From next-generation resequencing reads to a high-quality variant data set

Land use modified impacts of global change factors on soil microbial structure and function: A global hierarchical meta-analysis

Nitrogen cycling in terrestrial ecosystems is critical for biodiversity, vegetation productivity and biogeochemical cycling. However, little is known about the response of functional nitrogen cycle genes to global change factors in soils under different land uses. Here, we conducted a multiple hierarchical mixed effects meta-analyses of global change factors (GCFs) including warming (W+), mean altered precipitation (MAP+/-), elevated carbon dioxide concentrations (eCO2), and nitrogen addition (N+), using 2706 observations extracted from 200 peer-reviewed publications. The results showed that GCFs had significant and different effects on soil microbial communities under different types of land use. Under different land use types, such as Wetland, Tundra, Grassland, Forest, Desert and Agriculture, the richness and diversity of soil microbial communities will change accordingly due to differences in vegetation cover, soil management practices and environmental conditions. Notably, soil bacterial diversity is positively correlated with richness, but soil fungal diversity is negatively correlated with richness, when differences are driven by GCFs. For functional genes involved in nitrification, eCO2 in agricultural soils and the interaction of N+ with other GCFs in grassland soils stimulate an increase in the abundance of the AOA-amoA gene. In agricultural soil, MAP+ increases the abundance of nifH. W+ in agricultural soils and N+ in grassland soils decreased the abundance of nifH. The abundance of the genes nirS and nirK, involved in denitrification, was mainly negatively affected by W+ and positively affected by eCO2 in agricultural soil, but negatively affected by N+ in grassland soil. This meta-analysis was important for subsequent research related to global climate change. Considering data limitations, it is recommended to conduct multiple long-term integrated observational experiments to establish a scientific basis for addressing global changes in this context.

Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control

Rapid advances in high-throughput DNA sequencing technologies have enabled large-scale whole genome sequencing (WGS) studies. Before performing association analysis between phenotypes and genotypes, preprocessing and quality control (QC) of the raw sequence data need to be performed. Because many biostatisticians have not been working with WGS data so far, we first sketch Illumina's short-read sequencing technology. Second, we explain the general preprocessing pipeline for WGS studies. Third, we provide an overview of important QC metrics, which are applied to WGS data: on the raw data, after mapping and alignment, after variant calling, and after multisample variant calling. Fourth, we illustrate the QC with the data from the GENEtic SequencIng Study Hamburg-Davos (GENESIS-HD), a study involving more than 9000 human whole genomes. All samples were sequenced on an Illumina NovaSeq 6000 with an average coverage of 35× using a PCR-free protocol. For QC, one genome in a bottle (GIAB) trio was sequenced in four replicates, and one GIAB sample was successfully sequenced 70 times in different runs. Fifth, we provide empirical data on the compression of raw data using the DRAGEN original read archive (ORA). The most important quality metrics in the application were genetic similarity, sample cross-contamination, deviations from the expected Het/Hom ratio, relatedness, and coverage. The compression ratio of the raw files using DRAGEN ORA was 5.6:1, and compression time was linear by genome coverage. In summary, the preprocessing, joint calling, and QC of large WGS studies are feasible within a reasonable time, and efficient QC procedures are readily available.

Next-generation data filtering in the genomics era

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Whole genome sequences of 70 indigenous Ethiopian cattle

Indigenous animal genetic resources play a crucial role in preserving global genetic diversity and supporting the livelihoods of millions of people. In Ethiopia, the majority of the cattle population consists of indigenous breeds. Understanding the genetic architecture of these cattle breeds is essential for effective management and conservation efforts. In this study, we sequenced DNA samples from 70 animals from seven indigenous cattle breeds, generating about two terabytes of pair-end reads with an average coverage of 14X. The sequencing data were pre-processed and mapped to the cattle reference genome (ARS-UCD1.2) with an alignment rate of 99.2%. Finally, the variant calling process produced approximately 35 million high-quality SNPs. These data provide a deeper understanding of the genetic landscape, facilitate the identification of causal mutations, and enable the exploration of evolutionary patterns to assist cattle improvement and sustainable utilization, particularly in the face of unpredictable climate changes.

Efficient wastewater sample filtration improves the detection of SARS-CoV-2 variants: An extensive analysis based on sequencing parameters

During the SARS-CoV-2 pandemic, many countries established wastewater (WW) surveillance to objectively monitor the level of infection within the population. As new variants continue to emerge, it has become clear that WW surveillance is an essential tool for the early detection of variants. The EU Commission published a recommendation suggesting an approach to establish surveillance of SARS-CoV-2 and its variants in WW, besides specifying the methodology for WW concentration and RNA extraction. Therefore, different groups have approached the issue with different strategies, mainly focusing on WW concentration methods, but only a few groups highlighted the importance of prefiltering WW samples and/or purification of RNA samples. Aiming to obtain high-quality sequencing data allowing variants detection, we compared four experimental conditions generated from the treatment of: i) WW samples by WW filtration and ii) the extracted RNA by DNase treatment, purification and concentration of the extracted RNA. To evaluate the best condition, the results were assessed by focusing on several sequencing parameters, as the outcome of SARS-CoV-2 sequencing from WW is crucial for variant detection. Overall, the best sequencing result was obtained by filtering the WW sample. Moreover, the present study provides an overview of some sequencing parameters to consider when optimizing a method for monitoring SARS-CoV-2 variants from WW samples, which can also be applied to any sample preparation methodology.

Comparison of structural variant callers for massive whole-genome sequence data

BACKGROUND

Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers.

RESULTS

Notably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing resources, and both Manta and MELT demonstrated relatively good precision regarding calling insertions. We confirmed that the copy number variation callers, Canvas and CNVnator, exhibited better performance in identifying long duplications as they employ the read-depth approach. Finally, we also verified the genotypes inferred from each SV caller using a phased long-read assembly dataset, and Manta showed the highest concordance in terms of the deletions and insertions.

CONCLUSIONS

Our findings provide a comprehensive understanding of the accuracy and computational efficiency of SV callers, thereby facilitating integrative analysis of SV profiles in diverse large-scale genomic datasets.

Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus: A pilot bioinformatics study

BACKGROUND

Helicobacter pylori (H. pylori) infection is related to various extragastric diseases including type 2 diabetes mellitus (T2DM). However, the possible mechanisms connecting H. pylori infection and T2DM remain unknown.

AIM

To explore potential molecular connections between H. pylori infection and T2DM.

