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OMGene: mutual improvement of gene models through optimisation of evolutionary conservation
Michael P. Dunne1 &
Steven Kelly ORCID: orcid.org/0000-0001-8583-53621
The accurate determination of the genomic coordinates for a given gene – its gene model – is of vital importance to the utility of its annotation, and the accuracy of bioinformatic analyses derived from it. Currently-available methods of computational gene prediction, while on the whole successful, frequently disagree on the model for a given predicted gene, with some or all of the variant gene models often failing to match the biologically observed structure. Many prediction methods can be bolstered by using experimental data such as RNA-seq. However, these resources are not always available, and rarely give a comprehensive portrait of an organism's transcriptome due to temporal and tissue-specific expression profiles.
Orthology between genes provides evolutionary evidence to guide the construction of gene models. OMGene (Optimise My Gene) aims to improve gene model accuracy in the absence of experimental data by optimising the consistency of multiple sequence alignments of orthologous genes from multiple species. Using RNA-seq data sets from plants, mammals, and fungi, considering intron/exon junction representation and exon coverage, and assessing the intra-orthogroup consistency of subcellular localisation predictions, we demonstrate the utility of OMGene for improving gene models in annotated genomes.
We show that significant improvements in the accuracy of gene model annotations can be made, both in established and in de novo annotated genomes, by leveraging information from multiple species.
The utility of any given genome is dependent on the comprehensiveness and accuracy of its gene model annotations. Inaccuracies in the annotated locations and structures of protein coding genes can lead to myriad downstream errors. These include misinformed conclusions about the biological properties of an organism, as well as errors in transcript quantification, phylogenetic tree inference, protein localisation, and protein structure predictions. It is therefore vital to downstream analysis, both computational and experimental, to ensure that gene annotations are as accurate as possible.
The absolute quantity of publicly available genomic data has grown exponentially over the past two decades, as has the number of taxa represented [1,2,3], owing to the consistently decreasing costs of acquiring whole genome sequences [4, 5]. Accordingly, the feasibility of manual gene model annotation has diminished progressively, with a corresponding increase in reliance on computational gene prediction software. As such there are numerous tools available for the de novo and data-assisted prediction of genes [6]. These tools typically rely on genetic signatures such as GC content, codon bias, feature length distributions, and various conserved DNA sequence motifs. Though many of these tools are highly proficient at gene prediction, mistakes are common. Gene prediction tools often disagree on the quantity of genes that they predict [7,8,9]. Furthermore, even when gene predictors agree on the location of a gene, the predicted intron-exon structure for that gene can vary considerably between the different methods [10]. Common errors include erroneous exon/intron retention/omission, inaccurate exon/intron boundaries, frame errors, misplaced start codons, and fragmentation/fusion of gene models.
When available, the use of extrinsic empirical data, most notably RNA-seq, is the most reliable currently available method for procuring gene models. For example, single contiguous RNA-seq reads obtained from mRNA sequencing can be split across multiple loci when mapped to the genome, providing evidence for the locations of splice junctions. Unfortunately, empirical data is generally not available for all genes in a given species: many genes are expressed in a cell-type or cell-cycle specific manner and for organisms with many disparate tissue types it can be difficult to obtain RNA-seq data that cover the full breadth of the transcriptome [11, 12]. In addition, not all gene sequences are amenable to reliable and accurate alignment, in particular identical duplicate genes and genes that contain repetitive regions found in multiple other genes [13]. Furthermore library preparation protocols and other statistical factors can make reliable gene model inferences difficult [14,15,16]. Finally, there are some aspects of gene models that are simply not revealed by RNA-seq analysis: for example the presence of 5'UTR sequences or internal methionine residues mean that there can often be multiple plausible start codons locations for a given open reading frame (ORF), and so start codon location cannot be inferred from RNA-seq data.
Feature locations (splice sites, exons, transcription start sites) have been shown to be highly conserved across evolutionary timescales, often more so than the constituent amino acid sequences they encapsulate [17, 18], despite alternative splicing being a driver of divergence [19]. Given various gene model predictions, if multiple highly similar (in sequence and structure) gene models exist for a gene across multiple taxa, they are more likely to be biologically correct than disparate alternatives. Several de novo gene prediction algorithms have utilised this concept to constrain gene searches by predicting genes in multiple genomes simultaneously: notably SLAM [20], SGP-1 [21], TWINSCAN/N-SCAN/CONTRAST [22, 23], and the most recent version of Augustus [24]. However, no tool currently exists that can systematically and automatically improve the annotations of already predicted genes by leveraging annotated gene models from other species.
By considering orthogroups of related genes, one can optimise the similarity of gene models across species by seeking conserved structure across the various taxa. In the absence of extrinsic data, it is parsimonious to choose gene models that maximise intra-orthogroup amino acid alignment agreement. OMGene (Optimise My Gene) aims to improve genome annotations by optimising the agreement between gene models for orthologous genes in multiple species. It is designed to function without the need for additional empirical data, utilising only the local genome sequences for the genes in question, and works on existing predicted gene models. A standalone implementation of the algorithm is available under the GPLv3 licence at https://github.com/mpdunne/omgene. The algorithm is available as a python script, instructions for which, along with example data sets, are included in the git repository.
Algorithm description
The input for OMGene is a set of gene model files in general transfer format (GTF) and a set of corresponding FASTA genome files. GTF is a variant of GFF3 with a more standardised structure. As many public gene datasets use GFF3 format, a tool for extracting well-formed gene entries from GFF3 files is included in the GitHub repository. Each GTF file should contain coordinates for one gene only. The FASTA file can contain one or multiple sequences but must contain the nucleotide sequence referenced by the GTF file. The set of GTF files and their corresponding genome files are inputted to OMGene using a tab-delimited CSV file: each line in the input should contain first the file path to a gene model's GTF file, followed by the path to the genome for that species. If the GTF contains multiple transcript variants then these are considered together as variants of a single gene. Further information regarding input formats can be found in the OMGene GitHub repository.
For each inputted gene, the algorithm defines its gene region to be the region spanning the first and last base of any of its corresponding gene models, with a user-selected number of buffer bases either side (default value is 600 bp). The initial step of OMGene is to cross-align the amino acid sequences from each gene with the gene regions of the other genes using Exonerate [25]. The rationale behind this step is to find exonic regions that are present in one or more gene models but absent from one or more annotated gene model. This is performed three times: first by cross-aligning the input protein sequences against all gene regions, second by cross-aligning the protein sequences that have been found in the first step against all gene regions, and finally by cross-aligning all individual exon sequences from the first step. This three-step process mitigates against lack of detection due to gene model errors in one or more of the input genes. This, together with the exons from the original gene sequences, comprises a set of potential gene parts, which may overlap and which may be incompatible in reading frame. Compatible combinations of gene parts (i.e. without frame-shift errors) are strung together to form a putative gene model. Many such putative gene models may exist: the set of putative gene models with the highest alignment score (see Alignment score calculation below) is carried forward to the next step. If multiple alternatives have the same alignment score, the choice is made randomly.
The set of putative gene models from the previous step are aligned, and the set of putative exons from all genes is divided into adjacency groups: sets of exons that overlap each other in the alignment (see below). Exons are added in sequentially in these adjacency groups, and at each stage a valid gene model is sought on the left hand side of the gene (i.e. starting at the start codon and seeking to adjoin exons in valid donor-acceptor pairs). Multiple options for each gene are produced at each new junction, by recursively seeking out, or "wiggling" splice junctions (or start codons) in each frame either side of the existing exons start and end points. This produces a set of junction options for each pair of exon ends. A multipartite choice function is then used to choose the optimal set of exons across all secies, as described below. In the event that a particular exon is very small (< 40 bp), or does not yield any valid junction sites, both that exon and the one before it are probed for removal, and the variant with the removed exon is compared against the other partial gene models in the evaluation step. Once this recursive step ceases to produce new gene modes, the gene model set with the highest alignment score is declared the winner, and the next putative exon from the next adjacency group is added. If multiple alternatives have the same alignment score, the choice is made randomly. This is repeated until there are no further exons to add.
