▁by ▁a ▁dynamic ▁programming ▁method . ▁Results ▁showed ▁that ▁all ▁algorithms ▁had ▁better ▁performance ▁in ▁Q ST AR ▁data ▁than ▁on ▁L C Q ▁data , ▁while ▁P E A KS ▁as ▁the ▁best ▁had ▁a ▁success ▁rate ▁of ▁ 4 9 . 7 % ▁in ▁Q ST AR ▁data , ▁and ▁Nov o H MM ▁as ▁the ▁best ▁had ▁a ▁success ▁rate ▁of ▁ 1 8 . 3 % ▁in ▁L C Q ▁data . ▁The ▁performance ▁order ▁in ▁Q ST AR ▁data ▁was ▁P E A KS ▁> ▁L ute f isk , ▁P ep N ovo ▁> ▁A UD EN S , ▁Nov o H MM , ▁and ▁in ▁L C Q ▁data ▁was ▁Nov o H MM ▁> ▁P ep N ovo , ▁P E A KS ▁> ▁L ute f isk ▁> ▁A UD EN S . ▁Comp ared ▁in ▁a ▁range ▁of ▁spectrum ▁quality , ▁P E A KS ▁and ▁Nov o H MM ▁also ▁showed ▁the ▁best ▁performance ▁in ▁both ▁data ▁among ▁all ▁ 5 ▁algorithms . ▁P E A KS ▁and ▁Nov o H MM ▁had ▁the ▁best ▁sens itivity ▁in ▁both ▁Q ST AR ▁and ▁L C Q ▁data ▁as ▁well . ▁However , ▁no ▁evaluated ▁algorithms ▁exceed ed ▁a ▁ 5 0 % ▁of ▁exact ▁identification ▁for ▁both ▁data ▁sets . ▁ ▁References ▁ ▁Category : M ass ▁spect rom etry ▁Category : Pro te om ics <0x0A> </s> ▁A cha ear ane a ▁hier og lyph ica ▁is ▁a ▁t angle ▁web