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Fig. 2 | Algorithms for Molecular Biology

Fig. 2

From: An improved approximation algorithm for the reversal and transposition distance considering gene order and intergenic sizes

Fig. 2

An instance \(I=(\pi ,\breve{\pi },\breve{\iota })\), where \(\pi =(0,3,2,1,4,5,6,7), \breve{\pi }=(4,0,3,0,5,2,0),\) and \(\breve{\iota }=(1,3,0,3,1,2,4)\). On top we have the source genome \((\pi ,\breve{\pi })\) and in the bottom the target genome \((\iota ,\breve{\iota })\). Breakpoints are indicated above the source genome. Note that \(b_h(I)=2\) and \(b_s(I)=2\), so \(b(I) = 4\). The hard breakpoints \((\pi _4,\pi _5)\) and \((\pi _6,\pi _7)\) are overcharged and undercharged, respectively. Breakpoints \((\pi _0,\pi _1)\) and \((\pi _3,\pi _4)\) are soft. The breakpoints \((\pi _0,\pi _1)\) and \((\pi _6,\pi _7)\) are connected, while \((\pi _3,\pi _4)\) and \((\pi _6,\pi _7)\) are not. Besides, the pair of breakpoints \((\pi _0,\pi _1)\) and \((\pi _3,\pi _4)\) are softly connected. The instance I has the increasing strips \((\pi _0)\) and \((\pi _4,\pi _5,\pi _6,\pi _7\)), and the decreasing strip \((\pi _1,\pi _2,\pi _3)\)

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