metapredict 1.2

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metapredict: A machine learning-based tool for predicting protein disorder.

metapredict uses a bidirectional recurrent neural network trained on the consensus disorder values from 8 disorder predictors from 12 proteomes that were obtained from MobiDB. The creation of metapredict was made possible by parrot.

What is metapredict?

metapredict is a bit different than your typical protein disorder predictor. Instead of predicting the percent chance that a residue within a sequence might be disordered, metapredict tries to predict the consensus disorder score for the residue. This is because metapredict was trained on consensus values from MobiDB. These values are the percent of other disorder predictors that predicted a residue in a sequence to be disordered. For example, if a residue in a sequence has a value of 1 from the MobiDB consensus values, then all disorder predictors predicted that residue to be disordered. If the value was 0.5, than half of the predictors predicted that residue to be disordered. In this way, metapredict can help you quickly determine the likelihood that any given sequence is disordered by giving you an approximation of what other predictors would predict (things got pretty 'meta' there, hence the name metapredict).

Why is metapredict useful?

A major drawback of consensus disorder databases is that they can only give you values of previously predicted protein sequences. Therefore, if your sequence of interest is not in their database, tough luck. In addition, installing multiple different predictors to generate consensus scores locally is computationally expensive, time consuming, and in some cases simply not possible. Fortunately, metapredict gives you a way around this problem!

metapredict allows for predicting disorder for any amino acid sequence, and predictions can be output as graphs or as raw values. Additionally, metapredict allows for predicting disorder values for protein sequences from .fasta files either directly in Python or from the command-line. This gives maximum flexibility so the user can easily predict/graph disorder from a single sequence or for an entire proteome.

For full documentation, please see: https://metapredict.readthedocs.io/en/latest/getting_started.html

For disorder predictions using our server, please see: https://metapredict.net

Installation:

metapredict is available through PyPI - to install simply run

$ pip install metapredict

Alternatively, you can get metapredict directly from GitHub.

To clone the GitHub repository and gain the ability to modify a local copy of the code, run

$ git clone https://github.com/idptools/metapredict.git
$ cd metapredict
$ pip install .

This will install metapredict locally.

Usage:

There are two ways you can use metapredict:

1. Directly from the command-line
2. From within Python

Using metapredict from the command-line:

Predicting Disorder

The metapredict-predict-disorder command from the command line takes a .fasta file as input and returns disorder scores for the sequences in the FASTA file.

$ metapredict-predict-disorder <Path to .fasta file>

Example

$ metapredict-predict-disorder /Users/thisUser/Desktop/interestingProteins.fasta 

Additional Usage

Save the output - If you would like to save the ouptut, simply use the -o or --output-file flag and then specify the file path. By default this will save the output file as disorder.csv. However, you can specify the file name in the output path.

Example

$ metapredict-predict-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/disorder_predictions/my_disorder_predictions.csv

Quick predict

metapredict-quick-predict is a command that will let you input a sequence and get disorder values immediately printed to the terminal. The only argument that can be input is the sequence.

Example:

$ metapredict-quick-predict ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN

Graphing Disorder

The metapredict-graph-disorder command from the command line takes a .fasta file as input and returns a graph for every sequence within the .fasta file. Warning This will return a graph for every sequence in the FASTA file. These graphs will have to be closed sequentially. Therefore, it is not recommended to use this command without specifying an output directory specifying where to save the files.

$ metapredict-graph-disorder <Path to .fasta file> 

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta 

Additional Usage

Saving the output - To save the output, simply use the -o or --output-directory flag to specify where to save the file.

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/FolderForCoolPredictions

Changing resolution of saved graphs - By default, the output graphs have a DPI of 150. However, the user can change the DPI of the output (higher values have greater resolution but take up more space). To change the DPI simply add the flag -D or --dpi followed by the wanted DPI value.

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ -D 300

Changing the file type - By default the graphs will save as .png files. However, you can specify the file type by calling --filetype and then specifying the file type. Any matplotlib compatible file extension should work (for example, pdf).

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ --filetype pdf

Indexing file names - If you would like to index the file names with a leading unique integer starting at 1, use the --indexed-filenames flag.

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ --indexed-filenames

Changing the disorder threshhold line on the graph - If you would like to change the disorder threshold line plotted on the graph, use the --disorder-threshold flag followed by some value between 0 and 1. Default is 0.3.

