Quick start/testing

Installation

If you have (or can get) pip, running it on this repo is the easiest way to install the most recent version of intronIC (if you have multiple versions of Python installed, be sure to use the appropriate Python 3 version e.g. python3 instead of python in the following commands):

python -m pip install git+https://github.com/glarue/intronIC

Alternatively, you can get the last stable version published to PyPI:

python -m pip install intronIC

If successful, intronIC should now be callable from the command-line.

To upgrade to the latest version from a previous one, include --upgrade in either of the previous pip commands, e.g.

python -m pip install git+https://github.com/glarue/intronIC --upgrade

Otherwise, you can simply clone this repository to your local machine using git:

git clone https://github.com/glarue/intronIC.git
cd intronIC/intronIC

If you clone the repo, you may also wish to add intronIC/intronIC to your system PATH (how best to do this depends on your platform).

Dependencies

• Python >=3.3
• numpy & scipy
• scikit-learn >=0.20.1
• biogl
• matplotlib (optional, required for plotting)

To install dependencies separately using pip, do

python3 -m pip install numpy scipy matplotlib scikit-learn biogl

intronIC was built and tested on Linux, but should run on Windows or Mac OSes without too much trouble (I say that now...).

Useful arguments

The required arguments for any classification run include a name (-n; see note below), along with:

1. Genome (-g) and annotation/BED (-a, -b) files or,
2. Intron sequences file (-q) (see Training data and PWMS for formatting information, which matches the reference sequence format)

By default, intronIC includes non-canonical introns, and considers only the longest isoform of each gene. Helpful arguments may include:

• -p parallel processes, which can significantly reduce runtime

• -f cds use only CDS features to identify introns (by default, uses both CDS and exon features)

• --no_nc exclude introns with non-canonical (non-GT-AG/GC-AG/AT-AC) boundaries

• -i include introns from multiple isoforms of the same gene (default: longest isoform only)

Running on test dataset

• If you have installed via pip, first download the chromosome 19 FASTA and GFF3 sample files into a directory of your choice.

• If you have cloned the repo, first change to the /intronIC/intronIC/test_data subdirectory, which contains Ensembl annotations and sequence for chromosome 19 of the human genome. Replace intronIC with ../intronIC.py in the following examples.

Classify annotated introns

intronIC -g Homo_sapiens.Chr19.Ensembl_91.fa.gz -a Homo_sapiens.Chr19.Ensembl_91.gff3.gz -n homo_sapiens

The various output files contain different information about each intron; information can be cross-referenced by using the intron label (usually the first column of the file). U12-type introns are those (by default) with probability scores >90%, or equivalently (depending on the output file) relative scores >0. For example, here is an example U12-type AT-AC intron from the meta.iic file:

HomSap-gene:ENSG00000141837@transcript:ENST00000614285-intron_1(47);[c:-1]      10.0    AT-AC   GCC|ATATCCTTTT...TTTTCCTTAATT...AATAC|TCC       CACCTCCAACACCCTTCTTTTCTTTGAACAAGAT[TTTTCCTTAATT]CCCCAATAC       50719   transcript:ENST00000614285      gene:ENSG00000141837    1       47      3.9
     2       u12     cds

To retrieve all U12-type introns from this file, one can filter based on the relative score (2nd column; U12-type introns have relative scores >0), e.g.

awk '($2!="." && $2>0)' homo_sapiens.meta.iic

Extract all annotated intron sequences

If you just want to retrieve all annotated intron sequences (without classification), add the -s flag:

intronIC -g Homo_sapiens.Chr19.Ensembl_91.fa.gz -a Homo_sapiens.Chr19.Ensembl_91.gff3.gz -n homo_sapiens -s

See the rest of the Wiki for more extensive details about output files, usage info, etc.

A note on the -n (name) argument

By default, intronIC expects names in binomial (genus, species) form separated by a non-alphanumeric character, e.g. 'homo_sapiens', 'homo.sapiens', etc. intronIC then formats that name internally into a tag that it uses to label all output intron IDs, ignoring anything past the second non-alphanumeric character.

Output files, on the other hand, are named using the full name supplied via -n. If you'd prefer to have it leave whatever argument you supply to -n unmodified, use the --na flag.

If you are running multiple versions of the same species and would like to keep the same species abbreviations in the output intron data, simply add a tag to the end of the name, e.g. "homo_sapiens.v2"; the tags within files will be consistent ("HomSap"), but the file names across runs will be distinct.

Resource usage

For genomes with a large number of annotated introns, memory usage can be on the order of gigabytes. This should rarely be a problem even for most modern personal computers, however. For reference, the Ensembl 95 release of the human genome requires ~5 GB of memory.

For many non-model genomes, intronIC should run fairly quickly (e.g. tens of minutes). For human and other very well annotated genomes, runtime may be longer (the human Ensembl 95 release takes ~20-35 minutes in testing); run time scales relatively linearly with the total number of annotated introns, and can be improved by using parallel processes via -p.