High-coverage Data Research Articles

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

Susceptibility to Treatment-Resistant Depression Within Families

Antidepressant responses and the phenotype of treatment-resistant depression (TRD) are believed to have a genetic basis. Genetic susceptibility between the TRD phenotype and other psychiatric disorders has also been established in previous genetic studies, but population-based cohort studies have not yet provided evidence to support these outcomes. To estimate the TRD susceptibility and the susceptibility between TRD and other psychiatric disorders within families in a nationwide insurance cohort with extremely high coverage and comprehensive health care data. This cohort study assessed data from the Taiwan national health insurance database across entire population (N = 26 554 001) between January 2003 and December 2017. Data analysis was performed from August 2021 to April 2023. TRD was defined as having experienced at least 3 distinct antidepressant treatments in the current episode, each with adequate dose and duration, based on the prescribing records. Then, we identified the first-degree relatives of individuals with TRD (n = 34 467). A 1:4 comparison group (n = 137 868) of first-degree relatives of individuals without TRD was arranged for the comparison group, matched by birth year, sex, and kinship. Modified Poisson regression analyses were performed and adjusted relative risks (aRRs) and 95% CIs were calculated for the risk of TRD, the risk of other major psychiatric disorders, and different causes of mortality. This study included 172 335 participants (88 330 male and 84 005 female; mean [SD] age at beginning of follow-up, 22.9 [18.1] years). First-degree relatives of individuals with TRD had lower incomes, more physical comorbidities, higher suicide mortality, and increased risk of developing TRD (aRR, 9.16; 95% CI, 7.21-11.63) and higher risk of other psychiatric disorders than matched control individuals, including schizophrenia (aRR, 2.36; 95% CI, 2.10-2.65), bipolar disorder (aRR, 3.74; 95% CI, 3.39-4.13), major depressive disorder (aRR, 3.65; 95% CI, 3.44-3.87), attention-deficit/hyperactivity disorders (aRR, 2.38; 95% CI, 2.20-2.58), autism spectrum disorder (aRR, 2.26; 95% CI, 1.86-2.74), anxiety disorder (aRR, 2.71; 95% CI, 2.59-2.84), and obsessive-compulsive disorder (aRR, 3.14; 95% CI, 2.70-3.66). Sensitivity and subgroup analyses validated the robustness of the findings. To our knowledge, this study is the largest and perhaps first nationwide cohort study to demonstrate TRD phenotype transmission across families and coaggregation with other major psychiatric disorders. Patients with a family history of TRD had an increased risk of suicide mortality and tendency toward antidepressant resistance; therefore, more intensive treatments for depressive symptoms might be considered earlier, rather than antidepressant monotherapy.

SNP heterozygosity, relatedness and inbreeding of whole genomes from the isolated population of the Faroe Islands

BackgroundThe population of the Faroe Islands is an isolated population but very little is known about it from whole genome sequencing. The population of about 50000 people has a high incidence of rare diseases e.g., 1:300 for Primary Carnitine Deficiency. A screening programme was implemented, and eleven persons were also whole genome sequenced at x37 coverage for diagnostic purposes of those cases that were not affected by the known mutations. The purpose of our study is to utilize the high coverage data to explore the genomic variation and the ancestral history of the population. We study the SNP heterozygosity, the pairwise relatedness from kinship, the inbreeding from runs of homozygosity ROH, and we find the minor allele frequency distribution. We estimate the population ancestry and the timing of the founding event by using the whole genomes from eight consenting individuals.ResultsWe find the number of SNPs and the heterozygosity for the eight individual samples, and for merged samples, for which we also study the relatedness. We find close relatedness between the supposedly unrelated individuals. From ROH, we interpret the high relatedness as an ancient property of the isolated population. A bottleneck event is estimated starting between years sim 50-300 with a maximum consanguineous population in year sim 600 and similarly consanguineous between years 500-700. The ancestry analysis shows the population descends from founders of >99% European and <1% Admixed American ancestry. A distinct clustering near the central European and British populations of the 1000 Genome Project is likely the result of the population isolation and genetic drift. The minor allele frequency distribution suggests many rare variants.ConclusionsThe ancestry is mainly European while the inbreeding is higher compared to European populations and population isolates. The Faroese population has inbreeding more like ancient Europeans. We discovered a bottlenecked and consanguineous population event and estimated it starting in the 1st-4th century as compared to the oldest archaeological findings from the 4th-6th century.

XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker

In view of the urgent need for high coverage and high-resolution atmospheric CO2 data in the study of carbon neutralization and global CO2 change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO2 datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO2 dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO2 interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R2 is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO2 dataset.

Dynamic origin–destination flow estimation for urban road network solely using probe vehicle trajectory data

Dynamic origin–destination (OD) flow is a fundamental input for dynamic network models and simulators. Numerous studies have conducted dynamic OD estimations based on fixed detectors, where a high device coverage rate and data quality are often required to accomplish the desired results. Several existing methods have used probe vehicle trajectories as an additional data source, and generalized least squares (GLS) is commonly recognized as an effective framework. However, the prior matrices used in these models either came from historical data or data obtained by uniform scaling that neglected the variation in penetration rates and suffer from sparsity issues. Moreover, the microscopic information contained in the high-resolution probe vehicle trajectories has not been fully utilized. The possibility of estimating OD flows using only vehicle trajectories without external information is rarely discussed in current literature. Therefore, this paper introduces a dynamic OD flow estimation model solely using probe vehicle trajectories. In the proposed model, two methods based on probe OD pair distribution are proposed to infer prior OD flows. Then the GLS framework is extended by including link travel times as another objective term, and the solution algorithm is adapted to deal with uncertain priors. To validate the proposed model, extensive experiments were conducted on a simulation network. The results show that the proposed model could reliably estimate dynamic OD flows and showed superiority to two existing models. In sensitivity analysis concerning the penetration rate and degree of saturation, the proposed model presented satisfactory performance and could adapt to various conditions.

Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data

Seaports play an important role in the global shipping network. Shipping participants often attach great importance to the measurement of container port connectivity, as it reflects countries' access to world markets. As a result, various port connectivity index systems have been proposed by members of the shipping industry and scholars. In recent years, technological developments especially the advancement of high coverage and real-time Automatic Identification System (AIS) data, have provided a chance to improve the scope and frequency of the existing index systems. An improved system is expected to reflect the dynamic changes in a port's connectivity which may be induced by either local disruptions or shocks in the wider economy. This study builds a monthly container port connectivity index system by applying big data mining techniques, graph theory, and principal component analysis (PCA) to AIS data, taking both port factors and shipping network factors into consideration. AIS records from 2020 are used to calculate the connectivity score of 25 major container ports. We also compare our system with the connectivity index commonly used in the shipping industry, the Liner Shipping Connectivity Index (LSCI). Our results show that the measurement of connectivity can be improved over indices that depend primarily on indicators of traffic volume. Ports like Antwerp and Tanjung Pelepas rank high in the proposed system due to their sound performance on their accessibility and strategic position in the local region instead of their traffic volume. The monthly index system is also proven to reflect timely changes in the shipping industry through its accurate portrayal of changes in port connectivity during the COVID-19 outbreak.

An empirical evaluation of genotype imputation of ancient DNA.

With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase the power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle v4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference, and study sample size. Making use of five ancient individuals with high-coverage data available, we evaluated imputed data for accuracy, reference bias, and genetic affinities as captured by principal component analysis. We obtained genotype concordance levels of over 99% for data with 1× coverage, and similar levels of accuracy and reference bias at levels as low as 0.75×. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1×. We also show that a large and varied phased reference panel as well as the inclusion of low- to moderate-coverage ancient individuals in the study sample can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for postprocessing and validation prior to downstream analysis.

Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

Unmanned air vehicles (UAVs) used as aerial base stations (ABSs) can provide communication services in areas where cellular network is not functional due to a calamity. ABSs provide high coverage and high data rates to the user because of the advantage of a high altitude. ABSs can be static or mobile; they can adjust their position according to real-time location of ground user and maintain a good line-of-sight link with ground users. In this paper, a reinforcement learning framework is proposed to maximize the number of served users by optimizing the ABS 3D location and power. We also design a reward function that prioritize the emergency users to establish a connection with the ABS using Q-learning. Simulation results reveal that the proposed scheme clearly outperforms the baseline schemes.

Targeted long-read sequencing reveals clonally expanded HBV-associated chromosomal translocations in patients with chronic hepatitis B

Background & AimsHBV infects over 257 million people worldwide and is associated with the development of hepatocellular carcinoma (HCC). Integration of HBV DNA into the host genome is likely a key driver of HCC oncogenesis. Here, we utilise targeted long-read sequencing to determine the structure of HBV DNA integrations as well as full isoform information of HBV mRNA with more accurate quantification than traditional next generation sequencing platforms.MethodsDNA and RNA were isolated from fresh frozen liver biopsies collected within the GS-US-174-0149 clinical trial. A pan-genotypic panel of biotinylated oligos was developed to enrich for HBV sequences from sheared genomic DNA (∼7 kb) and full-length cDNA libraries from poly-adenylated RNA. Samples were sequenced on the PacBio long-read platform and analysed using a custom bioinformatic pipeline.ResultsHBV-targeted long-read DNA sequencing generated high coverage data spanning entire integrations. Strikingly, in 13 of 42 samples (31%) we were able to detect HBV sequences flanked by 2 different chromosomes, indicating a chromosomal translocation associated with HBV integration. Chromosomal translocations were unique to each biopsy sample, suggesting that each originated randomly, and in some cases had evidence of clonal expansion. Using targeted long-read RNA sequencing, we determined that upwards of 95% of all HBV transcripts in patients who are HBeAg-positive originate from cccDNA. In contrast, patients who are HBeAg-negative expressed mostly HBsAg from integrations.ConclusionsTargeted lso-Seq allowed for accurate quantitation of the HBV transcriptome and assignment of transcripts to either cccDNA or integration origins. The existence of multiple unique HBV-associated inter-chromosomal translocations in non-HCC CHB patient liver biopsies suggests a novel mechanism with mutagenic potential that may contribute to progression to HCC.Lay summaryFresh frozen liver biopsies from patients infected with HBV were subjected to targeted long-read RNA and DNA sequencing. Long-read RNA sequencing captures entire HBV transcripts in a single read, allowing for resolution of overlapping transcripts from the HBV genome. This resolution allowed us to quantify the burden of transcription from integrations vs. cccDNA origin in individual patients. Patients who were HBeAg-positive had a significantly larger fraction of the HBV transcriptome originating from cccDNA compared with those who were HBeAg-negative. Long-read DNA sequencing captured entire integrated HBV sequences including multiple kilobases of flanking host sequence within single reads. This resolution allowed us to describe integration events flanked by 2 different host chromosomes, indicating that integrated HBV DNA are associated with inter-chromosomal translocations. This may lead to significant transcriptional dysregulation and drive progression to HCC.

