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In DNA sequencing, we often need to infer an unknown sequence from a collection of its corrupted copies. Each copy cannot faithfully tell the truth due to DNA fragmentation, point mutations, and measurement errors. The theoretical guarantee of unique reconstruction is thus of concern. This motivated the study of sequence reconstruction problems three decades ago. Recently, synthetic DNA has been regarded as an ultra-dense data storage medium. Sequence reconstruction is a crucial step in achieving reliable and efficient data readout.

Recent advancements in DNA storage show that composite DNA letters can significantly enhance storage capacity. We model this process as a multinomial channel and propose an optimization algorithm to determine its capacity-achieving input distribution (CAID) for an arbitrary number of output reads. Our empirical results match a scaling law that determines that the support size grows exponentially with capacity.

In this work, we present a generalization of the recently proposed quantum Tanner codes by Leverrier and Zémor, which contains a construction of asymptotically good quantum low-density parity-check codes. Quantum Tanner codes have so far been constructed equivalently from groups, Cayley graphs, or square complexes constructed from groups. We show how to enlarge this to graphs with labeled local views and a family of square complexes, which is the largest possible in a certain sense.

Emerging DNA storage technologies use composite DNA letters, where information is represented by a probability vector, leading to higher information density and lower synthesis costs. However, it faces the problem of information leakage in sharing the DNA vessels among untrusted vendors. This paper introduces an asymptotic ramp secret sharing scheme (ARSSS) for secret information storage using composite DNA letters.

In this paper, we study error exponents for an index-based concatenated coding based class of DNA storage codes in which the number of reads performed can be variable. That is, the decoder can sequentially perform reads and choose whether to output the final decision or take more reads, and we are interested in minimizing the average number of reads performed rather than a fixed pre-specified value.