intronIC is a program that can be used to classify intron sequences as minor (U12-type) or major (U2-type), using a genome and annotation or the sequences themselves. Alternatively,
intronIC can simply extract all intron sequences without classification (using
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 in the following commands):
python3 -m pip install git+https://github.com/glarue/intronIC
Alternatively, you can get the last stable version published to PyPI:
python3 -m pip install intronIC
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.
python3 -m pip install git+https://github.com/glarue/intronIC --upgrade
Otherwise, you can simply clone this repository to your local machine using
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).
See the wiki for more detail information about configuration/run options.
To install dependencies separately using
python3 -m pip install numpy scipy matplotlib 'scikit-learn>=0.22' biogl
intronIC was built and tested on Linux, but should run on Windows or Mac OSes without too much trouble (I say that now...).
The required arguments for any classification run include a name (
-n; see note below), along with:
- Genome (
-g) and annotation/BED (
-b) files or,
- Intron sequences file (
-q) (see Training-data-and-PWMs for formatting information, which matches the reference sequence format)
intronIC includes non-canonical introns, and considers only the longest isoform of each gene. Helpful arguments may include:
-pparallel processes, which can significantly reduce runtime
-f cdsuse only
CDSfeatures to identify introns (by default, uses both
--no_ncexclude introns with non-canonical (non-
-iinclude introns from multiple isoforms of the same gene (default: longest isoform only)
Running on test data
- 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_datasubdirectory, which contains Ensembl annotations and sequence for chromosome 19 of the human genome. Replace
../intronIC.pyin 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
HomSap-gene:[email protected]: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
intronIC -g Homo_sapiens.Chr19.Ensembl_91.fa.gz -a Homo_sapiens.Chr19.Ensembl_91.gff3.gz -n homo_sapiens -s
A note on the
-n (name) argument
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
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.
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
See the rest of the wiki for more detailed instructions.
If you find this tool useful, please cite:
Devlin C Moyer, Graham E Larue, Courtney E Hershberger, Scott W Roy, Richard A Padgett, Comprehensive database and evolutionary dynamics of U12-type introns, Nucleic Acids Research, Volume 48, Issue 13, 27 July 2020, Pages 7066–7078, https://doi.org/10.1093/nar/gkaa464
intronIC was written to provide a customizable, open-source method for identifying minor (U12-type) spliceosomal introns from annotated intron sequences. Minor introns usually represent ~0.5% (at most) of a given genome's introns, and contain distinct splicing motifs which make them amenable to bioinformatic identification.
Earlier minor intron resources (U12DB, SpliceRack, ERISdb, etc.), while important contributions to the field, are static by design. As such, these databases fail to reflect the dramatic increase in available genome sequences and annotation quality of the last decade.
In addition, other published identification methods employ a certain amount of heuristic fuzziness in defining the classification criteria of their U12-type scoring systems (i.e how "U12-like" does an intron need to look before being called a U12-type intron).
intronIC relegates this decision to the well-established support-vector machine (SVM) classification method, which produces an easy-to-interpret "probability of being U12-type" score for each intron.
intronIC provides researchers the opportunity to tailor the underlying training data/position-weight matrices, should they have species-specific data to take advantage of.
intronIC performs a fair amount of bookkeping during the intron collection process, resulting in (potentially) useful metadata about each intron including parent gene/transcript, ordinal index and phase, information which (as far as I'm aware) is otherwise somewhat non-trivial to acquire.