METHODS

We extracted gene expression arrays from three online datasets (GSE60427, GSE27411 and GSE115601). Differentially expressed genes (DEGs) commonly present in patients with H. pylori infection and T2DM were identified. Hub genes were validated using human gastric biopsy samples. Correlations between hub genes and immune cell infiltration, miRNAs, and transcription factors (TFs) were further analyzed.

RESULTS

A total of 67 DEGs were commonly presented in patients with H. pylori infection and T2DM. Five significantly upregulated hub genes, including TLR4, ITGAM, C5AR1, FCER1G, and FCGR2A, were finally identified, all of which are closely related to immune cell infiltration. The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links. TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs, the largest number of TFs among the 5 hub genes.

CONCLUSION

We identified five hub genes that may have molecular connections between H. pylori infection and T2DM. This study provides new insights into the pathogenesis of H. pylori-induced onset of T2DM.

Two-speed genomes of Epichloe fungal pathogens show contrasting signatures of selection between species and across populations

Antagonistic selection between pathogens and their hosts can drive rapid evolutionary change and leave distinct molecular footprints of past and ongoing selection in the genomes of the interacting species. Despite an increasing availability of tools able to identify signatures of selection, the genetic mechanisms underlying coevolutionary interactions and the specific genes involved are still poorly understood, especially in heterogeneous natural environments. We searched the genomes of two species of Epichloe plant pathogen for evidence of recent selection. The Epichloe genus includes highly host-specific species that can sterilize their grass hosts. We performed selection scans using genome-wide SNP data from seven natural populations of two co-occurring Epichloe sibling species specialized on different hosts. We found evidence of recent (and ongoing) selective sweeps across the genome in both species. However, selective sweeps were more abundant in the species with a larger effective population size. Sweep regions often overlapped with highly polymorphic AT-rich regions supporting the role of these genome compartments in adaptive evolution. Although most loci under selection were specific to individual populations, we could also identify several candidate genes targeted by selection in sweep regions shared among populations. The genes encoded small secreted proteins typical of fungal effectors and cell wall-degrading enzymes. By investigating the genomic signatures of selection across multiple populations and species, this study contributes to our understanding of complex adaptive processes in natural plant pathogen systems.

Novel Insights into the Landscape of Crossover and Noncrossover Events in Rhesus Macaques (Macaca mulatta)

Meiotic recombination landscapes differ greatly between distantly and closely related taxa, populations, individuals, sexes, and even within genomes; however, the factors driving this variation are yet to be well elucidated. Here, we directly estimate contemporary crossover rates and, for the first time, noncrossover rates in rhesus macaques (Macaca mulatta) from four three-generation pedigrees comprising 32 individuals. We further compare these results with historical, demography-aware, linkage disequilibrium-based recombination rate estimates. From paternal meioses in the pedigrees, 165 crossover events with a median resolution of 22.3 kb were observed, corresponding to a male autosomal map length of 2,357 cM-approximately 15% longer than an existing linkage map based on human microsatellite loci. In addition, 85 noncrossover events with a mean tract length of 155 bp were identified-similar to the tract lengths observed in the only other two primates in which noncrossovers have been studied to date, humans and baboons. Consistent with observations in other placental mammals with PRDM9-directed recombination, crossover (and to a lesser extent noncrossover) events in rhesus macaques clustered in intergenic regions and toward the chromosomal ends in males-a pattern in broad agreement with the historical, sex-averaged recombination rate estimates-and evidence of GC-biased gene conversion was observed at noncrossover sites.

Genome-wide assessment of genetic diversity and transcript variations in 17 accessions of the model diatom Phaeodactylum tricornutum

Diatoms, a prominent group of phytoplankton, have a significant impact on both the oceanic food chain and carbon sequestration, thereby playing a crucial role in regulating the climate. These highly diverse organisms show a wide geographic distribution across various latitudes. In addition to their ecological significance, diatoms represent a vital source of bioactive compounds that are widely used in biotechnology applications. In the present study, we investigated the genetic and transcriptomic diversity of 17 accessions of the model diatom Phaeodactylum tricornutum including those sampled a century ago as well as more recently collected accessions. The analysis of the data reveals a higher genetic diversity and the emergence of novel clades, indicating an increasing diversity within the P. tricornutum population structure, compared to the previous study and a persistent long-term balancing selection of genes in old and newly sampled accessions. However, the study did not establish a clear link between the year of sampling and genetic diversity, thereby, rejecting the hypothesis of loss of heterozygoty in cultured strains. Transcript analysis identified novel transcript including noncoding RNA and other categories of small RNA such as PiwiRNAs. Additionally, transcripts analysis using differential expression as well as Weighted Gene Correlation Network Analysis has provided evidence that the suppression or downregulation of genes cannot be solely attributed to loss-of-function mutations. This implies that other contributing factors, such as epigenetic modifications, may play a crucial role in regulating gene expression. Our study provides novel genetic resources, which are now accessible through the platform PhaeoEpiview (https://PhaeoEpiView.univ-nantes.fr), that offer both ease of use and advanced tools to further investigate microalgae biology and ecology, consequently enriching our current understanding of these organisms.

Whole genome sequencing reveals population diversity and variation in HIV-1 specific host genes

HIV infection continues to be a major global public health issue. The population heterogeneity in susceptibility or resistance to HIV-1 and progression upon infection is attributable to, among other factors, host genetic variation. Therefore, identifying population-specific variation and genetic modifiers of HIV infectivity can catapult the invention of effective strategies against HIV-1 in African populations. Here, we investigated whole genome sequences of 390 unrelated HIV-positive and -negative individuals from Botswana. We report 27.7 million single nucleotide variations (SNVs) in the complete genomes of Botswana nationals, of which 2.8 million were missing in public databases. Our population structure analysis revealed a largely homogenous structure in the Botswana population. Admixture analysis showed elevated components shared between the Botswana population and the Niger-Congo (65.9%), Khoe-San (32.9%), and Europeans (1.1%) ancestries in the population of Botswana. Statistical significance of the mutational burden of deleterious and loss-of-function variants per gene against a null model was estimated. The most deleterious variants were enriched in five genes: ACTRT2 (the Actin Related Protein T2), HOXD12 (homeobox D12), ABCB5 (ATP binding cassette subfamily B member 5), ATP8B4 (ATPase phospholipid transporting 8B4) and ABCC12 (ATP Binding Cassette Subfamily C Member 12). These genes are enriched in the glycolysis and gluconeogenesis (p < 2.84e-6) pathways and therefore, may contribute to the emerging field of immunometabolism in which therapy against HIV-1 infection is being evaluated. Published transcriptomic evidence supports the role of the glycolysis/gluconeogenesis pathways in the regulation of susceptibility to HIV, and that cumulative effects of genetic modifiers in glycolysis/gluconeogenesis pathways may potentially have effects on the expression and clinical variability of HIV-1. Identified genes and pathways provide novel avenues for other interventions, with the potential for informing the design of new therapeutics.

