blast2cap3 merges contigs together from transcriptome assemblies
using protein homology and CAP3.
RNA-seq has given rise to a powerful way of exploring the genes of non-model organisms lacking a full genome. Howver, unlike genomic DNA sequencing, RNA-seq coverage is dependent upon gene expression levels, which can confound the assembly process. There are transcriptome-specific assmeblers (such as Trinity and Oases) that address this; however, the best transcriptome assemblies are often those that incorporate many assemblies with varying sized k-mers. C. Titus Brown has a nice introduction to the importance of k in transcriptome assemblies.
After multiple assemblies with different k-mer sizes, the resulting contigs should be merged. The first step is to cluster nucleotide sequences via a program like CD-HIT, so contigs that are fully contained in another contig are merged. The second step would be to join contigs that may have partially assembled across differently sized k-mers. For example:
|----------- contig k31-a ---------------| |----------- contig k61-b -----------|
Assemblers like CAP3 are then often used to merge contigs based on the overlapping region. However, this strategy often either (1) overwhelms CAP3 with too many contigs to consider merging or (2) incorrectly merges two contigs with nucleotide similarity.
blast2cap3 alleviates these issues by narrowing the pool of possible
contigs to merge to those that share a BLASTX hit to a common subject
protein of a relative.
blast2cap3 uses protein similarity because
most transcriptome contigs will be protein coding, and leveraging this
increases specifity. Both the narrowing of the merging pool and the
use of similarity in protein space rather than nucleotide space leads
to post-assembly merges with more specificity (more testing is needed,
but we're using it in production).
blast2cap3 is best used after ORF prediction. Contigs with full ORFs
shouldn't be candidates for a merge (unless you're interested in
assembly the 5' and 3' UTRs; if this is the case, use a different
approach). An iterative approach of ORF prediction,
blast2cap3, etc may be a good strategy.
Please contact vsbuffaloAAAAA@gmail.com (with the poly-A tail removed) if you wish to cite this program in your work.