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Introduction: Accurate genotyping of Killer cell Immunoglobulin-like Receptor (KIR) genes plays a pivotal role in enhancing our understanding of innate immune responses, disease correlations, and the advancement of personalized medicine. However, due to the high variability of the KIR region and high level of sequence similarity among different KIR genes, the generic genotyping workflows are unable to accurately infer copy numbers and complete genotypes of individual KIR genes from next-generation sequencing data. Thus, specialized genotyping tools are needed to genotype this complex region. Methods: Here, we introduce Geny, a new computational tool for precise genotyping of KIR genes. Geny utilizes available KIR allele databases and proposes a novel combination of expectation-maximization filtering schemes and integer linear programming-based combinatorial optimization models to resolve ambiguous reads, provide accurate copy number estimation, and estimate the correct allele of each copy of genes within the KIR region. Results & Discussion: We evaluated Geny on a large set of simulated short-read datasets covering the known validated KIR region assemblies and a set of Illumina short-read samples sequenced from 40 validated samples from the Human Pangenome Reference Consortium collection and showed that it outperforms the existing state-of-the-art KIR genotyping tools in terms of accuracy, precision, and recall. We envision Geny becoming a valuable resource for understanding immune system response and consequently advancing the field of patient-centric medicine. [ABSTRACT FROM AUTHOR]

Plant viral infections cause significant economic losses, totalling $350 billion USD in 2021. With no treatment for virus-infected plants, accurate and efficient diagnosis is crucial to preventing and controlling these diseases. High-throughput sequencing (HTS) enables cost-efficient identification of known and unknown viruses. However, existing diagnostic pipelines face challenges. First, many methods depend on subjectively chosen parameter values, undermining their robustness across various data sources. Second, artifacts (e.g. false peaks) in the mapped sequence data can lead to incorrect diagnostic results. While some methods require manual or subjective verification to address these artifacts, others overlook them entirely, affecting the overall method performance and leading to imprecise or labour-intensive outcomes. To address these challenges, we introduce IIMI, a new automated analysis pipeline using machine learning to diagnose infections from 1583 plant viruses with HTS data. It adopts a data-driven approach for parameter selection, reducing subjectivity, and automatically filters out regions affected by artifacts, thus improving accuracy. Testing with in-house and published data shows IIMI's superiority over existing methods. Besides a prediction model, IIMI also provides resources on plant virus genomes, including annotations of regions prone to artifacts. The method is available as an R package (iimi) on CRAN and will integrate with the web application www.virtool.ca , enhancing accessibility and user convenience. [ABSTRACT FROM AUTHOR]

Summary Natural killer (NK) cells are essential components of the innate immune system, with their activity significantly regulated by Killer cell Immunoglobulin-like Receptors (KIRs). The diversity and structural complexity of KIR genes present significant challenges for accurate genotyping, essential for understanding NK cell functions and their implications in health and disease. Traditional genotyping methods struggle with the variable nature of KIR genes, leading to inaccuracies that can impede immunogenetic research. These challenges extend to high-quality phased assemblies, which have been recently popularized by the Human Pangenome Consortium. This article introduces BAKIR (Biologically informed Annotator for KIR locus), a tailored computational tool designed to overcome the challenges of KIR genotyping and annotation on high-quality, phased genome assemblies. BAKIR aims to enhance the accuracy of KIR gene annotations by structuring its annotation pipeline around identifying key functional mutations, thereby improving the identification and subsequent relevance of gene and allele calls. It uses a multi-stage mapping, alignment, and variant calling process to ensure high-precision gene and allele identification, while also maintaining high recall for sequences that are significantly mutated or truncated relative to the known allele database. BAKIR has been evaluated on a subset of the HPRC assemblies, where BAKIR was able to improve many of the associated annotations and call novel variants. BAKIR is freely available on GitHub, offering ease of access and use through multiple installation methods, including pip, conda, and singularity container, and is equipped with a user-friendly command-line interface, thereby promoting its adoption in the scientific community. Availability and implementation BAKIR is available at github.com/algo-cancer/bakir [ABSTRACT FROM AUTHOR]

Motivation: Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples. Results: We have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq datasets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations, fusions in particular, were private, demonstrating the extensive intergenomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Availability and implementation: MiStrVar is available for download at https://bitbucket.org/comp bio/mistrvar, and ProTIE is available at https://bitbucket.org/compbio/protie. [ABSTRACT FROM AUTHOR]

Introduction: Accurate genotyping of Killer cell Immunoglobulin-like Receptor (KIR) genes plays a pivotal role in enhancing our understanding of innate immune responses, disease correlations, and the advancement of personalized medicine. However, due to the high variability of the KIR region and high level of sequence similarity among different KIR genes, the generic genotyping workflows are unable to accurately infer copy numbers and complete genotypes of individual KIR genes from next-generation sequencing data. Thus, specialized genotyping tools are needed to genotype this complex region., Methods: Here, we introduce Geny, a new computational tool for precise genotyping of KIR genes. Geny utilizes available KIR allele databases and proposes a novel combination of expectation-maximization filtering schemes and integer linear programming-based combinatorial optimization models to resolve ambiguous reads, provide accurate copy number estimation, and estimate the correct allele of each copy of genes within the KIR region., Results & Discussion: We evaluated Geny on a large set of simulated short-read datasets covering the known validated KIR region assemblies and a set of Illumina short-read samples sequenced from 40 validated samples from the Human Pangenome Reference Consortium collection and showed that it outperforms the existing state-of-the-art KIR genotyping tools in terms of accuracy, precision, and recall. We envision Geny becoming a valuable resource for understanding immune system response and consequently advancing the field of patient-centric medicine., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Zhou, Ghezelji, Hari, Ford, Holley, Sahinalp and Numanagić.)

Natural killer (NK) cells are essential components of the innate immune system, with their activity significantly regulated by Killer cell Immunoglobulin-like Receptors (KIRs). The diversity and structural complexity of KIR genes present significant challenges for accurate genotyping, essential for understanding NK cell functions and their implications in health and disease. Traditional genotyping methods struggle with the variable nature of KIR genes, leading to inaccuracies that can impede immunogenetic research. These challenges extend to high-quality phased assemblies, which have been recently popularized by the Human Pangenome Consortium. This paper introduces BAKIR (Biologically-informed Annotator for KIR locus), a tailored computational tool designed to overcome the challenges of KIR genotyping and annotation on high-quality, phased genome assemblies. BAKIR aims to enhance the accuracy of KIR gene annotations by structuring its annotation pipeline around identifying key functional mutations, thereby improving the identification and subsequent relevance of gene and allele calls. It uses a multi-stage mapping, alignment, and variant calling process to ensure high-precision gene and allele identification, while also maintaining high recall for sequences that are significantly mutated or truncated relative to the known allele database. BAKIR has been evaluated on a subset of the HPRC assemblies, where BAKIR was able to improve many of the associated annotations and call novel variants. BAKIR is freely available on GitHub, offering ease of access and use through multiple installation methods, including pip, conda, and singularity container, and is equipped with a user-friendly command-line interface, thereby promoting its adoption in the scientific community.