To ensure that the optimisation process did not overlook potentially better variants in the user-supplied gene models, the process above is repeated. This time, instead of varying exons start and end sites, the set of newly created junctions are compared against the original junctions, aiming to find the optimal combination of new and old junctions.
The final step involves filtering the changes based on a selection of categories that have been observed to over-fix gene models. Firstly, we require the alignment score α of a 10 amino acid region each side of the change to have either remained the same or improved. This is a basic requirement which should be met in most cases due to the way in which sequence variants are chosen. Secondly, changes that have opened gaps in the alignment in three or more of the sequences are not allowed: this is a common occurrence due to sequences proximal to exon termini that by chance feature valid splice junction sequences that are in frame with the adjacent exons and are evolutionarily conserved. These tend not to be correct. Thirdly, very small changes are forbidden: changes that have resulted in two or fewer amino acids being changed in a gapless region of the alignment, such that the new alignment is also gapless, are ignored. Similar changes to larger regions require an α (see Alignment score below) increase of 4 or more. This is to avoid changes that reflect multiple choices of donor-acceptor pairs for essentially identical sequences. Finally, the alignment in the region of the change must be of reasonable quality: for unchanged 5 amino acid regions either side of the region under consideration, the adjusted alignment score \( \overline{\alpha} \) must be 3 or higher (or all gaps) for some subset of three sequences containing the sequence of interest. Similarly the resulting score for the changed region must also be higher than 3 or all gaps. Exon boundaries that do not pass the filters are discarded and the genes are reconstructed a final time, allowing only the surviving boundaries and those that were present in the original gene. The resultant genes are outputted in GTF, amino acid FASTA and CDS FASTA format.
For algorithm development and evaluation, three species sets were selected (Table 1): a set of five fungal genomes, a set of five plant genomes, and a set of five mammal genomes. Each species set contained at least one well-annotated model organism. Orthogroups were inferred using OrthoFinder [26]. For the plant data set, where multiple transcript variants were available, the primary transcript was used as listed in Phytozome [27]. For the mammal data set, no primary transcripts were marked, and so for each gene the longest available transcript was used. RNA-seq data sources are listed in Table 2. and were downloaded from the Sequence Read Archive [28].
Table 1 Species sets used for algorithm validation
Table 2 SRA RNA-seq data sources
De novo gene prediction
De novo gene predictions were made using Augustus [24] version 3.2.2. Training was performed using all well-formed gene models from each species, and using the autoAugTrain.pl script included with the software. Augustus was run individually on each genome with the default settings.
Alignment score
An amino acid alignment can be considered as an ordered sequence \( A={\left({C}_n\right)}_{n=1}^{n=l} \) of columns \( {C}_n=\left({c}_1^n,\dots, {c}_l^n\right) \). The column score γ for a column C n is defined as the average pairwise Blosum62 score for amino acids in that column:
$$ \gamma \left({C}_n\right)=\frac{\sum_{1\le i<j\le l} Blos\left({c}_i^n,{c}_j^n\right)}{l} $$
The Blosum62 matrix was used as it is the most widely used amino acid substitution matrix. The alignment score α for an alignment A is constructed column-wise as:
$$ \alpha (A)=\sum \limits_{n=1}^l\gamma \left({C}_n\right) $$
The adjusted alignment score \( \overline{\alpha} \) is defined as \( \overline{\alpha}=\frac{\alpha }{l} \), where l is the alignment length.
Multipartite choice function
The multipartite choice function (Fig. 1) aims, for a set of k gene regions and a set of l k gene model variants for each gene region, to choose an optimal set containing one gene model variant from each gene region such that the alignment score is maximised. This problem is equivalent to finding the heaviest maximal clique in an edge-weighted complete multipartite graph. This function replaces the naïve approach of calculating pairwise alignment scores between all options and choosing an optimal subset, which, while optimal, is computationally infeasible.
Multipartite Choice Function. The choice function aims to find optimal variants from a set of protein sequences. a) Sequences are aligned; b) A consensus alignment is produced: on a column-by-column basis the choice of amino acid for each sequence that optimises the alignment score for that column is chosen as a representative; c) A binary representation is produced from the original alignment: for each position in alignment, a 1 is assigned if the amino acid matches the consensus, and a 0 is assigned if it does not. This leaves a sequence of vertical binary strings. The aim is to find a single vertical binary string that agrees with (i.e. is a bitwise subset of) as many as possible of these, and that is also compatible with the category constraints. The best such string in this case is shown to the right in green. d) The result
To reduce the complexity of the problem, options are chosen by comparison with a reference consensus alignment, produced by taking the most consistent set of amino acids for each column in a global alignment individually (Fig. 1a-b). This column-wise optimisation is fast, and provides a basis for the sequence-wide optimisation. To produce the consensus, The set of ∑l k options is aligned to the reference (the original alignment) using MAFFT –add [29]. The inconsistent regions are then isolated and re-aligned using the more accurate but more computationally intensive MAFFT L-INS-i. For each column in the alignment, the set of amino acid choices (one for each gene region) that optimises the alignment score for that column is chosen.
For each option i a binary string \( {H}_i=\left\{{h}_1^i,\dots, {h}_n^i\right\} \) is produced describing for each position in the alignment whether or not that option matches the consensus (Fig. 1c). The chosen subset will be the set of options that globally maximises agreement with the consensus. If the strings {H i } i are stacked vertically, such that they can be read as columns \( {\left\{{V}_j\right\}}_{j=1}^n \) then the task is equivalent to finding a columnar binary string V with one nonzero entry for each gene region such that |V i : V ⊆ V i | is maximised.
Given the set \( {A}_0={\left\{{V}_j\right\}}_{j=1}^n \), an optimal subset is deduced by sequential random sampling. Ignoring all-1 strings, an initial W0 = V k is chosen at random from A0. For sets S1, S2 and a set of "checkpoints" R, the set S1 is compatible with S2with respect to R = {R i } i if the binary intersection S1 ∩ S2 ∩ R i is nonzero for all i. Define A n = {a ∩ Wn − 1 : a, Wn − 1 compatible w. r. t G}, where G is the set of binary strings which are zero for all but one gene region, at each stage choosing W n at random from A n . The process A0, A1, A2, … eventually converges on a single binary string. This reduction is performed a user-selected number of times, the default being 1000. The result that is a subset of the largest number of V i is declared the winner. In the event that the result still contains more than one option for each gene region, subsets of options are calculated and their multiple alignment score α is calculated, the winner being the subset with the highest α. In the event that multiple subsets exhibit the same maximal α, a subset is chosen arbitrarily from them.
Adjacency group calculation
OMGene builds genes sequentially by iteratively adding in putative exons to multiple genes simultaneously. Care must be taken to ensure the gene parts (which in turn become exons once gene models are constructed) are added in a way conducive to vertical comparison of relevant regions (see Fig. 2). In OMGene, gene parts are considered in sequential adjacency groups based on their coordinates in a multiple sequence alignment. Prototype gene models are formed by stringing together amino acid sequences for individual putative exons for each gene region: these are then aligned, and a graph is formed from this alignment. Each putative exon is a node on the graph, and two exons are connected by an edge if and only if one of the exons overlaps the other by a third or more of its length. The adjacency groups are then defined to be cliques in this graph. Cliques are determined using the python implementation of the NetworkX package [30].
