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Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Background: Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors., Results: In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins., Conclusions: We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis., (© 2017 Cameron et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion)., Results: We present CLOVE, an algorithm for integrating the results of multiple breakpoint or SV callers and classifying the results as a particular SV. CLOVE is based on a graph data structure that is created from the breakpoint information. The algorithm looks for patterns in the graph that are characteristic of more complex rearrangement types. CLOVE is able to integrate the results of multiple callers, producing a consensus call., Conclusions: We demonstrate using simulated and real data that re-classified SV calls produced by CLOVE improve on the raw call set of existing SV algorithms, particularly in terms of accuracy. CLOVE is freely available from http://www.github.com/PapenfussLab .

Background: The increasing affordability of DNA sequencing has allowed it to be widely deployed in pathology laboratories. However, this has exposed many issues with the analysis and reporting of variants for clinical diagnostic use. Implementing a high-throughput sequencing (NGS) clinical reporting system requires a diverse combination of capabilities, statistical methods to identify variants, global variant databases, a validated bioinformatics pipeline, an auditable laboratory workflow, reproducible clinical assays and quality control monitoring throughout. These capabilities must be packaged in software that integrates the disparate components into a useable system., Results: To meet these needs, we developed a web-based application, PathOS, which takes variant data from a patient sample through to a clinical report. PathOS has been used operationally in the Peter MacCallum Cancer Centre for two years for the analysis, curation and reporting of genetic tests for cancer patients, as well as the curation of large-scale research studies. PathOS has also been deployed in cloud environments allowing multiple institutions to use separate, secure and customisable instances of the system. Increasingly, the bottleneck of variant curation is limiting the adoption of clinical sequencing for molecular diagnostics. PathOS is focused on providing clinical variant curators and pathology laboratories with a decision support system needed for personalised medicine. While the genesis of PathOS has been within cancer molecular diagnostics, the system is applicable to NGS clinical reporting generally., Conclusions: The widespread availability of genomic sequencers has highlighted the limited availability of software to support clinical decision-making in molecular pathology. PathOS is a system that has been developed and refined in a hospital laboratory context to meet the needs of clinical diagnostics. The software is available as a set of Docker images and source code at https://github.com/PapenfussLab/PathOS .

Motivation: The application of a genomics assay to samples from a cohort is a frequently applied experimental design in cancer genomics studies. The collection and analysis of cancer sequencing data in the clinical setting is an elaborate process that may involve consenting patients, obtaining possibly-multiple DNA samples, sequencing and analysis. Many of these steps are manual. At any stage mistakes can occur that cause a DNA sample to be labelled incorrectly. However, there is a paucity of methods in the literature to identify such swaps specifically in cancer studies., Results: Here, we introduce a simple method, HYSYS, to estimate the relatedness of samples and test for sample swaps and contamination. The test uses the concordance of homozygous SNPs between samples. The method is motivated by the observation that homozygous germline population variants rarely change in the disease and are not affected by loss of heterozygosity. Our tools include visualization and a testing framework to flag possible sample swaps. We demonstrate the utility of this approach on a small cohort., Availability and Implementation: http://github.com/PapenfussLab/HaveYouSwappedYourSamples., Contact: papenfuss@wehi.edu.au., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

We report a high-quality draft of the genome sequence of the grey, short-tailed opossum (Monodelphis domestica). As the first metatherian ('marsupial') species to be sequenced, the opossum provides a unique perspective on the organization and evolution of mammalian genomes. Distinctive features of the opossum chromosomes provide support for recent theories about genome evolution and function, including a strong influence of biased gene conversion on nucleotide sequence composition, and a relationship between chromosomal characteristics and X chromosome inactivation. Comparison of opossum and eutherian genomes also reveals a sharp difference in evolutionary innovation between protein-coding and non-coding functional elements. True innovation in protein-coding genes seems to be relatively rare, with lineage-specific differences being largely due to diversification and rapid turnover in gene families involved in environmental interactions. In contrast, about 20% of eutherian conserved non-coding elements (CNEs) are recent inventions that postdate the divergence of Eutheria and Metatheria. A substantial proportion of these eutherian-specific CNEs arose from sequence inserted by transposable elements, pointing to transposons as a major creative force in the evolution of mammalian gene regulation.

