High-throughput ChIPmentation: freely scalable, single day ChIPseq data generation from very low cell-numbers.

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.

Understanding and utilizing crop genome diversity via high-resolution genotyping.

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.

Rational experiment design for sequencing-based RNA structure mapping.

Structure mapping is a classic experimental approach for determining nucleic acid structure that has gained renewed interest in recent years following advances in chemistry, genomics, and informatics. The approach encompasses numerous techniques that use different means to introduce nucleotide-level modifications in a structure-dependent manner. Modifications are assayed via cDNA fragment analysis, using electrophoresis or next-generation sequencing (NGS). The recent advent of NGS has dramatically increased the throughput, multiplexing capacity, and scope of RNA structure mapping assays, thereby opening new possibilities for genome-scale, de novo, and in vivo studies. From an informatics standpoint, NGS is more informative than prior technologies by virtue of delivering direct molecular measurements in the form of digital sequence counts. Motivated by these new capabilities, we introduce a novel model-based in silico approach for quantitative design of large-scale multiplexed NGS structure mapping assays, which takes advantage of the direct and digital nature of NGS readouts. We use it to characterize the relationship between controllable experimental parameters and the precision of mapping measurements. Our results highlight the complexity of these dependencies and shed light on relevant tradeoffs and pitfalls, which can be difficult to discern by intuition alone. We demonstrate our approach by quantitatively assessing the robustness of SHAPE-Seq measurements, obtained by multiplexing SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) chemistry in conjunction with NGS. We then utilize it to elucidate design considerations in advanced genome-wide approaches for probing the transcriptome, which recently obtained in vivo information using dimethyl sulfate (DMS) chemistry.

A glimpse into past, present, and future DNA sequencing.

Current advances in DNA sequencing technologies are dropping down sequencing cost while increasing throughput at a pace never shown before. Past-decade great milestones, as the establishment of a reference human genome (amongst others) and large-scale human genetic variation study in the 1000 Genome project are, in conjunction with the use of these techniques, triggering advances in many areas of basic and applied science. These tools, stored in and combined with the vast amount of information present in biological online databases are, with the use of automated interpretation and analysis tools, allowing the fulfillment of increasingly ambitious studies in many areas and also are democratizing the access to information, interpretation and technologies, being the first opportunity for researchers to assess the influence of genetics in complex events as multifactorial diseases, evolutionary studies, metagenomics, transcriptomics, etc. In this review, we present the current state of the art of these technologies, focusing on second generation sequencing, from sample and library preparation to sequencing chemistries and bioinformatic software available for final data analysis and visualisation, with its possible applications. We also make an overview of first and third generation, due to its historical importance and for being the upcoming future tools for genetic analysis, respectively.

Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision?

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

Draft genome sequence of Streptomyces sp. KD18, isolated from industrial soil.

The present study scrutinizes the presence of Streptomyces strains in the soil sample collected from industrial area of Bahadurgarh (Haryana) India. The morphological approach manifested the isolated strain belong to Streptomyces species and named as Streptomyces sp. KD18. Sequencing of Streptomyces sp. KD18 genome was performed by Illumina Nextseq500 platform. 65 contigs were generated via SPAdes v3.11.1 and harboured genome size of 7.2 Mb. AntiSMASH server revealed the presence of 25 biosynthetic gene clusters in KD18 genome where BGC of lipstatin was of more interest from industrial and pharmaceutical purpose. The draft genome sequence represented via ANI values claimed that the KD18 strain belongs to Streptomyces toxytricini and finally named as S. toxytricini KD18. The LC-MS analysis of the extracted metabolite confirmed the production of lipstatin. The genome sequence data have been deposited to NCBI under the accession number of GCA_014748315.1. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03453-3.

Detection of Z-DNA Structures in Supercoiled Genome.

