Coded Trace Reconstruction Motivated by averagecase trace reconstruction and coding for portable DNAbased storage systems, we initiate the study of coded trace reconstruction, the design and analysis of highrate efficiently encodable codes that can be efficiently decoded with high probability from few reads (also called traces) corrupted by edit errors. Codes used in current portable DNAbased storage systems with nanopore sequencers are largely based on heuristics, and have no provable robustness or performance guarantees even for an error model with i.i. d. deletions and constant deletion probability. Our work is a first step towards the design of efficient codes with provable guarantees for such systems. We consider a constant rate of i.i. d. deletions, and begin by analyzing markerbased codeconstructions coupled with worstcase trace reconstruction algorithms. Then, we show how a more careful design of the code allows us to exploit ideas from averagecase trace reconstruction to reduce the number of traces required with the same redundancy.

Sparse Binary Relation Representations for Genome Graph Annotation Highthroughput DNA sequencing data is accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and allow for efficient query of sequences. In particular, the concept of colored de Bruijn graphs has been explored by several groups. While there has been good progress towards representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the labels. In this work, we present a systematic analysis of five different stateoftheart annotation compression schemes that evaluates key metrics on both artificial and realworld data and discusses how different data characteristics influence the compression performance. In addition, we present a new approach, MultiBRWT, that shows an up to 50% improvement in compression performance over the current stateoftheart and is adaptive to different kinds of input data. Using our comprehensive test datasets, we show that this improvement can be robustly reproduced for different representative realworld datasets.