Human Sequencing Data Research Articles

Using MARRVEL v1.2 for Bioinformatics Analysis of Human Genes and Variant Pathogenicity.

One of the greatest challenges in the bioinformatic analysis of human sequencing data is identifying which variants are pathogenic. Numerous databases and tools have been generated to address this difficulty. However, these many useful data and tools are broadly dispersed, requiring users to search for their variants of interest through human genetic databases, variant function prediction tools, and model organism databases. To solve this problem, we collected data and observed workflows of human geneticists, clinicians, and model organism researchers to carefully select and display valuable information that facilitates the evaluation of whether a variant is likely to be pathogenic. This program, Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) v1.2, allows users to collect relevant data from 27 public sources for further efficient bioinformatic analysis of the pathogenicity of human variants. © 2019 by John Wiley & Sons, Inc.

Noise-cancelling repeat finder: uncovering tandem repeats in error-prone long-read sequencing data.

SummaryTandem DNA repeats can be sequenced with long-read technologies, but cannot be accurately deciphered due to the lack of computational tools taking high error rates of these technologies into account. Here we introduce Noise-Cancelling Repeat Finder (NCRF) to uncover putative tandem repeats of specified motifs in noisy long reads produced by Pacific Biosciences and Oxford Nanopore sequencers. Using simulations, we validated the use of NCRF to locate tandem repeats with motifs of various lengths and demonstrated its superior performance as compared to two alternative tools. Using real human whole-genome sequencing data, NCRF identified long arrays of the (AATGG)n repeat involved in heat shock stress response.Availability and implementationNCRF is implemented in C, supported by several python scripts, and is available in bioconda and at https://github.com/makovalab-psu/NoiseCancellingRepeatFinder.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 730: Genomic characterization of hereditary leiomyomatosis and renal cell carcinoma (HLRCC), a rare and aggressive kidney cancer

Abstract Background Hereditary Leiomyomatosis and Renal Cell Carcinoma (HLRCC) is a rare autosomal-dominant syndromic disorder associated with germline FH mutations. Affected individuals have a predisposition to cutaneous and uterine leiomyoma’s, and are at increased risk of developing a renal cell carcinoma (HLRCC- associated RCC) with aggressive clinical outcome. However, aside from FH bi-allelic loss, additional genomic features of HLRCC are currently poorly understood. Method Five cases of HLRCC-associated RCC were characterized using integrated next generation sequencing (NGS) technology. This included an index case where multiple tumor sites (kidney, lung, liver, lymph node, skin, and uterine) from a single patient were characterized. For integrative analysis, in-house data were combined with five RCC cases with FH bi-allelic loss from The Cancer Genome Atlas Study. Single cell sequencing was carried out on normal human kidney to study the cell of origin for HLRCC- associated RCC. Results: Integrative NGS profiling and analysis of HLRCC-associated RCC tumors (n=10) revealed several important observations such as 1) a high frequency loss of whole chromosomes, which is reminiscent of tumors arising from the distal nephron, 2) low mutational burden similar to that observed in other kidney tumors and, 3) a HLRCC specific gene expression signature and discovery of a novel therapeutic candidate (HLRCC-TC1). Importantly, among renal tumors, HLRCC-TC1 was specifically expressed in HLRCC and other RCCs with poor clinical outcome. Application of siRNAs and neutralizing antibodies to HLRCC-TC1 reduced cell proliferation of the HLRCC cell line UOK262, but did not for that of control cell lines. Conclusion: The observed FH bi-allelic mutation along with recurrent chromosome-wide losses and integrative analysis with human kidney single cell sequencing data support a distal nephron origin for HLRCC- associated RCC. In addition, our study provides evidence for nominating HLRCC-TC1 as a novel cancer-specific therapeutic target. Further studies are needed to conduct an in-depth characterization of HLRCC-TC1. Citation Format: Pankaj Vats, Yuping Zhang, Sarvana Mohan Dhanashekaran, Narayanan Sathiya Pandi, Xuhong Cao, Fengyun Su, Sudhanshu Shukla, Rui Wang, Marcin Cieslik, Jincheng Pan, Christopher J. Ricketts, Prem Kumar Kumpati, Ajjai Alva, Marston W. Linehan, Rohit Mehra, Arul M. Chinnaiyan. Genomic characterization of hereditary leiomyomatosis and renal cell carcinoma (HLRCC), a rare and aggressive kidney cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 730.

Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data

BackgroundWhole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination of aligner-variant caller tools for detecting accurate single nucleotide variants (SNVs) and small insertion and deletion (InDels) separately. Moreover, many important aspects of InDel detection are overlooked while comparing the performance of tools, particularly its base pair length.ResultsWe assessed the performance of variant calling pipelines using the combinations of four variant callers and five aligners on human NA12878 and simulated exome data. We used high confidence variant calls from Genome in a Bottle (GiaB) consortium for validation, and GRCh37 and GRCh38 as the human reference genome. Based on the performance metrics, both BWA and Novoalign aligners performed better with DeepVariant and SAMtools callers for detecting SNVs, and with DeepVariant and GATK for InDels. Furthermore, we obtained similar results on human NA24385 and NA24631 exome data from GiaB.ConclusionIn this study, DeepVariant with BWA and Novoalign performed best for detecting accurate SNVs and InDels. The accuracy of variant calling was improved by merging the top performing pipelines. The results of our study provide useful recommendations for analysis of WES data in clinical genomics.

VCF/Plotein: visualization and prioritization of genomic variants from human exome sequencing projects.

MotivationIdentifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data.ResultsWe have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user’s CPU to ensure the security of patient data.Availability and implementationFreely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein.Supplementary information Supplementary data are available at Bioinformatics online.

A host subtraction database for virus discovery in human cell line sequencing data

The human cell lines HepG2, HuH-7, and Jurkat are commonly used for amplification of the RNA viruses present in environmental samples. To assist with assays by RNAseq, we sequenced these cell lines and developed a subtraction database that contains sequences expected in sequence data from uninfected cells. RNAseq data from cell lines infected with Sendai virus were analyzed to test host subtraction. The process of mapping RNAseq reads to our subtraction database vastly reduced the number non-viral reads in the dataset to allow for efficient secondary analyses.

Estimating Sample-Specific Regulatory Networks

SummaryBiological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or “edge,” in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.

An estimate of the total number of true human miRNAs.

While the number of human miRNA candidates continuously increases, only a few of them are completely characterized and experimentally validated. Toward determining the total number of true miRNAs, we employed a combined in silico high- and experimental low-throughput validation strategy. We collected 28 866 human small RNA sequencing data sets containing 363.7 billion sequencing reads and excluded falsely annotated and low quality data. Our high-throughput analysis identified 65% of 24 127 mature miRNA candidates as likely false-positives. Using northern blotting, we experimentally validated miRBase entries and novel miRNA candidates. By exogenous overexpression of 108 precursors that encode 205 mature miRNAs, we confirmed 68.5% of the miRBase entries with the confirmation rate going up to 94.4% for the high-confidence entries and 18.3% of the novel miRNA candidates. Analyzing endogenous miRNAs, we verified the expression of 8 miRNAs in 12 different human cell lines. In total, we extrapolated 2300 true human mature miRNAs, 1115 of which are currently annotated in miRBase V22. The experimentally validated miRNAs will contribute to revising targetomes hypothesized by utilizing falsely annotated miRNAs.

Statistical modeling on human microbiome sequencing data

Research studies have shown that human microbiome is associated with many diseases through the linkage between bacterial taxa and environmental and genetic factors. Typical human microbiome sequencing data that obtained by next generation sequencing technologies of the 16S rRNA gene are high dimensional and sparse because most taxa are not shared among the samples. As a result, the data is often over-dispersed and with excess zeros. These features rise statistical challenges for compositional data analysis. We review the recent statistical methodology development for this setting. In particular, we summarize some current popular parametric probability models including the cases when repeated measurements of the microbiome are applicable. Multivariate analyses methods that are based on distance measurement for testing differences between microbes community are introduced. Statistical models which are developed to assess the association between genetic variants on X-chromosome and microbial components are highlighted. We discuss some applications on analysis of the association of host genome, microbial compositions and human diseases. Despite sophisticated approaches to statistical analysis of taxa count data, we suggest some future research directions on how to classify and predict clinical outcomes with microbial compositions.

PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data.