Example

$ metapredict-graph-disorder /Users/thisUser/Desktop/interestingProteins.fasta -o /Users/thisUser/Desktop/DisorderGraphsFolder/ --disorder-threshold 0.5

Quick graph

metapredict-quick-graph is a command that will let you input a sequence and get a plot of the disorder back immediately. You cannot input fasta files for this command. The command only takes two arguments, 1. the sequence and 2. which is optional is the DPI -D or --dpi of the ouput graph which defaults to 150 DPI

Example:

$ metapredict-quick-graph ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN

Example:

$ metapredict-quick-graph ISQQMQAQPAMVKSQQQQQQQQQQHQHQQQQLQQQQQLQMSQQQVQQQGIYNNGTIAVAN -D 200

metapredict-uniprot

metapredict-uniprot is a command that will let you input any Uniprot ID and get a plot of the disorder for the corresponding protein. The default behavior is to have a plot automatically appear. Apart from the Uniprot ID which is required for this command, the command has four possible additional optional arguments, 1. DPI can be changed with the -D or --dpi flags, default is 150 DPI, 2. Using -o or --ourput-file will save the plot to a specified directory (default is current directory) - filenames and file extensions (pdf, jpg, png, etc) can be specified here. If there is no file name specified, it will save as the Uniprot ID and as a .png, 3. -t or --title will let you specify the title of the plot. By defualt the title will be Disorder for followed by the Uniprot ID.

Example:

$ metapredict-uniprot Q8RYC8

Example:

$ metapredict-uniprot Q8RYC8 -D 300

Example:

$ metapredict-uniprot Q8RYC8 -o /Users/ThisUser/Desktop/MyFolder/DisorderGraphs

Example:

$ metapredict-uniprot Q8RYC8 -o /Users/ThisUser/Desktop/MyFolder/DisorderGraphs/my_graph.png

Example:

$ metapredict-uniprot Q8RYC8 -t ARF19

Using metapredict in Python:

In addition to using metapredict from the command line, you can also use metapredict directly in Python.

First import metapredict -

import metapredict as meta

Once metapredict is imported you can work with individual sequences or .fasta files.

Predicting Disorder

The predict_disorder function will return a list of predicted disorder values for each residue of the input sequence. The input sequence should be a string. Running -

meta.predict_disorder("DSSPEAPAEPPKDVPHDWLYSYVFLTHHPADFLR")

would output -

[1, 1, 1, 1, 0.957, 0.934, 0.964, 0.891, 0.863, 0.855, 0.793, 0.719, 0.665, 0.638, 0.576, 0.536, 0.496, 0.482, 0.306, 0.152, 0.096, 0.088, 0.049, 0.097, 0.235, 0.317, 0.341, 0.377, 0.388, 0.412, 0.46, 0.47, 0.545, 0.428]

By default, output prediction values are normalized between 0 and 1. However, some of the raw values from the predictor are slightly less than 0 or slightly greater than 1. The negative values are simply replaced with 0 and the values greater than 1 are replaced with 1 by default. However, the user can get the raw prediction values by specifying normalized=False as a second argument in meta.predict_disorder. There is not a very good reason to do this, and it is generally not recommended. However, we wanted to give users the maximum amount of flexibility when using metapredict, so we made it an option.

meta.predict_disorder("DAPTSQEHTQAEDKERDSKTHPQKKQSPS", normalized=False)

Predicting Disorder Domains

The predict_disorder_domains function takes in an amino acid sequence and returns a 4-position tuple with: 0. the raw disorder scores from 0 to 1 where 1 is the highest probability that a residue is disordered, 1. the smoothed disorder score used for boundary identification, 2. a list of elements where each element is a list where 0 and 1 define the IDR location and 2 gives the actual sequence, and 3. a list of elements where each element is a list where 0 and 1 define the folded domain location and 2 gives the actual sequence

meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS")

