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The specificity of small interfering RNA (siRNA)-mediated gene silencing is a critical consideration for the application of RNA interference (RNAi). While the discovery of potential off-target effects by siRNAs is of concern, no systematic analysis has been conducted to explore the specificity of RNAi. Here, we present a study where a functionally validated siRNA (siCD46) was examined for silencing specificity on all possible 57 permutated target sites, each carrying a single-nucleotide mutation that would generate a mismatch when paired with siRNA antisense strand. We found that it was not only the position of the mismatched base pair, but also the identity of the nucleotides forming the mismatch that influenced silencing. Surprisingly, mismatches formed between adenine (A) and cytosine (C), in addition to the G:U wobble base pair, were well tolerated and target sites containing such mismatches were silenced almost as efficiently as its fully matched counterpart by siCD46. Northern blots showed that the silencing of fusion genes harboring the mutated target sites involved target mRNA degradation. This study provides direct evidence that the target recognition of siRNA is far more degenerative than previously considered. This finding is instrumental in the understanding of RNAi specificity and may aid the computational prediction of RNA secondary structure.

Abstract-During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice.Index Terms-API usage, usage pattern, sequence mining, software reuse, mining software repositories.

Pooled-DNA sequencing strategies enable fast, accurate, and cost-effect detection of rare variants, but current approaches are not able to accurately identify short insertions and deletions (indels), despite their pivotal role in genetic disease. Furthermore, the sensitivity and specificity of these methods depend on arbitrary, user-selected significance thresholds, whose optimal values change from experiment to experiment. Here, we present a combined experimental and computational strategy that combines a synthetically engineered DNA library inserted in each run and a new computational approach named SPLINTER that detects and quantifies short indels and substitutions in large pools. SPLINTER integrates information from the synthetic library to select the optimal significance thresholds for every experiment. We show that SPLINTER detects indels (up to 4 bp) and substitutions in large pools with high sensitivity and specificity, accurately quantifies variant frequency (r = 0.999), and compares favorably with existing algorithms for the analysis of pooled sequencing data. We applied our approach to analyze a cohort of 1152 individuals, identifying 48 variants and validating 14 of 14 (100%) predictions by individual genotyping. Thus, our strategy provides a novel and sensitive method that will speed the discovery of novel disease-causing rare variants.

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