Era Of Next-generation Sequencing Research Articles

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

Translational Biomedical Informatics and Computational Systems Medicine

Translational Biomedical Informatics and Computational Systems Medicine

EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window.

Biological assessment of abnormal genitalia

Biological assessment of abnormal genitalia is based on an ordered sequence of endocrine and genetic investigations that are predicated on knowledge obtained from a suitable history and detailed examination of the external genital anatomy. Investigations are particularly relevant in 46,XY DSD where the diagnostic yield is less successful than in the 46,XX counterpart. Advantage should be taken of spontaneous activity of the pituitary-gonadal axis in early infancy rendering measurements of gonadotrophins and sex steroids by sensitive, validated assays key to assessing testicular function. Allied measurement of serum anti-Müllerian hormone completes a comprehensive testis profile of Leydig and Sertoli cell function.Genetic assessment is dominated by analysis of a plethora of genes that attempts to delineate a cause for gonadal dysgenesis. In essence, this is successful in up to 20% of cases from analysis of SRY and SF1 (NR5A1) genes. In contrast, gene mutation analysis is highly successful in 46,XY DSD due to defects in androgen synthesis or action. The era of next generation sequencing is increasingly being applied to investigate complex medical conditions of unknown cause, including DSD. The challenge for health professionals will lie in integrating vast amounts of genetic information with phenotypes and counselling families appropriately. How tissues respond to hormones is apposite to assessing the range of genital phenotypes that characterise DSD, particularly for syndromes associated with androgen resistance. In vitro methods are available to undertake quantitative and qualitative analysis of hormone action. The in vivo equivalent is some assessment of the degree of under-masculinisation in the male, such as an external masculinisation score, and measurement of the ano-genital distance. This anthropometric marker is effectively a postnatal readout of the effects of prenatal androgens acting during the masculinisation programming window. For investigation of the newborn with abnormal genitalia, a pragmatic approach can be taken to guide the clinician using appropriate algorithms.

Understanding pathogens in the era of next generation sequencing

What is a pathogen? Medical textbooks usually define a pathogen as any microorganism that causes disease. However, this widespread definition is problematic on a number of counts [1, 11]. Moreover, a generally accepted definition is not forthcoming among medical microbiologists, immunologists, and physicians. Here it is argued that there is another, and more pressing question to be asked, namely: what makes some organisms pathogenic and others not? Asking these questions instead allows for distinguishing pathogens from non-pathogens in a more flexible way, while at the same time emphasizing the roles of ecological and evolutionary processes in determining pathogenicity in infectious diseases.

Using Maximal Segmental Score in Genome‐Wide Association Studies

Genome-wide association studies (GWAS) have become the method of choice for identifying disease susceptibility genes in common disease genetics research. Despite successes in these studies, much of the heritability remains unexplained due to lack of power and low resolution. High-density genotyping arrays can now screen more than 5 million genetic markers. As a result, multiple comparison has become an important issue especially in the era of next-generation sequencing. We propose to use a two-stage maximal segmental score procedure (MSS) which uses region-specific empirical P-values to identify genomic segments most likely harboring the disease gene. We develop scoring systems based on Fisher's P-value combining method to convert locus-specific significance levels into region-specific scores. Through simulations, our result indicated that MSS increased the power to detect genetic association as compared with conventional methods provided type I error was at 5%. We demonstrated the application of MSS on a publicly available case-control dataset of Parkinson's disease and replicated the findings in the literature. MSS provides an efficient exploratory tool for high-density association data in the current era of next-generation sequencing. R source codes to implement the MSS procedure are freely available at http://www.csjfann.ibms.sinica.edu.tw/EAG/program/programlist.htm.

Statistical model-based testing to evaluate the recurrence of genomic aberrations

Motivation: In cancer genomes, chromosomal regions harboring cancer genes are often subjected to genomic aberrations like copy number alteration and loss of heterozygosity. Given this, finding recurrent genomic aberrations is considered an apt approach for screening cancer genes. Although several permutation-based tests have been proposed for this purpose, none of them are designed to find recurrent aberrations from the genomic dataset without paired normal sample controls. Their application to unpaired genomic data may lead to false discoveries, because they retrieve pseudo-aberrations that exist in normal genomes as polymorphisms.Results: We develop a new parametric method named parametric aberration recurrence test (PART) to test for the recurrence of genomic aberrations. The introduction of Poisson-binomial statistics allow us to compute small P-values more efficiently and precisely than the previously proposed permutation-based approach. Moreover, we extended PART to cover unpaired data (PART-up) so that there is a statistical basis for analyzing unpaired genomic data. PART-up uses information from unpaired normal sample controls to remove pseudo-aberrations in unpaired genomic data. Using PART-up, we successfully predict recurrent genomic aberrations in cancer cell line samples whose paired normal sample controls are unavailable. This article thus proposes a powerful statistical framework for the identification of driver aberrations, which would be applicable to ever-increasing amounts of cancer genomic data seen in the era of next generation sequencing.Availability: Our implementations of PART and PART-up are available from http://www.hgc.jp/~niiyan/PART/manual.html.Contact: aniida@ims.u-tokyo.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