Calculation of adjacency groups. a) Amino acid sequences for individual putative exons are strung together and aligned. b) A graph is formed with vertices formed by gene parts (or exons), and edges drawn when the overlap between two parts is greater than or equal to one third the length of one of them. c) Cliques are extracted and then ordered lexicographically to form the adjacency groups
Junction F-score
The junction F-score for a gene is a measure of how well the splice junctions observed in mapped RNA-seq data are represented in the gene model. For a gene model G and corresponding gene region R, define J G to be the set of individual intron beginning and end coordinates in the gene model, and define J R to be the set of mapping junction beginning and end coordinates in the mapped RNA-seq data. A minimum of 10 reads is required for a given RNA-seq junction to be counted. We may then define the junction F-score as:
$$ jF\left({J}_G,{J}_R\right)=\frac{2\bullet jP\left({J}_G,{J}_R\right)\bullet jR\left({J}_G,{J}_R\right)}{jR\left({J}_G,{J}_R\right)+ jP\left({J}_G,{J}_R\right)} $$
$$ jP\left({J}_G,{J}_R\right)=\frac{\left|{J}_G\cap {J}_R\right|}{\left|{J}_R\right|};\kern1.5em jR\left({J}_G,{J}_R\right)=\frac{\left|{J}_G\cap {J}_R\right|}{\left|{J}_G\right|} $$
The direction of each junction site (start or end of a junction) is taken into account when considering the intersection of the two sets.
Coverage score
The coverage score is a measure of how well RNA-seq data represents a given gene. Given that gene expression levels can vary considerably and irregularly across the length of a transcript [13,14,15,16], care must be taken to ensure the expression profile for a gene region is properly interpreted. For example, sample preparation methods can bias coverage towards the centre and 3′ ends of the transcript; furthermore, jagged read profiles and transcription of antisense regions [31] and other intronic ncRNAs can cause expression profiles to be highly non-binary. To mitigate this, a rolling threshold approach is used. For a gene region R, and a genomic coordinate x ∈ R, the expression characteristic χ is defined as:
$$ \chi (x)=\min \left(\max \left(\left\{\rho (y):y\in R,y<x\right\}\right),\max \left(\left\{\rho (y):y\in R,y>x\right\}\right)\right) $$
Where ρ(y) is the read count at genomic coordinate y. Bases in the gene region to which the RNA-seq data has been mapped are categorised based on whether they are likely to correspond to exonic or non-exonic regions: a base x is considered to be exonic or on (i.e. likely included in the mature mRNA) if \( \rho (x)>\frac{\chi (x)}{5} \), and intronic or off (i.e. likely not included in the mature mRNA) if \( \rho (x)<\frac{\chi (x)}{5} \). The coverage score for a gene model G = {G1, …, G n }, where the G i are alternately exons and introns, is defined as:
$$ C(G)=\frac{1}{n}\ \left(\sum \limits_{G_i\ \mathrm{exonic}}\frac{\ \left|\left\{x\in {G}_i:x\ \mathrm{on}\right\}\right|}{\left|{G}_i\right|}+\sum \limits_{G_j\ \mathrm{intronic}}\frac{\left|\left\{x\in {G}_i:x\ \mathrm{off}\right\}\right|}{\left|{G}_j\right|}\ \right) $$
that is, the average length-adjusted coverage score for each individual feature (exon or intron) in the gene model.
RNA-seq data
RNA-seq data were downloaded from the Sequence Read Archive, assessed using FastQC [32], trimmed using SeqTK [33] and aligned to the genome with Hi-SAT2 [34, 35] using default parameters and single or paired-end methods as appropriate. Per-base coverage was calculated using SAMtools mpileup [36].
Subcellular localisation analysis
Subcellular localisation predictions for all datasets were obtained using TargetP [37]. For the plant dataset only, TargetP was run with the –P option to predict chloroplast targeting sequences. The localisation consistency for an orthogroup O was calculated as an entropy score across the categories for each gene:
$$ H(O)=-\frac{1}{\left|O\right|}\sum \limits_{C\epsilon \mathcal{C}(O)}\frac{\left|C\right|}{\left|O\right|}\bullet \log \left(\frac{\left|C\right|}{\left|O\right|}\right) $$
where \( \mathcal{C}(O)=\left\{{C}_1,\dots, {C}_n\right\} \) is the partition of genes in O into their localisation categories.
Problem definition, algorithm overview and evaluation criteria
An overview of the OMGene algorithm is provided in Fig. 3. OMGene aims to find the most consistent set of representative gene models for a set of inputted genes by seeking to maximise the agreement of their aligned amino acid sequences, returning the single best gene model for each gene. The algorithm constructs gene models based on relatively simple constraints: AUG for start codons; GU or GC for splice donor sites, AG for splice acceptor sites, and UAA, UGA, or UAG for stop codons. Other features such as codon bias or poly-pyrimidine tracts are not considered. OMGene can also use non-canonical translation initiation and splice sites if inputted by the user as a command-line option.
Simplified overview of OMGene workflow. a) Gene regions are extracted from around the gene model; b) Exonerate is used to cross-align all constituent exons and full open reading frames to construct basic prototype gene models; c) The exonic regions from these prototype gene models are sorted into adjacency groups, which are then sequentially optimised using the multipartite choice function; d) Results are compared against the original gene models to incorporate potentially overlooked combinations, and filtered under various criteria to produce results
The input for OMGene is a user-selected set of gene models, in general transfer format (GTF), which are assumed to belong to a single orthogroup. For a given set of species, an orthogroup is the set of genes descended from a single ancestral gene in the last common ancestor of those species [26]: this set may contain paralogous as well as orthologous genes. The suggested pipeline for using OMGene is to determine orthogroups using OrthoFinder [26], and to apply OMGene to each or a chosen subset of these orthogroups.
OMGene uses Exonerate [25] as an initial step to cross-align amino acid sequences from all user-supplied genes to the genomic regions of the genes from all species in question, in order to find conserved translatable features. It then combines this information with the original gene models to produce an initial set of prototype exonic regions, or gene parts, for optimisation. The amino acid sequences for these prototype gene models are then aligned, and the constituent gene parts are split into adjacency groups based on overlaps in the alignment (see Implementation). Adjacency groups are sequentially appended to the gene models, and the genetic coordinates are recursively adjusted and assessed to optimise the agreement of the amino acid sequences between species. The resultant gene models are then subject to stringent filtering criteria before the finalised set of gene models are presented as sets of GTF coordinates, amino acid FASTA and coding sequence (CDS) FASTA sequences.
To demonstrate the utility of OMGene, it was applied to orthogroups formed from three sets of test species: a set of five plant species, a set of five mammal species and a set of five fungal species (Table 1). Prokaryotes were not considered, as their protein coding genes lack introns and intergenic regions are either very small or absent and thus gene prediction is comparatively trivial. For plants and fungi, 600 buffer bases either side of each gene model were included, for mammals, which have larger introns, 2000 buffer bases were used. OMGene was applied to orthogroups that contained exactly one gene from each species, referred to as single-copy ubiquitous (SCU) orthogroups. In addition, OMGene was run on the same set but with all genes from three representative species – A. thaliana, H. sapiens and S. cerevisiae – replaced with de novo predicted genes, obtained by running the Augustus [24] gene finder on those genomes. These species were chosen as they have the best annotated genomes and thus the existing gene models will provide the best possible training set for Augustus de novo prediction. This de novo prediction analysis was done to simulate a typical genome-sequencing project where a user has generated a well-trained set of gene models solely using computational prediction.
OMGene was assessed in three ways: RNA-seq data were used to compare the accuracy of gene models before and after application of OMGene, from both coverage (i.e. the proportion of the predicted gene that is encompassed by reads mapped from RNA-seq data) and splice junction perspectives. To assess the accuracy of start codon prediction, OMGene-modified gene models were subject subcellular localisation prediction and the results were evaluated for consistency across the orthogroup. The RNA-seq data used to assess the success of OMGene were downloaded from the NCBI Sequence Read Archive [28] and are listed in Table 2.