The article offers information on the genomic characterization to discuss the diagnosis, prognosis and treatment of haematological malignancy which is performed on tumour specimens including lymph node biopsy or bone marrow aspirate. It focuses on the detection of genomic abnormalities of DNA from tumour cells that can be detected in the plasma.

Within the human host, the malaria parasite Plasmodium falciparum is exposed to multiple selection pressures. The host environment changes dramatically in severe malaria, but the extent to which the parasite responds to—or is selected by—this environment remains unclear. From previous studies, the parasites that cause severe malaria appear to increase expression of a restricted but poorly defined subset of the PfEMP1 variant, surface antigens. PfEMP1s are major targets of protective immunity. Here, we used RNA sequencing (RNAseq) to analyse gene expression in 44 parasite isolates that caused severe and uncomplicated malaria in Papuan patients. The transcriptomes of 19 parasite isolates associated with severe malaria indicated that these parasites had decreased glycolysis without activation of compensatory pathways; altered chromatin structure and probably transcriptional regulation through decreased histone methylation; reduced surface expression of PfEMP1; and down-regulated expression of multiple chaperone proteins. Our RNAseq also identified novel associations between disease severity and PfEMP1 transcripts, domains, and smaller sequence segments and also confirmed all previously reported associations between expressed PfEMP1 sequences and severe disease. These findings will inform efforts to identify vaccine targets for severe malaria and also indicate how parasites adapt to—or are selected by—the host environment in severe malaria. [ABSTRACT FROM AUTHOR]

Background: The Major Histocompatibility Complex (MHC) class I family of genes encode for molecules that have well-conserved structures, but have evolved to perform diverse functions. The availability of the gray, short-tailed opossum, Monodelphis domestica whole genome sequence has allowed for analysis of MHC class I gene content in this marsupial. Utilization of a novel method to search for MHC related domain structures revealed a previously unknown family of MHC class I-related genes. These genes, named UT1-17, are clustered on chromosome 1 in the opossum, unlinked to the MHC region. UT genes are only found in marsupial and monotreme genomes, consistent with being ancient in mammals yet lost in eutherian mammals. This study investigates the expression and polymorphism of the UT loci in the opossum to gain insight into their possible function. Results: Of the 17 opossumUT genes, most have restricted tissue transcription patterns, with the thymus and skin being the most common sites. Full-length structure of 11 UT transcripts revealed genes varying between five and eight exons, typical for class I family members. There were only two alternative splice variants found. The UT genes also have limited polymorphism and little evidence of positive selection. One locus, UT8, was chosen for further analysis due to its conservation amongst marsupials and generic characteristics. UT8 transcription is limited to developing αβ thymocytes, and is absent from mature αβ T cells in peripheral lymphoid tissues. Conclusion: The overall characteristics and features of UT genes including low polymorphism and restricted tissue expression make it likely that the molecules encoded by UT genes perform roles other than antigenic peptide presentation. [ABSTRACT FROM AUTHOR]

The scabies mite, Sarcoptes scabiei, is an obligate parasite of the skin that infects humans and other animal species, causing scabies, a contagious disease characterized by extreme itching. Scabies infections are a major health problem, particularly in remote Indigenous communities in Australia, where co-infection of epidermal scabies lesions by Group A Streptococci or Staphylococcus aureus is thought to be responsible for the high rate of rheumatic heart disease and chronic kidney disease. We collected and separately sequenced mite DNA from several pools of thousands of whole mites from a porcine model of scabies (S. scabiei var. suis) and two human patients (S. scabiei var. hominis) living in different regions of northern Australia. Our sequencing samples the mite and its metagenome, including the mite gut flora and the wound micro-environment. Here, we describe the mitochondrial genome of the scabies mite. We developed a new de novo assembly pipeline based on a bait-and-reassemble strategy, which produced a 14 kilobase mitochondrial genome sequence assembly. We also annotated 35 genes and have compared these to other Acari mites. We identified single nucleotide polymorphisms (SNPs) and used these to infer the presence of six haplogroups in our samples, Remarkably, these fall into two closely-related clades with one clade including both human and pig varieties. This supports earlier findings that only limited genetic differences may separate some human and animal varieties, and raises the possibility of cross-host infections. Finally, we used these mitochondrial haplotypes to show that the genetic diversity of individual infections is typically small with 1–3 distinct haplotypes per infestation. [ABSTRACT FROM AUTHOR]