Single-cell RNA sequencing is an increasingly used method to measure gene expression at the single cell level and build cell-type atlases of tissues. Hundreds of single-cell sequencing datasets have already been published. However, studies are frequently deposited as raw data, a format difficult to access for biological researchers due to the need for data processing using complex computational pipelines. We have implemented an online database, PanglaoDB, accessible through a user-friendly interface that can be used to explore published mouse and human single cell RNA sequencing studies. PanglaoDB contains pre-processed and pre-computed analyses from more than 1054 single-cell experiments covering most major single cell platforms and protocols, based on more than 4 million cells from a wide range of tissues and organs. The online interface allows users to query and explore cell types, genetic pathways and regulatory networks. In addition, we have established a community-curated cell-type marker compendium, containing more than 6000 gene-cell-type associations, as a resource for automatic annotation of cell types.

NovoGraph: Human genome graph construction from multiple long-read de novo assemblies.

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data

BackgroundMouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary.ResultsWe developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations.ConclusionsXenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR.

NovoGraph: Human genome graph construction from multiple long-read de novo assemblies

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Interred with Their DNA

Interred with Their DNA

Abstract A04: Microbial and immunologic characterization of gastroesophageal tissue biopsy samples: A multiparametric analysis

Abstract Gastric cancer carcinogenesis is associated with chronic inflammation, most commonly the result of Helicobacter pylori chronic infection in the stomach antrum. The development of gastric cancer in the context of chronic H. pylori infection is multifactorial, encompassing both bacterial factors and the altered immune microenvironment. However, a comprehensive analysis of the relation between inflammation and host microbial population in patient tissue samples has not previously been explored. We proposed an unbiased study to evaluate the relationships among microbiome composition, host immune response and genomic characterization from next-generation sequencing of gastric biopsy samples. Patients undergoing upper endoscopy without chronic inflammatory disease or chronic NSAID use were eligible for participation. Endoscopic biopsies from gastric fundus, body, and antrum were collected from patients with active H. pylori infection (n=21), prior infection (n=22) and no prior infection (n=26), and were sequenced at 10X to 30X coverage. In total, 77 gastric biopsies from 69 patients were freshly frozen for whole genome sequencing (WGS) and transcriptome (RNASeq) analysis. Detecting the microbiome from human biopsy sequencing data directly is challenging due to the low microbial content. A novel computational pipeline was developed to address this problem specifically (Zhang et al., Genome Biology 2015). A robust H. pylori signal was detected in samples from clinically verified H. pylori infected patients, and the results were further validated by qPCR. In our analysis population, in addition to identification of H. pylori, several bacteria associated with other cancers were also detected in several biopsy samples, such as Prevotella melaninogenica, Veillonella parvula and Fusobacterium nucleatum. H. pylori infection was associated with reduced microbial biodiversity compared to prior infection or control tissue (p=0.02). H. pylori active infection samples have a distinct non-H. pylori microbiome compared to prior infection and control samples. We also identified 5 patients with prior infection and 1 control patient with occult H. pylori infection (e.g., asymptomatic patients). To characterize the immune infiltration in the mucosal biopsy samples, we developed a 176-gene panel, collected from multiple published studies, to define the immune signatures. The expression profile of this immune gene panel was used evaluate the immune infiltration levels of multiple immune cell types. The result of unsupervised clustering revealed a much higher immune infiltration in H. pylori positive samples compared to uninfected samples, especially for CD8+, Th2 and Th17 cell populations. Two orthogonal experimental essays (ELISA and Flow Cytometry) were performed independently to verify the results. ELISA results confirmed the RNAseq-based expression profiling of inflammatory cytokines such as GRO, IL8, TNFa and SCD40L. Importantly, in 2 patients with prior H. pylori infection, the pro-inflammatory immune signature characteristic of H. pylori persisted. Finally, amongst H. pylori active infection patients, the biodiversity of the other bacteria present was inversely correlated with local immune infiltration (e.g., greater the bacterial diversity, the less robust the proinflammatory immune signature). In summary, this study established a methodology for microbiome and immune profiling of gastric biopsy samples. We have identified a proinflammatory immune response signature associated with H. pylori infection, confirmed and validated by orthogonal assays. Notably, in several samples from previously eradicated H. pylori infection, the proinflammatory immune signature persisted, suggesting a potential long-term impact of H. pylori infection on mucosal immunity, that might contribute to gastric tumorigenesis. Finally, we demonstrate that the biodiversity of the bacterial species correlates with the character of immune infiltration. Citation Format: Chao Zhang, Prashant Vijay Thakkar, Felice Schnoll-Sussman, Bridget McClure, Michelle Bigg, Greg Sonnenberg, Doron Betel, Manish Shah. Microbial and immunologic characterization of gastroesophageal tissue biopsy samples: A multiparametric analysis [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr A04.