Application of OMGene to publicly available datasets
Quantities and nature of changes made
The full plant data set contained 3694 SCU orthogroups, containing 18,470 genes. Application of OMGene to this test set resulted in gene model changes to one or more genes in 1543 (41.8%) of these orthogroups. In total, 2017 of the inputted genes (10.9%) were altered. Of these altered versions, 154 genes (7.6% of 2017) were present in the original genome annotations as alternative (non-primary) transcripts for the inputted gene. Figure 4 shows examples of various types of gene model alteration for genes in A. thaliana. A full breakdown of per-species change quantities for all species sets can be found in Table 3, Figs. 5 and 6; Table 4 and Fig. 7 show the distribution of the types of changes made. All gene models that were changed by OMGene are included in the supplementary material as a set of GTF files.
Examples of individual gene model changes for genes in A. thaliana. a) AT1G01320.1, orthogroup OG0010924, exon extension, splice acceptor side; b) AT1G76280.3.TAIR10, orthogroup OG10336, exon contraction, splice acceptor side; c) AT1G22860.1, orthogroup OG0010738, novel exon introduced; d) AT2G38720.1, orthogroup OG0009331, removed exon; e) AT3G01980.3, orthogroup OG0011814, novel intron introduced; f) AT4G14590.1, orthogroup OG0010029, intron removed; g) AT3G01380.1, orthogroup OG0012127, moved start codon; h) AT5G11490.2, orthogroup OG0013306, complex event: exon has been removed and the previous exon boundary has been extended to include the stop codon
Table 3 Per-species gene change breakdown
Chart showing the number of changes made. a) C. papaya and T. cacao experienced the most changes in the plant data set. The de novo predicted gene models for the A. thaliana genome underwent three times more changes than the publicly available one. b) The number of changes in mammals was roughly the same as in plants. As expected, M. musculus and H. sapiens experienced the fewest changes. The de novo predicted gene models for H. sapiens underwent considerably more changes than the curated genome annotation. c) The number of changes made was significantly less for the fungi data set. As for the plants and mammals, the de novo predicted genes for S. cerevisiae underwent more changes than the curated version
The average number of nucleotides added or removed from gene models as a result of changes made by OMGene. The units of the y-axis are the number of nucleotides. a) Average magnitudes of each change for plants; b) Average magnitudes for changes made to mammal genes; c) Average magnitudes for changes made to fungal genes. In all cases, predicted gene sequences were shortened to a greater extent than they were lengthened.
Table 4 Summary of gene model change categories
Distribution of types of changes made in the three data sets. a) The most common change in plants was exon deletion. b) Moved start codons and removed exons were most common in mammals. c) In fungi, the most common change was overwhelmingly a moved start codon
The plant species that experienced the highest number of changes were C. papaya and T. cacao, which is consistent with them being more recently published and less well-studied genomes. For all species, more nucleotides were removed than were added, indicating either that gene models predictions tend to be over-cautious or that OMGene is more proficient at removing material than at adding it in. In terms of the types of changes made, exon deletion was by far the most commonly seen change, followed by moved start codon and exon boundary adjustment (Fig. 7). It should be noted that exon deletion events also encapsulate the separation of erroneously fused gene models, which can contribute many exon deletion events simultaneously.
For the mammal data set, 8771 SCU orthogroups were considered, containing 43,855 genes. Of these, 2100 orthogroups (23.9%) saw some change, with 2686 genes (6.1%) undergoing alterations. Most gene changes occurred in C. lupus, M. domestica, and O. cuniculus, which, compared to M. musculus and H. sapiens, are less well studied and more recently annotated. By far the most common alterations here were removed exons and moved start codons, which is likely partially due to the choice of representative gene model for input to OMGene – the longest.
For the full fungal data set, 2710 SCU orthogroups were considered, containing 13,550 genes. Of these, 100 orthogroups (3.7%) exhibited some change, and 109 genes (0.8%) were altered. In this case, the genes in E. gossypii were the most commonly altered, consistent again with it being one of the lesser-studied species on the list. By far the most common change type in the fungal data set was a moved start codon, consistent with the fact that splicing is a rare event in fungal genes (on average 5.09 exons for plants, 11.23 exons for mammals, 1.08 exons for fungi).
To simulate a de novo genome annotation project, OMGene was also applied to the same three data sets with de novo predicted gene models for representative species, A. thaliana, H. sapiens and S. cerevisiae. These species were chosen as they have the most complete annotations of their respective data sets, and therefore these genes are likely to be the most reliable for training a gene finding algorithm. The genome annotation tool used was Augustus (see Implementation) as it is one of the best and most frequently used gene prediction algorithms.
For the plants data set with Augustus predictions for A. thaliana, 3694 SCU orthogroups were considered. Of these, 598 (16.2%) saw some change in an A. thaliana gene. Similarly, for the mammal data set, 7311 SCU orthogroups were considered, of which 2907 saw changes in a H. sapiens gene. For both A. thaliana and H. sapiens most changed genes underwent several individual changes. For the fungi data set, 2710 SCU orthogroups were considered. Of these, 19 (0.7%) saw some change in a S. cerevisiae gene. Tables 3 and 4 show a full breakdown of the types and amounts of changes made. As expected, in both cases, the total number changes and the average size of change made is greater for the de novo predicted gene models than the curated gene models. However, the distribution of types of changes made remained roughly the same.
Splice junction and feature coverage analysis
To assess the validity of changes made by OMGene, both the original and the updated gene model sets were compared using publicly available RNA-seq data from the NCBI Sequence Read Archive [28] (see Implementation and Table 2). Data were downloaded for all species except E. gossypii and D. hansenii, for which no adequate data were available. Each amended gene was assessed in two ways relative to this data: firstly by comparing the exact splice junction locations with RNA-seq derived splice junctions; secondly by evaluating the coverage of exonic regions with RNA-seq. To control for unreliable data, some genic regions were omitted from this analysis. Gene regions in which the RNA-seq data suggested there were indels in the reference genome, or that were within 1000 bp of the end of a contig or scaffold, or that contained 10 or more contiguous "N" nucleotide bases were omitted from the analysis (see Implementation). Regions with these characteristics prevent the creation of reliable gene models, and so are deemed here "unassessable" and not useful for determining gene model accuracy. Gene regions with very low coverage were also omitted from the junction score analysis: gene regions required at least one base with a read depth of 10 or more.
Gene models outputted by OMGene were assessed on whether or not their junction and coverage F-scores (see Implementation) had improved or been reduced. The full results can be seen in Table 5. For the plant data set, OMGene improved the agreement of the gene model with the splice junctions inferred from RNA-seq data for 729 assessable genes, while 125 assessable gene models exhibited reduced agreement (85.3% improved). Similarly, when assessing RNA-seq coverage of gene models OMGene improved the agreement of the models with the data for 1026 genes, while 167 genes exhibited reduced agreement (86.0% improved). For the de novo predicted A. thaliana genes, the success rates were essentially the same as for the public data (87.3% and 91.1% improved by junction and coverage F-scores respectively), but the absolute quantity of genes exhibiting a changed score increased roughly four-fold. This difference represents the considerable effort and evidence-based curation that has been invested in the A. thaliana genome annotation.
Table 5 RNA-seq coverage and junction F-scores
For the mammals, 948 assessable changed genes (79.2%) had an improved junction score compared to the original gene model. In addition, 82.83% of assessable genes had improved RNA-seq coverage scores. For the de novo data, 89.4% of assessable predicted H. sapiens had an improved splice junction score after application of OMGene, and 88.74% had an improved coverage score. The numbers of changed genes were again considerably more for the de novo predicted H. sapiens data, again indicating the high degree of attention that has been afforded to the annotation of this species.