Personalized medicine for laparoscopic gastrectomy in gastric cancer

Predictive medicine is crucial for improving medical quality and health. Traditionally, epidemiological studies are used for patient’s outcome with a disease in the pretreatment setting to predict outcomes before therapeutic intervention with surgery, drugs, or both. Given the heterogeneity and complexity of chronic multifactorial disorders, such as cancer, emerging biomedical research is based on genomics, epigenomics, and microRNAs along with systems computational biology and mathematical models for the development of novel biomarkers and more accurate predictions [1, 2]. Based on the establishment and success of laparoscopic resection for colorectal cancer to improve quality of life by equal oncological outcomes compared with open surgery, laparoscopic gastrectomy has rapidly evolved during the past decade [3–10]. Pretreatment identification of gastric patients at risk for developing early postoperative complications or recurrence after laparoscopic gastrectomy has crucial clinical implications. In general for cancer, such markers can guide a tailored treatment for improving patient’s outcomes and this is a major goal of personalized medicine [11]. In the December issue of Surgical Endoscopy, Yoshikawa et al. [12] evaluated several markers that might be clinical useful for patients who are going to undergo laparoscopic gastrectomy for gastric cancer. The authors have classified 66 patients who underwent laparoscopy assisted gastrectomy (LAG) into two groups: body mass index (BMI) [\25 BMI-L group: n = 53; C25 BMI-H group: n = 13] and visceral fat areas (VFAs) [\100 cm AF-L group: n = 35; C100 cm AF-H group: n = 31]. Yoshikawa et al. [12] report that the BMI did not impact postoperative complications (p = 0.18) or blood loss (p = 0.21), whereas by contrast VFA significantly influenced complications, blood loss, and number of lymph nodes retrieved. The authors conclude that the area of visceral fat tissue assessed by CT and software was useful to predict risks of LAG and postoperative complications with higher precision compared with BMI. This study evaluated the area of visceral fat tissue and provides a new aspect for identifying predictive and prognostic markers for LAG for gastric cancer. However, it is limited not only by its retrospective nature but also by the very small number of patients in subgroups evaluated. Therefore, it could be considered an underpowered study for assessing valid significant differences. However, the data provided are promising and the authors may perform a study with large and accurate samples of patients to validate the potential impact of the area of visceral fat tissue in patients undergoing laparoscopic gastrectomy. Development of robust biomarkers for personalized cancer treatment has been proven to be much more difficult and complicated than we have supposed. Personalized medicine can improve dramatically the outcome in public health, but much more research work is needed. In the era of next-generation sequencing for whole-genome sequencing, functional genomics, transcriptomics, and epigenomics, new directions are being shaped. Assembling large-scale omics data together with clinical data and integrating all of this genome and clinical information into computational and mathematical models could result in the development of systems biology and systems medicinebased biomarkers [13–32]. C. Hottenrott (&) Chirurgische Klinik, St. Elisabethenkrankenhaus, Ginnheimer Strase 3, 60487 Frankfurt, Germany e-mail: info@gastricbreastcancer.com

Motif discovery and transcription factor binding sites before and after the next-generation sequencing era

Motif discovery has been one of the most widely studied problems in bioinformatics ever since genomic and protein sequences have been available. In particular, its application to the de novo prediction of putative over-represented transcription factor binding sites in nucleotide sequences has been, and still is, one of the most challenging flavors of the problem. Recently, novel experimental techniques like chromatin immunoprecipitation (ChIP) have been introduced, permitting the genome-wide identification of protein–DNA interactions. ChIP, applied to transcription factors and coupled with genome tiling arrays (ChIP on Chip) or next-generation sequencing technologies (ChIP-Seq) has opened new avenues in research, as well as posed new challenges to bioinformaticians developing algorithms and methods for motif discovery.

Regulatory variations in the era of next-generation sequencing: Implications for clinical molecular diagnostics

With the successful identification of many protein-coding genes, the focus has now shifted toward deciphering functions of non-protein-coding regions that direct spatiotemporal and quantitative aspects of protein expression. Recent advances in our understanding of the regulatory architecture of the human genome coincide with growing evidence that changes in regulatory sequences are associated with human disease. Several recent reviews have highlighted disease-causing potential of aberrations in transcriptional and splicing regulatory elements as well as non-protein-coding RNA. Although changes in regulatory sequences generally produce milder biological effects than their protein-coding counterparts, many act as independent risk factors for common complex disorders or as genetic modifiers for "primary" disease-causing loci. Here, we review bioinformatics and experimental approaches that are used to identify regulatory sequences and assess pathogenicity of regulatory changes. We describe the current state of knowledge on disease-causing changes in regulatory sequences, challenge protein-centric views, and discuss complexities and solutions pertaining to the interpretation of regulatory changes in the next-generation sequencing era.

