DNA, with remarkable properties of high density, durability, and replicability, is one of the most appealing storage media. Emerging DNA storage technologies use composite DNA letters, where information is represented by probability vectors, leading to higher information density and lower synthesizing costs than regular DNA letters. However, it faces the problem of inevitable noise and information corruption. This paper explores the channel of composite DNA letters in DNA-based storage systems and introduces block codes for limited-magnitude probability errors on probability vectors. First, outer and inner bounds for limited-magnitude probability error correction codes are provided. Moreover, code constructions are proposed where the number of errors is bounded by t, the error magnitudes are bounded byl, and the probability resolution is fixed ask. These constructions focus on leveraging the properties of limited-magnitude probability errors in DNA-based storage systems, leading to improved performance in terms of complexity and redundancy. In addition, the asymptotic optimality for one of the proposed constructions is established. Finally, systematic codes based on one of the proposed constructions are presented, which enable efficient information extraction for practical implementation.

DNA, with remarkable properties of high density and stability, particularly for long-term data archiving, is one of the most appealing storage media. Emerging DNA storage technologies use composite DNA letters, where information is represented by a probability vector, leading to higher information density and lower synthesizing cost than single DNA letters. However, it faces the problem of inevitable noise and information corruption. This paper studies the channel of composite DNA letters in DNA storage and block codes for symmetric limited-magnitude errors on probability vectors. We provide outer and inner bounds for limited-magnitude probability error correction codes. Moreover, we propose code constructions where the number of errors is bounded by t, the error magnitudes are bounded by l, and the probability resolution is fixed as k. Our constructions exploit the properties of the limited-magnitude errors, and improve the performance in terms of complexity and redundancy.