The results for the fungal data set (see Table 6) were not as good. Notably very few gene models showed any change in junction F-score, with only 8 genes exhibiting a changed score. This is due to the relatively simple exon structure of fungal genes, for which splicing is very rare, and splicing events predicted by OMGene are much less likely to be correct. In this case 3 genes had an improved score, and 5 had a reduced score (37.5% success), with all 5 of the losing genes coming from Y. lipolytica. The most common change made to fungal genes was a moved start codon, which, although not detectable in the junction F-score, can be detectable in the coverage F-score. This is reflected in the results, where 30 genes showed an improved coverage F-score and 10 genes showed a worse coverage F-score (75% improved). In the de novo case, again the numbers increased while the percentage success remained roughly the same, with 4 (100%) genes improving by junction for S. cerevisiae and 11 (64.7%) improving by coverage score. The highly compact nature of fungal genomes, with few exons and limited space between genes means that the accuracy of de novo predicted genes is higher than in plants and mammals. Thus the utility of OMGene on these comparatively simpler genomes is limited.
Table 6 Subcellular localisation predictions
Many of the cases for which OMGene results differ from RNA-seq evidence are attributable to real biological variability that confounds the evaluation criteria of the algorithm. For example, there are some instances where the most evolutionary conserved splice site was not the splice site observed in the RNA-seq data. Such events, by definition, cannot be detected by OMGene. Furthermore, RNA-seq mapping errors also contributed to reduced scores, as did artefacts resulting from spliced UTRs, and jagged read profiles, particularly in the fungal data, that made some coverage scores difficult to calculate reliably. Finally, the presence of multiple transcript isoforms within the RNA-seq data can reduce the score for a valid transcript even if it is the best choice for that particular gene. While users of OMGene should be aware of these confounding factors, the above data demonstrates that, in general, OMGene is much more likely to improve a given gene model than not even for extensilvey curated genomes.
Assessment of subcellular localisation predictions for 5′ end analysis
Given that genes from the same orthogroup are, by definition, descended from a single ancestral gene, it is reasonable to assume that they should be consistent in their predicted subcellular localisation. Several sub-cellular targeting sequences are located at the N-termini of genes [38], thus one expects genes with inaccurately predicted start codons to yield inaccurate results when assessing their targeting signals. Genes belonging to orthogroups changed by OMGene were assessed to determine whether the changes resulted in increased consistency of the predicted subcellular localisation of all genes in the orthogroup. Targeting predictions were made using TargetP [37], and Shannon entropy was calculated to assess the consistency of the predictions within the orthogroups (see Implementation). Entropy scores were compared only for orthogroups in which at least one gene model was altered by OMGene. An entropy score of 0 indicates that all members of the orthogroup are predicted to localise to the same sub-cellular compartment; the worst possible entropy score given five genes and four possible localisations identified by TargetP (chloroplast, mitochondrion, secreted, cytoplasmic) is \( -\frac{2}{5}{\log}_2\left(\frac{1}{5}\right)-\frac{3}{5}{\log}_2\left(\frac{1}{5}\right)\approx 1.92 \), indicating that only two of the genes agree. An example orthogroup whose prediction entropy score has been improved by start codon adjustment can be seen in Fig. 8.
Example change in subcellular localisation prediction for a gene. Thecc1EG021604t1.CGDv1.1 from T. cacao has undergone a change in start codon, revealing a signal peptide at its 5′ end. In this case, what was previously assumed to be cytosolic has been found to be targeted to the secretory pathway, the same as the other members of the orthogroup (OG0009265). In this case, the Shannon entropy score for the orthogroup has fallen from 0.72 to 0
The 1543 plant orthogroups in which one or more genes were altered were subjected to subcellular prediction analysis (Table 6). Of these, gene model changes made by OMGene resulted in changes in predicted subcellular localisation for one or more constituent members of 55 orthogroups. In total, 74 improved agreement between gene models (74%), 13 remained the same (13%), and 13% increased disagreement between predicted subcellular localisation of gene models. The effect was more profound for the mammal data: 509 orthogroups showed a change in localisation predictions for one or more of their genes, 444 of which (87.2%) resulted in improved predicted localisation consistency. The data for fungi were sparser: only 7 out of 95 changed orthogroups exhibited a change in subcellular localisation prediction, with 6 of these changes improving the consistency of localisation prediction (85.7%) and 1 increasing disagreement (14.3%).
Similar results were obtained for the de novo annotation analysis in plants, although again the data were sparse here. Orthogroups containing the de novo predicted A. thaliana genes were considered together with the four original genes for the other species. Here, 11 of the A. thaliana genes experienced a change in subcellular localisation following application of OMGene. Of the 11 orthogroups containing these, 9 improved consistency (81.9%) and 2 reduced the consistency (18.2%). For de novo H. sapiens predictions, 527 of the start codon changes resulted in a change in subcellular localisation prediction change for H. sapiens. In 458 (86.9%) of these cases, this improved the consistency of the orthogroup. For the fungal data set, the data were extremely sparse, with only one gene experiencing a change in its targeting prediction, which reduced the consistency for its orthogroup. Thus, although data were sparse for the fungal dataset, in each of the plant, mammal, and fungal datasets the consistency of gene models was on the whole improved from a subcellular targeting perspective.
Here we present OMGene, an automated method for improving the consistency of gene model annotations across species. OMGene is intended for use in computational de novo genome annotation projects where no empirical data (such as RNA-seq data) is available to train or correct gene model predictions, or to assist the construction of gene models for genes that are not expressed in the data available. OMGene is also designed to help users who wish to leverage conservation information to correct gene models of a single gene of interest across a set of species. Thus OMGene is suitable for both large and small scale analyses.
OMGene is run as a python script with a tab-delimited input file, each line of which contains the path to a GTF file, containing coordinates for a single gene, and the path to its corresponding genome FASTA file. Full instructions for running OMGene can be found at the OMGene GitHub repository (https://github.com/mpdunne/omgene).
OMGene results reflect differences in gene model complexity between species sets
To demonstrate the utility and performance characteristics of OMGene, it was applied to three separate datasets of well-annotated plant, mammal and fungal genomes. When applied to the plant data set, OMGene altered the gene models of one or more genes in 41.8% of the orthogroups that were evaluated; when applied to the mammal data set, 23.9% of SCU orthogroups saw some change. In contrast, only 3.7% of orthogroups were subject to modification in the fungal data set. This result reflects the differences in gene model complexity between the three species groups. Specifically, gene models in plants tend to have more exons than fungi (mean = 5.09 exons for plants, 11.23 for mammals, 1.08 exons for fungi) and thus there is considerably more potential for gene model variation in plants and mammals than in fungi. In light of this it was unsurprising that the most frequently observed change made in fungi was a change in choice of start codon. This is also reflected in the high number of removed exons from plant and mammal genes, which is contributed to partly by the separation of erroneously fused adjacent genes.
OMGene works well on complex gene models
The changes made by OMGene were assessed relative to splice-mapped RNA-seq data to assess the level to which it had improved the gene models. For the plant data set, the results from OMGene clearly resembled the empirical data more closely, with 85.4% and 86.0% of genes improving in terms of their splice junctions and their coverage respectively. The profiles were different for different species, with many more changes being made for C. papaya and T.cacao; in addition the number of successes for B. rapa was slightly lower than for the other species.
In mammals, the three non-model species, C. lupus, M. domestica, and O. cuniculus faired well in terms of their junction scores. H. sapiens genes also typically scored highly, but M. musculus genes did not do so well. The reason why mouse genes changes were less successful than human gene changes is unknown. These two species also saw considerably fewer gene model changes than the other species. In all cases, mammal genes were mostly contracted via the removal of exons or by moving the start codon – this is at least partly due to biases in the choice of longest gene model as representatives for these genes.
