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Canzar Lab - Research

Comprehensive Isoform Discovery and Abundance Estimation (CIDANE)

Further complicating matters, one subgroup of transcripts is particularly hard to reconstruct. When transcripts are rare in the sample, key data about the connectivity of basic informational blocks that make up transcripts, known as exons, may not be evident from RNA sequencing. The researchers call these “invisible transcripts.” Existing methods being used to reassemble RNA ignore such transcripts for computational reasons.

The technique pioneered by our group, Comprehensive Isoform Discovery and Abundance Estimation, or CIDANE, mixes techniques from machine learning and combinatorial optimization to reconstruct transcripts.
CIDANE can also use existing information from known, and experimentally validated, gene structures to improve the accuracy of the RNA assembly. While such information is not necessary, CIDANE can use any existing data to improve its ability to reconstruct RNA.

“In our experience, any prior knowledge can help in piecing together the genetic puzzle.”

CIDANE is also able to reassemble the elusive invisible transcripts, detecting patterns that other techniques miss. Using a technique from large-scale optimization, CIDANE can discover those transcripts using an optional stage of the algorithm that kicks in on-demand when invisible transcripts appear to be involved. While the importance of invisible transcripts is still uncertain, by recognizing them, CIDANE gives genetics researchers more and better information about their existence and value.

Ultimately, CIDANE is more accurate than all existing reconstruction methods. The most sensitive reassembly technique after CIDANE, when applied to human blood and monocyte samples, correctly pieced together 11,473 and 11,117 transcripts respectively. CIDANE correctly predicted 14,885 and 14,254, which is 80 to 90 percent more transcripts than the third most sensitive, and most widely used, reassembly technique.

CIDANE: comprehensive isoform discovery and abundance estimation.
Canzar S, Andreotti S, Weese D, Reinert K, Klau GW.
Genome Biol. 2016 Jan 30;17:16. doi: 10.1186/s13059-015-0865-0.