Exploring the cancer genome in the era of next-generation sequencing

The emergence of next-generation sequencing technologies has led to dramatic advances in cancer genome studies. The increased efficiency and resolution of next-generation sequencing greatly facilitate the detection of genetic, genomic, and epigenomic alterations, such as single nucleotide mutations, small insertions and deletions, chromosomal rearrangements, copy number variations, and DNA methylation. Comprehensive analysis of cancer genomes through approaches of whole genome, exome, and transcriptome sequencing has significantly improved the understanding of cancer biology, diagnosis, and therapy. The present study briefly reviews the recent pioneering studies on cancer genome sequencing and provides an unprecedented insight into the landscape of genomic alterations in human sporadic cancers.

Epigenetic Control of Reprogramming and Cellular Differentiation

Epigenetic Control of Reprogramming and Cellular Differentiation

Tackling Skeletal Muscle Cells Epigenome in the Next-Generation Sequencing Era

Recent advances in high-throughput technologies have transformed methodologies employed to study cell-specific epigenomes and the approaches to investigate complex cellular phenotypes. Application of next-generation sequencing technology in the skeletal muscle differentiation field is rapidly extending our knowledge on how chromatin modifications, transcription factors and chromatin regulators orchestrate gene expression pathways guiding myogenesis. Here, we review recent biological insights gained by the application of next-generation sequencing techniques to decode the epigenetic profile and gene regulatory networks underlying skeletal muscle differentiation.

Unraveling the multiple myeloma genome in the next-generation sequencing era: challenges to translating knowledge into the clinic

The origin and progression of human cancers is caused by the acquisition of genetic and epigenetic alterations. Over the last decades, significant efforts and technological advances were key in the characterization of the cancer genome in a wide range of tumors. In 1976, Peter Nowell, following the observation of karyotypic heterogeneity in multiple tumor genomes, proposed a multistep process to explain the molecular basis of tumorigenesis [1]. This model was not only the first comprehensive ana lysis of genetic heterogeneity and instability in the tumor clone, but also led for the first time to the concept of personalized medicine, where therapy should be individualized according to the genetic background observed in the tumor clone. In the mid-1980s, at the time that the molecular techniques to search for oncogenes and tumor suppressor genes were developed, Renato Dulbecco pointed out in an influential article that sequencing the human genome was a critical priority, saying that the research community was faced with two options: either keep discovering the genes important in cancer individually, or use a massive approach and sequence the whole genome [2]. 4 years later, the Human Genome Project was started. The Human Genome Project required several years of collaborative work from worldwide leading sequencing institutes and a budget of US$300 million to sequence the first single genome with seven-times coverage [3]. Now, 10 years after the completion of the first draft of the Human Genome Project, the incorporation of the massively parallel sequencing (also known as nextgeneration sequencing) technologies has revolutionized the search for genetic alterations in tumor genomes. The simultaneous catalog of all mutations, copy number aberrations and structural abnormalities in an entire cancer genome can be performed in one week using a single sequencer and for only US$5000. With the ultimate goal of offering the test for US$1000 in the near future, the implementation of next-generation sequencing in clinical practice will soon be a reality.

Pathogen comparative genomics in the next-generation sequencing era: genome alignments, pangenomics and metagenomics

As soon as whole-genome sequencing entered the scene in the mid-1990s and demonstrated its use in revealing the entire genetic potential of any given microbial organism, this technique immediately revolutionized the way pathogen (and many other fields of) research was carried out. The ability to perform whole-genome comparisons further transformed the field and allowed scientists to obtain information linking phenotypic dissimilarities among closely related organisms and their underlying genetic mechanisms. Such comparisons have become commonplace in examining strain-to-strain variability, as well as comparing pathogens to less, or nonpathogenic near neighbors. In recent years, a bloom in novel sequencing technologies along with continuous increases in throughput has occurred, inundating the field with various types of massively parallel sequencing data and further transforming comparative genomics research. Here, we review the evolution of comparative genomics, its impact in understanding pathogen evolution and physiology and the opportunities and challenges presented by next-generation sequencing as applied to pathogen genome comparisons.