An improved approach for reconstructing consensus repeats from short sequence reads

BackgroundRepeat elements are important components of most eukaryotic genomes. Most existing tools for repeat analysis rely either on high quality reference genomes or existing repeat libraries. Thus, it is still challenging to do repeat analysis for species with highly repetitive or complex genomes which often do not have good reference genomes or annotated repeat libraries. Recently we developed a computational method called REPdenovo that constructs consensus repeat sequences directly from short sequence reads, which outperforms an existing tool called RepARK. One major issue with REPdenovo is that it doesn’t perform well for repeats with relatively high divergence rates or low copy numbers. In this paper, we present an improved approach for constructing consensus repeats directly from short reads. Comparing with the original REPdenovo, the improved approach uses more repeat-related k-mers and improves repeat assembly quality using a consensus-based k-mer processing method.ResultsWe compare the performance of the new method with REPdenovo and RepARK on Human, Arabidopsis thaliana and Drosophila melanogaster short sequencing data. And the new method fully constructs more repeats in Repbase than the original REPdenovo and RepARK, especially for repeats of higher divergence rates and lower copy number. We also apply our new method on Hummingbird data which doesn’t have a known repeat library, and it constructs many repeat elements that can be validated using PacBio long reads.ConclusionWe propose an improved method for reconstructing repeat elements directly from short sequence reads. The results show that our new method can assemble more complete repeats than REPdenovo (and also RepARK). Our new approach has been implemented as part of the REPdenovo software package, which is available for download at https://github.com/Reedwarbler/REPdenovo.

Abstract A62: Canine pedigree analysis as a model of hereditary breast cancer

Abstract Genetic homology with humans and breeding practices that limit intrabreed heterogeneity make canines a useful genetic model for identifying heritable disease markers that have parallels in humans. Ultimately, the homogeneity of individual canine breeds is advantageous in identifying the modes of inheritance for human disorders that have resisted investigative efforts for decades. Particularly few genetic research studies have focused on identifying inherited risk factors for canine mammary tumor (CMT), and as such there has been little exploration of the potential overlaps between the ~65% of human hereditary breast cancer cases that are currently genetically unsolved. DNA samples from 85 CMT-affected canines were extracted from blood/buccal swabs for whole-genome sequencing. These included 32 different canine breeds from six of the seven American Kennel Club (AKC) recognized breed groups; the most represented breeds were Golden Retrievers (n=20), Siberian Huskies (n=8), and Standard Schnauzers (n=7). The cohort also included 3 male CMT cases, which is a hallmark of hereditary CMT and comparable to hereditary breast cancer incidence in human males. Furthermore, pedigree analyses identified dogs within the CMT cohort who stem from a breed-specific common ancestor, and this indicates increased genetic homogeneity within a breed. Currently, these dogs exist in twelve different canine pedigrees corresponding to twelve different breeds and representing 72% of the CMT cohort. The most impressively linked pedigrees contain 20/20 Golden Retrievers, 7/8 Siberian Huskies, 7/7 Standard Schnauzers, 5/5 Welsh Springer Spaniels, 4/4 Newfoundlands, 4/4 Golden Setters, 3/3 Dalmatians, and 3/3 Doberman Pinschers. These CMT pedigrees highlight the characteristics of hereditary disease and demonstrate potential to identify mutations that are specific to a breed or kennel. Through pedigree analysis, fourteen samples were selected for genome sequencing, and they represented Golden Retrievers, Siberian Huskies, Standard Schnauzers, and Dalmatians. Following genome sequencing, orthologs of human genes are being investigated for comparison to human hereditary breast cancer sequencing data that were generated in our laboratory. This comparative genomics aspect may lead to the discovery of novel genes that influence breast cancer risk. Citation Format: Anna L.W. Huskey, Carlos Lloveras-Fuentes, Katie Goebel, Nancy D. Merner. Canine pedigree analysis as a model of hereditary breast cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr A62.

A host subtraction database for virus discovery in human cell line sequencing data.

