Hello, my name is Marina DiStefano and I'm a clinical molecular genetics fellow
at Harvard, and a postdoc in the ClinGen Resource. This is part one of a
three-part training series on transcript annotation and curation. Part 1 will
discuss transcript annotation. This is an outline of the entire series. As I said,
in this first part I will cover why transcript curation matters, citing
examples from the hearing loss clinical domain. I will also provide an overview
of transcript annotation bodies and their differences. Please continue on to
part 2 for more discussion on transcript curation resources, and part 3 to hear
about a specific transcript curation strategy that we deployed in the hearing
loss domain. As many bio-curators already know, gene curation is required for
variant curation. If a gene is not associated with disease, the effect of a
variant on the gene cannot be determined. This is best represented by the graph on
the left here, modified from Daniel MacArthur, with variant-level evidence
plotted on the y-axis and gene-level evidence plotted on the x-axis. You can
have a very well characterized variant and a well characterized gene on the top
right, such as Delta F508 in CFTR, which is definitively associated
with cystic fibrosis. You can also have a less characterized variant of uncertain
significance, or VUS, and a well characterized gene on the bottom right.
You can even have a VUS in a GUS, a variant of uncertain significance in a
gene of uncertain significance on the bottom left. But you can never have a
variant with significant evidence in a gene with little to no evidence. I'm
hoping in this talk to convince you that transcript curation represents an
additional level of curation required for variant curation.
Transcript curation is critical for variant interpretation because
transcript choice could alter the molecular consequence of the variant, and
I will provide examples of this. Clinical labs often choose the most severe
molecular consequence for interpretation, which may not be the most biologically-
relevant. Transcript choice can vary depending on the disease being evaluated
and the tissue expression profile of the transcripts. Clinical labs also often
choose the longest transcript for variant annotation, which may not be the most
biologically-relevant choice depending on tissue distribution of the
transcripts. This is an example from the hearing loss domain that supports the
idea of the need for transcript curation. TBC1D24 is a gene that is
associated with non-syndromic hearing loss, DOOR syndrome, which is a syndrome
characterized by deafness, nail abnormalities, bone abnormalities, and
seizures, and a spectrum of epilepsy conditions. As you can see, RefSeq, a
transcript annotation body which we will talk about shortly, lists two curated
transcripts for this gene. The top one is expressed in mouse neurons and contains
an extra exon 3. The bottom one is expressed in mouse cochlea and non-
neuronal tissues. In the literature, a frameshift variant was identified in
this unique exon of the transcript that is solely expressed in neurons, leading
to a loss of function of this transcript. The individuals that harbored this
variant had severe lethal epileptic encephalopathy without hearing loss,
which is supported by the tissue- specific expression in mouse.
I'll now take some time to discuss the most commonly used transcript
annotations. The first set is the Ensembl/GENCODE set which is annotated
by EMBL-EBI. This was previously largely done in an automatic fashion, but
EsembL curators have informed me that more and more manual curation is being
used for these transcripts. They are pulled and aligned directly to the
genome build. As I'm sure you've seen before, they are labeled with the ENST
label (ENSP is for the translated protein product). It's important to know
which curation references use these transcripts. The most critical ones are
the reference population databases gnomAD, ExAC, and 100,000 genomes project. GTEx,
which I'll discuss more in video two, is a very useful transcript curation
resource and also uses Ensembl transcripts. Decipher and COSMIC use them
too. This is a screenshot of the Ensembl website. You can see each transcript
listed along with the corresponding RefSeq transcript, which is a helpful
feature for curation. The flags listed at the end are evidence flags for each
transcript. If you mouse over each tag, it will specify what each one means. For
example, TSL1 is Transcript Support Level 1, which means it is supported by
at least one non-suspect mRNA.
RefSeq is the transcript annotation effort from NCBI. These transcripts are
designated by curation and are independent of the genome build. This can
mean that the genome build and RefSeq can differ. This can pose a problem
because clinical pipelines often call variants off of the genome build and
then annotate them with RefSeq transcripts. RefSeqGene is the most
thoroughly curated set of RefSeq transcripts. The two prefixes for the
strands of these transcripts are N and X. N transcripts are supported by some
evidence, either literature or GenBank cDNA data. GenBank is a database hosted
by NIH that contains sequences from 260 thousand species collected from
individual labs and batch submissions from large-scale sequencing projects. The
second letter in the prefix denotes the type of transcript. M is for mRNA, R is
for non-coding RNA, and P is for protein. So, for example, the most common prefix
you see is "NM" which means it is a curated mRNA transcript. RefSeq is most
commonly used by clinical labs and research publications, which is important
to note for bio-curators. This is a screenshot of different parts of the
RefSeqGene website. It was too large to capture it all, but I've included useful
parts. The top is the gene-level summary. Below is their genome browser that lists
all of the RefSeq transcripts. Further down on the website, each transcript is
listed with a short explanatory sentence. These annotations are pulled into
Element, if you use that genome browser. The top right is a picture of the
bibliography for the gene. I've been informed by RefSeq curators that these
references are a random listing of all the references, so it is important to click
and evaluate all of the references. There is also a graph of RNAseq data. I
personally prefer the exon-level expression of GTEx, but this set is also
useful. I'm now going to talk about the EMBL-EBI and
NCBI collaboration efforts. The first effort that was developed was the Locus
Reference Genomic or LRG effort. These transcripts are highly curated and their
purpose is to ensure efficient and consistent variant reporting by choosing
a clinically relevant transcript. This usually corresponds to one transcript
per gene, and is relatively stable. As of a few weeks ago, there are only 12
hundred or so LRGs, indicating that it is a time-consuming effort. LRG has
curators, but it also solicits transcript curation from content experts. For
example, we have created a hundred or so hearing loss genes, and we worked with
LRG to submit those curations and generate LRG transcripts for all of
those genes. This is the workflow that the LRG effort used for incorporating our
hearing loss transcript curation. As a part of this effort, NMs and ENSTs are
100% harmonized, and an LRG is created using these harmonized transcripts. This
effort will feed into the main effort which I'll discuss shortly.
This is the display of one of the hearing loss transcripts we curated. You
can see the harmonized and mapped RefSeq and Ensembl transcripts. The newest
collaboration from EMBL-EBI and RefSeq is the matched annotation from NCBI and
EMBL-EBI project, or the MANE project. This is a collaborative transcript set
including the best that RefSeq and Ensembl offer. The transcripts are
aligned to the newer genome build GRCh3, anticipating that clinical labs can
switch to this set when the population reference databases switch to this build.
Transcripts are fairly stable, highly curated, and have 100% identity between
annotation efforts. The MANE Select set is the first set to be released which
includes one transcript per locus, and fifty percent of these transcripts were
released in December last year. The remainder will hopefully be
released later this year. The second set is the MANE Plus set which continues on
with LRG project, providing highly curated transcripts for particular user
groups like clinical labs or researchers. These will be completed in the future. EMBL-
EBI and NCBI recently had a very nice explanatory webinar on this effort, and I
highly recommend reviewing the slides from that webinar or the recording for
more information. The 50% of the MANE select transcripts are available as a
download on NCBI's FTP site. In conclusion, I hope that I've convinced
you that transcript curation is critical for variant interpretation. I've
explained the existing annotation efforts and how they differ, but they
are in the process of harmonization. Please check out my other two recordings
to find more information about curating transcripts. Thank you