An Integrated Next-Generation Sequencing System for Analyzing DNA Mutations, Gene Fusions, and RNA Expression in Lung Cancer.

We developed and characterized a next-generation sequencing (NGS) technology for streamlined analysis of DNA and RNA using low-input, low-quality cancer specimens. A single-workflow, targeted NGS panel for non-small cell lung cancer (NSCLC) was designed covering 135 RNA and 55 DNA disease-relevant targets. This multiomic panel was used to assess 219 formalin-fixed paraffin-embedded NSCLC surgical resections and core needle biopsies. Mutations and expression phenotypes were identified consistent with previous large-scale genomic studies, including mutually exclusive DNA and RNA oncogenic driver events. Evaluation of a second cohort of low cell count fine-needle aspirate smears from the BATTLE-2 trial yielded 97% agreement with an independent, validated NGS panel that was used with matched surgical specimens. Collectively, our data indicate that broad, clinically actionable insights that previously required independent assays, workflows, and analyses to assess both DNA and RNA can be conjoined in a first-tier, highly multiplexed NGS test, thereby providing faster, simpler, and more economical results.

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies.

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench ("wet bench") and data analyses conducted using bioinformatics pipelines ("dry bench"). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible "tag" PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

The 3,000 rice genomes project: new opportunities and challenges for future rice research.

Rice is the world's most important staple grown by millions of small-holder farmers. Sustaining rice production relies on the intelligent use of rice diversity. The 3,000 Rice Genomes Project is a giga-dataset of publically available genome sequences (averaging 14× depth of coverage) derived from 3,000 accessions of rice with global representation of genetic and functional diversity. The seed of these accessions is available from the International Rice Genebank Collection. Together, they are an unprecedented resource for advancing rice science and breeding technology. Our immediate challenge now is to comprehensively and systematically mine this dataset to link genotypic variation to functional variation with the ultimate goal of creating new and sustainable rice varieties that can support a future world population that will approach 9.6 billion by 2050.

Discrimination between thermodynamic models of cis-regulation using transcription factor occupancy data.

Many studies have identified binding preferences for transcription factors (TFs), but few have yielded predictive models of how combinations of transcription factor binding sites generate specific levels of gene expression. Synthetic promoters have emerged as powerful tools for generating quantitative data to parameterize models of combinatorial cis-regulation. We sought to improve the accuracy of such models by quantifying the occupancy of TFs on synthetic promoters in vivo and incorporating these data into statistical thermodynamic models of cis-regulation. Using chromatin immunoprecipitation-seq, we measured the occupancy of Gcn4 and Cbf1 in synthetic promoter libraries composed of binding sites for Gcn4, Cbf1, Met31/Met32 and Nrg1. We measured the occupancy of these two TFs and the expression levels of all promoters in two growth conditions. Models parameterized using only expression data predicted expression but failed to identify several interactions between TFs. In contrast, models parameterized with occupancy and expression data predicted expression data, and also revealed Gcn4 self-cooperativity and a negative interaction between Gcn4 and Nrg1. Occupancy data also allowed us to distinguish between competing regulatory mechanisms for the factor Gcn4. Our framework for combining occupancy and expression data produces predictive models that better reflect the mechanisms underlying combinatorial cis-regulation of gene expression.

Functional DNA quantification guides accurate next-generation sequencing mutation detection in formalin-fixed, paraffin-embedded tumor biopsies.

The formalin-fixed, paraffin-embedded (FFPE) biopsy is a challenging sample for molecular assays such as targeted next-generation sequencing (NGS). We compared three methods for FFPE DNA quantification, including a novel PCR assay ('QFI-PCR') that measures the absolute copy number of amplifiable DNA, across 165 residual clinical specimens. The results reveal the limitations of commonly used approaches, and demonstrate the value of an integrated workflow using QFI-PCR to improve the accuracy of NGS mutation detection and guide changes in input that can rescue low quality FFPE DNA. These findings address a growing need for improved quality measures in NGS-based patient testing.

Support for international agricultural research: current status and future challenges.

The success of the first Green Revolution in the form of abundant food supplies and low prices over the past two decades has diverted the world's attention from agriculture to other pressing issues. This has resulted in lower support for the agricultural research work primarily undertaken by the 15 research centers of the Consultative Group on International Agricultural Research (CGIAR). The total support in real dollars for most of the last three decades has been more or less flat although the number of centers increased from 4 to 15. However, since 2000, the funding situation has improved for the CGIAR centers, with almost all the increase coming from grants earmarked for specific research projects. Even for some centers such as the International Rice Research Institute (IRRI), the downward trend continued as late as 2006 with the budget in real dollars reaching the 1978 level of support. The recent food crisis has renewed the call for a second Green Revolution by revitalizing yield growth to feed the world in the face of growing population and a shrinking land base for agricultural use. The slowdown in yield growth because of decades of neglect in agricultural research and infrastructure development has been identified as the underlying reason for the recent food crisis. For the second Green Revolution to be successful, the CGIAR centers will have to play a complex role by expanding productivity in a sustainable manner with fewer resources. Thus, it is crucial to examine the current structure of support for the CGIAR centers and identify the challenges ahead in terms of source and end use of funds for the success of the second Green Revolution. The objective of this paper is to provide a historical perspective on the support to the CGIAR centers and to examine the current status of funding, in particular, the role of project-specific grants in rebuilding capacity of these centers. The paper will also discuss the nature of the support (unrestricted vs. project-specific grants) that will be needed for a much-desired second Green Revolution.

A cis-regulatory logic simulator.

BACKGROUND: A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence. RESULTS: We developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence. CONCLUSION: We developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material.