A computational framework for improving genetic variants identification from 5,061 sheep sequencing data

BACKGROUND

Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation. Joint calling is routinely used to combine identified variants across multiple related samples. However, the improvement of variants identification using the mutual support information from multiple samples remains quite limited for population-scale genotyping.

RESULTS

In this study, we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples' data. The variants were accurately identified from multiple samples by using four steps: (1) Probabilities of variants from two widely used algorithms, GATK and Freebayes, were calculated by Poisson model incorporating base sequencing error potential; (2) The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification (rHID) variants database; (3) The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate (FDR) using rHID database; (4) To avoid the elimination of potentially true variants from rHID database, the variants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants. The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154).

CONCLUSION

The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.

miRNA326-5p Targets DKC1 Gene to Regulate Apoptosis-Related Proteins and Intervene in the Development of Neuroblastoma

Objective

To study the effect of congenital dyskeratosis 1 (DKC1) on neuroblastoma and its regulation mechanism.

Methods

The expression of DKC1 in neuroblastoma was analyzed by TCGA database and molecular assay. NB cells were transfected with siDKC1 to observe the effects of DKC1 on proliferation, cloning, metastasis, and invasion, and apoptosis and apoptosis-related proteins. The tumor-bearing mouse model was constructed, shDKC1 was transfected to observe the tumor growth and tumor tissue changes, and the expression of DKC1 and Ki-67 was detected. Screening and identification of miRNA326-5p targeting DKC1. NB cells were treated with miRNA326-5p mimic or inhibitors to detect the expression of DKC1. NB cells were transfected with miRNA326-5p and DKC1 mimics to detect cell proliferation, apoptosis, and apoptotic protein expression.

Results

DKC1 was highly expressed in NB cells and tissues. The activity, proliferation, invasion, and migration of NB cells were significantly decreased by DKC1 gene knockout, while apoptosis was significantly increased. The expression level of B-cell lymphoma-2 in shDKC1 group was significantly lower than that of the control group, while the expression level of BAK, BAX, and caspase-3 was significantly higher than that of the control group. The results of experiments on tumor-bearing mice were consistent with the above results. The results of miRNA assay showed that miRNA326-5p could bind DKC1 mRNA to inhibit the protein expression, thereby inhibiting the proliferation of NB cells, promoting their apoptosis, and regulating the expression of apoptotic proteins.

Conclusion

miRNA326-5p targeting DKC1 mRNA regulates apoptosis-related proteins to inhibit neuroblastoma proliferation and promote the apoptotic process.

Performance evaluation of six popular short-read simulators

High-throughput sequencing data enables the comprehensive study of genomes and the variation therein. Essential for the interpretation of this genomic data is a thorough understanding of the computational methods used for processing and analysis. Whereas "gold-standard" empirical datasets exist for this purpose in humans, synthetic (i.e., simulated) sequencing data can offer important insights into the capabilities and limitations of computational pipelines for any arbitrary species and/or study design-yet, the ability of read simulator software to emulate genomic characteristics of empirical datasets remains poorly understood. We here compare the performance of six popular short-read simulators-ART, DWGSIM, InSilicoSeq, Mason, NEAT, and wgsim-and discuss important considerations for selecting suitable models for benchmarking.

Whole-genome analyses disentangle reticulate evolution of primroses in a biodiversity hotspot

Biodiversity hotspots, such as the Caucasus mountains, provide unprecedented opportunities for understanding the evolutionary processes that shape species diversity and richness. Therefore, we investigated the evolution of Primula sect. Primula, a clade with a high degree of endemism in the Caucasus. We performed phylogenetic and network analyses of whole-genome resequencing data from the entire nuclear genome, the entire chloroplast genome, and the entire heterostyly supergene. The different characteristics of the genomic partitions and the resulting phylogenetic incongruences enabled us to disentangle evolutionary histories resulting from tokogenetic vs cladogenetic processes. We provide the first phylogeny inferred from the heterostyly supergene that includes all species of Primula sect. Primula. Our results identified recurrent admixture at deep nodes between lineages in the Caucasus as the cause of non-monophyly in Primula. Biogeographic analyses support the 'out-of-the-Caucasus' hypothesis, emphasizing the importance of this hotspot as a cradle for biodiversity. Our findings provide novel insights into causal processes of phylogenetic discordance, demonstrating that genome-wide analyses from partitions with contrasting genetic characteristics and broad geographic sampling are crucial for disentangling the diversification of species-rich clades in biodiversity hotspots.

Identifying high-confidence variants in human cytomegalovirus genomes sequenced from clinical samples

Understanding the intrahost evolution of viral populations has implications in pathogenesis, diagnosis, and treatment and has recently made impressive advances from developments in high-throughput sequencing. However, the underlying analyses are very sensitive to sources of bias, error, and artefact in the data, and it is important that these are addressed adequately if robust conclusions are to be drawn. The key factors include (1) determining the number of viral strains present in the sample analysed; (2) monitoring the extent to which the data represent these strains and assessing the quality of these data; (3) dealing with the effects of cross-contamination; and (4) ensuring that the results are reproducible. We investigated these factors by generating sequence datasets, including biological and technical replicates, directly from clinical samples obtained from a small cohort of patients who had been infected congenitally with the herpesvirus human cytomegalovirus, with the aim of developing a strategy for identifying high-confidence intrahost variants. We found that such variants were few in number and typically present in low proportions and concluded that human cytomegalovirus exhibits a very low level of intrahost variability. In addition to clarifying the situation regarding human cytomegalovirus, our strategy has wider applicability to understanding the intrahost variability of other viruses.

Selection against admixture and gene regulatory divergence in a long-term primate field study

Genetic admixture is central to primate evolution. We combined 50 years of field observations of immigration and group demography with genomic data from ~9 generations of hybrid baboons to investigate the consequences of admixture in the wild. Despite no obvious fitness costs to hybrids, we found signatures of selection against admixture similar to those described for archaic hominins. These patterns were concentrated near genes where ancestry is strongly associated with gene expression. Our analyses also show that introgression is partially predictable across the genome. This study demonstrates the value of integrating genomic and field data for revealing how "genomic signatures of selection" (e.g., reduced introgression in low-recombination regions) manifest in nature; moreover, it underscores the importance of other primates as living models for human evolution.

