High-throughput Genome Sequencing Technologies Research Articles

Joint inference of clonal structure using single-cell genome and transcriptome sequencing data.

Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.

Markov chain composite likelihood and its application in genetic recombination model

Phylogenetic Trees are critical in human genome research for investigating human evolution and identifying disease-associated genetic markers. New high-throughput genome sequencing technologies raise an urgent need to develop statistical methods that can construct phylogenetic trees from long genome sequences with quick computation speeds, while considering various biological complexities. Though an ancestral mixture model has been proposed [Chen SC, Lindsay BG. Building mixture trees from binary sequence data. Biometrika. 2006;93(4):843–860. doi: 10.1093/biomet/93.4.843] to this end by allowing genetic mutations over generations, another essential evolution factor, genetic recombination, is missed. Therefore, in this paper, we develop a novel genetic recombination model for tree construction and propose to use Markov chain composite likelihood (MCCL) to make model estimation computationally feasible. To further reduce computation complexity, a hierarchical estimator is constructed to estimate unknown ancestral distributions through MCCL. Simulation studies and real data example show that our proposed methods perform well and fast, so have the potential for implementation in long sequence genome data.

Investigation of blood group genotype prevalence in Korean population using large genomic databases

Blood group antigens, which are prominently expressed in red blood cells, are important in transfusion medicine. The advent of high-throughput genome sequencing technology has facilitated the prediction of blood group antigen phenotypes based on genomic data. In this study, we analyzed data from a large Korean population to provide an updated prevalence of blood group antigen phenotypes, including rare ones. A robust dataset comprising 72,291 single nucleotide polymorphism arrays, 5318 whole-exome sequences, and 4793 whole-genome sequences was extracted from the Korean Genome and Epidemiology Study, Genome Aggregation Database, and Korean Variant Archive and then analyzed. The phenotype prevalence of clinically significant blood group antigens, including MNSs, RHCE, Kidd, Duffy, and Diego, was predicted through genotype analysis and corroborated the existing literature. We identified individuals with rare phenotypes, including 369 (0.51%) with Fy(a−b+), 188 (0.26%) with Di(a+b−), and 16 (0.02%) with Jr(a−). Furthermore, we calculated the frequencies of individuals with extremely rare phenotypes, such as p (0.000004%), Kell-null (0.000310%), and Jk(a−b−) (0.000438%), based on allele frequency predictions. These findings offer valuable insights into the distribution of blood group antigens in the Korean population and have significant implications for enhancing the safety and efficiency of blood transfusion.

Distributed RMI-DBG model: Scalable iterative de Bruijn graph algorithm for short read genome assembly problem

Genome assembly is the computational process of merging short parts of DNA into larger sequences called contigs. Rapid growth of high-throughput genome sequencing technologies and production of large amount of data have led to the genome assembly paradigms shift from shared memory to distributed memory systems in the recent years. Among the existing assembly algorithms, the iterative de Bruijn Graph is a leading approach for assembling short reads. This approach by exploring the advantages of all k between kmin to kmax, generates high quality assembly. However, the assembly operations are decelerated especially in the larger data sets. RMI-DBG is an agile iterative de Bruijn Graph algorithm that has the computational efficiency of de Bruijn Graph methods and the flexibility of overlap-based algorithms. In this paper, we suggest a distributed iterative DBG model based on RMI-DBG, named DRMI-DBG. The proposed idea is to address the problem of parallelizing the de Bruijn Graph construction and processing on distributed memory systems at each iteration of the algorithm. DRMI-DBG is a scalable iterative DBG framework over a Hadoop cluster by applying the power of Spark (a batch processing engine) and Giraph (a distributed big graph processing system). Experiments on a variety of real data sets show that DRMI-DBG accelerates the performance of RMI-DBG algorithm and IDBA-UD assembler up to 4.8 times with comparable or better results in the quality of the assembly. For more evaluation, performance of the proposed model is compared to ScalaDBG, as the state-of-the-art distributed assembler based on the multiple k-values strategy.

