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High‐resolution genome analysis technologies provide an unprecedented level of insight into structural diversity across crop genomes. Low‐cost discovery of sequence variation has become accessible for all crops since the development of next‐generation DNA sequencing technologies, using diverse methods ranging from genome‐scale resequencing or skim sequencing, reduced‐representation genotyping‐by‐sequencing, transcriptome sequencing or sequence capture approaches. High‐density, high‐throughput genotyping arrays generated using the resulting sequence data are today available for the assessment of genomewide single nucleotide polymorphisms in all major crop species. Besides their application in genetic mapping or genomewide association studies for dissection of complex agronomic traits, high‐density genotyping arrays are highly suitable for genomic selection strategies. They also enable description of crop diversity at an unprecedented chromosome‐scale resolution. Application of population genetics parameters to genomewide diversity data sets enables dissection of linkage disequilibrium to characterize loci underlying selective sweeps. High‐throughput genotyping platforms simultaneously open the way for targeted diversity enrichment, allowing rejuvenation of low‐diversity chromosome regions in strongly selected breeding pools to potentially reverse the influence of linkage drag. Numerous recent examples are presented which demonstrate the power of next‐generation genomics for high‐resolution analysis of crop diversity on a subgenomic and chromosomal scale. Such studies give deep insight into the history of crop evolution and selection, while simultaneously identifying novel diversity to improve yield and heterosis.

Aim Most of the fundamental questions in conservation biogeography require the description of species geographic boundaries and the identification of discrete biological units within these boundaries. International conservation efforts and institutions rely mainly on traditional taxonomic approaches for defining these boundaries, resulting in significant cryptic diversity going undetected and often extinct. Here, we combine high‐throughput genomic data with publicly available environmental data to identify cryptic diversity in the threatened bird's‐eye primrose (Primula farinosa). We aim to characterize evolutionary lineages and test whether they co‐occur with changes in environmental conditions. These lineages can be used as intraspecific units for conservation to enhance assessments regarding the status of threatened species. Location Europe and temperate Asia (latitude, 40–65°N; longitude, 10°E–115°W). Methods We genotyped 93 individuals from 71 populations at 1,220 loci (4,089 SNPs) across the Eurasian distribution of P. farinosa. We used phylogenomic and population structure approaches to identify intraspecific lineages. We further extracted statistically derived and remotely sensed environmental information, that is land cover, climate and soil characteristics, to define the biotic and abiotic environment inhabited by each lineage and test for niche similarities among lineages. Additionally, we tested for isolation by distance among populations and applied linear and polynomial regressions to identify lineage‐environment associations. Results Analyses of genomic data revealed six major lineages within P. farinosa corresponding to distinct geographic areas. Niche similarity tests indicated that lineages occupy distinct abiotic and biotic space. Isolation by distance indicated that geography alone cannot explain genetic divergence within P. farinosa, while lineage‐environment associations suggested potential adaptation to different abiotic conditions across lineages. However, relationships with the land cover classes, a proxy for habitat, were weaker. Main conclusion Our results highlight the need for incorporating intraspecific diversity in global assessments of species conservation status and the utility of genomic and publicly available environmental data in conservation biogeography.

Background Chromatin immunoprecipitation coupled to sequencing (ChIP-seq) is widely used to map histone modifications and transcription factor binding on a genome-wide level. Results We present high-throughput ChIPmentation (HT-ChIPmentation) that eliminates the need for DNA purification prior to library amplification and reduces reverse-crosslinking time from hours to minutes. Conclusions The resulting workflow is easily established, extremely rapid, and compatible with requirements for very low numbers of FACS sorted cells, high-throughput applications and single day data generation.

High-throughput genomic data reveal thousands of gene variants per patient, and it is often difficult to determine which of these variants underlies disease in a given individual. However, at the population level, there may be some degree of phenotypic homogeneity, with alterations of specific physiological pathways underlying the pathogenesis of a particular disease. We describe here the human gene connectome (HGC) as a unique approach for human Mendelian genetic research, facilitating the interpretation of abundant genetic data from patients with the same disease, and guiding subsequent experimental investigations. We first defined the set of the shortest plausible biological distances, routes, and degrees of separation between all pairs of human genes by applying a shortest distance algorithm to the full human gene network. We then designed a hypothesis-driven application of the HGC, in which we generated a Toll-like receptor 3-specific connectome useful for the genetic dissection of inborn errors of Toll-like receptor 3 immunity. In addition, we developed a functional genomic alignment approach from the HGC. In functional genomic alignment, the genes are clustered according to biological distance (rather than the traditional molecular evolutionary genetic distance), as estimated from the HGC. Finally, we compared the HGC with three state-of-the-art methods: String, FunCoup, and HumanNet. We demonstrated that the existing methods are more suitable for polygenic studies, whereas HGC approaches are more suitable for monogenic studies. The HGC and functional genomic alignment data and computer programs are freely available to noncommercial users from http://lab.rockefeller.edu/casanova/HGC and should facilitate the genome-wide selection of disease-causing candidate alleles for experimental validation.

