Consalign: simultaneous rna structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring

HIGHLIGHTS

SUMMARY

In folding single RNA sequences, modern parameter training methods optimize secondary_structure scoring parameters based on both training RNA sequences and training RNA secondary_structures (Do et_al, 2006a; Zakov et_al, 2011; Rivas et_al, 2012; Singh et_al, 2019; Sato et_al, 2021). The machine-learning methods of SAF have fallen behind those of folding single RNA sequences due to the complexity of the former (Dowell and Eddy, 2006; Do et_al, 2008). In the γ-centroid estimator principle (Ding et_al, 2005; Hamada et_al, 2009a,b,c, 2011), the authors define a γ-centroid pairwise SAF recursion like LocARNA (Will et_al, 2007 . . .

 



If you want to have access to all the content you need to log in!

Thanks :)

Username or Email

Password

Remember me

Lost your password?

If you don't have an account, you can create one here.