Phenotyping CHST3 skeletal dysplasia from freezer-induced urine sediments

Skeletal dysplasias are a group of rare genetic disorders that affect growth and development of the skeleton, leading to physical deformities and other medical problems. High-throughput genome sequencing technologies have made it easier to genotype the disorder, but do not always reflect the phenotypic outcome. CHST3-related skeletal dysplasia is caused by the reduced function of the carbohydrate sulfotransferase that sulfates chondroitin sulfate glycosaminoglycans. We show in this pilot study that we were able to phenotype patients with CHST3-related skeletal dysplasia by profiling the glycosaminoglycans and identifying their potential protein carriers sequentially using freezer-induced patient urine sediments.

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.

In-depth analysis of genomes and functional genomics of orchid using cutting-edge high-throughput sequencing.

High-throughput sequencing technology has been facilitated the development of new methodologies and approaches for studying the origin and evolution of plant genomes and subgenomes, population domestication, and functional genomics. Orchids have tens of thousands of members in nature. Many of them have promising application potential in the extension and conservation of the ecological chain, the horticultural use of ornamental blossoms, and the utilization of botanical medicines. However, a large-scale gene knockout mutant library and a sophisticated genetic transformation system are still lacking in the improvement of orchid germplasm resources. New gene editing tools, such as the favored CRISPR-Cas9 or some base editors, have not yet been widely applied in orchids. In addition to a large variety of orchid cultivars, the high-precision, high-throughput genome sequencing technology is also required for the mining of trait-related functional genes. Nowadays, the focus of orchid genomics research has been directed to the origin and classification of species, genome evolution and deletion, gene duplication and chromosomal polyploidy, and flower morphogenesis-related regulation. Here, the progressing achieved in orchid molecular biology and genomics over the past few decades have been discussed, including the evolution of genome size and polyploidization. The frequent incorporation of LTR retrotransposons play important role in the expansion and structural variation of the orchid genome. The large-scale gene duplication event of the nuclear genome generated plenty of recently tandem duplicated genes, which drove the evolution and functional divergency of new genes. The evolution and loss of the plastid genome, which mostly affected genes related to photosynthesis and autotrophy, demonstrated that orchids have experienced more separate transitions to heterotrophy than any other terrestrial plant. Moreover, large-scale resequencing provide useful SNP markers for constructing genetic maps, which will facilitate the breeding of novel orchid varieties. The significance of high-throughput sequencing and gene editing technologies in the identification and molecular breeding of the trait-related genes in orchids provides us with a representative trait-improving gene as well as some mechanisms worthy of further investigation. In addition, gene editing has promise for the improvement of orchid genetic transformation and the investigation of gene function. This knowledge may provide a scientific reference and theoretical basis for orchid genome studies.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data.

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

WBTC: a new approach for efficient storage of genomic data

With the improvement in high-throughput genome sequencing technology, huge amount of genomic data are generated every day. These data are used in numerous applications: sequence alignment, drug discovery and personalized medicine, etc. To efficiently handle genome data for storage, processing, and transmission, some specific genomic data compression approach is a need of today. In this paper, a hybrid approach-WBTC (Word Based Compression Technique) based on statistical and substitution model is proposed for genome compression. WBTC can support genomic data in raw forms as well as Fasta/Multi-fasta file formats. WBTC is a lossless genome compression algorithm in which searching is possible without full decompression. Experiments show that the proposed algorithm-WBTC outperforms in comparison to other state-of-the-art algorithms with respect to compression ratio, compression time, decompression time, compression memory and decompression memory.

Comparative genomics of Mycoplasma pneumoniae isolated from children with pneumonia: South Korea, 2010\u20132016

