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A pipeline that automates this process is currently in development. The current pseudogene pipeline utilized by MAKER defines pseudogenes as intergenic sequences with significant resemblance to annotated proteins in that genome. This protocol can be adapted to find pseudogenes without similarity to protein coding genes in the organism but similar to genes in closely related species by modifying the input sequences to the pipeline.

Currently all snoscan annotations are being promoted to the final annotation set. To increase specificity and overall accuracy, a filter based on AED will soon be implemented. Navigate your browser to:.

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Let's take a look at this. Then you can follow the detailed install instructions in the file. Before we begin with example data I want everyone to note that there are finished examples are located in example data folder, so if you fall behind you can always find MAKER control files datasets and final results in there. The directory contains much of the final results for the example think of it as the pre-baked food in a cooking show , the finished.

Each of the other examples will contain a similar results directory and control file so if you fall behind or are impatient, then you can easily jump forward to the results. The example files are found in the Let's take a look at one of theses files to see what the format looks like. FASTA format is fairly simple. The file we are looking at contains protein sequences, so the sequence uses the single letter code for amino acids. Because there can be many variables and options involved in annotation, command line options would be too numerous and cumbersome. You can create a set of generic configuration files in the current working directory by typing the following.

Control files are run-specific and a separate set of control files will need to be generated for each genome annotated with MAKER. MAKER will look for control files in the current working directory, so it is recommended that MAKER be run in a separate directory containing unique control files for each genome. You will see the names of a number of MAKER supported executables as well as the path to their location. If you followed the installation instructions correctly, including the instructions for installing prerequisite programs, all executable paths should show up automatically for you.

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If the value is a file name, you can use relative paths and environment variables, i. Note that for all control files the comments written to help users begin with a pound sign. We can just leave the remaining values as the default. Here we need to set the location of the genome, EST, and protein input files we will be using. These come from the supplied example files. There are a lot of options in this file, and we'll discuss many of them in more detail later on in other examples.

Below are the options we adjust with a text editor:. You should now see a large amount of status information flowing past your screen. Why do we need to do this? Repetitive elements can make up a significant portion of the genome. These repeats fall into to basic classes:. The low information content of the low complexity repeats sequence can produce sequence alignments with high statistical significance to low-complexity protein regions creating a false homology think evidence for genes throughout the genome.

Because these complex repeats contain real protein coding genes they play havoc with ab initio gene predictors. For example, a transposable element that occurs within the intron of one of the organism's own protein encoding genes might cause a gene predictor to include extra exons as part of this gene. Thus, sequence which really only belongs to a transposable element is included in your final gene annotation set. Analysis of the repeat structure of a new genome is an important goal, but the presence of those repeats both simple and complex makes it nearly impossible to generate a useful annotation set of the organisms own genes.

For this reason it is critical to identify and mask these repetitive regions of the genome. Masking sequence from the annotation pipeline especially hard masking may seem like it might cause us to lose real protein coding genes that are important for the organism's biology. It is true that repeat derived genes can be co-opted and expressed by the organism and repeat masking will affect our ability to annotate these genes. However, these genes are rare and the number of gene models and sequence alignments improved by the repeat masking step far outweighs the few gene models that may be negatively affected.

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You do have the option to run ab initio gene predictors on both the masked and unmasked sequence if repeat masking worries you though. You do this by setting unmask: Following repeat masking, MAKER runs ab initio gene predictors specified by the user to produce preliminary gene models. Because the patterns of gene structure are going to differ from organism to organism, you must train gene predictors before you can use them.

I will discuss how to do this later on. A simple way to indicate if a sequence region is likely associated with a gene is to identify A if the region is actively being transcribed or B if the region has homology to a known protein.

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Remember now that we are aligning against the repeat-masked genomic sequence. How is this going to affect our alignments? For one thing we won't be able to align against low-complexity regions. Some real proteins contain low-complexity regions and it would be nice to identify those, but if I let anything align to a low-complexity region, then I will get spurious alignments all over the genome.

Wouldn't it be nice if there was a way to allow BLAST to extend alignments through low-complexity regions, but only if there is is already alignment somewhere else? You can do this with soft-masking. If you remember soft-masking is using lower case letters to mask sequence without losing the sequence information.

BLAST allows you to use soft-masking to keep alignments from seeding in low-complexity regions, but allows you to extend through them. This of course will allow some of the spurious alignments you were trying to avoid, but overall you still end up suppressing the majority of poor alignments while letting through enough real alignments to justify the cost. BLAST will align regions any where it can, even if the algorithm aligns regions out of order, with multiple overlapping alignments in the exact same region, or with slight overhangs around splice sites.

Exonerate realigns each sequences identified by BLAST around splice sites and forces the alignments to occur in order. Polished alignments are produced using the est2genome and protein2genome options for Exonerate. Because of amplification steps involved in building an EST library and limitations involved in some high throughput sequencing technologies, you don't necessarily know whether you're really aligning the forward or reverse transcript of an mRNA.

However, if you take splice sites into account, you can only align to one strand correctly. Once you have ab initio predictions, EST alignments, and protein alignments you can integrate this evidence to produce even better gene predictions. MAKER does this by communicating with the gene prediction programs.

1 December, 2018

MAKER takes all the evidence, generates "hints" to where splice sites and protein coding regions are located, and then passes these "hints" to programs that will accept them. MAKER then takes the entire pool of ab initio and evidence informed gene predictions, updates features such as 5' and 3' UTRs based on EST evidence, tries to determine alternative splice forms where EST data permits, produces quality control metrics for each gene model this is included in the output , and then MAKER chooses from among all the gene model possibilities the one that best matches the evidence.

There are only entries describing a single contig because there was only one contig in the example file. Other possible entries include:. Knowing where the output is stored may seem trivial, however, input genome fasta files can contain thousands even hundreds-of-thousands of contigs, and many file-systems have performance problems with large numbers of sub-directories and files within a single directory.

Even when the underlying file-system handles things gracefully, access via network file-systems can still be an issue. To deal with this problem, MAKER creates a hierarchy of nested sub-directory layers, starting from a 'base', and places the results for a given contig within these datastore of possibly thousands of nested directories. Now you will se a number of new files that represent the merged output for the entire assembly in this case the assembly only contained a single contig though.

While you can identify individual features such as genes, mRNAs, and exons, trying to interpret those features in the context of thousands of other genes and thousands of bases of sequence really can't be done by directly looking at the GFF3 file. For sanity check purposes it would be nice to have a graphical view of what's in the GFF3 file. I'm not going to explain how to setup JBrowse that's for another course. So instead here is a link to example 1 output loaded into JBrowse.

Click to see example 1 in JBrowse. The remainder of this page addresses issues that can be encountered during the annotation process. If you are involved in a genome project for an emerging model organism, you should already have an EST database, or more likely now mRANA-Seq data, which would have been generated as part of the original sequencing project. However a trained ab initio gene predictor is a much more difficult thing to generate. Gene predictors require existing gene models on which to base prediction parameters.

However, with emerging model organisms you are not likely to have any pre-existing gene models. So how then are you supposed to train your gene prediction programs? You can then use these imperfect gene models to train gene predictor program. Once you have re-run MAKER with the newly trained gene predictor, you can use the second set of gene annotations to train the gene predictors yet again.

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This boot-strap process allows you to iteratively improve the performance of ab initio gene predictors.