Unigene Assembly Process Overview
The "unigene problem" consists of two fundamental
questions:
- Are these two sequences from the same
gene/transcript?
- Where are the sequencing errors in this
sequence?
The ability to answer either question correctly and
consistently enables an algorithm for precise assembly of
a unigene build from EST sequences. It is plain to see
that if (1) is yes, then answers for (2) are known
(errors are where the sequence differs, barring allelic
variation). As well, if (2) were determined, then (1) is
easily settled by examining an alignment of the sequences
for true differences in the overlapping region.
Constructing a unigene build must attempt to solve
both questions simultaneously. This is different from
genomic DNA assembly, for the following important
reasons:
EST sequencing methodology does not yield an
expectation of stochastic oversampling of each DNA
base. In genomic sequencing, with 8X expected coverage
for example, answering (2) above becomes easier as
there are several observations for each base once
proper alignment is determined. The optimal outcome of assembling a BAC is exactly
one contig. The implied answer to question (1) above is
then always yes: all subclones belong in the same
contig.
There are no widely used, freely available assemblers
for EST data, so we do the next best thing: use a genomic
assembler such as phrap (P. Green) or CAP3 (X.
Huang [1]). CAP3 is typically preferred
for EST assembly (see [2] for a
discussion), being less aggressive at splitting apart
contigs.
In general deciding whether or not to assemble two
sequences together is a very easy question as long as the
observed differences between the sequences are
significant. When the observed differences in two
sequences approaches the rate of sequencing error,
determining whether or not two different genes are
represented by the sequences becomes theoretically
impossible without collecting more data. Since error
rates in a collection of sequences appear as a
distribution, the result is a range of observed
differences where actual differences and sequencing
errors make assembly decisions arbitrary.
The likely result is the over-representation or
under-representation of gene families which contain
recently diverged paralogs. Additionally, if the organism
sequenced is heterozygous at many loci with significant
allelic variation, similar results may occur.
This may be controlled by selection of threshold
parameters governing the assembly process, but there is
no "one size fits all" threshold that accurately decides
all cases. Particular choices of thresholds may either
(a) promote false detection of distinct but similar genes
(b) promote false detection of alleles (by assembling
close paralogs together) or (c) do both (neutral choice
of parameters). For SGN's assembly, we have decided to
proceed with option (b), to attempt to minimize the
number of false isolations of unique
transcripts.
Future versions of SGN's unigene build process will
include the option for the user to inspect an assembly's
multiple sequence alignment (MSA) as well as view the
major alternatives incorporated in any given
assembly.
References:
- Huang, X. and Madan, A. (1999) CAP3: A DNA Sequence
Assembly Program. Genome Research, 9:
868-877
- Liang Feng, et. al. (2000) An optimized
protocol for analysis of EST sequences Nucleic
Acids Research 28, 3657-3665
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