Proceedings of the 2011 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference

The Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011) was held in College Station, Texas on April 1-2, 2011. The Conference General Chair was Ulisses Braga-Neto, the MCBIOS President for the 2010-2011 term, from Texas A&M University. There were nearly 200 registrants and 140 abstracts were submitted, divided into 48 oral presentation abstracts and 92 poster session abstracts. In addition, participants attended talks by very distinguished Keynote speakers. Joan W. Bennett, from Rutgers University and Member of the National Academy of Science, presented the talk “Chromosomal Composition and Computational Competence;” Donald Geman, from The Johns Hopkins University and co-inventor of the Gibbs Sampler and Random Forest Classifiers, lectured on “Measuring Network Regulation and Differential Expression by Rank Conservation;” while John Weinstein, Chair of the Department of Bioinformatics and Computational Biology at the University of Texas M.D. Anderson Cancer Center, talked on “Personalizing Cancer Medicine in the Era of Next-Generation Sequencing: Omics and Informatics.” The conference also benefited from invited dinner and lunch speakers, who gave informal, highly informative and entertaining talks. Edward R. Dougherty, from Texas A&M University and Director of Computational Biology at the Translational Genomics Institute, asked the audience “Is Biological Science Delightful?” whereas Ernesto Marques, from the Center for Vaccine Research at the University of Pittsburgh, gave the talk “Activation of the Complement System in Dengue Infection: Opportunities for Computational Modeling.” Participants also had the opportunity to attend hands-on workshops on NCBI tools, presented by Peter Cooper from NCBI/NIH, and on protein evolution, presented by Hugh Nicholas and Troy Wymore, from the Pittsburgh Supercomputing Center. The winners of conference awards were: Best Oral Presentations (students): First place: Suzanne Matthews, Texas A&M University Second place (3-way tie): Lori Dalton, Texas A&M University Second place: Shelton Griffith, Oak Ridge National Lab Second place: Winston Haynes, Hendrix College Best Oral Presentations (Post-Doctoral fellows): Yan Li, NCTR Fan Zhang, IUPUI Best Poster (Computation): First place: Tianchuan Du, Southern University Second place: Christopher Cathey, Jackson State University Third place: Ralph Crosby, Texas A&M University Best Poster (Biology): First place: Awantika Singh, UALR/UAMS Second place: Mohammed Shahrokh Esfahani, Texas A&M University Third place: Fang-Han Hsu, Texas A&M University MCBIOS Outstanding Service Award: Dr. Jonathan D. Wren, Oklahoma Medical Research Foundation

A Blueprint for Advancing Genetics-Based Cancer Therapy

In the era of next-generation sequencing, there are significant challenges to harnessing cancer genome information to develop novel therapies. Key research thrusts in both academia and industry will speed this transition, and lessons learned for cancer will more broadly shape the process for genetic contributions to the therapy of disease more broadly.

Next-generation analysis on hereditary neurodegenerative disorders using next-generation sequencers

With the throughput of next-generation sequencers, even whole genome analysis is a reality. Traditionally, it was important to collect as many samples as possible and to perform sequence analysis of many genes in candidate regions. There has been a paradigm shift in the era of next-generation sequencing; to reveal the significance of raviants produced by next-generation sequencers rather than just to perform sequence analysis becomes the key to elucidate causes of disorders. We performed target capture and next-generation sequencing analyses of a small consanguineous family in which only two members were affected by posterior column ataxia with retinitis pigmentosa (PCARP). We successfully identified a causative mutation in FLVCR1 which cosegregated with the disease. The fact that we could identify the causative gene even from a small family means that the advent of next-generation sequencers has brought us to a next-generation analysis on hereditary disorders. In the near future, many causative genes of hereditary neurodegenerative disorders particularly with small number of affected members will be revealed, which must provide considerable insights into pathogenesis. Approaches utilizing bioinformatics to further narrow down the numerous variations produced by next-generation sequencers are demanded in order to study remaining small families, or sporadic diseases.

Fine-scale population structure and the era of next-generation sequencing

Fine-scale population structure characterizes most continents and is especially pronounced in non-cosmopolitan populations. Roughly half of the world's population remains non-cosmopolitan and even populations within cities often assort along ethnic and linguistic categories. Barriers to random mating can be ecologically extreme, such as the Sahara Desert, or cultural, such as the Indian caste system. In either case, subpopulations accumulate genetic differences if the barrier is maintained over multiple generations. Genome-wide polymorphism data, initially with only a few hundred autosomal microsatellites, have clearly established differences in allele frequency not only among continental regions, but also within continents and within countries. We review recent evidence from the analysis of genome-wide polymorphism data for genetic boundaries delineating human population structure and the main demographic and genomic processes shaping variation, and discuss the implications of population structure for the distribution and discovery of disease-causing genetic variants, in the light of the imminent availability of sequencing data for a multitude of diverse human genomes.