would output -

[[0.828, 0.891, 0.885, 0.859, 0.815, 0.795, 0.773, 0.677, 0.66, 0.736, 0.733, 0.708, 0.66, 0.631, 0.601, 0.564, 0.532, 0.508, 0.495, 0.458, 0.383, 0.373, 0.398, 0.36, 0.205, 0.158, 0.135, 0.091, 0.09, 0.102, 0.126, 0.129, 0.114, 0.106, 0.097, 0.085, 0.099, 0.114, 0.093, 0.119, 0.117, 0.043, 0.015, 0.05, 0.139, 0.172, 0.144, 0.121, 0.124, 0.128, 0.147, 0.173, 0.129, 0.152, 0.169, 0.2, 0.172, 0.22, 0.216, 0.25, 0.272, 0.308, 0.248, 0.255, 0.301, 0.274, 0.264, 0.28, 0.25, 0.235, 0.221, 0.211, 0.235, 0.185, 0.14, 0.168, 0.307, 0.509, 0.544, 0.402], array([0.87596856, 0.86139124, 0.84596224, 0.82968293, 0.81255466,
   0.79457882, 0.77575677, 0.75608988, 0.73557951, 0.71422703,
   0.69203382, 0.66900124, 0.63956894, 0.62124099, 0.60188696,
   0.57893168, 0.55241615, 0.52131925, 0.4859528 , 0.44109689,
   0.39353789, 0.35264348, 0.31495776, 0.28      , 0.24661615,
   0.21469814, 0.18500621, 0.15963478, 0.13604845, 0.1172087 ,
   0.10798882, 0.1026882 , 0.09419503, 0.08462484, 0.08256398,
   0.08832671, 0.0908559 , 0.09263851, 0.09438758, 0.09309938,
   0.09102733, 0.09338137, 0.09665342, 0.10073913, 0.10392671,
   0.11010311, 0.11402981, 0.11898634, 0.12430683, 0.13169441,
   0.1381764 , 0.15245093, 0.16746957, 0.17518385, 0.18167578,
   0.18893043, 0.20013416, 0.21581491, 0.23015652, 0.2420559 ,
   0.25209814, 0.25817391, 0.26588944, 0.27456894, 0.27429068,
   0.26411925, 0.24452671, 0.23076894, 0.22834783, 0.21689842,
   0.20887549, 0.20564427, 0.20856996, 0.21901779, 0.23835296,
   0.26794071, 0.30914625, 0.36333478, 0.43187154, 0.51612174]), [[0, 20, 'MKAPSNGFLPSSNEGEKKPI']], [[20, 80, 'NSQLWHACAGPLVSLPPVGSLVVYFPQGHSEQVAASMQKQTDFIPNYPNLPSKLICLLHS']]]

Additional Usage

Altering the disorder theshhold - To alter the disorder theshold, simply set disorder_threshold=my_value where my_value is a float. The higher the treshold value, the more conservative metapredict will be for designating a region as disordered. Default = 0.42

Example

meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", disorder_threshold=0.3)

Altering minimum IDR size - The minimum IDR size will define the smallest possible region that could be considered an IDR. In other words, you will not be able to get back an IDR smaller than the defined size. Default is 12.

Example

meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_IDR_size = 10)

Altering the minimum folded domain size - The minimum folded domain size defines where we expect the limit of small folded domains to be. NOTE this is not a hard limit and functions more to modulate the removal of large gaps. In other words, gaps less than this size are treated less strictly. Note that, in addition, gaps < 35 are evaluated with a threshold of 0.35 x disorder_threshold and gaps < 20 are evaluated with a threshold of 0.25 x disorder_threshold. These two lengthscales were decided based on the fact that coiled-coiled regions (which are IDRs in isolation) often show up with reduced apparent disorder within IDRs but can be as short as 20-30 residues. The folded_domain_threshold is used based on the idea that it allows a 'shortest reasonable' folded domain to be identified. Default=50.

Example

meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", minimum_folded_domain = 60)

Altering gap_closure - The gap closure defines the largest gap that would be closed. Gaps here refer to a scenario in which you have two groups of disordered residues seprated by a 'gap' of not disordered residues. In general large gap sizes will favour larger contigous IDRs. It's worth noting that gap_closure becomes relevant only when minimum_region_size becomes very small (i.e. < 5) because really gaps emerge when the smoothed disorder fit is "noisy", but when smoothed gaps are increasingly rare. Default=10.

Example

meta.predict_disorder_domains("MKAPSNGFLPSSNEGEKKPINSQLWHACAGPLV", gap_closure = 5)

Predicting Disorder Domains using a Uniprot ID

In addition to inputting a sequence, you can predict disorder domains by inputting a Uniprot ID by using the predict_disorder_domains_uniprot function. This function has the exact same functionality as predict_disorder_domains except you can now input a Uniprot ID.

