Large Genomic Datasets Research Articles

Transcriptomics of the depressed and PTSD brain

Stress is the response of an organism to demands for change, yet excessive or chronic stress contributes to nearly all psychiatric disorders. The advent of high-throughput transcriptomic methods such as single cell RNA sequencing poses new opportunities to understand the neurobiology of stress, yet substantial barriers to understanding stress remain. Stress adaptation is an organismal survival mechanism conserved across all organisms, yet there is an infinity of potential stressful experiences. Unraveling shared and separate transcriptional programs for adapting to stressful experience remains a challenge, despite methodological and analytic advances. Here we review the state of the field focusing on the technologies used to study the transcriptome for the stress neurobiologist, and also attempt to identify central questions about the heterogeneity of stress for those applying transcriptomic approaches. We further explore how postmortem transcriptome studies aided by preclinical animal models are converging on common molecular pathways for adaptation to aversive experience. Finally, we discuss approaches to integrate large genomic datasets with human neuroimaging and other datasets.

Population genomics of Drosophila suzukii reveal longitudinal population structure and signals of migrations in and out of the continental United States.

Drosophila suzukii, or spotted-wing drosophila, is now an established pest in many parts of the world, causing significant damage to numerous fruit crop industries. Native to East Asia, D. suzukii infestations started in the United States a decade ago, occupying a wide range of climates. To better understand invasion ecology of this pest, knowledge of past migration events, population structure, and genetic diversity is needed. In this study, we sequenced whole genomes of 237 individual flies collected across the continental United States, as well as several sites in Europe, Brazil, and Asia, to identify and analyze hundreds of thousands of genetic markers. We observed strong population structure between Western and Eastern US populations, but no evidence of any population structure between different latitudes within the continental United States, suggesting that there are no broad-scale adaptations occurring in response to differences in winter climates. We detect admixture from Hawaii to the Western United States and from the Eastern United States to Europe, in agreement with previously identified introduction routes inferred from microsatellite analysis. We also detect potential signals of admixture from the Western United States back to Asia, which could have important implications for shipping and quarantine policies for exported agriculture. We anticipate this large genomic dataset will spur future research into the genomic adaptations underlying D. suzukii pest activity and development of novel control methods for this agricultural pest.

Quantifying bacterial evolution in the wild: A birthday problem for Campylobacter lineages.

Measuring molecular evolution in bacteria typically requires estimation of the rate at which nucleotide changes accumulate in strains sampled at different times that share a common ancestor. This approach has been useful for dating ecological and evolutionary events that coincide with the emergence of important lineages, such as outbreak strains and obligate human pathogens. However, in multi-host (niche) transmission scenarios, where the pathogen is essentially an opportunistic environmental organism, sampling is often sporadic and rarely reflects the overall population, particularly when concentrated on clinical isolates. This means that approaches that assume recent common ancestry are not applicable. Here we present a new approach to estimate the molecular clock rate in Campylobacter that draws on the popular probability conundrum known as the ‘birthday problem’. Using large genomic datasets and comparative genomic approaches, we use isolate pairs that share recent common ancestry to estimate the rate of nucleotide change for the population. Identifying synonymous and non-synonymous nucleotide changes, both within and outside of recombined regions of the genome, we quantify clock-like diversification to estimate synonymous rates of nucleotide change for the common pathogenic bacteria Campylobacter coli (2.4 x 10−6 s/s/y) and Campylobacter jejuni (3.4 x 10−6 s/s/y). Finally, using estimated total rates of nucleotide change, we infer the number of effective lineages within the sample time frame–analogous to a shared birthday–and assess the rate of turnover of lineages in our sample set over short evolutionary timescales. This provides a generalizable approach to calibrating rates in populations of environmental bacteria and shows that multiple lineages are maintained, implying that large-scale clonal sweeps may take hundreds of years or more in these species.

NPARS-A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science.

Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating matters, in the field of genomic-based science high-throughput sequencing technologies generate considerable amounts of data that needs to be stored, manipulated, and analyzed using a plethora of software tools. Researchers are rarely able to reproduce published genomic studies. Results: Presented is a novel approach which facilitates accuracy and reproducibility for large genomic research data sets. All data needed is loaded into a portable local database, which serves as an interface for well-known software frameworks. These include python-based Jupyter Notebooks and the use of RStudio projects and R markdown. All software is encapsulated using Docker containers and managed by Git, simplifying software configuration management. Conclusion: Accuracy and reproducibility in science is of a paramount importance. For the biomedical sciences, advances in high throughput technologies, molecular biology and quantitative methods are providing unprecedented insights into disease mechanisms. With these insights come the associated challenge of scientific data that is complex and massive in size. This makes collaboration, verification, validation, and reproducibility of findings difficult. To address these challenges the NGS post-pipeline accuracy and reproducibility system (NPARS) was developed. NPARS is a robust software infrastructure and methodology that can encapsulate data, code, and reporting for large genomic studies. This paper demonstrates the successful use of NPARS on large and complex genomic data sets across different computational platforms.

Alignment-free methods for polyploid genomes: Quick and reliable genetic distance estimation.

Polyploid genomes pose several inherent challenges to population genetic analyses. While alignment-based methods are fundamentally limited in their applicability to polyploids, alignment-free methods bypass most of these limits. We investigated the use of Mash, a k-mer analysis tool that uses the MinHash method to reduce complexity in large genomic data sets, for basic population genetic analyses of polyploid sequences. We measured the degree to which Mash correctly estimated pairwise genetic distance in simulated haploid and polyploid short-read sequences with various levels of missing data. Mash-based estimates of genetic distance were comparable to alignment-based estimates, and were less impacted by missing data. We also used Mash to analyse publicly available short-read data for three polyploid and one diploid species, then compared Mash results to published results. For both simulated and real data, Mash accurately estimated pairwise genetic differences for polyploids as well as diploids as much as 476 times faster than alignment-based methods, though we found that Mash genetic distance estimates could be biased by per-sample read depth. Mash may be a particularly useful addition to the toolkit of polyploid geneticists for rapid confirmation of alignment-based results and for basic population genetics in reference-free systems or those with only poor-quality sequence data available.

POLYGENIC RISK SCORES, PGT-P, AND IVF: SIBLING AND NON-EUROPEAN ANCESTRY VALIDATIONS

Polygenic Risk Scores (PRS) are under intense investigation by research groups all around the world. Large genomic datasets and machine learning methods have produced predictors for common disease conditions such as coronary artery disease, diabetes, schizophrenia, and breast cancer. The objective of this study is to further validate PRS and related health and longevity indices, using sibling (family) cohorts and non-European (African, South Asian, and East Asian) ancestry groups.

Gauging the spectrum of human mutations

Human Genetics It has become increasing clear that mutation affects phenotypic variation and disease risk across humans. However, there are many different types of mutation. Seplyarskiy et al. applied a matrix factorization method to large human genomic datasets to identify germline mutational processes in an unsupervised manner. From this survey, nine robust mutational components were identified and specific mechanisms generating seven of these processes were proposed from correlations with genomic features. These results confirm and improve upon our understanding of mutational processes and reveal likely mechanisms of mutation in the human genome. Science , aba7408, this issue p. [1030][1] [1]: /lookup/doi/10.1126/science.aba7408

Distributed hybrid-indexing of compressed pan-genomes for scalable and fast sequence alignment.