The number of junction changes made for the fungal data set was considerably lower: only 8 changed genes had an altered junction F-score, 62.5% of which become worse after OMGene. Though this is less than the plant data set, it should be noted that the resolution of this data set does not lend itself to accurate conclusions about the general validity of changes made to fungal genes. The resolution and success rate for fungal genes from a coverage perspective was slightly higher, with 75% of the genes with changed scores improving. The low resolution of junction data for fungal genes reflects the rarity of complex gene models in these species, and thus the low likelihood that deviations from simple, single-exon gene models are correct. Thus, while OMGene does not always produce gene models that agree with transcriptome data, it does improve the overall quality of gene model annotations even for relatively simple fungal genomes.
The improvements in gene model accuracy made by OMGene for the de novo predicted gene models were much the same as for the publicly available, curated gene models. However, the number of changes made to the de novo predicted set was much greater, indicating that the considerable labour that has been applied to these model organisms has successfully controlled for potential errors. It should be noted that, although OMGene managed to improve many of the gene models outputted by Augustus, the two agreed in most cases (86.1%, 60.3% and 98.6% for plants, mammals and fungi respectively), indicating that the basic implementation of a well-trained Augustus de novo prediction produces genes that are highly consistent between species.
OMGene improves the consistency of subcellular localisation predictions
In addition to assessment of coverage and splice junctions, gene models were assessed by the consistency of their predicted subcellular localisation. Given that the orthogroups used in this analysis comprise ubiquitously conserved single copy genes, it is logical to assume that these genes should generally have the same subcellular localisation. For the full plant data set, of all orthogroups whose genes had different subcellular targeting predictions after application of OMGene, 76.4% had improved intra-orthogroup consistency, with 85.5% either improving or remaining the same. In mammals, 87.2% of orthogroups whose subcellular localisation predictions were changed by OMGene showed improved consistency. For the full fungal data set, although the data were sparse, 85.7% of the orthogroups considered had improved consistency.
The results for the plant data set were similar for the de novo annotated set (85.7% improvement), as was also the case for mammals (86.9% improvement). For fungal orthogroups containing de novo predicted S. cerevisiae genes, the only gene whose localisation prediction changed caused the consistency of its orthogroup to decrease, however the resolution of the data in this case is not sufficient to draw any conclusions. Thus, application of OMGene improves the accuracy of start codon specification in de novo predicted gene models.
When applied to publicly available plant, mammal and fungal data sets, OMGene demonstrates proficiency in improving gene models from multiple perspectives. Due to stringent filtering criteria, it does not fix all errant gene models, however the gene models that it does fix represent an improvement the majority of the time. The overall improvement is larger for genomes with complex gene models.
Availability and requirements
Project name: OMGene.
Project home page: https://github.com/mpdunne/omgene
Operating system: Unix.
Programming languages: Python v2.7.x.
Other requirements: Bedtools, Exonerate, Python packages (scipy, numpy, pyfaidx, networkx, BioPython).
License: GPLv3.
GTF:
General transfer format
SCU:
Single copy, ubiquitous
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Dujon B, Sherman D, Fischer G, Durrens P, Casaregola S, Lafontaine I, De Montigny J, Blanchin S, Beckerich J-M, Beyne E, Bleykasten C, Babour A, Boyer J, Cattolico L, Confanioleri F, De Daruvar A, Despons L, Fabre E, De Montigny J, Marck C, Neuvéglise C, Talla E, Goffard N, Frangeul L, Aigle M, Anthouard V, Babour A, Barbe V, Barnay S, Blanchin S, Beckerich J-M, Beyne E, Bleykasten C, Boisramé A, Boyer J, Cattolico L, Confanioleri F, De Daruvar A, Despons L, Fabre E, Fairhead C, Ferry-Dumazet H, Groppi A, Hantraye F, Hennequin C, Jauniaux N, Joyet P, Kachouri R, Kerrest A, Koszul R, Lemaire M, Lesur I, Ma L, Muller H, Nicaud J-M, Nikolski M, Oztas S, Ozier-Kalogeropoulos O, Pellenz S, Potier S, Richard G-F, Straub M-L, Suleau A, Swennen D, Tekaia F, Wésolowski-Louvel M, Westhof E, Wirth B, Zeniou-Meyer M, Zivanovic I, Bolotin-Fukuhara M, Thierry A, Bouchier C, Caudron B, Scarpelli C, Gaillardin C, Weissenbach J, Wincker P, Souciet J-L. Genome evolution in yeasts. Nature. 2004;430(6995):35–44.
Sacerdot C, Casaregola S, Lafontaine I, Tekaia F, Dujon B, Ozier-kalogeropoulos O. Promiscuous DNA in the nuclear genomes of hemiascomycetous yeasts. FEMS Yeast Res. 2008;8:846–57.
Gnerre S, Maccallum I, Przybylski D, Ribeiro FJ, Burton JN, Walker BJ, Williams L, Nicol R, Gnirke A, Nusbaum C, Lander ES, Jaffe DB, Sharpe T, Hall G, Shea TP, Sykes S, Berlin AM, Aird D, Costello M, Daza R, Williams L, Nicol R, Gnirke A, Nusbaum C, Lander ES, Jaffe DB. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. PNAS. 2011;108(4):1513–8.
SK is a Royal Society University Research Fellow. This work was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement number 637765. MPD is supported by an EPSRC studentship through EP/G03706X/1.
The software is available under the GPLv3 licence at https://github.com/mpdunne/omgene.
Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
Michael P. Dunne & Steven Kelly
Michael P. Dunne
SK conceived the project. MPD developed the algorithm. SK and MPD analysed the data and wrote the manuscript. Both authors read and approved the final manuscript.
Correspondence to Steven Kelly.
Dunne, M.P., Kelly, S. OMGene: mutual improvement of gene models through optimisation of evolutionary conservation. BMC Genomics 19, 307 (2018). https://doi.org/10.1186/s12864-018-4704-z
Annotation errors
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Mayberry Jr. to play for the Mets in '15
John Mayberry Jr. leaving the confines of Toronto for New York in '15
A signing by the New York Mets will have John Mayberry Jr. coming back to Philadelphia to not play for the Phillies again, but he will be in a Mets uniform as the Metropolitans picked up Mayberry pending a physical.
Now the fans in New York will have a nice chance to see the ex-Phillies player who we expected to do a lot better in his career, but never blossomed into that semi-star that perhaps a few tweaks to his game he could have been.
John Mayberry Jr and Domonic Brown were touted as being the next big stars that the Phillies would cultivate from their own farm system, but neither one really took off, although Brown did find his swing briefly and started hitting home runs faster than Ryan Howard for a short time a few seasons ago.
Ruben Amaro Jr. and John Mayberry are both alumni from Stanford University, and that's what a lot of people said helped keep Mayberry on the Phillies roster through his tougher times in his early career.
If Mayberry can get back to his double digit home runs that he has hit in 3 seasons in a row from 2011-2013, he'll be a welcome addition to that Mets team.
Labels: Mayberry Jr goes to the Mets in 2015
Hit or miss: Phillies 2015 season
Merry Christmas to you
Fleeing Phillies to leave much different team
It's taken long, but Rollins will be a Dodger for ...
Rollins going out to the sun and fun of LA?
Holding the line on the Phils best players is admi...
Former Phillie Jayson Werth to trade uniform for p...
NL East Baseball Podcast Simulcast hosted by Miami...
Everyone knows the Phillies need change as Winter ...
Holiday Selfies this afternoon at Citizens Bank Pa...