Example

meta.predict_disorder_domains_uniprot('Q8N6T3')

Graphing Disorder

The graph_disorder function will show a plot of the predicted disorder consensus values across the input amino acid sequence.

meta.graph_disorder("DAPTSQEHTQAEDKERDSKTHPQKKQSPS")

Additional Usage

Changing the title of the generated graph - There are two parameters that the user can change for graph_disorder. The first is the name of the title for the generated graph. The name by default is blank and the title of the graph is simply Predicted protein disorder. However, the title can be specified by specifing title = "my cool title" would result in a title of my cool title.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERD", title="Name of this nonexistant protein")

Changing the resolution of the generated graph - By default, the output graph has a DPI of 150. However, the user can change the DPI of the generated graph (higher values have greater resolution). To do so, simply specify DPI="Number" where the number is an integer.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERD", DPI=300)

Changing the disorder threshold line - The disorder threshold line for graphs defaults to 0.3. However, if you want to change where the line designating the disorder cutoff is, simply specify disorder_threshold = Float where Float is some decimal value between 0 and 1.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERD", disorder_threshold=0.5)

Adding shaded regions to the graph - If you would like to shade specific regions of your generated graph (perhaps shade the disordered regions), you can specify shaded_regions=[[list of regions]] where the list of regions is a list of lists that defines the regions to shade.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERD", shaded_regions=[[1, 20], [30, 40]])

In addition, you can specify the color of the shaded regions by specifying shaded_region_color. The default for this is red. You can specify any matplotlib color or a hex color string.

Example

meta.graph_disorder("DAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERDDAPPTSQEHTQAEDKERD", shaded_regions=[[1, 20], [30, 40]], shaded_region_color="blue")

Saving the graph - By default, the graph will automatically appear. However, you can also save the graph if you'd like. To do this, simply specify output_file = path_where_to_save/filename.file_extension. For example, output_file=/Users/thisUser/Desktop/cool_graphs/myCoolGraph.png. You can save the file with any valid matplotlib extension (.png, .pdf, etc.).

Example

meta.graph_disorder("DAPPTSQEHTQAEDKER", output_file=/Users/thisUser/Desktop/cool_graphs/myCoolGraph.png)

Calculating Percent Disorder

The percent_disorder function will return the percent of residues in a sequence that have predicted consensus disorder values of 30% or more (as a decimal value).

Example

meta.percent_disorder("DAPPTSQEHTQAEDKERD")

By default, this function uses a cutoff value of equal to or greater than 0.3 for a residue to be considered disordered.

Additional Usage

Changing the cutoff value - If you want to be more strict in what you consider to be disordered for calculating percent disorder of an input sequence, you can simply specify the cutoff value by adding the argument cutoff=decimal where the decimal corresponds to the percent you would like to use as the cutoff (for example, 0.8 would be 80%).

Example

meta.percent_disorder("DAPPTSQEHTQAEDKERD", cutoff=0.8)

The higher the cutoff value, the higher the value any given predicted residue must be greater than or equal to in order to be considered disordered when calculating the final percent disorder for the input sequence.

Predicting Disorder From a .fasta File

By using the predict_disorder_fasta function, you can predict disorder values for the amino acid sequences in a .fasta file. By default, this function will return a dictionary where the keys in the dictionary are the fasta headers and the values are the consensus disorder predictions of the amino acid sequence associated with each fasta header in the original .fasta file.

Example

meta.predict_disorder_fasta("file path to .fasta file/fileName.fasta")

An actual filepath would look something like:

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta")

Additional Usage

Save the output values - By default the predict_disorder_fasta function will immediately return a dictionary. However, you can also save the output to a .csv file by specifying output_file = "location you want to save the file to". When specifying the file path, you also want to specify the file name. The first cell of each row will contain a fasta header and the subsequent cells in that row will contain predicted consensus disorder values for the protein associated with the fasta header.

Example

meta.predict_disorder_fasta("file path to .fasta file/fileName.fasta", output_file="file path where the output .csv should be saved")

An actual filepath would look something like:

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_file="/Users/thisUser/Desktop/cool_predictions.csv")

Get raw prediction values - By default, this function will output prediction values that are normalized between 0 and 1. However, some of the raw values from the predictor are slightly less than 0 or slightly greater than 1. The negative values are simply replaced with 0 and the values greater than 1 are replaced with 1 by default. If you want the raw values simply specify normalized=False. There is not a very good reason to do this, and it is generally not recommended. However, we wanted to give users the maximum amount of flexibility when using metapredict, so we made it an option.