Genomic studies have estimated there are approximately 10³–10⁶ bacterial species per gram of soil. The microbial species found in soil associated with decomposing human remains (gravesoil) have been investigated and recognized as potential molecular determinants for estimates of time since death. The nascent era of high-throughput amplicon sequencing of the conserved 16S ribosomal RNA (rRNA) gene region of gravesoil microbes is allowing research to expand beyond more subjective empirical methods used in forensic microbiology. The goal of the present study was to evaluate microbial communities and identify taxonomic signatures associated with the gravesoil human cadavers. Using 16S rRNA gene amplicon-based sequencing, soil microbial communities were surveyed from 18 cadavers placed on the surface or buried that were allowed to decompose over a range of decomposition time periods (3–303 days). Surface soil microbial communities showed a decreasing trend in taxon richness, diversity, and evenness over decomposition, while buried cadaver-soil microbial communities demonstrated increasing taxon richness, consistent diversity, and decreasing evenness. The results show that ubiquitous Proteobacteria was confirmed as the most abundant phylum in all gravesoil samples. Surface cadaver-soil communities demonstrated a decrease in Acidobacteria and an increase in Firmicutes relative abundance over decomposition, while buried soil communities were consistent in their community composition throughout decomposition. Better understanding of microbial community structure and its shifts over time may be important for advancing general knowledge of decomposition soil ecology and its potential use during forensic investigations.

Background Batch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tests resulting in actual effects going unidentified. At their worst, they constitute confounds and render datasets useless. Attempting to remove batch effects will result in some of the biologically meaningful component of the measurement (i.e. signal) being lost. We present and benchmark a novel technique, called Harman . Harman maximises the removal of batch noise with the constraint that the risk of also losing biologically meaningful component of the measurement is kept to a fraction which is set by the user. Results Analyses of three independent publically available datasets reveal that Harman removes more batch noise and preserves more signal at the same time, than the current leading technique. Results also show that Harman is able to identify and remove batch effects no matter what their relative size compared to other sources of variation in the dataset. Of particular advantage for meta-analyses and data integration is Harman’s superior consistency in achieving comparable noise suppression - signal preservation trade-offs across multiple datasets, with differing number of treatments, replicates and processing batches. Conclusion Harman’s ability to better remove batch noise, and better preserve biologically meaningful signal simultaneously within a single study, and maintain the user-set trade-off between batch noise rejection and signal preservation across different studies makes it an effective alternative method to deal with batch effects in high-throughput genomic datasets. Harman is flexible in terms of the data types it can process. It is available publically as an R package ( https://bioconductor.org/packages/release/bioc/html/Harman.html ), as well as a compiled Matlab package ( http://www.bioinformatics.csiro.au/harman/ ) which does not require a Matlab license to run.

Low permeability oil reservoirs hold an important position in the global oil resource reserves. They boast abundant reserves and serve as one of the crucial sources for crude oil reserve replacement in China and even the world. The mechanisms for improving the oil recovery rate in high-oil-bearing reservoirs include improving fluid properties, enhancing displacement efficiency, etc. However, their development is quite challenging, requiring continuous exploration and innovation in development technologies. This study addresses the unclear distribution patterns of microbial communities and the incomplete understanding of microbial enhanced oil recovery (MEOR) mechanisms in low permeability reservoirs. Utilizing high-throughput genomics and functional gene analysis techniques, combined with laboratory and field data, the study investigates the distribution and growth patterns of microbial communities in a low permeability reservoir, exemplified by the S169 block. Additionally, the potential of MEOR to enhance oil recovery and its underlying mechanisms are explored. The results indicate that microbial communities in low permeability reservoirs exhibit strong heterogeneity, with their distribution closely correlated to geological factors such as reservoir permeability and porosity. The diversity of microbial communities is positively correlated with oil recovery efficiency, and highly active microbial populations promote the production of metabolites that enhance oil recovery. The metabolic products of microorganisms help reduce the interfacial tension between oil and water, improve the fluidity of oil, and enhance the recovery rate. In addition, the structural changes in microbial communities are closely related to factors such as the permeability and porosity of reservoirs. This study provides a theoretical basis for the optimization of microbial enhanced oil recovery (MEOR) technology.

Abstract Cell2Read is a novel automated method for complete integration of cell lysis and sample preparation for next-generation sequencing (NGS). It optimizes diffusion kinetics and complex thermal geometries to allow for effective use down to low inputs of cells. This allows for DNA analysis from a low cellular input, whether this be for in vitro analysis or diagnostic applications from dissociated tumor biopsies. We demonstrate that the system can process input cell suspensions as low as 1500 cells without compromising sequencing integrity. We also demonstrate the breadth of the protocol in its ability to repeatably process many cell types, including HepG2, Caov3, HEY A8, OVCAR 8, MDA-MB-231, and Human Primary Ovarian Epithelial Cells. The workflow integrates and fully automates cell lysis, DNA extraction, and library preparation into a single automated platform, offering high sensitivity and reproducibility. Our results show that the system yields consistent DNA quantities (≥10 ng) with high sequencing quality, even at low cell inputs, with alignment rates exceeding 95% for inputs of 3125 cells or greater. The automated method’s sequencing performance was comparable to manual protocols, with no significant differences in quality scores or GC bias across processing methods. We also demonstrated effective, non-biased sequencing of heterogeneous cell suspensions, through comprehensive testing of spiked concentrations of cancerous cells with non-cancerous ovarian cells. Sequencing output showed proportional DNA representation of cancer markers to the concentration of cancer cells inputted. The Cell2Read workflow offers a technically validated, scalable solution that expands accessibility to genomic analysis and supports reproducible, high-quality sequencing from low-input human samples. This robustness across a range of cell types, makes Cell2Read an ideal solution for sequencing applications, including oncology research and clinical diagnostics.

Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.

This chapter contains sections titled: Introduction Technologies for Genomic Analysis Applications Conclusion References