The main idea is here.
As of now, all the existing de novo assemblers use a de Bruijn graph to represent the assembly problem, which processes each sequence into a set of overlapping substrings of length k bps, called k-mers, where k is a parameter, and recover the splicing isoforms from the graph . Generally speaking, larger k values tend to perform better on transcripts with high gene- expression levels or longer contigs, while smaller k values perform better on
transcripts with low gene-expression levels or shorter contigs. It seems unlikely that a single k value will yield an optimal overall assembly. Hence some assemblers, such as Trans- ABySS , Oases-M  (multiple-k version of Oases) and IDBA-Tran , use a multiple-k strategy, in which multiple assemblies using different k values are merged to get a higher sensitivity, but at the cost of introducing more false-positive transcripts.
In this paper, we present a new assembler, Bridger, aiming to build a bridge between the key ideas of two popular assemblers, the reference-based assembler Cufflinks  and de novo assembler Trinity . Specifically, we have generalized the main techniques employed by Cufflinks to overcome the limitations of Trinity, hence to develop a more general de novo assembler better than the state of the art. We have tested Bridger on two standard RNA- seq datasets, one dog and one human dataset, and on one strand-specific mouse RNA-seq data. In each case, Bridger assembled more reference transcripts than the other de novo assemblers, while reporting 10,000 to 30,000 fewer candidate transcripts, which greatly reduced the false positive assemblies. In addition, Bridger runs much faster and requires less memory space than most of compared methods, and exhibits competitive CPU time. The performance of Bridger is even comparable with the reference-based assembler Cufflinks in both sensitivity and accuracy. In addition, a multiple-k version of Bridger, Bridger-M, can further improve the assembly sensitivity by merging assemblies from different k values.