Example

meta.predict_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", normalized=False)

Predict Disorder Using Uniprot ID

By using the predict_disorder_uniprot function, you can return predicted consensus disorder values for the amino acid sequence of a protein by specifying the Uniprot ID.

Example

meta.predict_disorder_uniprot("Q8N6T3")

Generating Graphs From a .fasta File

By using the graph_disorder_fasta function, you can graph predicted consensus disorder values for the amino acid sequences in a .fasta file. The graph_disorder_fasta function takes a .fasta file as input and by default will return the graphs immediately. However, you can specify output_dir=path_to_save_files which result in a a .png file saved to that directory for every sequence within the .fasta file. You cannot specify the output file name here! By default, the file name will be the first 14 characters of the FASTA header followed by the filetype as specified by filetype. If you wish for the files to include a unique leading number (i.e. X_rest_of_name where X starts at 1 and increments) then set indexed_filenames = True. This can be useful if you have sequences where the 1st 14 characters may be identical, which would otherwise overwrite an output file. By default this will return a single graph for every sequence in the FASTA file.

WARNING - This command will generate a graph for every sequence in the .fasta file. If you have 1,000 sequences in a .fasta file and you do not specify the output_dir, it will generate 1,000 graphs that you will have to close sequentially. Therefore, I recommend specifying the output_dir such that the output is saved to a dedicated folder.

Example

meta.graph_disorder_fasta("file path to .fasta file/fileName.fasta", output_dir="file path of where to save output graphs")

An actual filepath would look something like:

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs")

Additional Usage

Changing resolution of saved graphs - By default, the output files have a DPI of 150. However, the user can change the DPI of the output files (higher values have greater resolution but take up more space). To change the DPI, specify DPI=Number where Number is an integer.

Example

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", DPI=300, output_dir="/Users/thisUser/Desktop/folderForGraphs")

Changing the output File Type - By default ths output file is a .png. However, you can specify the output file type by using output_filetype="file_type" where file_type is some matplotlib compatible file type (such as .pdf).

Example

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs", output_filetype = "pdf")

Indexing generated files - If you would like to index the file names with a leading unique integer starting at 1, set indexed_filenames=True.

Example

meta.graph_disorder_fasta("/Users/thisUser/Desktop/coolSequences.fasta", output_dir="/Users/thisUser/Desktop/folderForGraphs", indexed_filenames=True)

Generating Graphs Using Uniprot ID

By using the graph_disorder_uniprot function, you can graph predicted consensus disorder values for the amino acid sequence of a protein by specifying the Uniprot ID.

Example

meta.graph_disorder_uniprot("Q8N6T3")

This function carries all of the same functionality as graph_disorder including specifying disorder_threshold, title of the graph, the DPI, and whether or not to save the output.

Example

meta.graph_disorder_uniprot("Q8N6T3", disorder_threshold=0.5, title="my protein", DPI=300, output_file="/Users/thisUser/Desktop/my_cool_graph.png")

metapredict isn't working!

I have recieved occassional feedback that metapredict is not working for a user. A common problem is that the user is using a different version of Python than metapredict was made on. metapredict was made using Python version 3.7, and I recommend using this version while using metapredict to avoid problems (I haven't done extensive testing using other versions of Python, so if you're not using 3.7, do so at your own risk). A convenient workaround is to use a conda environment that has Python 3.7 set as the default version of Python. For more info on conda, please see https://docs.conda.io/projects/conda/en/latest/index.html

Once you have conda installed, simply use the command

conda create --name my_env python=3.7

where you can replace the name of your environment with whatever you'd like. Then, use metapredict from within this conda environment.

If you are having other problems, please report them to the issues section on the metapredict Github page at https://github.com/idptools/metapredict/issues

Known Installation/Execution Issues

Below we include documentation on known issues.

macOS libiomp clash

PyTorch current ships with its own version of the OpenMP library (libiomp.dylib). Unfortunately when numpy is installed from conda (although not from pip) this leads to a collision because the conda-derived numpy library also includes a local copy of the libiomp5.dylib library. This leads to the following error message (included here for google-ability).

OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized. OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.