A customised target capture sequencing tool for molecular identification of Aloe vera and relatives

Plant molecular identification studies have, until recently, been limited to the use of highly conserved markers from plastid and other organellar genomes, compromising resolution in highly diverse plant clades. Due to their higher evolutionary rates and reduced paralogy, low-copy nuclear genes overcome this limitation but are difficult to sequence with conventional methods and require high-quality input DNA. Aloe vera and its relatives in the Alooideae clade (Asphodelaceae, subfamily Asphodeloideae) are of economic interest for food and health products and have horticultural value. However, pressing conservation issues are increasing the need for a molecular identification tool to regulate the trade. With > 600 species and an origin of ± 15 million years ago, this predominantly African succulent plant clade is a diverse and taxonomically complex group for which low-copy nuclear genes would be desirable for accurate species discrimination. Unfortunately, with an average genome size of 16.76 pg, obtaining high coverage sequencing data for these genes would be prohibitively costly and computationally demanding. We used newly generated transcriptome data to design a customised RNA-bait panel targeting 189 low-copy nuclear genes in Alooideae. We demonstrate its efficacy in obtaining high-coverage sequence data for the target loci on Illumina sequencing platforms, including degraded DNA samples from museum specimens, with considerably improved phylogenetic resolution. This customised target capture sequencing protocol has the potential to confidently indicate phylogenetic relationships of Aloe vera and related species, as well as aid molecular identification applications.

HLA-G genetic diversity and evolutive aspects in worldwide populations

HLA-G is a promiscuous immune checkpoint molecule. The HLA-G gene presents substantial nucleotide variability in its regulatory regions. However, it encodes a limited number of proteins compared to classical HLA class I genes. We characterized the HLA-G genetic variability in 4640 individuals from 88 different population samples across the globe by using a state-of-the-art method to characterize polymorphisms and haplotypes from high-coverage next-generation sequencing data. We also provide insights regarding the HLA-G genetic diversity and a resource for future studies evaluating HLA-G polymorphisms in different populations and association studies. Despite the great haplotype variability, we demonstrated that: (1) most of the HLA-G polymorphisms are in introns and regulatory sequences, and these are the sites with evidence of balancing selection, (2) linkage disequilibrium is high throughout the gene, extending up to HLA-A, (3) there are few proteins frequently observed in worldwide populations, with lack of variation in residues associated with major HLA-G biological properties (dimer formation, interaction with leukocyte receptors). These observations corroborate the role of HLA-G as an immune checkpoint molecule rather than as an antigen-presenting molecule. Understanding HLA-G variability across populations is relevant for disease association and functional studies.

A survey of maritime communications: From the wireless channel measurements and modeling perspective

Maritime communications, including inland waterways and sea communications, are key technologies for ensuring communication and navigational safety during maritime activities. New mobile communication technologies, such as 5G and 6G, focus on the full coverage of land, sea, and space. However, the current maritime communications primarily depend on satellite communication systems that have high costs, limited coverage, and low data rate. Thus, research on maritime communications based on broadband technology has garnered attention in recent years, and it requires a deeper understanding of the characteristics of wireless channels. In this paper, we outline the difference between the terrestrial and maritime propagation properties and present a survey of maritime communications in terms of the wireless channel measurements and modeling. Furthermore, the research challenges and directions that may be encountered in future maritime communications are discussed.

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

Ancestral Spectrum Analysis With Population-Specific Variants.

With the advance of sequencing technology, an increasing number of populations have been sequenced to study the histories of worldwide populations, including their divergence, admixtures, migration, and effective sizes. The variants detected in sequencing studies are largely rare and mostly population specific. Population-specific variants are often recent mutations and are informative for revealing substructures and admixtures in populations; however, computational methods and tools to analyze them are still lacking. In this work, we propose using reference populations and single nucleotide polymorphisms (SNPs) specific to the reference populations. Ancestral information, the best linear unbiased estimator (BLUE) of the ancestral proportion, is proposed, which can be used to infer ancestral proportions in recently admixed target populations and measure the extent to which reference populations serve as good proxies for the admixing sources. Based on the same panel of SNPs, the ancestral information is comparable across samples from different studies and is not affected by genetic outliers, related samples, or the sample sizes of the admixed target populations. In addition, ancestral spectrum is useful for detecting genetic outliers or exploring co-ancestry between study samples and the reference populations. The methods are implemented in a program, Ancestral Spectrum Analyzer (ASA), and are applied in analyzing high-coverage sequencing data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP). In the analyses of American populations from the 1000 Genomes Project, we demonstrate that recent admixtures can be dissected from ancient admixtures by comparing ancestral spectra with and without indigenous Americans being included in the reference populations.

Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

Microbiome Fingerprint as Biomarker for Geographical Origin and Heredity in Crocus sativus: A Feasibility Study

Host–microbiome interactions are specific and not random, making them defining entities for the host. The hypothesis proposed by various researchers earlier, that both plants and animals harbor specific inheritable core microbiome, is being augmented in the present study. Additionally, a case for using microbial fingerprint as a biomarker, not only for plant identification but also as a geographical indicator, has been investigated, taking Crocus sativus, saffron, as a study material. Crocus sativus, a monogenetic herb, on account of its male sterility and vegetative propagation, is reported to lack genome based molecular markers. Cormosphere microbiome (microbiome associated with corm) has been compared across three geographical locations, in two continents, to identify the core and unique microbiome, during the vegetative phase of its growth. Microbiome analysis done at phylum and genus level, using next generation sequencing technology, revealed that cormosphere at three locations harbored common phyla. At genus level, 24 genera were found common to all three geographical locations, indicating them to be part of the core microbiome of saffron. However, there were some bacterial genera unique to Kashmir, Kishtwar, and Morocco that can be used to develop microbial markers/geographical indicators for saffron grown in these regions. This is a preliminary study, indicating that the location specific bacterial community can be used to develop microbial barcodes but needs further augmentation with high coverage data from other saffron growing geographical regions.

SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks

BackgroundOnce aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data.ResultsIn this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads that takes raw reads as the input and detects structural variants of size larger than 50 bp. Our pipeline utilizes state-of-the-art long-read aligners, namely NGMLR and Minimap2, and structural variation callers, videlicet Sniffle and SVIM. We found that by using a neural network, we can extract features from Minimap2 output to detect a subset of reads that provide useful information for structural variation detection. By only mapping this subset with NGMLR, which is far slower than Minimap2 but better serves downstream structural variation detection, we can increase the sensitivity in an efficient way. As a result of using multiple tools intelligently, SVNN achieves up to 20 percentage points of sensitivity improvement in comparison with state-of-the-art methods and is three times faster than a naive combination of state-of-the-art tools to achieve almost the same accuracy.ConclusionSince prohibitive costs of using high-coverage data have impeded long-read applications, with SVNN, we provide the users with a much faster structural variation detection platform for PacBio reads with high precision and sensitivity in low-coverage scenarios.

Quantifying Scales of Spatial Variability of Cyanobacteria in a Large, Eutrophic Lake Using Multiplatform Remote Sensing Tools

Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.

Fine-scale population structure in five rural populations from the Spanish Eastern Pyrenees using high-coverage whole-genome sequence data

The area of the Spanish Pyrenees is particularly interesting for studying the demographic dynamics of European rural areas given its orography, the main traditional rural condition of its population and the reported higher patterns of consanguinity of the region. Previous genetic studies suggest a gradient of genetic continuity of the area in the West to East axis. However, it has been shown that micro-population substructure can be detected when considering high-quality NGS data and using spatial explicit methods. In this work, we have analyzed the genome of 30 individuals sequenced at 40× from five different valleys in the Spanish Eastern Pyrenees (SEP) separated by less than 140 km along a west to east axis. Using haplotype-based methods and spatial analyses, we have been able to detect micro-population substructure within SEP not seen in previous studies. Linkage disequilibrium and autozygosity analyses suggest that the SEP populations show diverse demographic histories. In agreement with these results, demographic modeling by means of ABC-DL identify heterogeneity in their effective population sizes despite of their close geographic proximity, and suggests that the population substructure within SEP could have appeared around 2500 years ago. Overall, these results suggest that each rural population of the Pyrenees could represent a unique entity.

Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.