BackgroundMycoplasma pneumoniae is a common cause of respiratory tract infections in children and adults. This study applied high-throughput whole genome sequencing (WGS) technologies to analyze the genomes of 30 M. pneumoniae strains isolated from children with pneumonia in South Korea during the two epidemics from 2010 to 2016 in comparison with a global collection of 48 M. pneumoniae strains which includes seven countries ranging from 1944 to 2017.ResultsThe 30 Korean strains had approximately 40% GC content and ranged from 815,686 to 818,669 base pairs, coding for a total of 809 to 828 genes. Overall, BRIG revealed 99% to > 99% similarity among strains. The genomic similarity dropped to approximately 95% in the P1 type 2 strains when aligned to the reference M129 genome, which corresponded to the region of the p1 gene. MAUVE detected four subtype-specific insertions (three in P1 type 1 and one in P1 type 2), of which were all hypothetical proteins except one tRNA insertion in all P1 type 1 strains. The phylogenetic associations of 30 strains were generally consistent with the multilocus sequence typing results. The phylogenetic tree constructed with 78 genomes including 30 genomes from Korea formed two clusters and further divided into two sub-clusters. eBURST analysis revealed two clonal complexes according to P1 typing results showing higher diversity among P1 type 2 strains.ConclusionsThe comparative whole genome approach was able to define high genetic identity, unique structural diversity, and phylogenetic associations among the 78 M. pneumoniae strains isolated worldwide.

Human mitochondrial genome compression using machine learning techniques

BackgroundIn recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effectively compression genome sequences data remains an unsolved problem.ResultsIn this paper, we propose a compression method using machine learning techniques (DeepDNA), for compressing human mitochondrial genome data. The experimental results show the effectiveness of our proposed method compared with other on the human mitochondrial genome data.ConclusionsThe compression method we proposed can be classified as non-reference based method, but the compression effect is comparable to that of reference based methods. Moreover, our method not only have a well compression results in the population genome with large redundancy, but also in the single genome with small redundancy. The codes of DeepDNA are available at https://github.com/rongjiewang/DeepDNA.

Review of current diagnostic methods and advances in Helicobacter pylori diagnostics in the era of next generation sequencing.

Helicobacter pylori (H. pylori) infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers, mucosa associated tissue lymphoma and gastric adenocarcinoma. In recent years, an alarming increase in antimicrobial resistance and subsequently failing empiric H. pylori eradication therapies have been noted worldwide, also in many European countries. Therefore, rapid and accurate determination of H. pylori’s antibiotic susceptibility prior to the administration of eradication regimens becomes ever more important. Traditionally, detection of H.pylori and its antimicrobial resistance is done by culture and phenotypic drug susceptibility testing that are cumbersome with a long turn-around-time. Recent advances in diagnostics provide new tools, like real-time polymerase chain reaction (PCR) and line probe assays, to diagnose H. pylori infection and antimicrobial resistance to certain antibiotics, directly from clinical specimens. Moreover, high-throughput whole genome sequencing technologies allow the rapid analysis of the pathogen’s genome, thereby allowing identification of resistance mutations and associated antibiotic resistance. In the first part of this review, we will give an overview on currently available diagnostic methods for detection of H. pylori and its drug resistance and their implementation in H. pylori management. The second part of the review focusses on the use of next generation sequencing technology in H. pylori research. To this end, we conducted a literature search for original research articles in English using the terms “Helicobacter”, “transcriptomic”, “transcriptome”, “next generation sequencing” and “whole genome sequencing”. This review is aimed to bridge the gap between current diagnostic practice (histology, rapid urease test, H. pylori culture, PCR and line probe assays) and new sequencing technologies and their potential implementation in diagnostic laboratory settings in order to complement the currently recommended H. pylori management guidelines and subsequently improve public health.

High density linkage map construction and QTL mapping for runner production in allo-octoploid strawberry Fragaria\u2009\xd7\u2009ananassa based on ddRAD-seq derived SNPs

Recent advances in high-throughput genome sequencing technologies are now making the genetic dissection of the complex genome of cultivated strawberry easier. We sequenced Maehyang (short-day cultivar) × Albion (day-neutral cultivar) crossing populations using double digest restriction-associated DNA (ddRAD) sequencing technique that yielded 978,968 reads, 80.2% of which were aligned to strawberry genome allowing the identification of 13,181 high quality single nucleotide polymorphisms (SNPs). Total 3051 SNPs showed Mendelian segregation in F1, of which 1268 were successfully mapped to 46 linkage groups (LG) spanning a total of 2581.57 cM with an average interval genetic distance of 2.22 cM. The LGs were assigned to the 28 chromosomes of Fragaria × ananassa as determined by positioning the sequence tags on F. vesca genome. In addition, seven QTLs namely, qRU-5D, qRU-3D1, qRU-1D2, qRU-4D, qRU-4C, qRU-5C and qRU-2D2 were identified for runner production with LOD value ranging from 3.5–7.24 that explained 22–38% of phenotypic variation. The key candidate genes having putative roles in meristem differentiation for runnering and flowering within these QTL regions were identified. These will enhance our understanding of the vegetative vs sexual reproductive behavior in strawberry and will aid in setting breeding targets for developing perpetual flowering and profuse runnering cultivar.