Baseball Winter Meetings just a week away

redpajama_set_name: RedPajamaCommonCrawl


The term factories and beautiful doesn't seem to go hand in hand right? But yes, appearances are surely deceiving. Who would have imagined inside those beautifully sculpted architectural buildings, a factory is functioning. These 25 buildings break the age-old concept of a rusty, blackened, dusty, old factory images we have had since childhood. Now, if factories look like this, then I guess one wouldn't mind going for a trip to a factory! Recently Chennai architects conducted a search on the best factory design in the world. Their findings also coincide with the search conducted by forbes.com on the same topic, and the results are here.
This was built in the year 2002 by Architect Gunter Henn. The major aspect of this factory is, as the name itself denotes, a major part of its exteriors were made of glasses so that visitors can view part of the production process through it. It symbolically refers to the company's transparency in production and open minded approach.
This factory was built in the year 2008 by RE: Vision Architecture. Aerzen bags the position of first Gold LEED certified manufacturing plant in Pennsylvania. This is the greatest environmental privilege a building can receive. The building relies completely on solar energy. Sunlight sweeps through factory and offices. Here walls are made with plastered straw bales and the building is surrounded by carefully restored wild meadow. It also has a rooftop garden with water recycling facility.
This was built by architects William McDonough and Partners in the year 1995. The beauty of this building is that it was planned to fit into the landscape as naturally as a tree would stand there. Sheer windows were provided to receive consistent daylight all through the structure. The most desirable part of the building is The Street, a hall that runs through the entire building, connecting all offices and manufacturing units together.
Guillermo Hevia Architects built it in the year 2009. Here distinctive brown panels sync beautifully with the desert like environment outside. The stark yard is lined with wood columns. This olive oil factory has the unique property of being rustic and futuristic at the same time. The structure is made of concrete structure coated with wood and glass. Another unique quality is that the entire structure including the factory materials is fully made of biodegradable materials.
This was built in the year 2005 by architects Zaha Hadid. Here BMW stuck to the traditions and traditional united office and traditional factory together into one complex. This building inter-wines factory with office and improves the business. Here the employees can see manufacturing process going on in the bottom floor through sheer windows.
Foster and Partners built this in the year 2004. This building spreads through the sides of a lake in the shape of letter S. The height of the building was lowered intentionally so that visitors can see the trees over the top of the building. Originally it was designed as an office. But later it was doubled in size to include production space for automobiles. The interior is very large, that it could hold nine Boeing 747 jumbo jets.
This was made by Joerg Leeser, with BEL in the year 2006. This building is actually a prototype that has been created in the same image all around the globe. Triangular skylight domes cover the ceiling. Thus daylight provides the light required for the daytime.
This was built in the year 2010 by Design Studio E-nporpaMMa. Here the walls are lined with brilliant colors. This more looks like the part of an amusement park, but in reality, it's a pipe rolling factory.
This was built in the year 1937 by Richard Elmore. This factory gives the feel of a fairy tale, with wood paneled walls, amber -gold brewing vats and rustic, spiral staircases.
It is unknown who had built this unusually beautiful building and when was it built. The ground smokes up with fog, the walls curve and holds dials, nozzles, tangled metalworks that surprisingly vanishes round the bend. This top secret rocket factory is just amazing making it unrealistically beautiful. Pictures are forbidden inside this place but once an adventurous photographer Lana Sator snuck inside and clicked some pics. This plant belongs to NPO Energomash, leading manufacturer of military grade liquid propellant rocket engines.
This was built in the year 1996 by Strada LLC. This is one of the country's oldest and best-known baseball equipment manufacturers. The factory has a side by side museum in the same building that pays tribute to legendary athletes in this field. The structure has a big baseball bat in front of it that is free standing.
This factory was built in the year 1989 by KSP Jurgen Engel Architekten. This Siemens factory was the winner of 2007, ''Best Factory in Europe" award. This electronics factory looks like the inside of a clock working.
This factory was built by Harley, Ellington and Day in the year 1996. In this, a maze of well polished wooden steps and beautifully painted white railings leads to a brewing floor. This looks more like a library and less like a factory. The brewery gets its power from solar panels on the roof.
This was built in the year 1904 by some unknown architects. Guiness beer is an international icon by itself. Now when the factory is also aesthetically first class, it just makes it the perfect destination for the world to come and revel in its beauty and taste some first class beer as well.
Some unknown architect also builds this one in the year 1940. This plant includes 8 million square feet industrial space, where the world's most advanced fighter jet production takes place.
D.Villanueva Arquitectos built this factory in the year 2008. It is located on the Atlantic Coastal hills of Spain covered up with lush green vegetation. Inside the factory, there is no particular distinction between factory space and office space, thus promoting cooperation among the workers.
KHR Architects built this in the year 2008. This factory has an artistic interior and looks like two elevated rectangles facing each other across a lengthy courtyard. They are the world's oldest production units of Hifi, Audio, TV, and other electronics.
This was built by ARAD( Architectural Research and Design) in the year 2006. It is a 3700 square meter open space and 350 square meter mezzanine over the production floor.
This was built in the year 1967 by unknown architects. Boeing produces some of the world's largest jetliners. The production floor is a maze of moving jet pieces and workers, centered by a large frame of single aircraft production space.
The Rolton Group built this in the year 2003. Here the warehouse is a mirror of the cars it produces. All the walls and tables are white in color. All the mechanics and designers are also dressed up in white.
METRO Arquitetos built this in the year 2011. This comprises of two towering red spires made of glass stand head held high on plain, rural Brazil. The towers are an extra addition to the chocolate factory, which accommodates both workers and visitors, thus controlling the traffic flow.
This was built in 1909 by Martin Hammitzsch. This looks more like a mosque and less like a cigarette factory. Hence it is called Tobacco Mosque. The design is due to the unusual codes in the city which prohibited any creation to look like a factory.
Built by an unknown architect in the year 2007, this plant is home to Shell Nanhai, subsidiary of Royal Dutch Shell, the oil giant. The plant produces its electricity and contains an efficient system for managing waste.
Unknown architects built this in 2007, this one covers up one million square feet area. The main feature of the plant is it is rated as Class 10 clean room which means ten or fewer particles are measuring 0.5 microns or larger per 1 cubic foot of air.


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The Wondrous and Wonderful Festival of Fantasy
Blake Taylor / Wednesday, July 1st, 2015
Tags: Attraction Reviews / Festival of Fantasy Parade / parade
Hear ye, hear ye! Ladies and gentlemen, boys and girls, welcome to the Magic Kingdom! You are warmly invited to join Mickey Mouse and his Fantasyland friends for a magical celebration in the streets. Dreams will come true, hearts will soar, and you will become a part of the magic. For the time has come to take your places and prepare to welcome the wondrous and wonderful Festival of Fantasy!
These words herald across the airwaves of Magic Kingdom Park each afternoon just before 3:00 pm. They prompt anticipation, signal excitement, and mean one thing: the parade is on its way.
A good, old-fashioned parade is a tradition dating back to the very first day of operation for any Disney theme park, July 17, 1955, the opening day of Disneyland. It would become a staple included as part of Magic Kingdom's opening celebrations in October 1971. Over the years, different styles of parades have come and gone, from minimalist, intimate marching band processions to energetic street parties to nighttime spectacles to seasonal holiday favorites. Generally, though, when I say "Disney parade," you think of the extravagant, no-expenses-spared [3:00] signature of the Magic Kingdom bringing to life scenes and characters from Disney's most treasured animated classics. Of this genre, there have been many iterations, and the current one, Disney Festival of Fantasy Parade, is among the park's finest.
With parades being steeped in such deep history in the Disney parks, Festival of Fantasy taps into this tradition with expertise. The parade debuted on March 9, 2014, just as the park was about to conclude its multi-year New Fantasyland project. Where story is concerned, few parades have a literal narrative, but all have traces of a theme. For Festival of Fantasy's story, New Fantasyland was turned to as a starting point. Steven Davison, Creative Director of Walt Disney Parks & Resorts Creative Entertainment, said of the process:
[Walt Disney World] had just opened the new expansion of Fantasyland and we said, "Hey. What would be really great is if the characters exploded out of Fantasyland and said, 'Come to our new land. Come help us celebrate.'"
Paying close attention to the opening monologue, it's inferred that Mickey Mouse is the person putting on this festival, as it were. With this idea in mind, Davison and his team began to develop the parade. Davison's Disney resume also includes Disneyland favorites like World of Color and Mickey's Soundsational Parade, and the same sense of innovation of those productions is evident here in Festival of Fantasy. As far as characters go, the choices here go for iconic rather than rare, a decision that ultimately works well within the context of this particular parade. The energy of the performers matches the energy of elated guests as they spot their all-time favorites coming their way. It's like Disney took a ranking of most posted-about characters on fanboy Tumblr accounts and plopped them all in one parade. The result is one that leaves everyone anticipating who will come next.
There is a famous story of Disney CEO Bob Iger attending the 2005 opening of Hong Kong Disneyland and watching the parade. He noticed that of all the characters appearing in the parade, the only ones representing films created in the past ten years were the Toy Story gang. As legend has it, this is what initially planted the seed for Iger to move forward with the Disney acquisition of Pixar in January 2006. With this tale in mind, the assumption can be made that Disney parades are a pulse of the state of Disney animation. They are indicative of the company's successes, both for timeless masterpieces and more recent wins. If a character is given the honor of being in the parade, one can assume Disney values the films in which those characters appear. Likewise, the assumption can be made that the only characters Disney includes in their parades are the ones they are confident of withstanding generations to be part of the Disney legacy long-term. If this is true, then Festival of Fantasy is reassurance in the best way that Disney's animation department is at the top of its game. Of the thirteen films and franchises represented in Festival of Fantasy, four of those were just introduced to the world in the past six years. Nearly 31%! That's a far cry from only one new film having the legs to appear alongside the classics. The Princess and the Frog, Tangled, Brave, and Frozen all stand their ground rubbing equal elbows with the likes of Peter Pan and The Little Mermaid. It's a nice reminder of this special time in Disney history.
The floats carrying these Disney icons are worthy complements to each fantastic film, the scope of which is unlike anything the park has seen and doesn't use anything recycled from previous Florida parades. The floats' soaring heights are so tall that they mandated a complete restructuring of Main Street, U.S.A's Christmas decorations. (Typically the street is adorned with swooping garland, but if those remained, Festival of Fantasy floats would tear them down as the processions glided through. Instead, 2014 saw the debut of faux candlelit wreaths attached to Main Street storefronts.) Size isn't the only quality of the floats' wow factor, though. They are all kinetic, having some form of movement, from giant swinging axes to a conga line of dancing robotic fish. The fire-breathing Maleficent dragon is especially impressive. There is so much to look at, and it's impossible to soak it all in with just one viewing.
The ongoing movement accentuates the energy of the parade, something different it brings to the table and what will likely become a precedence for future parades. One can't help but dance along. With all the motion happening everywhere in the foreground, in the background is a nearly perfect soundtrack. It weaves a new theme song as its central, recurring tune that is stylized according to each unit's film and paired with familiar songs from that movie. Of particular note is the joyous orchestral arrangement of the princess float and the rustic, celebrational feel of the Tangled unit. The choreography of the Lost Boys in the Peter Pan unit was even inspired by Newsies.
Disney Festival of Fantasy Parade takes what had unfortunately become a stale, tired part of the Magic Kingdom entertainment lineup and brings refreshing life back into the esteemed tradition of the [3:00] parade. It is exactly what you want it to be, infusing the characters it knows its guests will go crazy for, and who also symbolize important moments in the story of this magical place as a whole. It is the embodiment of what the Magic Kingdom itself emulates: The belief that, if only for a fleeting moment, fantasy can become real with the help of timeless heroes ready for a transportive experience that will leave you wanting to come back to see it all over again.
Images © Disney.
Blake studies Electronic Media and Film at Appalachian State University. He enjoys making his family of six watch the parade in Frontierland and then sprint to Main Street in time to see it again. You can find him on Twitter @olddirtyblake or at BlakeOnline.com.
Tags: Attraction Reviews Festival of Fantasy Parade parade


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Benny Gordon To Wheel Grant Enfinger Racing Ford Fusion Powered By ROUSHYATES Engines At Daytona
DAYTONA BEACH, Florida (February 8, 2011) – – For Benny Gordon, it's an opportunity of a life time. For Grant Enfinger Racing (GER), it's a chance to step up to the plate. This Saturday when the ARCA Racing Series presented by Menards takes the green flag at Daytona (Fla.) International Speedway, Gordon will pilot the No. 09 VSI Ford Fusion with all intentions of putting the Grant Enfinger-owned automobile in victory lane.
Set to make his ARCA debut at "The World Center of Racing", Gordon, a famed competitor in the USARacing Pro Cup Series returns to the 2.5-mile superspeedway on the heels of setting the fastest time at the official ARCA DIS test just three weeks ago. Not to mention, Gordon will have his biggest cheerleader actually competing in the race as car owner Grant Enfinger will drive a car fielded by 2008 ARCA championship team, Allgaier Motorsports.
Howard Bixman, the team's crew chief also hopes to guide Gordon to victory circle in his ARCA debut, the first time that would happen since Matt Hawkins succeeded in 2008 at Iowa Speedway.
"We're hoping for a real strong weekend for the Grant Enfinger Racing Ford Fusion," said Bixman. "Benny (Gordon) did a tremendous job last month along with this entire GER team. We have since put a lot of time into this car along with the ROUSHYATES Engine program to find a little more speed that will give us that little extra push for the race."
He continued, "While our focus and attention will be on Benny and the No. 09 VSI Ford Fusion, we're also going to be keeping our eyes on that No. 36 with Grant (Enfinger) and hoping that the two cars can work together and maybe pull off one of the best finishes in ARCA history."
Bixman concluded by saying, "without a second thought, this will probably be one of the most competitive and most nerve racking ARCA races ever held at Daytona. The track is incredibly smooth since it's been re-paved which pretty much throws handling and tires out the window. The race will be determined by smart driving and being at the right place at the right time."
"It's going to be fun racing against my own car," said Enfinger, who nearly soared to victory lane in Gordon's machine at Talladega (Ala.) Superspeedway last April. "I know Benny is going to do a great job for my team and our sponsors. Hopefully all the cards will pan out and we can hook-up at the end and be the duo that everyone is chasing."
Enfinger concluded "I'm very thankful for all our marketing partners that support us, Beasley Allen Law Firm, ROUSHYATES Engines, ROUUSHYATES Performance Parts, FORD Racing and Stanley Industries."
The Lucas Oil Slick Mist 200 at Daytona (80 laps / 200 miles) is the first of 19 events on the 2011 ARCA Racing Series presented by Menards schedule. The 48th ARCA race held at Daytona is a three-day show. Practice begins Thursday, February 10 with a four hour practice session planned from 1:00-5:00 p.m. Menards pole qualifying presented by Ansell is set for Friday, February 11 slated to begin at 2:00 p.m. A forty-five minute final practice session is etched in on Saturday, February 12th from 10:00 a.m. – 10:45 a.m. The event will take the green flag later that day shortly after 4:30 p.m. The race will be televised live on SPEED with additional race coverage including live timing and scoring and radio broadcast through the ARCA Racing Network (ARN) online at arcaracing.com. All times are local.
PHOTO CAPTION: The No. 09 VSI Ford Fusion for Grant Enfinger Racing (GER) powered by ROUSHYATES Engines driven by Benny Gordon.
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