Genomic and transcriptomic data are quite different in some fundamental aspects, here a incomplete list:

Coverage: typical short read assembler (including those you mentioned) use kmer based graph structures for the reconstruction of the underlying sequence. For genomes, the kmer-coverage profile is quite distinct, with kmers form non-repetitive regions clustering around the sequencing depth of your sample, erroneous kmers at low frequencies and repeat stuff at high frequencies. This spectrum is used in assemblers during error correction, graph optimization /evaluation etc. The underlying assumptions, however, are not true at all for RNA-seq data. Here, the abundance of each transcript determines the frequency of corresponding kmers and you will get very different spectra.

Structural variants: In a (haploid) genome data set, you don't expect a lot of structural variances, and if you do, you often want to merge them into a single haplotype assembly. In transcriptomes, quite the opposite is the case. Alternative splicing produces a plentitude of structural variants for the same regions. This characteristic cannot be captured with denovo genome assemblers and most likely will result in individual fragments corresponding to single exons.

(There are other transcriptome assemblers, e.g.: OASIS)

You should clarify how you are differentiating a "de novo assembler" and a "transcript assembler". I feel like there might be some confusion on the usage of these terms. Do you mean to say genome assembler vs transcriptome assembler?

The biggest difference between genome and transcriptome assembly is coverage. Barring repetitive or highly conserved regions, a genome ideally would have even coverage. A transcriptome, on the other hand, have differential coverage across each transcript depending on it's expression. Think of each transcript as a whole "genome" and a transcriptome assembly as trying to assemble many mini-genomes from a pool of mixed genomic reads (like a meta-genomic assembly).