Motivation: Methods for detecting somatic genome rearrangements in tumours using next-generation sequencing are vital in cancer genomics. Available algorithms use one or more sources of evidence, such as read depth, paired-end reads or split reads to predict structural variants. However, the problem remains challenging due to the significant computational burden and high false-positive or false-negative rates.Results: In this article, we present Socrates (SOft Clip re-alignment To idEntify Structural variants), a highly efficient and effective method for detecting genomic rearrangements in tumours that uses only split-read data. Socrates has single-nucleotide resolution, identifies micro-homologies and untemplated sequence at break points, has high sensitivity and high specificity and takes advantage of parallelism for efficient use of resources. We demonstrate using simulated and real data that Socrates performs well compared with a number of existing structural variant detection tools.Availability and implementation: Socrates is released as open source and available from http://bioinf.wehi.edu.au/socrates.Contact: papenfuss@wehi.edu.auSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Targeted resequencing by massively parallel sequencing has become an effective and affordable way to survey small to large portions of the genome for genetic variation. Despite the rapid development in open source software for analysis of such data, the practical implementation of these tools through construction of sequencing analysis pipelines still remains a challenging and laborious activity, and a major hurdle for many small research and clinical laboratories. We developed TREVA (Targeted REsequencing Virtual Appliance), making pre-built pipelines immediately available as a virtual appliance. Based on virtual machine technologies, TREVA is a solution for rapid and efficient deployment of complex bioinformatics pipelines to laboratories of all sizes, enabling reproducible results. The analyses that are supported in TREVA include: somatic and germline single-nucleotide and insertion/deletion variant calling, copy number analysis, and cohort-based analyses such as pathway and significantly mutated genes analyses. TREVA is flexible and easy to use, and can be customised by Linux-based extensions if required. TREVA can also be deployed on the cloud (cloud computing), enabling instant access without investment overheads for additional hardware. TREVA is available at http://bioinformatics.petermac.org/treva/. [ABSTRACT FROM AUTHOR]

Animal venoms represent a vast library of bioactive peptides and proteins with proven potential, not only as research tools but also as drug leads and therapeutics. This is illustrated clearly by marine cone snails (genus Conus), whose venoms consist of mixtures of hundreds of peptides (conotoxins) with a diverse array of molecular targets, including voltage- and ligand-gated ion channels, G-protein coupled receptors and neurotransmitter transporters. Several conotoxins have found applications as research tools, with some being used or developed as therapeutics. The primary objective of this study was the large-scale discovery of conotoxin sequences from the venom gland of an Australian cone snail species, Conus victoriae. Using cDNA library normalization, high-throughput 454 sequencing, de novo transcriptome assembly and annotation with BLASTX and profile hidden Markov models, we discovered over 100 unique conotoxin sequences from 20 gene superfamilies, the highest diversity of conotoxins so far reported in a single study. Many of the sequences identified are new members of known conotoxin superfamilies, some help to redefine these superfamilies and others represent altogether new classes of conotoxins. In addition, we have demonstrated an efficient combination of methods to mine an animal venom gland and generate a library of sequences encoding bioactive peptides. [ABSTRACT FROM AUTHOR]

Animal venoms represent a vast library of bioactive peptides and proteins with proven potential, not only as research tools but also as drug leads and therapeutics. This is illustrated clearly by marine cone snails (genus Conus), whose venoms consist of mixtures of hundreds of peptides (conotoxins) with a diverse array of molecular targets, including voltage- and ligand-gated ion channels, G-protein coupled receptors and neurotransmitter transporters. Several conotoxins have found applications as research tools, with some being used or developed as therapeutics. The primary objective of this study was the large-scale discovery of conotoxin sequences from the venom gland of an Australian cone snail species, Conus victoriae. Using cDNA library normalization, high-throughput 454 sequencing, de novo transcriptome assembly and annotation with BLASTX and profile hidden Markov models, we discovered over 100 unique conotoxin sequences from 20 gene superfamilies, the highest diversity of conotoxins so far reported in a single study. Many of the sequences identified are new members of known conotoxin superfamilies, some help to redefine these superfamilies and others represent altogether new classes of conotoxins. In addition, we have demonstrated an efficient combination of methods to mine an animal venom gland and generate a library of sequences encoding bioactive peptides. [ABSTRACT FROM AUTHOR]