Keygen Php Maker Tutorial
Mina Delahoussaye
This is the original publication for the MAKER2 gene prediction pipeline. This version of maker includes the addition of multiple gene ab-initio prediction tools as well as AED score support for gene models.MAKER2 Publication
Is there a specific order to run different predictors in Maker?While there are multiple ways to run Maker, all tips and tutorials that I have run across have always done the maker annotation first, and then incorporated the other ab-initio predictors subsequently. This leads us to the first step of finding all transcriptional and protein resources for the initial MAKER prediction.
Plants are notoriously hard annotation targets. Plant genomes are commonly large and highly repetitive; they contain a large number of pseudogenes, and novel protein coding and non-coding genes. To address these challenges we optimized MAKER's performance on large computing clusters such at TACC, developed tutorials for custom repeat library generation, provide a pseudogene identification protocol for use with standard MAKER outputs, and incorporated non-coding RNA annotation capabilities into MAKER.
Many of the interesting genomes we are currently sequencing as a genomics community are not being sequenced because of their similarities to previously sequenced genomes but because of their dis-similarities. These phylogenetically distant organisms not only present unique protein coding genes but also a multitude of previously unseen repetitive elements. For the best annotation results a species specific repeat library should be used in masking the genome prior to annotation. We have provided basic Repeat Library Construction--Basic and advanced Repeat Library Construction--Advanced tutorials for creating these libraries. A pipeline that automates this process is currently in development.
tRNAscan-SE and snoscan are now integrated into the MAKER framework. Annotating tRNAs is now as simple as setting a single option in the maker_opts.ctl file. tRNAscan-SE runs quickly and accurately. Annotating snoRNAs requires the user to pass a file containing annotated rRNAs for the organism of interest in fasta format to MAKER through the maker_opts.ctl file. 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. miR-PREFeR was developed for miRNA annotation as part of the MAKER tool kit and has yet to be incorporated into the MAKER framework. At this time miR-PREFeR is run as a stand-alone tool and the output can be passed to MAKER in the maker_opts.ctl as 'other_gff=' for inclusion in the final gff3 file.
When you install, MAKER it comes with some example input files to test the installation and to familiarize the user with how to run the pipline. The example files are found in the .../maker/data directory.
I've already copied these data files into the /maker/tutorial/example_01_basic directory for you, but if you're following this tutorial outside of the course you can run directly inside the data directory to follow the first example or copy the files into a directory of your choice:
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. However if the location to any of the executables is not set in your PATH environment variable, as per installation instructions, you will have to add these manually to the maker_exe.ctl file every time you run MAKER.
This is the primary configuration file for MAKER specific options. 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. If you are following this in class you can replace the maker_opts.ctl file with the opts.txt which is has options pre-filled for you. 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:
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. 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:1 in the maker_opt.ctl configuration file.
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. You can turn this behavior off though if it bothers you by setting softmask=0 in the maker_bopt.ctl file.
If you look in the current working directory, you will see that MAKER has created an output directory called dpp_contig.maker.output. The name of the output directory is based on the input genomic sequence file, which in this case was dpp_contig.fasta.
JBrowse ia convenient way to view and distribute MAKER GFF3 output, and it comes with a simple script called maker2jbrowse that makes loading MAKER's output into JBrowse extremely easy. I will demonstrate how to load a GFF3 into JBrowse, but for all the examples we do today, I've already provided links for viewing the results. So loading additional data into JBrowse will be an exercise left to the user outside of this tutorial. Below is a link to example 1.1 output loaded into JBrowse.
Many of the steps used by MAKER can be computationally demanding, so MAKER will most commonly be run using MPI. However because of limited resources available for a large group this will be the only exercise where we actually run MAKER with MPI during the tutorial. We already covered briefly how to install MAKER with MPI support, and to load the currently installed MPI configuration for MAKER on the class servers you will need to load a couple of modules.
MAKER is now configured to generate annotations from the EST and aligned protein data, so start the program (this normally takes about 20 min to run because it's a long contig). But we are going to unpack the partial.tgz file before running maker to get some of our raw data precomputed and give it that nice "cooking show" feel (... or not it's your choice). We will use the -base command line flag to affect the output directory so we can run multiple ways and preserve output in separate directories (otherwise MAKER will overwrite to the same directory).
Here we have our MAKER output GFF3 and FASTA files for proteins and transcripts (Click to see GFF3 in JBrowse). We also have output reports from the program InterProScan (a domain finder) ran on the MAKER proteins and a BLAST report of homology of the MAKER proteins to UniProt/Swiss-Prot. How to run these programs is not part of this tutorial, but how to integrate their output is.
Here's my story. I had no experience in any programming or game development, but I am passionately driven by my creativity. I bought VX Ace on sale and started following a tutorial, learning all the basics as I went. I started on a dream project, but other things got in the way and the sands of time wore down my knowledge of the software.
Is there a good tutorial that covers everything without going into unnecessary specifics, what-ifs and lengthy examples? Where every episode is heavily planned and scripted, rather than improvised and breaking off into tangents?
In this tutorial you will learn how to perform a genome annotation, and how to evaluate its quality. You will see how training ab-initio predictors is an important step to produce good results. Finally, you will learn how to use the JBrowse genome browser to visualise the results.
This is the extended version. We will perform the complete training of ab-initio predictors and discuss the results in detail.If you would like to run through the tutorial a bit quicker and focus on the mainanalysis steps, please see the shorter version of this tutorial
Just as we did for the genome at the beginning, we can use BUSCO to check the quality of this first Maker annotation. Instead of looking for known genes in the genome sequence, BUSCO will inspect the transcript sequences of the genes predicted by Maker. This will allow us to see if Maker was able to properly identify the set of genes that Busco found in the genome sequence at the beginning of this tutorial.
Usually no more than two rounds of training is needed to get the best results from the ab-initio predictors. You can try to retrain Augustus and SNAP, but you will probably notice very few changes. We will keep the final annotation we obtained for the rest of this tutorial.
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