NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks

Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs using long-range haplotype information, then phases long reads with called SNPs and calls indels with local realignment. Evaluation on 8 human genomes demonstrates that NanoCaller generally achieves better performance than competing approaches. We experimentally validate 41 novel variants in a widely used benchmarking genome, which could not be reliably detected previously. In summary, NanoCaller facilitates the discovery of novel variants in complex genomic regions from long-read sequencing.

Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

Reconstruction of neural circuitry at single-synapse resolution is a key target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.

Boosting alignment accuracy through adaptive local realignment

MotivationWhile mutation rates can vary across the residues of a protein, when computing alignments of protein sequences the same setting of values for substitution score and gap penalty parameters is typically used across their entire length. We provide for the first time a new method called adaptive local realignment that automatically uses diverse parameter settings in different regions of the input sequences when computing multiple sequence alignments. This allows parameter settings to adapt to more closely match the local mutation rate across a protein.MethodOur method builds on our prior work on global alignment parameter advising with the Facet alignment accuracy estimator. Given a computed alignment, in each region that has low estimated accuracy, a collection of candidate realignments is generated using a precomputed set of alternate parameter settings. If one of these alternate realignments has higher estimated accuracy than the original subalignment, the region is replaced with the new realignment, and the concatenation of these realigned regions forms the final alignment that is output.ResultsAdaptive local realignment significantly improves the quality of alignments over using the single best default parameter setting. In particular, this new method of local advising, when combined with prior methods for global advising, boosts alignment accuracy by as much as 26% over the best default setting on hard-to-align benchmarks (and by 6.4% over using global advising alone).AvailabilityA new version of the Opal multiple sequence aligner that incorporates adaptive local realignment using Facet for parameter advising, is available free for non-commercial use at http://[email protected]

Realignment and Recidivism

California’s 2011 Public Safety Realignment created an unprecedented policy experiment by transferring the authority over lower-level felony offenders from the state correctional system to fifty-eight county jail and probation systems. While centered in California, these changes are reflective of an ongoing national conversation about the appropriate level of government at which to focus crime control efforts. In this article, we first situate Realignment in criminological and sociolegal literatures, showing how the reform offers opportunities to further inquiry as to the effectiveness of a wide variety of correctional strategies, implementation, and local variation in correctional law and policy. We then review early research focused on the statewide effect of Realignment on recidivism, which has produced mixed findings depending on the measure of recidivism applied. We then examine variation in recidivism outcomes across county sites and present findings that indicate there is an important relationship between local Realignment implementation strategies and recidivism outcomes. Throughout, we focus on two overarching themes. The first is the challenge of disentangling the roles of offender behavior from justice system response in meaningfully interpreting changes in recidivism outcomes. The second is the challenge of evaluating the effects of policy or practice changes under limited data. Although the need for better and more expansive data is a common theme, we highlight it here in the context of a larger data collection that we have under way.