Investigation of Virulence Factors by "Omics" Approaches

Aspergillus fumigatus is the predominant fungal pathogen responsible for life-threatening systemic infections in humans. Recently developed high-throughput whole genome sequencing (WGS) and RNA-Seq technologies have proven to be powerful tools for systematically investigating pathogenic organisms. In this review, we present new virulence factors obtained through our "omics" researches on A. fumigatus. We first sequenced genomes of A. fumigatus stains isolated from one infected patient at different time points, and made an important finding that although the genome (microsatellites) type of the infected strain remained unchanged, the strain exhibited several genetic changes, including acquiring therapeutic drug resistance, during patient treatment for 1.5 years. Of the various presentations of aspergillosis, pulmonary aspergilloma (PA) is one of the most common forms of A. fumigatus infection, where fungus balls are composed of fungal hyphae, inflammatory cells, fibrin, mucus, and tissue debris. Chronic necrotizing pulmonary aspergillosis (CNPA), also known as semi-invasive or invasive aspergillosis, is locally invasive and predominantly seen in patients with mild immunodeficiency or with a chronic lung disease. We compared genomes of strains individually isolated from eight PA and eight CNPA patients in Japan, and found that the PA and CNPA strains show indiscernible genetic and ancestral backgrounds as far as genomic SNPs of the strains are concerned. The main route of infections caused by A. fumigatus is via inhalation of conidia. Inhaled conidia rapidly adhere to pulmonary epithelial cells. Nevertheless, little is known of the molecular mechanism of adherence in A. fumigatus resting conidia. We assumed corresponding adhesion molecules were highly expressed in high-adhesion conidia during conidia maturation, and exhaustively searched adhesion molecules by comparing gene expression levels in high- and low-adherence strains using the RNA-Seq technique. We found several factors involved in conidial adhesion and suggest that composite actions of these molecules have roles in conidial adhesion to human pulmonary epithelial cells.

Computational identification of micro-structural variations and their proteogenomic consequences in cancer.

Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples. We have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq datasets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations, fusions in particular, were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. MiStrVar is available for download at https://bitbucket.org/compbio/mistrvar, and ProTIE is available at https://bitbucket.org/compbio/protie. cenksahi@indiana.edu. Supplementary data are available at Bioinformatics online.

Detection of genomic loci associated with chromosomal recombination using high-density linkage mapping in Setaria

Meiotic recombination is essential to sexual reproduction and the generation of genetic diversity. Variation in recombination rates is presently of particular interest due to efforts being made to increase the rate of genetic gain in agricultural crops by breaking up large linkage disequilibrium blocks containing both beneficial and detrimental alleles. Here, a high-density genetic linkage map of Setaria was constructed using tunable genotyping by sequencing (tGBS) analysis of a population of recombinant inbred lines (RILs). Several regions of the Setaria genome exhibited significant levels of segregation distortion (SD), and recombination crossovers (COs) were also detected. The regions with high SD generally tended to have fewer COs, particularly for pericentromeric chromosomal areas. Recombination crossovers detected in Setaria were unevenly distributed across the genome and occurred more often in intergenic regions. Quantitative trait loci (QTLs) contributing towards the recombination frequency (Type I) and occurrence of COs in designated loci (Type II) were identified, and Type II QTLs garnered higher statistical power. The result of this study suggest that QTLs analysis of Type II traits using RILs might provide an opportunity to further understand meiotic recombination using high throughput genome sequencing and genotyping technologies.

PopNet: A Markov Clustering Approach to Study Population Genetic Structure.