Z-DNAs are nucleic acid secondary structures that form within a special pattern of nucleotides and are promoted by DNA supercoiling. Through Z-DNA formation, DNA encodes information by dynamic changes in its secondary structure. A growing body of evidence indicates that Z-DNA formation can play a role in gene regulation; it can affect chromatin architecture and demonstrates its association with genomic instability, genetic diseases, and genome evolution. Many functional roles of Z-DNA are yet to be discovered highlighting the need for techniques to detect genome-wide folding of DNA into this structure. Here, we describe an approach to convert linear genome into supercoiled genome sponsoring Z-DNA formation. Applying permanganate-based methodology and high-throughput sequencing to supercoiled genome allows genome-wide detection of single-stranded DNA. Single-stranded DNA is characteristic of the junctions between the classical B-form of DNA and Z-DNA. Consequently, analysis of single-stranded DNA map provides snapshots of the Z-DNA conformation over the whole genome.

Biomarker-driven therapies for metastatic uveal melanoma: A prospective precision oncology feasibility study.

BACKGROUND: Targeted therapies for metastatic uveal melanoma have shown limited benefit in biomarker-unselected populations. The Treat20 Plus study prospectively evaluated the feasibility of a precision oncology strategy in routine clinical practice. PATIENTS AND METHODS: Fresh biopsies were analyzed by high-throughput genomics (whole-genome, whole-exome, and RNA sequencing). A multidisciplinary molecular and immunologic tumor board (MiTB) made individualized treatment recommendations based on identified molecular aberrations, patient situation, drug, and clinical trial availability. Therapy selection was at the discretion of the treating physician. The primary endpoint was the feasibility of the precision oncology clinical program. RESULTS: Molecular analyses were available for 39/45 patients (87%). The MiTB provided treatment recommendations for 40/45 patients (89%), of whom 27/45 (60%) received ≥1 matched therapy. First-line matched therapies included MEK inhibitors (n = 15), MET inhibitors (n = 10), sorafenib (n = 1), and nivolumab (n = 1). The best response to first-line matched therapy was partial response in one patient (nivolumab based on tumor mutational burden), mixed response in two patients, and stable disease in 12 patients for a clinical benefit of 56%. The matched therapy population had a median progression-free survival and overall survival of 3.3 and 13.9 months, respectively. The growth modulation index with matched therapy was >1.33 in 6/17 patients (35%) with prior systemic therapy, suggesting clinical benefit. CONCLUSIONS: A precision oncology approach was feasible for patients with metastatic uveal melanoma, with 60% receiving a therapy matched to identify molecular aberrations. The clinical benefit after checkpoint inhibitors highlights the value of tumor mutational burden testing.

Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.

Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.

In Vivo Chemical Probing for G-Quadruplex Formation.

While DNA inside the cells is predominantly canonical right-handed double helix, guanine-rich DNAs have potential to fold into four-stranded structures that contain stacks of G-quartets (G4 DNA quadruplex). Genome sequencing has revealed G4 sequences tend to localize at the gene control regions, especially in the promoters of oncogenes. A growing body of evidence indicates that G4 DNA quadruplexes might have important regulatory roles in genome function, highlighting the need for techniques to detect genome-wide folding of DNA into this structure. Potassium permanganate in vivo treatment of cells results in oxidizing of nucleotides in single-stranded DNA regions that accompany G4 DNA quadruplexes formation, providing an excellent probe for the conformational state of DNA inside the living cells. Here, we describe a permanganate-based methodology to detect G4 DNA quadruplex, genome-wide. This methodology combined with high-throughput sequencing provides a snapshot of the DNA conformation over the whole genome in vivo.

Bringing microbiome-drug interaction research into the clinic.

Our understanding of the scope and clinical relevance of gut microbiota metabolism of drugs is limited to relatively few biotransformations targeting a subset of therapeutics. Translating microbiome research into the clinic requires, in part, a mechanistic and predictive understanding of microbiome-drug interactions. This review provides an overview of microbiota chemistry that shapes drug efficacy and toxicity. We discuss experimental and computational approaches that attempt to bridge the gap between basic and clinical microbiome research. We highlight the current landscape of preclinical research focused on identifying microbiome-based biomarkers of patient drug response and we describe clinical trials investigating approaches to modulate the microbiome with the goal of improving drug efficacy and safety. We discuss approaches to aggregate clinical and experimental microbiome features into predictive models and review open questions and future directions toward utilizing the gut microbiome to improve drug safety and efficacy.