To avoid this error we make the executive decision to ignore this clash. This has largely not appeared to have any deleterious issues on performance or accuracy accross the tests run. If you are uncomfortable with this then the code in metapredict/__init__.py can be edited with IGNORE_LIBOMP_ERROR set to False and metapredict re-installed from the source directory.

Testing

To see if your installation of metapredict is working properly, you can run the unit test included in the package by navigating to the metapredict/tests folder within the installation directory and

running:

$ pytest -v

Example Datasets

Example data that can be used with metapredict can be found in the metapredict/data folder on GitHub. The example data set is just a .fasta file containing 5 protein sequences.

Recent changes

This section is a log of recent changes with metapredict. My hope is that as I change things, this section can help you figure out why a change was made and if it will break any of your current work flows. The first major changes were made for the 0.56 release, so tracking will start there. Reasons are not provided for bug fixes for because the reason can assumed to be fixing the bug...

V1.2

Change: Major update. Changed some basic functionality. Made it such that you don't need to specify to save (for disorder prediction values or graphs). Rather, if a file path is specified, the files will be saved. Updated graphing functionality to allow for specifying the disorder cutoff line and to allow users to highlight various regions of the graph. Changed import such that you can now just use import metapredict as meta in Python (as opposed to import metapredict and then from metapredict import meta). Lots of backend changes to make metapredict more stable. Added additional testing. Updated documentation. Standardized file reading/writing. Made it so user can specify file type of saved graphs. Added backend meta_tools.py to handle the busywork. Changed version numbering for networks. Updated code to avoid OMPLIB issue (known bug in previous versions). Updated all command-line tools to match backend code.

V1.1

Change: Fixed some bugs.

V1.0

Change: Added functionality to generate graphs using a Uniprot ID as the input from command line. Added functionality to predict disorder domains. Added functionality to predict/graph disorder and predict disorder domains using a Uniprot ID from Python. Updated tests to include testing new functionality.

V0.61

Change: Added functionality to predict or graph a disordered sequence from the command line by directly inputting the sequence. This can only do one sequence at a time and does not save the disorder values or graph. It is meant to provide a very quick and easy way to check something out.

V0.60

Change: Added functionality to specify the horizontal lines that appear across the graphs rather than only having the option of having the dashed lines appear at intervals of 0.2. This functionality is in both Python and the command line.

V0.58

Change: Updated the network with a newly trained network (using the same dataset as the original) that is slightly more accurate.

Reason: I am always trying to find ways to make metapredict more accurate. When I manage to make the predictor better, I will update it.

V0.57

Change: Bug fix that could result in prediction values to six decimal places in some scenarios

Change: Changed titles for graphs generated by metapredict-graph-disorder to be 14 characters instead of 10. This is reflected in the title graph and the saved files.

Reason: The 10 character save file was occasionally the same for multiple proteins. This resulted in the inability to discern which protein corresponded to which graph and could result in overwriting previously generated graphs. The 14 characters should be long enough to keep unique names for all proteins being analyzed.

Change: Fixed bug that could result in crashing due to short fasta headers.

V0.56

Change: Number of decimals in predictions was reduced from 6 to 3.

Reason: It is not necessary to have accuracy out to 6 decimal places.

Change: Added functionality to use . to specify current directory from command line.

Reason: Improve functionality.

Change: -DPI flag changed to -dpi in command line graphing function

Reason: It was annoying to have to do all caps for this flag.

Change: The predict-disorder command is now metapredict-predict-disorder and the graph-disorder command is now metapredict-graph-disorder

Reason: This will help users be able to use auto complete functionality from the command line using tab to pull up the graph or predict disorder commands while only having to remember metapredict.

Change: The output for .csv files will now have a comma space between each value instead of just a comma.

Reason: Improve readability.

Copyright

Copyright (c) 2020-2021, Holehouse Lab - WUSM

Acknowledgements

IDP-Parrot, created by Dan Griffith, was used to generate the network used for metapredict. See https://pypi.org/project/idptools-parrot/ for some very cool machine learning stuff.

In addition to using Dan Griffith's tool for creating metapredict, the code for brnn_architecture.py and encode_sequence.py was written by Dan (originally for idp-parrot).

I would also like to thank the team at MobiDB for creating the database that was used to train this predictor. Check out their awesome stuff at https://mobidb.bio.unipd.it

Project based on the Computational Molecular Science Python Cookiecutter version 1.3.