties of Fe(D2O)n3+ clusters, with n = 4 and 6, have been studied with density-functional theory calculations and the BLYP functional, and the hydration of a single Fe3+ ion in a periodic box with 32 water molecules at room temperature has been studied with Car−Parrinello molecular dynamics and the same functional. We have compared the results from the CPMD simulation with classical MD simulations, using a flexible SPC-based water model and the same number of water molecules, to evaluate the relative strengths and weaknesses of the two MD methods. The classical MD simulations and the CPMD simulations both give Fe−water distances in good agreement with experiment, but for the intramolecular vibrations, the classical MD yields considerably better absolute frequencies and ion-induced frequency shifts. On the other hand, the CPMD method performs considerably better than the classical MD in describing the intramolecular geometry of the water molecule in the first hydration shell and the average first shell···second shell hydrogen-bond distance. Differences between the two methods are also found with respect to the second-shell water orientations. The effect of the small box size (32 vs 512 water molecules) was evaluated by comparing results from classical simulations using different box sizes; non-negligible effects are found for the ion−water distance and the tilt angles of the water molecules in the second hydration shell and for the O−D stretching vibrational frequencies of the water molecules in the first hydration shell. [ABSTRACT FROM AUTHOR]

The optimized geometry, energetics, and vibrational properties of Al(D2O)n3+ clusters, with n = 1,2,4, and 6, have been studied using plane waves, different local basis sets, different methodologies [density-functional theory, MP2, CCSD(T)], and different functionals (BLYP, PBE). Moreover, Car-Parrinello molecular-dynamics (MD) simulations using the BLYP functional, plane waves, and the Vanderbilt ultrasoft pseudopotentials have been performed for an aqueous Al3+ solution with 1 ion and 32 D2O molecules in a periodic box at room temperature, studied for 10 ps. The cluster calculations were performed to pinpoint possible shortcomings of the electronic structure description used in the Car-Parinello MD (CPMD) simulation. For the clusters, the hydration structure and interaction energies calculated with the 'BLYP/plane-wave' approach agree well with high-level ab initio methods but the exchange-correlation functional introduces errors in the OD stretching frequencies (both in the absolute values and in the ion-induced shifts). For the aqueous solution, the CPMD simulation yields structural properties in good agreement with experimental data. The CPMD-simulated OD stretching vibrational band for the first-shell water molecules around Al3+ is strongly downshifted by the influence of the ion and is compared with experimental data from the literature. To make such a comparison meaningful, the influences of a number of systematic effects have been addressed, such as the exchange-correlation functional, the fictitious electron mass, anharmonicity effects, and the small box size in the simulation. Each of these factors (except the last one) is found to affect the OD frequency by 100 cm(-1) or more. The final "corrected" frequencies agree with experiment within approximately 30 cm(-1) for bulk water but are too little downshifted for the first-shell Al3+ (aq) water molecules (by approximately 200 cm(-1)).