Computational pan-genomics utilizes information from multiple individual genomes in large-scale comparative analysis. Genetic variation between case-controls, ethnic groups, or species can be discovered thoroughly using pan-genomes of such subpopulations. Whole-genome sequencing (WGS) data volumes are growing rapidly, making genomic data compression and indexing methods very important. Despite current space-efficient repetitive sequence compression and indexing methods, the deployed compression methods are often sequential, computationally time-consuming, and do not provide efficient sequence alignment performance on vast collections of genomes such as pan-genomes. For performing rapid analytics with the ever-growing genomics data, data compression and indexing methods have to exploit distributed and parallel computing more efficiently. Instead of strict genome data compression methods, we will focus on the efficient construction of a compressed index for pan-genomes. Compressed hybrid-index enables fast sequence alignments to several genomes at once while shrinking the index size significantly compared to traditional indexes. We propose a scalable distributed compressed hybrid-indexing method for large genomic data sets enabling pan-genome-based sequence search and read alignment capabilities. We show the scalability of our tool, DHPGIndex, by executing experiments in a distributed Apache Spark-based computing cluster comprising 448 cores distributed over 26 nodes. The experiments have been performed both with human and bacterial genomes. DHPGIndex built a BLAST index for n = 250 human pan-genome with an 870:1 compression ratio (CR) in 342 minutes and a Bowtie2 index with 157:1 CR in 397 minutes. For n = 1,000 human pan-genome, the BLAST index was built in 1520 minutes with 532:1 CR and the Bowtie2 index in 1938 minutes with 76:1 CR. Bowtie2 aligned 14.6 GB of paired-end reads to the compressed (n = 1,000) index in 31.7 minutes on a single node. Compressing n = 13,375,031 (488 GB) GenBank database to BLAST index resulted in CR of 62:1 in 575 minutes. BLASTing 189,864 Crispr-Cas9 gRNA target sequences (23 MB in total) to the compressed index of human pan-genome (n = 1,000) finished in 45 minutes on a single node. 30 MB mixed bacterial sequences were (n = 599) were blasted to the compressed index of 488 GB GenBank database (n = 13,375,031) in 26 minutes on 25 nodes. 78 MB mixed sequences (n = 4,167) were blasted to the compressed index of 18 GB E. coli sequence database (n = 745,409) in 5.4 minutes on a single node.

Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene.

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations.

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations.

Analysis of 56,348 Genomes Identifies the Relationship between Antibiotic and Metal Resistance and the Spread of Multidrug-Resistant Non-Typhoidal Salmonella

Salmonella enterica is common foodborne pathogen that generates both enteric and systemic infections in hosts. Antibiotic resistance is common is certain serovars of the pathogen and of great concern to public health. Recent reports have documented the co-occurrence of metal resistance with antibiotic resistance in one serovar of S. enterica. Therefore, we sought to identify possible co-occurrence in a large genomic dataset. Genome assemblies of 56,348 strains of S. enterica comprising 20 major serovars were downloaded from NCBI. The downloaded assemblies were quality controlled and in silico serotyped to ensure consistency and avoid improper annotation from public databases. Metal and antibiotic resistance genes were identified in the genomes as well as plasmid replicons. Co-occurrent genes were identified by constructing a co-occurrence matrix and grouping said matrix using k-means clustering. Three groups of co-occurrent genes were identified using k-means clustering. Group 1 was comprised of the pco and sil operons that confer resistance to copper and silver, respectively. Group 1 was distributed across four serovars. Group 2 contained the majority of the genes and little to no co-occurrence was observed. Metal and antibiotic co-occurrence was identified in group 3 that contained genes conferring resistance to: arsenic, mercury, beta-lactams, sulfonamides, and tetracyclines. Group 3 genes were also associated with an IncQ1 class plasmid replicon. Metal and antibiotic co-occurrence from group 3 genes is mostly isolated to one clade of S. enterica I 4,[5],12:i:-.

Using replicate hybrid zones to understand the genomic basis of adaptive divergence.