The impact of frequently neglected model violations on bacterial recombination rate estimation: a case study in Mycobacterium canettii and Mycobacterium tuberculosis

Mycobacterium canettii is a causative agent of tuberculosis in humans, along with the members of the Mycobacterium tuberculosis complex. Frequently used as an outgroup to the M. tuberculosis complex in phylogenetic analyses, M. canettii is thought to offer the best proxy for the progenitor species that gave rise to the complex. Here, we leverage whole-genome sequencing data and biologically relevant population genomic models to compare the evolutionary dynamics driving variation in the recombining M. canettii with that in the nonrecombining M. tuberculosis complex, and discuss differences in observed genomic diversity in the light of expected levels of Hill-Robertson interference. In doing so, we highlight the methodological challenges of estimating recombination rates through traditional population genetic approaches using sequences called from populations of microorganisms and evaluate the likely mis-inference that arises owing to a neglect of common model violations including purifying selection, background selection, progeny skew, and population size change. In addition, we compare performance when full within-host polymorphism data are utilized, versus the more common approach of basing analyses on within-host consensus sequences.

Recommendations for improving statistical inference in population genomics

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

Comparing PCR-generated artifacts of different polymerases for improved accuracy of DNA metabarcoding

Accuracy of PCR amplification is vital for obtaining reliable amplicon-sequencing results by metabarcoding. Here, we performed a comparative analysis of error profiles in the PCR products by 14 different PCR kits using a mock eukaryotic community DNA sample mimicking metabarcoding analysis. To prepare a mock eukaryotic community from the marine environment, equal amounts of plasmid DNA from 40 microalgal species were mixed and used for amplicon-sequencing by a high-throughput sequencing approach. To compare the differences in PCR kits used for this experiment, we focused on the following seven parameters: 1) Quality, 2) Chimera, 3) Blast top hit accuracy, 4) Deletion, 5) Insertion, 6) Base substitution and 7) Amplification bias amongst species. The results showed statistically significant differences (p &lt; 0.05) for all of the seven parameters depending on the PCR kits used. These differences may result from the different DNA polymerases included in each kit, although the result can also be influenced by PCR reaction conditions. Simultaneous analysis of several parameters suggested that kits containing KOD plus Neo (TOYOBO) and HotStart Taq DNA polymerase (BiONEER, CA, US) at the annealing temperature of 65 °C displayed better results in terms of parameters associated with chimeras, top hit similarity and deletions.

The recombination landscapes of spiny lizards (genus Sceloporus)

Despite playing a critical role in evolutionary processes and outcomes, relatively little is known about rates of recombination in the vast majority of species, including squamate reptiles-the second largest order of extant vertebrates, many species of which serve as important model organisms in evolutionary and ecological studies. This paucity of data has resulted in limited resolution on questions related to the causes and consequences of rate variation between species and populations, the determinants of within-genome rate variation, as well as the general tempo of recombination rate evolution on this branch of the tree of life. In order to address these questions, it is thus necessary to begin broadening our phylogenetic sampling. We here provide the first fine-scale recombination maps for two species of spiny lizards, Sceloporus jarrovii and Sceloporus megalepidurus, which diverged at least 12 Mya. As might be expected from similarities in karyotype, population-scaled recombination landscapes are largely conserved on the broad-scale. At the same time, considerable variation exists at the fine-scale, highlighting the importance of incorporating species-specific recombination maps in future population genomic studies.

Genome-wide evolutionary response of European oaks during the Anthropocene

The pace of tree microevolution during Anthropocene warming is largely unknown. We used a retrospective approach to monitor genomic changes in oak trees since the Little Ice Age (LIA). Allelic frequency changes were assessed from whole-genome pooled sequences for four age-structured cohorts of sessile oak (Quercus petraea) dating back to 1680, in each of three different oak forests in France. The genetic covariances of allelic frequency changes increased between successive time periods, highlighting genome-wide effects of linked selection. We found imprints of parallel linked selection in the three forests during the late LIA, and a shift of selection during more recent time periods of the Anthropocene. The changes in allelic covariances within and between forests mirrored the documented changes in the occurrence of extreme events (droughts and frosts) over the last 300 years. The genomic regions with the highest covariances were enriched in genes involved in plant responses to pathogens and abiotic stresses (temperature and drought). These responses are consistent with the reported sequence of frost (or drought) and disease damage ultimately leading to the oak dieback after extreme events. They provide support for adaptive evolution of long-lived species during recent climatic changes. Although we acknowledge that other sources (e.g., gene flow, generation overlap) may have contributed to temporal covariances of allelic frequency changes, the consistent and correlated response across the three forests lends support to the existence of a systematic driving force such as natural selection.

Identification and Validation of Hub Genes in Acute Pancreatitis and Hypertriglyceridemia

BACKGROUND

The pathogenesis of acute pancreatitis (AP) and the relationship between acute pancreatitis and hypertriglyceridemia are complex and not fully understood. The purpose of this study was to identify the hub genes along with common differentially expressed genes (DEGs) between acute pancreatitis and hypertriglyceridemia.

METHODS

We downloaded three gene expression profiles of AP and one gene expression profile of hypertriglyceridemia from the Gene Expression Omnibus (GEO) database and filtered the DEGs based on the above four datasets. Next, we identified the hub genes by performing the Gene Ontology (GO) term analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) construction. We also constructed the miRNA-hub gene network and established mouse models with hypertriglyceridemia and AP using a high-fat diet and injection of caerulein (CAE), respectively. Finally, the immunohistochemical analysis was used to verify the differential expressions of hub genes in AP, hypertriglyceridemia, and normal pancreatic tissue.

RESULTS

A total of 105 DEGs associated with AP and 149 DEGs associated with hypertriglyceridemia were identified. Additionally, we identified six hub genes of AP, all of which were closely related to the cytoskeleton while two DEGs genes were common in both AP and hypertriglyceridemia. We also verified their expression in mouse models. Finally, a network of miRNA-mRNA was also constructed, and the top seven interactive miRNAs (hsa-mir-1-3p, hsa-mir-5195-3p, hsa-mir-145-5p, hsa-let-7b-5p, hsa-mir-10b-5p, hsa-mir-206, and hsa-mir-613) targeting the most hub genes were identified.