With the advent of low cost, high-throughput genome sequencing technology, population genomic data sets are being generated for hundreds of species of pathogenic, industrial, and agricultural importance. The challenge is how best to analyze and visually display these complex data sets to yield intuitive representations capable of capturing complex evolutionary relationships. Here we present PopNet, a novel computational method that identifies regions of shared ancestry in the chromosomes of related strains through clustering patterns of genetic variation. These relationships are subsequently visualized within a network by a novel implementation of chromosome painting. We apply PopNet to three diverse populations that feature differential rates of recombination and demonstrate its ability to capture evolutionary relationships as well as associate traits to specific loci. Compared with existing tools, PopNet provides substantial advances by both removing the need to predefine a single reference genome that can bias interpretation of population structure, as well as its ability to visualize multiple evolutionary relationships, such as recombination events and shared ancestry, across hundreds of strains.

Optimal hybrid sequencing and assembly: Feasibility conditions for accurate genome reconstruction and cost minimization strategy

Recent advances in high-throughput genome sequencing technologies have enabled the systematic study of various genomes by making whole genome sequencing affordable. Modern sequencers generate a huge number of small sequence fragments called reads, where the read length and the per-base sequencing cost depend on the technology used. To date, many hybrid genome assembly algorithms have been developed that can take reads from multiple read sources to reconstruct the original genome. However, rigorous investigation of the feasibility conditions for complete genome reconstruction and the optimal sequencing strategy for minimizing the sequencing cost has been conspicuously missing. An important aspect of hybrid sequencing and assembly is that the feasibility conditions for genome reconstruction can be satisfied by different combinations of the available read sources, opening up the possibility of optimally combining the sources to minimize the sequencing cost while ensuring accurate genome reconstruction. In this paper, we derive the conditions for whole genome reconstruction from multiple read sources at a given confidence level and also introduce the optimal strategy for combining reads from different sources to minimize the overall sequencing cost. We show that the optimal read set, which simultaneously satisfies the feasibility conditions for genome reconstruction and minimizes the sequencing cost, can be effectively predicted through constrained discrete optimization. Through extensive evaluations based on several genomes and different read sets, we verify the derived feasibility conditions and demonstrate the performance of the proposed optimal hybrid sequencing and assembly strategy.

Acute myeloid leukemia and prognosis in the era of molecular markers

Introduction. Acute myeloid leukemia is a malignant, clonal disease of a hematopoietic stem/progenitor cell, characterized by accumulation of acquired somatic genetic and epigenetic alterations. Acute myeloid leukemia is, basically, highly heterogeneous disease, so individual treatment approach is needed. With the use of high-throughput genome sequencing technologies, a full spectrum of recurrent gene mutations in acute myeloid leukemia has been discovered, which has also provided deeper insight into leukemogenesis and acute myeloid leukemia ontogeny. This review focuses on molecular markers with proven prognostic significance, which form, together with standard cytogenetics, basis for current acute myeloid leukemia risk stratification. Even in the era of molecular markers, standard prognostic factors (pre-treatment and postinduction factors) have a strong influence on the choice of postremission therapy. Conclusion. Evaluation of molecular markers and their impact on prognosis in acute myeloid leukemia should be interpreted in the context of complex gene interactions. Only comprehensive understanding of acute myeloid leukemia biology and integration of all prognostic markers enable us to timely plan risk adapted treatment.

Recent "omics" advances in Helicobacter pylori.

The development of high-throughput whole genome sequencing (WGS) technologies is changing the face of microbiology, facilitating the comparison of large numbers of genomes from different lineages of a same organism. Our aim was to review the main advances on Helicobacter pylori "omics" and to understand how this is improving our knowledge of the biology, diversity and pathogenesis of H.pylori. Since the first H.pylori isolate was sequenced in 1997, 510 genomes have been deposited in the NCBI archive, providing a basis for improved understanding of the epidemiology and evolution of this important pathogen. This review focuses on works published between April 2015 and March 2016. Helicobacter "omics" is already making an impact and is a growing research field. Ultimately these advances will be translated into a routine clinical laboratory setting in order to improve public health.