Combining hybrid zone analysis with genomic data is a promising approach to understanding the genomic basis of adaptive divergence. It allows for the identification of genomic regions underlying barriers to gene flow. It also provides insights into spatial patterns of allele frequency change, informing about the interplay between environmental factors, dispersal and selection. However, when only a single hybrid zone is analysed, it is difficult to separate patterns generated by selection from those resulting from chance. Therefore, it is beneficial to look for repeatable patterns across replicate hybrid zones in the same system. We applied this approach to the marine snail Littorina saxatilis, which contains two ecotypes, adapted to wave-exposed rocks vs. high-predation boulder fields. The existence of numerous hybrid zones between ecotypes offered the opportunity to test for the repeatability of genomic architectures and spatial patterns of divergence. We sampled and phenotyped snails from seven replicate hybrid zones on the Swedish west coast and genotyped them for thousands of single nucleotide polymorphisms. Shell shape and size showed parallel clines across all zones. Many genomic regions showing steep clines and/or high differentiation were shared among hybrid zones, consistent with a common evolutionary history and extensive gene flow between zones, and supporting the importance of these regions for divergence. In particular, we found that several large putative inversions contribute to divergence in all locations. Additionally, we found evidence for consistent displacement of clines from the boulder-rock transition. Our results demonstrate patterns of spatial variation that would not be accessible without continuous spatial sampling, a large genomic data set and replicate hybrid zones.

Discovery and Opportunities With Integrative Analytics Using Multiple-Omics Data.

In our era of post-information age, the generation of data increases at an astronomical pace. This progress has led to doubling the sum of medical knowledge to every 73 days in 2020 from every 50 years in 1950.(1) For example, recent efforts with large genomic datasets have allowed for unraveling unknown genetic associations with disease risk, and fueled therapeutic options.

Abstract 1821: Integrative genomic analysis of diffuse large B cell lymphoma (DLBCL) identifies recurrent oncogenic alterations associated with distinct immune-related clusters

Abstract Introduction: Genetic alterations in tumor cells shape the cancer immune landscape and influence response to checkpoint blockade therapy (CBT). Despite recent advances in defining DLBCL genomics, the impact of genetic alterations on the DLBCL immune environment is not well-characterized. Here, we aim to define unique immune landscapes in DLBCL and determine the impact of lymphoma-cell intrinsic alterations on shaping immune environments in this disease, which may have implications for CBT response Methods: Gene set variation analysis (GSVA) was used to generate sample-wise enrichment scores for immune-related gene sets in ~500 DLBCLs. A cell-of-origin (COO) signature that classifies DLBCLs based on transcriptional similarity to activated B cells (ABC) or germinal center B cells (GCB) was included to control for COO-related genomic heterogeneity. Samples were hierarchically clustered. Paired exome sequencing was analyzed to identify mutations associated with each cluster. Cas9-mediated gene editing was used to modulate genes of interest in a murine lymphoma model (A20) Results: GSVA revealed 4 DLBCL immune clusters - ABC hot, ABC cold, GCB hot and GCB cold. Concordant with previous work, PD-L1 gene amplifications were highly enriched in ABC hot DLBCLs, while double-hit signature DLBCLs fell within the GCB cold cluster. Mutations and oncogenic pathways associated with each cluster were identified. ABC cold DLBCLs harbored recurrent inactivating mutations in TMEM30A and PRDM1, while mutations in KLHL6 and SPEN were associated with ABC hot DLBCLs. The GBC cold cluster was enriched for mutations in TP53 and FOXO1, and inactivating SOCS1 mutations were prevalent in GCB hot DLBCLs. MYC activation signatures characterized ABC and GCB cold clusters. A20 lymphomas with inducible c-Myc activation (A20MycER) contained fewer CD8+ T cells following tamoxifen versus control treatment, confirming our computational findings. Moreover, as SOCS1 mutations strongly associated with GCB hot DLBCLs and are prevalent in CBT-sensitive lymphomas, the extent to which Socs1-deficient lymphoma cells showed increased sensitivity to the critical T cell effector cytokine IFNγ was examined. Upon IFNγ exposure, A20Socs1-/- cells showed increased pStat1 levels, enhanced expression of PD-L1 and MHC-I, and strikingly diminished proliferation compared to A20WT cells, suggesting that enhanced JAK/STAT signaling via Socs1-/- increases IFNγ sensitivity, rendering lymphoma cells vulnerable to CBT Conclusion: Through computational analyses in a large genomic dataset, we have defined unique immune-related DLBCL clusters and associated recurrent genomic alterations. Through ongoing studies in murine models, we aim to elucidate mechanisms by which particular oncogenic alterations in lymphoma cells shape the DLBCL immune landscape and impact CBT response Citation Format: Sravya Tumuluru, James K. Godfrey, Jovian Yu, Alan Cooper, Michael Leukam, Sonali M. Smith, Xiufen Chen, Justin P. Kline. Integrative genomic analysis of diffuse large B cell lymphoma (DLBCL) identifies recurrent oncogenic alterations associated with distinct immune-related clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1821.

PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets

BackgroundPlasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. Plasmid detection in large genomic datasets is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance propagation. Bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds).ResultsWe developed PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in partially assembled genomes. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with a F1 score of 0.950. Notably, it can detect 77.4% of plasmid contigs below 1 kb with 2.8% of false positives and 99.9% of plasmid contigs over 50 kb with 2.2% of false positives.ConclusionsPlasForest outperforms other currently available tools on genomic datasets by being both sensitive and precise. The performance of PlasForest on metagenomic assemblies are currently well below those of other k-mer-based methods, and we discuss how homology-based approaches could improve plasmid detection in such datasets.

Identifying High-Risk Phenotypes Using Three-Way Feature Interactions in Multiple Myeloma: An Analysis of Publically Available Mmrf Commpass Study

The introduction of next generation sequencing has exponentially increased the number of potential disease targets from a limited panel of probes to markers across the entire genome. Previous studies of NGS data in multiple myeloma have identified key features associated with outcome in additive models, typically showing involvement of TP53, gain of 1q, MYC translocations, and others.We modified the multi-factor dimensionality reduction method (MDR) method, a computationally efficient method for detecting non-linear patterns of gene-gene interactions in genome-wide association studies (GWAS) first proposed by Richie et al, to identify three-way interactions significantly associated with outcome in MM. Specifically, we altered the ranking strategy of the survival adapted Cox-MDR method of Lee et al from balanced accuracy (average of sensitivity and specificity) to the absolute sum of deviance residuals. Due to the high level of censoring (94 events in 559 cases), balanced accuracy ranking favored interactions that identified few high risk cases and failed to validate well on external data.The publically available MMRF CoMMpass trial database (release version IA10) was utilized as primary training set for our MDR method. We included in our comparisons 164 RNA-SEQ genes with significant association with overall survival, the 20 most abundant non-synonymous, non-IG variants, and 24 estimated whole chromosome arm copy number gains or loss calls from the long insert WGS data of 559 baseline cases with outcome and clinical data (median follow-up 1.8 years). We searched for all three-way interactions across 210 total features resulting in the ranking of over 1.5 million unique three-way interactions, adjusted for age, ISS, creatinine level, and treatment by modified MDR. The top three-way RNA-expression interactions from our discovery phase were validated on 540 cases with GEP microarray and clinical data from UAMS Total Therapy 2 and 3 with median follow-up of 4.66 years (publically available data from GSE2658, GSE31161, and GSE24080).Of the top 100 three-way gene expression interactions discovered that we could validate on Affymetrix GEP data, 84 were significant in the UAMS TT2 and TT3 data set (log rank p-value < 0.05). HMGB3, a proposed inhibitor of myeloid differentiation, and DDX51, known to be differentially methylated in ALL, were the most commonly involved genes in the top significant interactions across both data sets. Pathway analysis of the 150 most frequently observed genes across the top 5,000 interactions in CoMMpass revealed an enrichment of DNA replication and cell cycle associated genes: RFC5, MCM2, MCM3, MCM4, MCM6, CCNA2, CDK2, etc.Within the top interactions that included whole arm copy number variants, gain of 1q and loss of 13q were the most frequently observed. We note that the top three-way interactions with gain of 1q paired exclusively with ALYREF, a transcriptional promoter, and one additional gene. Of the top interactions that included a SNV, mutations of DIS3 and DNAH5 were the most frequently observed. Many of the top interactions including DIS3 mutations paired with HMGB3 or TRIB3, a negative regulator of NF-kB and sensitizer to apoptosis, and one additional gene.MDR models are capable of identifying complex interactions of features that are not strictly additive. We observed a variety of interaction models, and present below an example where presence of 2 or more features resulted in overall negative outcome while presence of 1 or fewer did not. This situation was observed for the interaction of high expression of HMGB3, DDX51, and PSMB2, a bortezomib sensitizer in MM cell lines, where high expression in 2 or more of these genes imparted a negative outcome in CoMMpass and TT2/TT3 data sets. In the TT2/TT3 data set, cases identified as high risk from this interaction were enriched for the PR subtype and GEP70 HR.This modified MDR method is an effective tool at mining large genomic data sets for all k -way interactions associated with outcome among binary or multi-level features, adjusted for clinical covariates. Our modifications to MDR method allow us to identify more relevant interactions and increase reliability in validation data. This method could potentially further knowledge of the complex relationships between key features in genomic landscape of MM by discovering novel interactions that identify high-risk phenotypes. [Display omitted] DisclosuresBergsagel:Phosplatin Therapeutics: Research Funding.