CONCLUSION

Overall, we identified six hub genes associated with AP and two common DEGs associated with AP and hypertriglyceridemia along with seven miRNAs that may regulate AP. This study could provide new ideas for further elucidation of the pathogenesis of hypertriglyceridemia-induced acute pancreatitis in the future.

Bioinformatics Analysis of Hub Genes and Potential Therapeutic Agents Associated with Gastric Cancer

Purpose

The current treatment methods available for advanced gastric cancer are not very promising. Hence, it is important to explore novel biomarkers and potential therapeutic agents to treat gastric cancer (GC). This study aimed to identify hub genes associated with GC prognosis and explore potential drugs for its treatment.

Materials and Methods

Three gene expression data of GC and normal tissues were downloaded from the Gene Expression Omnibus (GEO) and processed to identify the differentially expressed genes (DEGs). We conducted a comprehensive analysis of DEGs, including functional enrichment analysis, construction of protein-protein interaction (PPI) network, identification of hub genes, survival analysis and expression verification of hub genes. Finally, we constructed the network of miRNA-mRNA, and predicted the drugs that might be effective for GC treatment.

Results

A total of 340 DEGs, including 94 up-regulated and 246 down-regulated genes, were identified. Among the up-regulated DEGs, the enrichment terms were primarily related to tumorigenesis and tumor progression, extracellular matrix organization, and collagen catabolic process. Additionally, 10 hub genes (FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2, THBS1, COL5A1, SPARC, and COL4A1) were identified, out of which 7 genes were significantly associated with poor overall survival (OS) in GC. The expression levels of these 7 hub genes were verified using real-time PCR, immunohistochemistry, and the GEPIA2 (Gene Expression Profiling Interactive Analysis) server. A regulatory network of miRNA-mRNA was also constructed, and the top 4 interactive miRNAs (hsa-miR-29b-3p, hsa-miR-140-3p, hsa-miR-29a-3p, and hsa-miR-29c-3p) that targeted the most hub genes were identified. Finally, fourteen small molecules were predicted to be effective in treating GC.

Conclusion

The identification of the hub genes, miRNA-mRNA network, and potential candidate drugs associated with GC provides new insights into the molecular mechanisms and treatment of GC.

Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

High-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.

Comparison of microbial composition of cough swabs and sputum for pathogen detection in patients with cystic fibrosis

BACKGROUND

With the continued advancement of CFTR modulator therapies there is likely to be a burgeoning population of adult cystic fibrosis (CF) patients unable to expectorate sputum. Consequently, the detection and surveillance of pulmonary colonisation, previously reliant on sputum culture, needs re-examining. We hypothesised that cough swabs analysed with culture-independent analysis of the 16S gene could serve as a surrogate for colonisation of the lower airways.

METHODS

Cough swabs and sputum samples were prospectively collected from consecutive adults and children with CF across two sites at regular outpatient appointments. Conventional culture analysis and next generation sequencing were used to compare paired same day samples.

RESULTS

Twenty-two adults and 8 paediatric patients provided 75 paired cough swabs and sputum samples. Alpha diversity measures showed increased bacterial richness in sputum, while evenness and Simpson's diveristy index were higher in cough swabs. Within each sampling technique, microbial composition showed greater similarity when considering intra-patient variation. Poor concordance was observed between culture independent cough swabs and culture dependent/independent sputum analysis for specific pathogens, with cough swabs unable to accurately identify commonly associated CF pathogens (AUROCC range: 0.51 to 0.64).

CONCLUSION

Culture independent analysis of cough swabs provides an inaccurate diagnosis of lower respiratory tract colonisation and should not be used as a diagnostic test in patients with CF.

[Expression of nicotinamide-N-methyltransferase in gastric cancer and its biological and clinicopathological significance]

OBJECTIVE

To investigate the expression of nicotinamide-N-methyltransferase (NNMT) in gastric cancer (GC) and explore its biological and clinicopathological significance.

OBJECTIVE

We screened the candidate genes associated with the classification and prognosis of gastric cancer by analyzing GEO, Oncomine and TCGA datasets. The molecular pathways and protein interaction network involving these candidate genes were analyzed using STRING, GSEA, David and Cytoscape software. The expressions of the candidate genes in 28 pairs of gastric cancer and adjacent tissues were detected with qRTPCR, and CCK-8 assay, clone formation assay, wound healing assay and Transwell assay were carried out to analyze the effects of modulation of NNMT expression on proliferation, invasion and migration of different gastric cancer cell lines.

OBJECTIVE

NNMT was highly expressed in gastric cancer tissues and was negatively correlated with the prognosis of patients with gastric cancer. Pathway analysis showed that the high expression of NNMT was associated with adhesion-related pathway molecules such as extracellular matrix receptors, cell adhesion molecules, and cytokine receptors, while its low expression was associated with base mismatch repair and riboflavin metabolism. Protein interaction analysis showed that NNMT interacted with 16 differentially expressed proteins such as AURKA and was co-expressed with TAGLN, PTRF, AKAP12 and IGF2BP2. In clinical tissue specimens, qRT-PCR results showed that the expression of NNMT mRNA was significantly higher in gastric cancer tissues than in the adjacent tissues (P < 0.05). In gastric cancer cell lines, overexpression of NNMT was found to significantly promote cell proliferation, invasion and migration, while NNMT knockdown produced obvious inhibitory effects on cell proliferation, invasion and migration.

OBJECTIVE

NNMT is highly expressed in gastric cancer and negatively correlated with the prognosis of gastric cancer patients. The high expression of NNMT promotes the proliferation, invasion and metastasis of gastric cancer cells, suggesting the potential of NNMT as prognostic marker of gastric cancer.

Limits and convergence properties of the sequentially Markovian coalescent

Several methods based on the sequentially Markovian coalescent (SMC) make use of full genome sequence data from samples to infer population demographic history including past changes in population size, admixture, migration events and population structure. More recently, the original theoretical framework has been extended to allow the simultaneous estimation of population size changes along with other life history traits such as selfing or seed banking. The latter developments enhance the applicability of SMC methods to nonmodel species. Although convergence proofs have been given using simulated data in a few specific cases, an in-depth investigation of the limitations of SMC methods is lacking. In order to explore such limits, we first develop a tool inferring the best case convergence of SMC methods assuming the true underlying coalescent genealogies are known. This tool can be used to quantify the amount and type of information that can be confidently retrieved from given data sets prior to the analysis of the real data. Second, we assess the inference accuracy when the assumptions of SMC approaches are violated due to departures from the model, namely the presence of transposable elements, variable recombination and mutation rates along the sequence, and SNP calling errors. Third, we deliver a new interpretation of SMC methods by highlighting the importance of the transition matrix, which we argue can be used as a set of summary statistics in other statistical inference methods, uncoupling the SMC from hidden Markov models (HMMs). We finally offer recommendations to better apply SMC methods and build adequate data sets under budget constraints.

