Minimum information and guidelines for reporting a multiplexed assay of variant effect

Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

Minimum information and guidelines for reporting a Multiplexed Assay of Variant Effect

Multiplexed Assays of Variant Effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines has led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

Development and application of a computable genotype model in the GA4GH Variation Representation Specification

As the diversity of genomic variation data increases with our growing understanding of the role of variation in health and disease, it is critical to develop standards for precise inter-system exchange of these data for research and clinical applications. The Global Alliance for Genomics and Health (GA4GH) Variation Representation Specification (VRS) meets this need through a technical terminology and information model for disambiguating and concisely representing variation concepts. Here we discuss the recent Genotype model in VRS, which may be used to represent the allelic composition of a genetic locus. We demonstrate the use of the Genotype model and the constituent Haplotype model for the precise and interoperable representation of pharmacogenomic diplotypes, HGVS variants, and VCF records using VRS and discuss how this can be leveraged to enable interoperable exchange and search operations between assayed variation and genomic knowledgebases.

tmVar 3.0: an improved variant concept recognition and normalization tool

MOTIVATION

Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision.

RESULT

We propose tmVar 3.0: an improved variant recognition and normalization system. Compared to its predecessors, tmVar 3.0 recognizes a wider spectrum of variant-related entities (e.g. allele and copy number variants), and groups together different variant mentions belonging to the same genomic sequence position in an article for improved accuracy. Moreover, tmVar 3.0 provides advanced variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar 3.0 exhibits state-of-the-art performance with over 90% in F-measure for variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar 3.0 as well as annotations for the entire PubMed and PMC datasets are freely available for download.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ncbi/tmVar3.

Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data

Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.

The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification

Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced "verse"), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH).

C-N Coupling of DNA-Conjugated (Hetero)aryl Bromides and Chlorides for DNA-Encoded Chemical Library Synthesis

DNA-encoded chemical library (DECL) screens are a rapid and economical tool to identify chemical starting points for drug discovery. As a robust transformation for drug discovery, palladium-catalyzed C–N coupling is a valuable synthetic method for the construction of DECL chemical matter; however, currently disclosed methods have only been demonstrated on DNA-attached (hetero)aromatic iodide and bromide electrophiles. We developed conditions utilizing an N-heterocyclic carbene–palladium catalyst that extends this reaction to the coupling of DNA-conjugated (hetero)aromatic chlorides with (hetero)aromatic and select aliphatic amine nucleophiles. In addition, we evaluated steric and electronic effects within this catalyst series, carried out a large substrate scope study on two representative (hetero)aryl bromides, and applied this newly developed method within the construction of a 63 million-membered DECL.

Palladium-Catalyzed Hydroxycarbonylation of (Hetero)aryl Halides for DNA-Encoded Chemical Library Synthesis

A strategy for DNA-compatible, palladium-catalyzed hydroxycarbonylation of (hetero)aryl halides on DNA–chemical conjugates has been developed. This method generally provided the corresponding carboxylic acids in moderate to very good conversions for (hetero)aryl iodides and bromides, and in poor to moderate conversions for (hetero)aryl chlorides. These conditions were further validated by application within a DNA-encoded chemical library synthesis and subsequent discovery of enriched features from the library in selection experiments against two protein targets.

A Mild, DNA-Compatible Nitro Reduction Using B2(OH)4

A hypodiboric acid system for the reduction of nitro groups on DNA–chemical conjugates has been developed. This transformation provided good to excellent yields of the reduced amine product for a variety of functionalized aromatic, heterocyclic, and aliphatic nitro compounds. DNA tolerance to reaction conditions, extension to decigram scale reductions, successful use in a DNA-encoded chemical library synthesis, and subsequent target selection are also described.

Quantitative Comparison of Enrichment from DNA-Encoded Chemical Library Selections

DNA-encoded chemical libraries (DELs) provide a high-throughput and cost-effective route for screening billions of unique molecules for binding affinity for diverse protein targets. Identifying candidate compounds from these libraries involves affinity selection, DNA sequencing, and measuring enrichment in a sample pool of DNA barcodes. Successful detection of potent binders is affected by many factors, including selection parameters, chemical yields, library amplification, sequencing depth, sequencing errors, library sizes, and the chosen enrichment metric. To date, there has not been a clear consensus about how enrichment from DEL selections should be measured or reported. We propose a normalized z-score enrichment metric using a binomial distribution model that satisfies important criteria that are relevant for analysis of DEL selection data. The introduced metric is robust with respect to library diversity and sampling and allows for quantitative comparisons of enrichment of n-synthons from parallel DEL selections. These features enable a comparative enrichment analysis strategy that can provide valuable information about hit compounds in early stage drug discovery.

ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants

BACKGROUND

The success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations.

RESULTS

In this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org .

CONCLUSIONS

By enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.

Structure and function of the healthy pre-adolescent pediatric gut microbiome

BACKGROUND

The gut microbiome influences myriad host functions, including nutrient acquisition, immune modulation, brain development, and behavior. Although human gut microbiota are recognized to change as we age, information regarding the structure and function of the gut microbiome during childhood is limited. Using 16S rRNA gene and shotgun metagenomic sequencing, we characterized the structure, function, and variation of the healthy pediatric gut microbiome in a cohort of school-aged, pre-adolescent children (ages 7-12 years). We compared the healthy pediatric gut microbiome with that of healthy adults previously recruited from the same region (Houston, TX, USA).

RESULTS

Although healthy children and adults harbored similar numbers of taxa and functional genes, their composition and functional potential differed significantly. Children were enriched in Bifidobacterium spp., Faecalibacterium spp., and members of the Lachnospiraceae, while adults harbored greater abundances of Bacteroides spp. From a functional perspective, significant differences were detected with respect to the relative abundances of genes involved in vitamin synthesis, amino acid degradation, oxidative phosphorylation, and triggering mucosal inflammation. Children's gut communities were enriched in functions which may support ongoing development, while adult communities were enriched in functions associated with inflammation, obesity, and increased risk of adiposity.

CONCLUSIONS

Previous studies suggest that the human gut microbiome is relatively stable and adult-like after the first 1 to 3 years of life. Our results suggest that the healthy pediatric gut microbiome harbors compositional and functional qualities that differ from those of healthy adults and that the gut microbiome may undergo a more prolonged development than previously suspected.

Abstract 800: Simultaneous methylation and mutational analysis of breast cancer genomes using droplet-based targeted sequencing

Targeted sequencing using droplet-based PCR provides an established method to selectively and uniformly amplify thousands of genomic regions of interest for deep, cost-effective next generation sequencing. Recently, this approach has been adapted for the analysis of bisulfite-treated DNA, enabling both quantitative epigenomic analysis (strand-specific determination of cytosine methylation status with single-base pair resolution) and genomic mutation detection. Simultaneous collection of both methylation and genomic aberration information with a single method makes efficient use of precious clinical material and allows both researchers and clinicians to use this information to sub-type cancers.

Here we report on our sequencing results using a single breast cancer targeted panel for sequence specific analysis of both epigenomic methylation and genomic mutation in breast tumor samples. The Breast Cancer MethylSeq Panel includes PCR primer pairs designed to amplify 1000 targets, leaving room for an additional 3000 target loci of interest to be added by individual researchers in the future. This single panel provides information about both loci-specific CpG methylation and genomic mutations, enabling analysis of multiple content types including: A) cell subtype (CELL); B) genomic mutation (MUT); C) surrogate gene expression subtypes using promoter CpG islands or other methylation marks (EXP); D) copy-number variation (CNV); E) Loss of Heterozygosity (LOH); and F) methylation-mediated gene silencing and loss of imprinting (IMP). In addition, miRNA associated with breast cancer will be targeted for analysis (MIR).

The Breast Cancer MethylSeq Panel was tested and validated with a set of 64 bisulfite-converted DNA samples that included breast cancer cell lines, a range of ER positive and negative tumors and TCGA samples, and a set of 8 metastatic tumors from a single individual. Overall Illumina HiSeq sequencing success metrics, concordance with metadata on the various target classes (A-F above) using validated controls, and results from the patient samples will be presented.

The Breast Cancer MethylSeq Panel allows simultaneous analysis of epigenomic and genomic sequence alterations associated with breast tumor samples, enabling tumor sub-typing and correlation with clinical outcomes. This ‘core’ panel provides a unique (expandable) screening tool for the breast cancer research community, and a paradigm for efficient analysis of multiple endpoints from a single assay for other diseases.