The human cell lines HepG2, HuH-7, and Jurkat are commonly used for amplification of the RNA viruses present in environmental samples. To assist with assays by RNAseq, we sequenced these cell lines and developed a subtraction database that contains sequences expected in sequence data from uninfected cells. RNAseq data from cell lines infected with Sendai virus were analyzed to test host subtraction. The process of mapping RNAseq reads to our subtraction database vastly reduced the number non-viral reads in the dataset to allow for efficient secondary analyses.

Abstract 2086: Comprehensive microbial and immunological screening of gastroesophageal biopsies: A multi-parametric analysis to study carcinogenesis

Abstract The development of gastric cancer in the context of chronic H. pylori infection is multifactorial, encompassing both bacterial factors and the altered immune microenvironment. We proposed an unbiased study to evaluate the relationships among microbiome composition, host immune response and genomic characterization from next generation sequencing of gastric biopsy samples. Endoscopic biopsies collected from patients with active H. pylori infection (n=21), prior infection (n=22) and no prior infection (n=26), and were sequenced at 10X to 30X coverage. In total, 77 gastric biopsies from 69 patients were freshly frozen for whole genome sequencing and RNASeq analysis. Detecting the microbiome from human biopsy sequencing data directly is challenging due to the low microbial content. A novel computational pipeline was developed to address this problem specifically. In our analysis population, in addition to the robust H. pylori signal, several bacteria associated with other cancers were also detected in several biopsy samples, such as Prevotella melaninogenica, Veillonella parvula and Fusobacterium nucleatum. H. pylori infection was associated with reduced microbial biodiversity compared to prior infection or control tissue (p=0.02). H. pylori active infection samples have a distinct non-H. pylori microbiome compared to prior infection and control samples. We also identified 5 patients with prior infection and 1 control patient with occult H. pylori infection. To characterize the immune infiltration in the mucosal biopsy samples, we define a 176-gene immune panel. The result of unsupervised clustering of gene expression revealed a much higher immune infiltration in H. pylori positive samples compared to uninfected samples, especially for CD8+, Th2 and Th17 cell populations. Two orthogonal experimental essays (ELISA and Flow Cytometry) confirmed the RNAseq-based expression profiling of inflammatory cytokines such as GRO, IL8, TNFa and SCD40L. Importantly, in 2 patients with prior H. pylori infection, the pro-inflammatory immune signature characteristic of H. pylori persisted. Finally, amongst H. pylori active infection patients, the biodiversity of the other bacteria present was inversely correlated with local immune infiltration (eg. greater the bacterial diversity, the less robust the pro-inflammatory immune signature). In summary, this study established a methodology for microbiome and immune profiling of gastric biopsy samples. We have identified a pro-inflammatory immune response signature associated with H. pylori infection, confirmed and validated by orthogonal assays. Notably, in several samples from previously eradicated H. pylori infection, the pro-inflammatory immune signature persisted, suggesting a potential long-term impact of H. pylori infection on mucosal immunity, that might contribute to gastric tumorigenesis. Citation Format: Chao Zhang, Prashant V. Thakkar, Felice Schnoll-Sussman, Bridget McClure, Greg Sonnenberg, Doron Betel, Manish A. Shah. Comprehensive microbial and immunological screening of gastroesophageal biopsies: A multi-parametric analysis to study carcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2086.

VarAFT: a variant annotation and filtration system for human next generation sequencing data.

With the rapidly developing high-throughput sequencing technologies known as next generation sequencing or NGS, our approach to gene hunting and diagnosis has drastically changed. In <10 years, these technologies have moved from gene panel to whole genome sequencing and from an exclusively research context to clinical practice. Today, the limit is not the sequencing of one, many or all genes but rather the data analysis. Consequently, the challenge is to rapidly and efficiently identify disease-causing mutations within millions of variants. To do so, we developed the VarAFT software to annotate and pinpoint human disease-causing mutations through access to multiple layers of information. VarAFT was designed both for research and clinical contexts and is accessible to all scientists, regardless of bioinformatics training. Data from multiple samples may be combined to address all Mendelian inheritance modes, cancers or population genetics. Optimized filtration parameters can be stored and re-applied to large datasets. In addition to classical annotations from dbNSFP, VarAFT contains unique features at the disease (OMIM), phenotypic (HPO), gene (Gene Ontology, pathways) and variation levels (predictions from UMD-Predictor and Human Splicing Finder) that can be combined to optimally select candidate pathogenic mutations. VarAFT is freely available at: http://varaft.eu.