A Fine-Scale Genetic Map for Vervet Monkeys

Despite its important biological role, the evolution of recombination rates remains relatively poorly characterized. This owes, in part, to the lack of high-quality genomic resources to address this question across diverse species. Humans and our closest evolutionary relatives, anthropoid apes, have remained a major focus of large-scale sequencing efforts, and thus recombination rate variation has been comparatively well studied in this group-with earlier work revealing a conservation at the broad- but not the fine-scale. However, in order to better understand the nature of this variation, and the time scales on which substantial modifications occur, it is necessary to take a broader phylogenetic perspective. I here present the first fine-scale genetic map for vervet monkeys based on whole-genome population genetic data from ten individuals and perform a series of comparative analyses with the great apes. The results reveal a number of striking features. First, owing to strong positive correlations with diversity and weak negative correlations with divergence, analyses suggest a dominant role for purifying and background selection in shaping patterns of variation in this species. Second, results support a generally reduced broad-scale recombination rate compared with the great apes, as well as a narrower fraction of the genome in which the majority of recombination events are observed to occur. Taken together, this data set highlights the great necessity of future research to identify genomic features and quantify evolutionary processes that are driving these rate changes across primates.

Population Genomics of Fungal Plant Pathogens and the Analyses of Rapidly Evolving Genome Compartments

Genome sequencing of fungal pathogens have documented extensive variation in genome structure and composition between species and in many cases between individuals of the same species. This type of genomic variation can be adaptive for pathogens to rapidly evolve new virulence phenotypes. Analyses of genome-wide variation in fungal pathogen genomes rely on high quality assemblies and methods to detect and quantify structural variation. Population genomic studies in fungi have addressed the underlying mechanisms whereby structural variation can be rapidly generated. Transposable elements, high mutation and recombination rates as well as incorrect chromosome segregation during mitosis and meiosis contribute to extensive variation observed in many species. We here summarize key findings in the field of fungal pathogen genomics and we discuss methods to detect and characterize structural variants including an alignment-based pipeline to study variation in population genomic data.

Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis

Purpose

The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC.

Methods

Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR.

Results

A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3–1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells.

Conclusion

Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

The population genomics of within-host Mycobacterium tuberculosis

Recent progress in genomic sequencing from patient samples has allowed for the first detailed insight into the within-host genetic diversity of Mycobacterium tuberculosis (M.TB), revealing remarkably low levels of variation. While this has often been attributed to low mutation rates, other factors have been described, including resistance evolution (i.e., selective sweeps), widespread purifying and background selection, and, more recently, progeny skew. Here we review recent findings pertaining to the processes governing the evolutionary dynamics of M.TB, discuss their implications for improving our understanding of this important human pathogen, and make recommendations for future work. Significantly, this emerging evolutionary framework involving the joint estimation of demographic, selective, and reproductive processes is forming a new paradigm for the study of within-host pathogen evolution that will be widely applicable across organisms.

Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data

High-throughput sequencing technologies have rapidly developed during the past years and have become an essential tool in plant sciences. However, the analysis of genomic data remains challenging and relies mostly on the performance of automatic pipelines. Frequently applied pipelines involve the alignment of sequence reads against a reference sequence and the identification of sequence variants. Since most benchmarking studies of bioinformatics tools for this purpose have been conducted on human datasets, there is a lack of benchmarking studies in plant sciences. In this study, we evaluated the performance of 50 different variant calling pipelines, including five read mappers and ten variant callers, on six real plant datasets of the model organism Arabidopsis thaliana. Sets of variants were evaluated based on various parameters including sensitivity and specificity. We found that all investigated tools are suitable for analysis of NGS data in plant research. When looking at different performance metrics, BWA-MEM and Novoalign were the best mappers and GATK returned the best results in the variant calling step.

De Novo Mutation Rate Estimation in Wolves of Known Pedigree

Knowledge of mutation rates is crucial for calibrating population genetics models of demographic history in units of years. However, mutation rates remain challenging to estimate because of the need to identify extremely rare events. We estimated the nuclear mutation rate in wolves by identifying de novo mutations in a pedigree of seven wolves. Putative de novo mutations were discovered by whole-genome sequencing and were verified by Sanger sequencing of parents and offspring. Using stringent filters and an estimate of the false negative rate in the remaining observable genome, we obtain an estimate of ∼4.5 × 10-9 per base pair per generation and provide conservative bounds between 2.6 × 10-9 and 7.1 × 10-9. Although our estimate is consistent with recent mutation rate estimates from ancient DNA (4.0 × 10-9 and 3.0-4.5 × 10-9), it suggests a wider possible range. We also examined the consequences of our rate and the accompanying interval for dating several critical events in canid demographic history. For example, applying our full range of rates to coalescent models of dog and wolf demographic history implies a wide set of possible divergence times between the ancestral populations of dogs and extant Eurasian wolves (16,000-64,000 years ago) although our point estimate indicates a date between 25,000 and 33,000 years ago. Aside from one study in mice, ours provides the only direct mammalian mutation rate outside of primates and is likely to be vital to future investigations of mutation rate evolution.

The population genetics of crypsis in vertebrates: recent insights from mice, hares, and lizards

By combining well-established population genetic theory with high-throughput sequencing data from natural populations, major strides have recently been made in understanding how, why, and when vertebrate populations evolve crypsis. Here, we focus on background matching, a particular facet of crypsis that involves the ability of an organism to conceal itself through matching its color to the surrounding environment. While interesting in and of itself, the study of this phenotype has also provided fruitful population genetic insights into the interplay of strong positive selection with other evolutionary processes. Specifically, and predicated upon the findings of previous candidate gene association studies, a primary focus of this recent literature involves the realization that the inference of selection from DNA sequence data first requires a robust model of population demography in order to identify genomic regions which do not conform to neutral expectations. Moreover, these demographic estimates provide crucial information about the origin and timing of the onset of selective pressures associated with, for example, the colonization of a novel environment. Furthermore, such inference has revealed crypsis to be a particularly useful phenotype for investigating the interplay of migration and selection—with examples of gene flow constraining rates of adaptation, or alternatively providing the genetic variants that may ultimately sweep through the population. Here, we evaluate the underlying evidence, review the strengths and weaknesses of the many population genetic methodologies used in these studies, and discuss how these insights have aided our general understanding of the evolutionary process.

