MOTIVATION: Sequence alignment has been at the core of computational biology for half a century. Still, it is an open problem to design a practical algorithm for exact alignment of a pair of related sequences in linear-like time. RESULTS: We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm. In order to efficiently align long sequences with high divergence, we extend the recently proposed seed heuristic with match chaining, gap costs, and inexact matches. We additionally integrate the novel match pruning technique and diagonal transition to improve the A* search. We prove the correctness of our algorithm, implement it in the A*PA aligner, and justify our extensions intuitively and empirically. On random sequences of divergence d=4% and length n, the empirical runtime of A*PA scales near-linearly with length (best fit n1.06, n≤107 bp). A similar scaling remains up to d=12% (best fit n1.24, n≤107 bp). For n=107 bp and d=4%, A*PA reaches >500× speedup compared to the leading exact aligners Edlib and BiWFA. The performance of A*PA is highly influenced by long gaps. On long (n>500kb) ONT reads of a human sample it efficiently aligns sequences with d<10%, leading to 3× median speedup compared to Edlib and BiWFA. When the sequences come from different human samples, A*PA performs 1.7× faster than Edlib and BiWFA. AVAILABILITY AND IMPLEMENTATION: github.com/RagnarGrootKoerkamp/astar-pairwise-aligner.

We present an algorithm for the optimal alignment of sequences to genome graphs . It works by phrasing the edit distance minimization task as finding a shortest path on an implicit alignment graph. To find a shortest path, we instantiate the A \(^\star \) paradigm with a novel domain-specific heuristic function that accounts for the upcoming subsequence in the query to be aligned, resulting in a provably optimal alignment algorithm called AStarix . Experimental evaluation of AStarix shows that it is 1–2 orders of magnitude faster than state-of-the-art optimal algorithms on the task of aligning Illumina reads to reference genome graphs. Implementations and evaluations are available at https://github.com/eth-sri/astarix .

We present a novel approach for predicting debug information in stripped binaries. Using machine learning, we first train probabilistic models on thousands of non-stripped binaries and then use these models to predict properties of meaningful elements in unseen stripped binaries. Our focus is on recovering symbol names, types and locations, which are critical source-level information wiped off during compilation and stripping. Our learning approach is able to distinguish and extract key elements such as register-allocated and memory-allocated variables usually not evident in the stripped binary. To predict names and types of extracted elements, we use scalable structured prediction algorithms in probabilistic graphical models with an extensive set of features which capture key characteristics of binary code. Based on this approach, we implemented an automated tool, called Debin, which handles ELF binaries on three of the most popular architectures: x86, x64 and ARM. Given a stripped binary, Debin outputs a binary augmented with the predicted debug information. Our experimental results indicate that Debin is practically useful: for x64, it predicts symbol names and types with 68.8% precision and 68.3% recall. We also show that Debin is helpful for the task of inspecting real-world malware -- it revealed suspicious library usage and behaviors such as DNS resolver reader.