The Use of Psoralen Photobinding to Study Transcription-Induced Supercoiling.

Proteins manipulating intracellular DNA necessarily impart torsional stress, which redistributes across the DNA. Overtwisting and undertwisting of the double helix result in the manifestation of positive and negative DNA supercoiling. A growing body of evidence indicates that DNA topology is an important player in the key regulatory steps of genome function, highlighting the need for biochemical techniques to detect dynamic changes in the DNA structure. Psoralen binding to DNA in vivo is proportional to the level of supercoiling, providing an excellent probe for the topological state of nuclear DNA. Here we describe a psoralen-based methodology to detect transcription-induced DNA supercoiling genome-wide. The DNA samples generated with this approach can be hybridized to microarray platforms or high-throughput sequenced to provide a topological snapshot of the whole genome.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks.

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Divorcing Strain Classification from Species Names.

Confusion about strain classification and nomenclature permeates modern microbiology. Although taxonomists have traditionally acted as gatekeepers of order, the numbers of, and speed at which, new strains are identified has outpaced the opportunity for professional classification for many lineages. Furthermore, the growth of bioinformatics and database-fueled investigations have placed metadata curation in the hands of researchers with little taxonomic experience. Here I describe practical challenges facing modern microbial taxonomy, provide an overview of complexities of classification for environmentally ubiquitous taxa like Pseudomonas syringae, and emphasize that classification can be independent of nomenclature. A move toward implementation of relational classification schemes based on inherent properties of whole genomes could provide sorely needed continuity in how strains are referenced across manuscripts and data sets.

What to expect from high throughput genomics in metastatic breast cancers?

Breast cancer is a heterogeneous disease and its genomic characteristics have been widely studied in the last years. Although several progresses have been made, metastatic disease is still incurable in the majority of patients. Recent genomic studies have shown that a large number of candidate targets exist in breast cancer. Currently only two drivers have been validated (ER and HER2), but several others seem to be associated with objective response, such as PIK3CA mutations, FGFR1 amplifications, AKT1 mutations, EGFR amplifications and ERBB2 mutations. Beside driver identification, many other applications can be developed for genomics such as identification of lethal subclones, DNA repair defects or immune response against tumor. Most of the precision medicine programs currently use targeted sequencing. Nevertheless, whole exome sequencing, RNA sequencing, gene expression analysis, phosphoprotein detection, SNP arrays and ctDNA sequencing have been also proposed in clinical trials.

Vision from next generation sequencing: multi-dimensional genome-wide analysis for producing gene regulatory networks underlying retinal development, aging and disease.

Genomics and genetics have invaded all aspects of biology and medicine, opening uncharted territory for scientific exploration. The definition of "gene" itself has become ambiguous, and the central dogma is continuously being revised and expanded. Computational biology and computational medicine are no longer intellectual domains of the chosen few. Next generation sequencing (NGS) technology, together with novel methods of pattern recognition and network analyses, has revolutionized the way we think about fundamental biological mechanisms and cellular pathways. In this review, we discuss NGS-based genome-wide approaches that can provide deeper insights into retinal development, aging and disease pathogenesis. We first focus on gene regulatory networks (GRNs) that govern the differentiation of retinal photoreceptors and modulate adaptive response during aging. Then, we discuss NGS technology in the context of retinal disease and develop a vision for therapies based on network biology. We should emphasize that basic strategies for network construction and analyses can be transported to any tissue or cell type. We believe that specific and uniform guidelines are required for generation of genome, transcriptome and epigenome data to facilitate comparative analysis and integration of multi-dimensional data sets, and for constructing networks underlying complex biological processes. As cellular homeostasis and organismal survival are dependent on gene-gene and gene-environment interactions, we believe that network-based biology will provide the foundation for deciphering disease mechanisms and discovering novel drug targets for retinal neurodegenerative diseases.