We present a tool, keep me around (kma), a suite of python scripts and an R
package that finds retained introns in RNA-Seq experiments and incorporates
biological replicates to reduce the number of false positives when detecting
retention events. kma uses the results of existing quantification tools that
probabilistically assign multi-mapping reads, thus interfacing easily with
transcript quantification pipelines. The data is represented in a convenient,
database style format that allows for easy aggregation across introns, genes,
samples, and conditions to allow for further exploratory analysis

Conboy, John G.

Pachter, Lior

Pimentel, Harold

English

arXiv

We present a tool, keepme around (kma), a suite of python scripts and an R package that finds retained introns in RNA-Seq experiments and incorporates biological replicates to reduce the number of false positives when detecting retention events. kma uses the results of existing quantification tools that probabilistically assign multi-mapping reads, thus interfacing easily with transcript quantification pipelines. The data is represented in a convenient, database style format that allows for easy aggregation across introns, genes, samples, and conditions to allow for further exploratory analysis

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Keep Me Around: Intron Retention Detection and Analysis
Harold Pimentel1, John G. Conboy2, and Lior Pachter∗3
1Department of Computer Science, UC Berkeley.
2Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory.
3Departments of Computer Science, Mathematics and Molecular & Cell Biology, UC
Berkeley.
October 5, 2015
Abstract
Summary: We present a tool, keepme around (kma), a suite of python scripts and an R package that
finds retained introns in RNA-Seq experiments and incorporates biological replicates to reduce the number
of false positives when detecting retention events. kma uses the results of existing quantification tools
that probabilistically assign multi-mapping reads, thus interfacing easily with transcript quantification
pipelines. The data is represented in a convenient, database style format that allows for easy aggregation
across introns, genes, samples, and conditions to allow for further exploratory analysis.
Availability: The source code is available under the GPLv2 license and can be found at:
http://github.com/pachterlab/kma
Contact: lpachter@math.berkeley.edu
1 Motivation
Many organisms exhibit intron retention events that can be measured with RNA-Seq [Burgess, 2014, Braunschweig et al.,
2014], and recent publications suggest that these events are important constituents of transcriptome regu-
lation. While some existing tools can detect intron retention events [Katz et al., 2010, Anders et al., 2012,
Bai et al., 2015], none that we are aware of incorporate biological replicates to reduce the reporting of
false-positives. Other tools have been mentioned in the literature, but do not have freely available software
[Khodor et al., 2011, Wong et al., 2013, Braunschweig et al., 2014, Boutz et al., 2015]. There is therefore
a need for a robust intron retention detection method that is based on rigorous quantification of intron
retention followed by assessment of significance using biological replicates.
We present keep me around (kma), a set of tools for detecting intron retention in RNA-Seq experiments
that utilizes biological replicates to improve accuracy. kma currently uses the transcript quantification method
eXpress [Roberts and Pachter, 2013], but is compatible with with any RNA-Seq quantification pipeline.
2 Implementation
kma begins by performing a pre-processing step consisting of several python scripts that find “measurable”
intronic regions called inclusion regions (regions in which none of the overlapping isoforms contain an exon),
together with the corresponding isoforms which could retain the intron called overlap isoforms. kma then
outputs a table of intron-transcript relationships. This table includes the (1) intron coordinates, (2) intron
quantification coordinates (3) transcripts which could potentially retain the intron, and (4) the gene name.
The intron quantification coordinates differ from the exact intron coordinates by including a small region of
the neighboring exons which is several bases shorter than the read length (Figure 1). This exonic overlap
ensures that the reads spanning the intron-exon junctions are included into the intron expression. These
∗lpachter@math.berkeley.edu
1
Figure 1: Example of intron retention in orthochromatic erythroblasts from [Pimentel et al., 2014] in gene
SF3B1. The “Intron” track shows the regions of included pseudo-transcripts and that each pseudo-transcript
overlaps neighboring exons by 25 bases. The coverage track shows that the first few introns are covered by
very few reads, but intron 4 (chr2:198283675-198185151) shows intron retention in all replicates.
