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  1. Alkan, Can (Ed.)
    Abstract Motivation

    Detection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments.

    Results

    We show that HQAlign captures about 4%–6% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10%–50% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome.

    Availability and implementation

    https://github.com/joshidhaivat/HQAlign.git.

     
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  2. Player-replaceability is a property of a blockchain protocol that ensures every step of the protocol is executed by an unpredictably random (small) set of players; this guarantees security against a fully adaptive adversary and is a crucial property in building permissionless blockchains. Forensic Support is a property of a blockchain protocol that provides the ability, with cryptographic integrity, to identify malicious parties when there is a safety violation; this provides the ability to enforce punishments for adversarial behavior and is a crucial component of incentive mechanism designs for blockchains. Player-replaceability and strong forensic support are both desirable properties, yet, none of the existing blockchain protocols have both properties. Our main result is to construct a new BFT protocol that is player-replaceable and has maximum forensic support. The key invention is the notion of a ``transition certificate'', without which we show that natural adaptations of extant BFT and longest chain protocols do not lead to the desired goal of simultaneous player-replaceability and forensic support. 
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  3. Birol, Inanc (Ed.)
    Abstract Motivation

    Single-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad hoc effort that requires expert biological knowledge.

    Results

    Here, we introduce CellMeSH—a new automated approach to identifying cell types for clusters based on prior literature. CellMeSH combines a database of gene–cell-type associations with a probabilistic method for database querying. The database is constructed by automatically linking gene and cell-type information from millions of publications using existing indexed literature resources. Compared to manually constructed databases, CellMeSH is more comprehensive and is easily updated with new data. The probabilistic query method enables reliable information retrieval even though the gene–cell-type associations extracted from the literature are noisy. CellMeSH is also able to optionally utilize prior knowledge about tissues or cells for further annotation improvement. CellMeSH achieves top-one and top-three accuracies on a number of mouse and human datasets that are consistently better than existing approaches.

    Availability and implementation

    Web server at https://uncurl.cs.washington.edu/db_query and API at https://github.com/shunfumao/cellmesh.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  4. null (Ed.)