Citation Format: Michael L. Samuels, Matt Roth, Preethi Srinivasan, Kevin Riehle, Adam Brufsky, Ryan Hartmaier, David Boone, R. Alan Harris, Chia-Chin Wu, Steffi Oesterreich, Adrian Lee, Aleksandar Milosavljevic. Simultaneous methylation and mutational analysis of breast cancer genomes using droplet-based targeted sequencing. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 800. doi:10.1158/1538-7445.AM2013-800

Abstract P6-05-12: Lack of Frequent Estrogen Receptor Mutation in Primary Breast Tumors

The estrogen receptor alpha (ESR1) is a critical driver of breast tumorigenesis, and as such has been a target for therapy for many years. Early reports using Sanger sequencing and conformational assays reported that there were little if any mutations in ER-positive tumors, although a few somatic mutations have recently been described in tumors and cell lines. We set out to validate the authenticity of these reported somatic mutations by performing analysis of ER DNA sequence variants (DSVs) in 66 ER+ breast tumors, and 39 breast cancer cell lines. We utilized a combined approach of target capture sequencing of ESR1, and subsequent testing for novel and previously identified DSVs using an orthogonal mass spectrometry based sequencing approach. A DNA capture approach was designed to capture all exons, flanking splice sites, and the 3′ UTR of ESR1. DNA was captured and sequenced using SOLiD.

Using stringency with a cut-off with at least 2 variant reads, variants in at least 20% of the reads, and requiring evidence for the variants in both strands, we identified 4 previously reported SNPs (rs2077647; rs46432; rs1801132; rs2228480), and two previously reported somatic mutations H6Y and N532Y. We developed mass-spectrometry assays, including controls, for these DSVs, and in addition, we included 10 DSVs which had previously been reported as somatic mutations in breast (and other) cancer. Applying mass-spec based analysis to breast tumors and cell lines, we were able to confirm previously reported SNPs, and one previously reported mutations (H6Y). This mutation was found in one breast tumor and one cell line. None of the other reported somatic mutations could be confirmed in tumors or cell lines. Functional assays on the ESR1H6Y DSV failed to identify differences to wildtype receptor. The lack of a phenotype, and the infrequent occurrence of this DSV do not support a major driver role for ESR1H6Y.

This analysis suggests that ESR1 mutations are a rare event in ER+ primary breast cancer.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P6-05-12.

The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences

BACKGROUND

Microbial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings.

METHODS

To address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection.

RESULTS

We validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome.

CONCLUSIONS

By lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries.

A metagenomic approach to characterization of the vaginal microbiome signature in pregnancy

While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age.

Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome

BACKGROUND & AIMS

The intestinal microbiomes of healthy children and pediatric patients with irritable bowel syndrome (IBS) are not well defined. Studies in adults have indicated that the gastrointestinal microbiota could be involved in IBS.

METHODS

We analyzed 71 samples from 22 children with IBS (pediatric Rome III criteria) and 22 healthy children, ages 7-12 years, by 16S ribosomal RNA gene sequencing, with an average of 54,287 reads/stool sample (average 454 read length = 503 bases). Data were analyzed using phylogenetic-based clustering (Unifrac), or an operational taxonomic unit (OTU) approach using a supervised machine learning tool (randomForest). Most samples were also hybridized to a microarray that can detect 8741 bacterial taxa (16S rRNA PhyloChip).

RESULTS

Microbiomes associated with pediatric IBS were characterized by a significantly greater percentage of the class γ-proteobacteria (0.07% vs 0.89% of total bacteria, respectively; P < .05); 1 prominent component of this group was Haemophilus parainfluenzae. Differences highlighted by 454 sequencing were confirmed by high-resolution PhyloChip analysis. Using supervised learning techniques, we were able to classify different subtypes of IBS with a success rate of 98.5%, using limited sets of discriminant bacterial species. A novel Ruminococcus-like microbe was associated with IBS, indicating the potential utility of microbe discovery for gastrointestinal disorders. A greater frequency of pain correlated with an increased abundance of several bacterial taxa from the genus Alistipes.

CONCLUSIONS

Using 16S metagenomics by PhyloChip DNA hybridization and deep 454 pyrosequencing, we associated specific microbiome signatures with pediatric IBS. These findings indicate the important association between gastrointestinal microbes and IBS in children; these approaches might be used in diagnosis of functional bowel disorders in pediatric patients.