Is automated genomic reporting ready for prime time?

The process of reducing the large genomic datasets obtained from sequencing a genome, to a smaller and more manageable filtered set of variants for analysis, is already usually an automated pipelined process. Similarly, many laboratory and bioinformatic workflows are already amenable to basic laboratory automation.

Genomic alterations in gynecological malignancies: histotype-associated driver mutations, molecular subtyping schemes, and tumorigenic mechanisms.

There are numerous histological subtypes (histotypes) of gynecological malignancies, with each histotype considered to largely reflect a feature of the "cell of origin," and to be tightly linked with the clinical behavior and biological phenotype of the tumor. The recent advances in massive parallel sequencing technologies have provided a more complete picture of the range of the genomic alterations that can persist within individual tumors, and have highlighted the types and frequencies of driver-gene mutations and molecular subtypes often associated with these histotypes. Several large-scale genomic cohorts, including the Cancer Genome Atlas (TCGA), have been used to characterize the genomic features of a range of gynecological malignancies, including high-grade serous ovarian carcinoma, uterine corpus endometrial carcinoma, uterine cervical carcinoma, and uterine carcinosarcoma. These datasets have also been pivotal in identifying clinically relevant molecular targets and biomarkers, and in the construction of molecular subtyping schemes. In addition, the recent widespread use of clinical sequencing for the more ubiquitous types of gynecological cancer has manifested in a series of large genomic datasets that have allowed the characterization of the genomes, driver mutations, and histotypes of even rare cancer types, with sufficient statistical power. Here, we review the field of gynecological cancer, and seek to describe the genomic features by histotype. We also will demonstrate how these are linked with clinicopathological attributes and highlight the potential tumorigenic mechanisms.

Phylogeographic model selection using convolutional neural networks

The discipline of phylogeography has evolved rapidly in terms of the analytical toolkit used to analyse large genomic data sets. Despite substantial advances, analytical tools that could potentially address the challenges posed by increased model complexity have not been fully explored. For example, deep learning techniques are underutilized for phylogeographic model selection. In non-model organisms, the lack of information about their ecology and evolution can lead to uncertainty about which demographic models are appropriate. Here, we assess the utility of convolutional neural networks (CNNs) for assessing demographic models in South American lizards in the genus Norops. Three demographic scenarios (constant, expansion, and bottleneck) were considered for each of four inferred population-level lineages, and we found that the overall model accuracy was higher than 98% for all lineages. We then evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among the four lineages, identifying a single model with an estimated overall accuracy of 87% when using CNNs. The inferred demography of the lizard system suggests that gene flow between non-sister populations and changes in effective population sizes through time, probably in response to Pleistocene climatic oscillations, have shaped genetic diversity in this system. Approximate Bayesian computation (ABC) was applied to provide a comparison to the performance of CNNs. ABC was unable to identify a single model among the larger set of 26 models in the subsequent analysis. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographer's toolkit.

Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs.