"GMOD," or "Garry's Mod," is a sandbox-style game made with the "Half-Life 2" engine. Players have open-ended game play and no limitations for creation. The objective and goals of the game are left completely up to the players, with infinite tools at their disposal. Players can add extra models, skins, items and add-ons to the game to diversify game play. Adding extra models will give you more nonplayable character choices, allowing you to be more unique situations.
how to add custom models to gmod
Open up the "garrysmod" file on your computer, located where you installed the game on your hard drive. Locate the "models" folder in the "garrysmod" directory, or if there is not one, create a folder with that name.
In some cases, it can be helpful for a mapper if he can choose between different skins for the model. It's easy to add more variety to your custom models with multiple skins without creating a totally new model.
In some circumstances, you may choose to divide your model texture into more than one material. This allows for multiple combinations that otherwise would require a larger amount of individual models. If, for example, you wanted to create a light that had two housing styles but three bulb colors, you could create these combinations under the $texturegroup.
Accurate gene structure annotation is a fundamental but somewhat elusive goal of genome projects, as witnessed by the fact that (model) genomes typically undergo several cycles of re-annotation. In many cases, it is not only different versions of annotations that need to be compared but also different sources of annotation of the same genome, derived from distinct gene prediction workflows. Such comparisons are of interest to annotation providers, prediction software developers, and end-users, who all need to assess what is common and what is different among distinct annotation sources. We developed ParsEval, a software application for pairwise comparison of sets of gene structure annotations. ParsEval calculates several statistics that highlight the similarities and differences between the two sets of annotations provided. These statistics are presented in an aggregate summary report, with additional details provided as individual reports specific to non-overlapping, gene-model-centric genomic loci. Genome browser styled graphics embedded in these reports help visualize the genomic context of the annotations. Output from ParsEval is both easily read and parsed, enabling systematic identification of problematic gene models for subsequent focused analysis.
Tools such as the Eval package[1] and the GFPE program[2] represent some of the earliest efforts to provide a reusable, easy-to-use annotation comparison tool to the community. Eval in particular stands out based on the amount of detail provided by its reported comparison statistics and by the ability to visualize the distributions of these statistics. Eval takes as input annotation files in Gene Transfer Format (GTF) and calculates a rich set of descriptive statistics summarizing the differences between the annotations. Because whole-genome annotations typically include thousands (or tens of thousands) of genes, these statistics are intended to condense the information into a comprehensive yet concise summary (at the resolution of entire sequences or sets of sequences), facilitating targeted improvement of gene prediction software. Unfortunately, this condensing process discards large amounts of valuable information at the resolution of individual gene loci, making the tool unsuitable for analyses that target a particular gene, sets of genes, or gene loci with characteristics of interest from within a larger set of genes. Such locus-resolution comparisons are useful not only to software developers and annotation producers who need to know whether their software has distinct advantages or disadvantages, e.g., favoring long over shorter gene models on average, or failing in untranslated region (UTR) prediction, but they are of primary interest for specialists concerned with a particular gene family or pathway.
One common approach involves designating one set of annotations as the reference set and then using the coordinates of each reference gene annotation to define a distinct gene locus to serve as the basis for subsequent comparison (see Figure1). However, this approach is unfavorable for several related reasons. First, reference gene annotations that overlap are handled separately, when it makes more sense to associate them with the same locus and handle them together. Second, it forces a quality judgment between the two sets of annotations when their relative quality is often unknown. The two sets of annotations likely include complementary information, and unless there is a clear distinction in quality between the two, choosing one as a reference discards clearly related information from the other. Third, relevant information from predicted gene models that extend beyond the boundaries of the corresponding reference annotation is ignored.
In many cases, a single pair of model vectors (one for the reference, one for the prediction) is sufficient to fully represent annotated gene structure at a given locus. This is certainly true when both the reference and the prediction annotate a single gene with a single mRNA product at the locus. But even if the reference (or the prediction) annotates multiple genes or transcripts, non-overlapping annotations can be encoded in the same model vector and compared simultaneously with corresponding annotations from the other data set. However, if either the reference or the prediction contains annotations for overlapping transcripts, either because of alternative splicing or because of overlapping gene models, a single pair of model vectors is insufficient to represent the complete annotated gene structure at that locus. In these more complicated cases, the reference or the prediction or both will be associated with multiple model vectors. Thus, the algorithmic requirement is to represent all annotated transcript structures in the locus using the smallest number of model vectors.
When working with genome annotations, there is an increasing variety of cases in which no gold standard is available for comparison. For example, gene annotations for many model species are available from a variety of sources (i.e., UCSC versus Ensembl). The respective quality of these different annotation sets is not always clear, but comparison is still a necessary and fundamental task. Another example relates to genome projects that typically offer multiple releases of gene annotations between each major genome assembly release. Although newer releases may offer marginal improvements over the older ones, neither one can truly be considered a high-quality standard reference for comparison. An additional example relates to the increased affordability of genome sequencing and the number of new and exotic species for which genome sequence is available. Gene annotation software is based on complex statistical models containing many parameters, and it is not always initially clear which parameter values to use up front. Therefore, when annotating a newly sequenced genome, it is common to extract a subset of the genome on which to perform repeated optimization runs to determine the parameter values that should be used subsequently to annotate the entire genome. 2ff7e9595c
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