Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.Comment: Accepted by IEEE PES 2018 General Meetin

aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events

<div><p>Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The class contains tandem -type motif sequences, and the class contains alternating , and type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a -class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan <i>et al.</i> Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for -class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of <i>ycf</i>3. The aPPRove web server can be found at <a href="http://www.cs.colostate.edu/~approve" target="_blank">www.cs.colostate.edu/~approve</a>.</p></div

An illustration of the putative binding event of CLB19 and <i>ycf3</i>.

<p>This shows the alignment of the putative binding event of CLB19 and the binding site located upstream of the edit site at position 43,350 of the <i>Arabidopsis thaliana</i> plastid genome. Pairs highlighted in green are considered to be statistically correlated amino acid-nucleotide pairs as specified by Barkan <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160645#pone.0160645.ref006" target="_blank">6</a>]. The C highlighted in magenta is the edit site of the binding site.</p

An illustration demonstrating the relationship between the True Positive Rate (TPR) and False Positive Rate (FPR) of the aPPRove algorithm.

<p>(a) illustrates the FPR of all 55 PPR-RNA pairs. We compared the score of aligning <i>S</i>(6, 1′) of each PPR protein to their own binding site against every possible alignment to a database of decoy transcripts. The median and range of the FPR is 0.0076 and 0.12. (b) is the ROC curve that was computed by running aPPRove on all 55 PPR proteins and their known binding sites, using the set of <i>Arabidopsis thaliana</i> transcripts originating from the organelle that the PPR targets.</p

Illustrations that demonstrate the adjusted p-values with respect to the total number of amino acid binding pairs in the protein domain, and the adjusted p-values with respect to the total number of binding pairs that have statistically significant site preference according to Barkan <i>et al.</i> [6].

<p>The regression lines on both plots demonstrate that there is an negative correlation between the number of binding pairs in the protein domain and p-value. (a) has a Pearson’s Correlation sample estimate of −0.335024 with a p-value of 0.01241. (b) has a Pearson’s Correlation sample estimate of −0.3978517 with a p-value of 0.00263.</p

An illustration of the distribution of normalized scores and the corresponding adjusted p-values.

<p>Fig 2(a) is a boxplot of the 55 Benjamini Hochberg adjusted p-values of the normalized scores. The median p-value is 0.013. Fig 2(b) illustrates the distribution of normalized scores, which are calculated by finding all possible alignments of every protein to each possible binding site in the target database (including the known binding site) and then normalizing all of these scores. The normalized score of the known binding should have a relatively lower adjusted p-value and thus, be identified in the extreme right of the distribution. The green line indicates where the score of aligning MEF26 to its known binding site on <i>cox</i>3 is located on the distribution generated by aligning the <i>S</i>(6, 1′) sequence of MEF26 to the target database [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160645#pone.0160645.ref029" target="_blank">29</a>].</p

Demonstrating the combinatorial code for nucleotide specificity in Barkan <i>et. al</i> [6].

<p>The first and second column contain the amino acid at site 6 and 1′ respectively. The third column contains the nucleotide the combination of the two amino acids show preference towards.</p

DataSheet2_ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks.XLSX

Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/</p

DataSheet1_ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks.CSV

Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/</p

DataSheet4_ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks.CSV

Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/</p