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1. NPSV: A simulation-driven approach to genotyping structural variants in whole-genome sequencing data

   https://doi.org/10.1093/gigascience/giab046  

   Linderman, Michael D; Paudyal, Crystal; Shakeel, Musab; Kelley, William; Bashir, Ali; Gelb, Bruce D (July 2021, GigaScience)

   Abstract Background Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. Results We introduce NPSV, a machine learning–based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples, and variant types. NPSV can specifically identify putative de novo SVs in a trio context and is robust to offset SV breakpoints. Conclusions Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a “black box” that can be simulated, NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications. 

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2. HQAlign: aligning nanopore reads for SV detection using current-level modeling

   https://doi.org/10.1093/bioinformatics/btad580  

   Joshi, Dhaivat; Diggavi, Suhas; Chaisson, Mark J; Kannan, Sreeram (October 2023, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract MotivationDetection 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. ResultsWe 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 implementationhttps://github.com/joshidhaivat/HQAlign.git. 

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3. Pride and Professionalization in Volunteer Moderation: Lessons for Effective Platform-User Collaboration

   https://doi.org/10.54501/jots.v1i2.34  

   Seering, Joseph; Dym, Brianna; Kaufman, Geoff; Bernstein, Michael (February 2022, Journal of Online Trust and Safety)

   While most moderation actions on major social platforms are performed by either the platforms themselves or volunteer moderators, it is rare for platforms to collaborate directly with moderators to address problems. This paper examines how the group-chatting platform Discord coordinated with experienced volunteer moderators to respond to hate and harassment toward LGBTQ+ communities during Pride Month, June 2021, in what came to be known as the "Pride Mod" initiative. Representatives from Discord and volunteer moderators collaboratively identified and communicated with targeted communities, and volunteers temporarily joined servers that requested support to supplement those servers' existing volunteer moderation teams. Though LGBTQ+ communities were subject to a wave of targeted hate during Pride Month, the communities that received the requested volunteer support reported having a better capacity to handle the issues that arose. This paper reports the results of interviews with 11 moderators who participated in the initiative as well as the Discord employee who coordinated it. We show how this initiative was made possible by the way Discord has cultivated trust and built formal connections with its most active volunteers, and discuss the ethical implications of formal collaborations between for-profit platforms and volunteer users. 

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4. Meltos: multi-sample tumor phylogeny reconstruction for structural variants

   https://doi.org/10.1093/bioinformatics/btz737  

   Ricketts, Camir; Seidman, Daniel; Popic, Victoria; Hormozdiari, Fereydoun; Batzoglou, Serafim; Hajirasouliha, Iman; Valencia, Alfonso (October 2019, Bioinformatics)

   Abstract Motivation We propose Meltos, a novel computational framework to address the challenging problem of building tumor phylogeny trees using somatic structural variants (SVs) among multiple samples. Meltos leverages the tumor phylogeny tree built on somatic single nucleotide variants (SNVs) to identify high confidence SVs and produce a comprehensive tumor lineage tree, using a novel optimization formulation. While we do not assume the evolutionary progression of SVs is necessarily the same as SNVs, we show that a tumor phylogeny tree using high-quality somatic SNVs can act as a guide for calling and assigning somatic SVs on a tree. Meltos utilizes multiple genomic read signals for potential SV breakpoints in whole genome sequencing data and proposes a probabilistic formulation for estimating variant allele fractions (VAFs) of SV events. Results In order to assess the ability of Meltos to correctly refine SNV trees with SV information, we tested Meltos on two simulated datasets with five genomes in both. We also assessed Meltos on two real cancer datasets. We tested Meltos on multiple samples from a liposarcoma tumor and on a multi-sample breast cancer data (Yates et al., 2015), where the authors provide validated structural variation events together with deep, targeted sequencing for a collection of somatic SNVs. We show Meltos has the ability to place high confidence validated SV calls on a refined tumor phylogeny tree. We also showed the flexibility of Meltos to either estimate VAFs directly from genomic data or to use copy number corrected estimates. Availability and implementation Meltos is available at https://github.com/ih-lab/Meltos. Contact imh2003@med.cornell.edu Supplementary information Supplementary data are available at Bioinformatics online. 

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5. MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming

   https://doi.org/10.1145/3711032  

   Li, Yuan; Sun, Lichao; Zhang, Yixuan (April 2025, Proceedings of the ACM on Human-Computer Interaction)

   Significant advancements have occurred in the application of Large Language Models (LLMs) for social simulations. Despite this, their abilities to perform teaming in task-oriented social events are underexplored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behaviors and form efficient teams to solve tasks. To bridge this gap, we introduce MetaAgents, a social simulation framework populated with LLM-based agents. MetaAgents facilitates agent engagement in conversations and a series of decision making within social contexts, serving as an appropriate platform for investigating interactions and interpersonal decision-making of agents. In particular, we construct a job fair environment as a case study to scrutinize the team assembly and skill-matching behaviors of LLM-based agents. We take advantage of both quantitative metrics evaluation and qualitative text analysis to assess their teaming abilities at the job fair. Our evaluation demonstrates that LLM-based agents perform competently in making rational decisions to develop efficient teams. However, we also identify limitations that hinder their effectiveness in more complex team assembly tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations. 

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