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Despite recent improvements in nanopore basecalling accuracy, germline variant calling of small insertions and deletions (INDELs) remains poor. Although precision and recall for single nucleotide polymorphisms (SNPs) now exceeds 99.5%, INDEL recall remains below 80% for standard R9.4.1 flow cells. We show that read phasing and realignment can recover a significant portion of false negative INDELs. In particular, we extend Needleman-Wunsch affine gap alignment by introducing new gap penalties for more accurately aligning repeatedn-polymer sequences such as homopolymers ($$n=1$$$n=1$) and tandem repeats ($$2 \le n \le 6$$$2\le n\le 6$). At the same precision, haplotype phasing improves INDEL recall from 63.76 to$$70.66\%$$$70.66\%$and nPoRe realignment improves it further to$$73.04\%$$$73.04\%$.

 

Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool—LAMPrey—to process sequenced LAMP product. LAMPrey’s concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

 

Garbage collection (GC) support for unmanaged languages can reduce programming burden in reasoning about liveness of dynamic objects. It also avoids temporal memory safety violations and memory leaks.SoundGC for weakly-typed languages such as C/C++, however, remains an unsolved problem. Current value-based GC solutions examine values of memory locations to discover the pointers, and the objects they point to. The approach is inherently unsound in the presence of arbitrary type casts and pointer manipulations, which are legal in C/C++. Such language features are regularly used, especially in low-level systems code.

In this paper, we propose Dynamic Pointer Provenance Tracking to realize sound GC. We observe that pointers cannot be created out-of-thin-air, and they must have provenance to at least one valid allocation. Therefore, by tracking pointer provenance from the source (e.g., malloc) through both explicit data-flow and implicit control-flow, our GC has sound and precise information to compute the set of all reachable objects at any program state. We discuss several static analysis optimizations, that can be employed during compilation aided with profiling, to significantly reduce the overhead of dynamic provenance tracking from nearly 8× to 16% for well-behaved programs that adhere to the C standards. Pointer provenance based sound GC invocation is also 13% faster and reclaims 6% more memory on average, compared to an unsound value-based GC.

 

State-of-art secure processors like Intel SGX remain susceptible to leaking page-level address trace of an application via the page fault channel in which a malicious OS induces spurious page faults and deduces application's secrets from it. Prior works which fix this vulnerability do not provision for OS demand paging to be oblivious. In this work, we present InvisiPage which obfuscates page fault channel while simultaneously making OS demand paging oblivious. To do so, InvisiPage first carefully distributes page management actions between the application and the OS. Second, InvisiPage secures application's page management interactions with the OS using a novel construct which is derived from Oblivious RAM (ORAM) but is customized for page management. Finally, we lower overheads of our approach by reducing page management interactions with the OS via a novel memory partition. For a suite of cloud applications which process sensitive data we show that page fault channel can be tackled while enabling oblivious demand paging at low overheads. 

Dynamic information-flow tracking (DIFT) is useful for enforcing security policies, but rarely used in practice, as it can slow down a program by an order of magnitude. Static program analyses can be used to prove safe execution states and elide unnecessary DIFT monitors, but the performance improvement from these analyses is limited by their need to maintain soundness. In this paper, we present a novel optimistic hybrid analysis (OHA) to significantly reduce DIFT overhead while still guaranteeing sound results. It consists of a predicated whole-program static taint analysis, which assumes likely invariants gathered from profiles to dramatically improve precision. The optimized DIFT is sound for executions in which those invariants hold true, and recovers to a conservative DIFT for executions in which those invariants are false. We show how to overcome the main problem with using OHA to optimize live executions, which is the possibility of unbounded rollbacks. We eliminate the need for any rollback during recovery by tailoring our predicated static analysis to eliminate only safe elisions of noop monitors. Our tool, Iodine, reduces the overhead of DIFT for enforcing security policies to 9%, which is 4.4x lower than that with traditional hybrid analysis, while still being able to be run on live systems.