Understanding the World Through Code

Funded through the NSF Expeditions in Computing Program

An RNA splicing primer

The problem of predicting RNA splicing is of crucial importance to biology, and is one where formal methods and machine learning have a lot to contribute. In this tutorial we explain the basics of the RNA splicing problem. This content sumarizes data from multiple sourcesWangCooper07WangBurge08 and is expected to serve as a quick primer for researchers from the ML and PL community who want to work on this problem.

The basics of DNA

To understand the RNA splicing problem, it is important to first understand how cells make proteins. The starting point for the synthesis of a protein is a sequence of DNA (the primary sequence). DNA is composed of 4 basic nucleotides: adenine, guanine, thiamine and cytosine, usually abreviated as a, g, t and c. The first two (adenine and guanine) are known as purines, and the other two (thiamine and cytosine) are known as pyrimidines. The most important thing to know about DNA is that adenine and thiamine always match together as do guanine and cytosine.

DNA is generally aranged as two complementary sequences of nucleotides, where every base in the first sequence has a corresponding matching base in the second sequence as illustrated in the figure. Each sequence has a starting point, known as the 5' (five prime) end, and an end point, known as the 3' end.

A fragment of DNA that encodes for a protein is known as a gene. The first step in creating a protein from a gene is known as RNA transctiption.

Transcription at a glance

The goal of transcription is to create a strand of RNA that copies the information contained in the original DNA strand. During transcription, the two complementary strands of DNA are separated , and an enzyme called RNA polymerase assembles a strand of RNA by adding one nucleotide at a time to the 3' end of an RNA sequence. As a computer scientist, the most important thing to know about RNA is that it is also composed of nucleotides, but the thiamine is replaced with the pyrimidine uracil (abreviated as u).

This strand of RNA is called a messenger RNA (mRNA) and it will carry the information about how to construct the protein out of the nucleus and into the ribosomes where the proteins will be assembled. But before that happens, in fact as the RNA polymerase is assembling the RNA, this RNA must be spliced.

Splicing basics

Before the mRNA is spliced, it is called a pre-mRNA. The parts of the pre-mRNA that will be cut out (spliced) are known as introns. Those that will stay as part of the final RNA are called exons (the ex stands for expressed). Some RNA sequences are always spliced the same way (this is known as constitutive splicing), although in some instances, the same sequence can splice in multiple different ways depending on the environment (this is known as alternative splicing).

The fundamental question that we want to answer is: How does splicing work? More specifically, there are multiple different questions that we can answer.

The constitutive splicing code.

The simplest version of the question is, can we identify all the locations in an RNA sequence that could be the start or end of an intron.

Some of the standard measures of quality for such a model are:

In the simplest case, we can just train a model with a set of RNA sequences labeled with the start and end of all their introns and use it to make predictions for new sequences. However, we would like to have models that satisfy the following properties:

Alternative splicing.

For sequences that are alternatively spliced, can we label each splice site with the probability that it will splice at that point? And even better, can we identify the actual sequences that can be produced from a single sequence of pre-mRNA?

Some things we know about splicing

There is a broad literature on splicing, but below are a few of the things we know about how splicing works that would be useful to incorporate into any model.

First, introns tend to follow a common pattern. The start of an intron (the 5' end) often refered to as the donor site usually starts with the sequence 'GU'. The end of the intron, (the 3' end) often refered to as the acceptor site, usually ends with the sequence 'AG'. These patterns are known as consensus sequences, and you can find published data on the matrices that define these consensus sequences.

In the middle of the intron, there is a region known as the branch site that always includes an 'A', and often has a distinctive pattern. Between the branch site and the acceptor site, there is usually a pyrimidine tract, a sequence of 'U's or 'C's.

Splicing is performed by a complex of small nuclear ribonucleoproteins (snRNPs pronounced as "snurps") known as the spliceosome. The spliceosome is composed of 5 snRNPs called U1, U2, U4, U5 and U6, and is aided and regulated by a large number (about 150) of RNA-binding molecules.

The figure provides a cartoon illustration of how these snRNPs attach to the RNA and manipulate it in order to splice out the intron into a structure known as a lariat. The crucial question is: how do these snRNPs know where to attach? The consensus sequences at the ends of the intron are not specific enough to regulate this process. We cannnot just say "Every GU is the start of an intron and every AG is the end of one". Consensus sequences that don't correspond to actual start and ends of introns are known as cryptic splice sites.

We know the proces is regulated by additional RNA-binding molecules that attach to particular sequences of RNA known as Splicing Regulatory Elements (SREs).

Splicing regulatory elemsnts (SREs)

There are 4 main types of SREs: These splicing enhancer and silencers are known as cis-acting elements (cis==same) because they are a pattern in the RNA molecule that affects the splicing of that molecule. These elements work by recruiting trans-acting elements (trans==different), which are different proteins present in the nucleus which are recruited into the splicing machinery. For example, in the exon, we generally find ESE sequences close to the exon-intron boundary. These help recruit SR proteins (a type of RNA-binding protein) which then help the U1 snRNP attach to the donor site. A consensus sequence that does not have this ESE nearby is more likely to be a cryptic splice site. As another examples, ESSs can bind a type of proteins called hnRNP I, which can inhibit splicing by blocking the interaction between U1 and U2.

Usually, the SREs will correspond to short patterns. When modeling, it is common to assume they are hexamers (aka 6mers or 6-base long patterns). One important consideration is that some SRE sequences have been observed to be context dependent, so they can act as silencers or enhancers depending on the context. The patterns can also be species specific, and some categories of organisms rely more on intronic SREs, whereas others rely more on exonic SREs.

In many cases, the action of SREs is additive, so more enhancers nearby will increase the probability that a particular consensus sequence is a real splice site. However, sometimes the interactions are non-linear, so a particular combination of enhancers will have a strong effect even if each enhancer individually would have none.

A key aspect of the models we are trying to build will be to incorporate what is known about existing SREs to help train models with less data, as well as to generate new hypothesis about SREs and their interactions.