reads are often valuable information; if they are unique, they give strong evidence for the expression of the
intron. kma also outputs a BED track containing intronic quantification coordinates, as well as a FASTA
file containing the intronic sequences to quantify against. This pre-processing step only has to be performed
once assuming the transcriptome annotation does not change and read size is at least a few bases longer
than exon overlap.
kma is designed to leverage existing transcript quantification methods. This allows for the computation
of relative abundance of introns as well as transcripts while allowing multi-mapping reads to be processed
using well understood models already developed in existing tools [Pachter, 2011, Li et al., 2010]. After the
pre-processing step, the intronic sequences are added to the transcriptome and the chosen quantification
method is run using the augmented transcriptome. Any method can be used, provided it outputs expression
in a unit that is additive, e.g. transcripts per million (TPM).
Once introns and transcripts are quantified from all samples in the experiment, the data can be post-
processed and further analyzed in an R package [R Core Team, 2014] that is part of kma. We currently
provide functions to read data from eXpress, but it is quite simple to add a new function that reads in
other formats; all that is required is the target identifier and corresponding expression estimate. Once data
is read in, retention is computed by taking the intron expression (numerator) and summing the expression
of the overlapping transcripts plus the intron expression (denominator). This calculation leads to a natural
measurement of intron retention, the proportion of the transcript expression containing the intron, also
known as the proportion spliced in [Katz et al., 2010].
While we store a special object of class IntronRetention, the majority of the operations depend only on
the data stored in database-like data frames with each row being an intron observation from one sample.
A common row contains categorical fields intron, sample, condition which serve as a key, along with mea-
surements retention, numerator, denominator, unique reads and various columns for filters. This allows for
fast aggregation and manipulation via packages such as dplyr [Wickham and Francois, 2014]. Summaries
of retention across subgroups such as specific introns, conditions, or samples can be quickly computed by
simple queries. We provide common summaries as functions, but the raw data frame is always available for
further analysis. In addition to easy manipulation, this data format is suitable for exploratory analysis in
plotting tools such as ggplot2 [Wickham, 2009].
In certain situations, estimates of the retention level can be unreliable due to low coverage in the exonic
regions, or high variations in coverage due to biases or repetitive sequences. Coverage filters were implemented
based on relative expression or rank, along with a “zero coverage filter”. The zero coverage filter finds the
longest spanning region in an intron which has no reads starting in that region. Then, it computes the
probability of observing a region of length Z with no reads starting in it, given the intron’s expression. The
intron is removed from consideration if the probability is low.
Unlike other publicly available intron retention tools which simply provide an estimate for intron retention
per sample, our method provides a resampling hypothesis testing procedure to determine whether the mean
is greater than what one would expect due to reshuﬄing of the given data in those samples. The null
distribution is generated from the filtered list by randomly selecting a retention value from each sample per
condition B times. For each set of samples, the mean is computed. After the null distribution is generated,
the p-value is computed by finding the proportion of null values that the observed mean is greater than.
2
This allows for a lower false-positive rate when detecting IR events. This procedure also helps shield against
samples that have contamination of non-mature mRNA.
3 Discussion
We have developed an R package kma that addresses the issue of finding intron retention events. Since
this tool only slightly modifies existing quantification pipelines by introducing an augmented transcriptome,
kma can easily be introduced into existing RNA-Seq quantification pipelines. In R, the data is represented
in a database-like format that allows for flexible and fast aggregation allowing for exploratory analysis to
be carried out relatively easily. We also implemented a hypothesis testing procedure which reduces the
likelihood of finding false-positive intron retention events by incorporating biological replicates.
Acknowledgement
HP was supported by the NSF GRFP. JGC and LP were supported in part by NIH R01 DK094699.
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