Accurate sequence variant genotyping in cattle using variation-aware genome graphs

BACKGROUND

Genotyping of sequence variants typically involves, as a first step, the alignment of sequencing reads to a linear reference genome. Because a linear reference genome represents only a small fraction of all the DNA sequence variation within a species, reference allele bias may occur at highly polymorphic or divergent regions of the genome. Graph-based methods facilitate the comparison of sequencing reads to a variation-aware genome graph, which incorporates a collection of non-redundant DNA sequences that segregate within a species. We compared the accuracy and sensitivity of graph-based sequence variant genotyping using the Graphtyper software to two widely-used methods, i.e., GATK and SAMtools, which rely on linear reference genomes using whole-genome sequencing data from 49 Original Braunvieh cattle.

RESULTS

We discovered 21,140,196, 20,262,913, and 20,668,459 polymorphic sites using GATK, Graphtyper, and SAMtools, respectively. Comparisons between sequence variant genotypes and microarray-derived genotypes showed that Graphtyper outperformed both GATK and SAMtools in terms of genotype concordance, non-reference sensitivity, and non-reference discrepancy. The sequence variant genotypes that were obtained using Graphtyper had the smallest number of Mendelian inconsistencies between sequence-derived single nucleotide polymorphisms and indels in nine sire-son pairs. Genotype phasing and imputation using the Beagle software improved the quality of the sequence variant genotypes for all the tools evaluated, particularly for animals that were sequenced at low coverage. Following imputation, the concordance between sequence- and microarray-derived genotypes was almost identical for the three methods evaluated, i.e., 99.32, 99.46, and 99.24% for GATK, Graphtyper, and SAMtools, respectively. Variant filtration based on commonly used criteria improved genotype concordance slightly but it also decreased sensitivity. Graphtyper required considerably more computing resources than SAMtools but less than GATK.

CONCLUSIONS

Sequence variant genotyping using Graphtyper is accurate, sensitive and computationally feasible in cattle. Graph-based methods enable sequence variant genotyping from variation-aware reference genomes that may incorporate cohort-specific sequence variants, which is not possible with the current implementation of state-of-the-art methods that rely on linear reference genomes.

On the critical evaluation and confirmation of germline sequence variants identified using massively parallel sequencing

Although massively parallel sequencing (MPS) is becoming common practice in both research and routine clinical care, confirmation requirements of identified DNA variants using alternative methods are still topics of debate. When evaluating variants directly from MPS data, different read depth statistics, together with specialized genotype quality scores are, therefore, of high relevance. Here we report results of our validation study performed in two different ways: 1) confirmation of MPS identified variants using Sanger sequencing; and 2) simultaneous Sanger and MPS analysis of exons of selected genes. Detailed examination of false-positive and false-negative findings revealed typical error sources connected to low read depth/coverage, incomplete reference genome, indel realignment problems, as well as microsatellite associated amplification errors leading to base miss-calling. However, all these error types were identifiable with thorough manual revision of aligned reads according to specific patterns of distributions of variants and their corresponding reads. Moreover, our results point to dependence of both basic quantitative metrics (such as total read counts, alternative allele read counts and allelic balance) together with specific genotype quality scores on the used bioinformatics pipeline, stressing thus the need for establishing of specific thresholds for these metrics in each laboratory and for each involved pipeline independently.

The Evolutionary History of Nebraska Deer Mice: Local Adaptation in the Face of Strong Gene Flow

The interplay of gene flow, genetic drift, and local selective pressure is a dynamic process that has been well studied from a theoretical perspective over the last century. Wright and Haldane laid the foundation for expectations under an island-continent model, demonstrating that an island-specific beneficial allele may be maintained locally if the selection coefficient is larger than the rate of migration of the ancestral allele from the continent. Subsequent extensions of this model have provided considerably more insight. Yet, connecting theoretical results with empirical data has proven challenging, owing to a lack of information on the relationship between genotype, phenotype, and fitness. Here, we examine the demographic and selective history of deer mice in and around the Nebraska Sand Hills, a system in which variation at the Agouti locus affects cryptic coloration that in turn affects the survival of mice in their local habitat. We first genotyped 250 individuals from 11 sites along a transect spanning the Sand Hills at 660,000 single nucleotide polymorphisms across the genome. Using these genomic data, we found that deer mice first colonized the Sand Hills following the last glacial period. Subsequent high rates of gene flow have served to homogenize the majority of the genome between populations on and off the Sand Hills, with the exception of the Agouti pigmentation locus. Furthermore, mutations at this locus are strongly associated with the pigment traits that are strongly correlated with local soil coloration and thus responsible for cryptic coloration.

A Perfect Match Genomic Landscape Provides a Unified Framework for the Precise Detection of Variation in Natural and Synthetic Haploid Genomes

We present a conceptually simple, sensitive, precise, and essentially nonstatistical solution for the analysis of genome variation in haploid organisms. The generation of a Perfect Match Genomic Landscape (PMGL), which computes intergenome identity with single nucleotide resolution, reveals signatures of variation wherever a query genome differs from a reference genome. Such signatures encode the precise location of different types of variants, including single nucleotide variants, deletions, insertions, and amplifications, effectively introducing the concept of a general signature of variation. The precise nature of variants is then resolved through the generation of targeted alignments between specific sets of sequence reads and known regions of the reference genome. Thus, the perfect match logic decouples the identification of the location of variants from the characterization of their nature, providing a unified framework for the detection of genome variation. We assessed the performance of the PMGL strategy via simulation experiments. We determined the variation profiles of natural genomes and of a synthetic chromosome, both in the context of haploid yeast strains. Our approach uncovered variants that have previously escaped detection. Moreover, our strategy is ideally suited for further refining high-quality reference genomes. The source codes for the automated PMGL pipeline have been deposited in a public repository.

Identification of significant biomarkers and pathways associated with gastric carcinogenesis by whole genome-wide expression profiling analysis

The incidence of gastric cancer (GC) is extremely high in East Asia. GC is also one of the most common and lethal forms of cancer from a global perspective. However, to date, we have not been able to determine one or several genes as biomarkers in the diagnosis of GC and have also been unable to identify the genes which are important in the therapy of GC. In this study, we analyzed all genome-wide expression profiling arrays uploaded onto the Gene Expression Omnibus (GEO) database to filtrate the differentially expressed genes (DEGs) between normal stomach tissues and GC tissues. GSE13911, GSE19826 and GSE79973 were based on the GPL570 platform, and GSE29272 was based on the GPL96 platform. We screened out the DEGs from the two platforms and by selecting the intersection of these two platforms, we identified the common DEGs in the sequencing data from different laboratories. Finally, we obtained 3 upregulated and 34 downregulated DEGs in GC from 384 samples. As the number of downregulated DEGs was greater than that of the upregulated DEGs, functional analysis and pathway enrichment analysis were performed on the downregulated DEGs. Through our analysis, we identified the most significant genes associated with GC, such as secreted phosphoprotein 1 (SPP1), sulfatase 1 (SULF1), thrombospondin 2 (THBS2), ATPase H+/K+ transporting beta subunit (ATP4B), gastric intrinsic factor (GIF) and gastrokine 1 (GKN1). The prognostic power of these genes was corroborated in the Oncomine database and by Kaplan-Meier plotter (KM-plotter) analysis. Moreover, gastric acid secretion, collecting duct acid secretion, nitrogen metabolism and drug metabolism were significantly related to GC. Thus, these genes and pathways may be potential targets for improving the diagnosis and clinical effects in patients with GC.

Direct estimate of the spontaneous germ line mutation rate in African green monkeys

Here, I provide the first direct estimate of the spontaneous mutation rate in an Old World monkey, using a seven individual, three-generation pedigree of African green monkeys. Eight de novo mutations were identified within ∼1.5 Gbp of accessible genome, corresponding to an estimated point mutation rate of 0.94 × 10^-8 per site per generation, suggesting an effective population size of ∼12000 for the species. This estimation represents a significant improvement in our knowledge of the population genetics of the African green monkey, one of the most important nonhuman primate models in biomedical research. Furthermore, by comparing mutation rates in Old World monkeys with the only other direct estimates in primates to date-humans and chimpanzees-it is possible to uniquely address how mutation rates have evolved over longer time scales. While the estimated spontaneous mutation rate for African green monkeys is slightly lower than the rate of 1.2 × 10^-8 per base pair per generation reported in chimpanzees, it is similar to the lower range of rates of 0.96 × 10^-8 -1.28 × 10^-8 per base pair per generation recently estimated from whole genome pedigrees in humans. This result suggests a long-term constraint on mutation rate that is quite different from similar evidence pertaining to recombination rate evolution in primates.

The Demographic and Adaptive History of the African Green Monkey

Relatively little is known about the evolutionary history of the African green monkey (genus Chlorocebus) due to the lack of sampled polymorphism data from wild populations. Yet, this characterization of genetic diversity is not only critical for a better understanding of their own history, but also for human biomedical research given that they are one of the most widely used primate models. Here, I analyze the demographic and selective history of the African green monkey, utilizing one of the most comprehensive catalogs of wild genetic diversity to date, consisting of 1,795,643 autosomal single nucleotide polymorphisms in 25 individuals, representing all five major populations: C. a. aethiops, C. a. cynosurus, C. a. pygerythrus, C. a. sabaeus, and C. a tantalus. Assuming a mutation rate of 5.9 × 10-9 per base pair per generation and a generation time of 8.5 years, divergence time estimates range from 523 to 621 kya for the basal split of C. a. aethiops from the other four populations. Importantly, the resulting tree characterizing the relationship and split-times between these populations differs significantly from that presented in the original genome paper, owing to their neglect of within-population variation when calculating between population-divergence. In addition, I find that the demographic history of all five populations is well explained by a model of population fragmentation and isolation, rather than novel colonization events. Finally, utilizing these demographic models as a null, I investigate the selective history of the populations, identifying candidate regions potentially related to adaptation in response to pathogen exposure.

Whole-genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations

Whole-genome resequencing (WGR) is a powerful method for addressing fundamental evolutionary biology questions that have not been fully resolved using traditional methods. WGR includes four approaches: the sequencing of individuals to a high depth of coverage with either unresolved or resolved haplotypes, the sequencing of population genomes to a high depth by mixing equimolar amounts of unlabelled-individual DNA (Pool-seq) and the sequencing of multiple individuals from a population to a low depth (lcWGR). These techniques require the availability of a reference genome. This, along with the still high cost of shotgun sequencing and the large demand for computing resources and storage, has limited their implementation in nonmodel species with scarce genomic resources and in fields such as conservation biology. Our goal here is to describe the various WGR methods, their pros and cons and potential applications in conservation biology. WGR offers an unprecedented marker density and surveys a wide diversity of genetic variations not limited to single nucleotide polymorphisms (e.g., structural variants and mutations in regulatory elements), increasing their power for the detection of signatures of selection and local adaptation as well as for the identification of the genetic basis of phenotypic traits and diseases. Currently, though, no single WGR approach fulfils all requirements of conservation genetics, and each method has its own limitations and sources of potential bias. We discuss proposed ways to minimize such biases. We envision a not distant future where the analysis of whole genomes becomes a routine task in many nonmodel species and fields including conservation biology.

Genotyping-by-sequencing reveals three QTL for clubroot resistance to six pathotypes of Plasmodiophora brassicae in Brassica rapa

Clubroot, caused by Plasmodiophora brassicae, is an important disease of Brassica crops worldwide. F1 progeny from the Brassica rapa lines T19 (resistant) × ACDC (susceptible) were backcrossed with ACDC, then self-pollinated to produce BC1S1 lines, From genotyping-by-sequencing (GBS) of the parental lines and BC1 plants, about 1.32 M sequences from T19 were aligned into the reference genome of B. rapa with 0.4-fold coverage, and 1.77 M sequences with 0.5-fold coverage in ACDC. The number of aligned short reads per plant in the BC1 ranged from 0.07 to 1.41 M sequences with 0.1-fold coverage. A total of 1584 high quality SNP loci were obtained, distributed on 10 chromosomes. A single co-localized QTL, designated as Rcr4 on chromosome A03, conferred resistance to pathotypes 2, 3, 5, 6 and 8. The peak was at SNP locus A03_23710236, where LOD values were 30.3 to 38.8, with phenotypic variation explained (PVE) of 85-95%. Two QTLs for resistance to a novel P. brassicae pathotype 5x, designated Rcr8 on chromosome A02 and Rcr9 on A08, were detected with 15.0 LOD and 15.8 LOD, and PVE of 36% and 39%, respectively. Bulked segregant analysis was performed to examine TIR-NBS-LRR proteins in the regions harboring the QTL.