Statistical Challenges in Sequence-Based Association Studies with Population- and Family-Based Designs

Over the past few years, association analysis has become the primary tool for finding genes that underlie complex traits. Both population-based and family-based designs are commonly used designs in genetic association studies. Recent technological advances in exome and whole genome sequencing afford the next generation of sequence-based association studies. We review here recent developments in statistical methodology and remaining challenges related to sequence-based association studies with both population-based and family-based designs.     

Leveraging the Power of High Performance Computing for Next Generation Sequencing Data Analysis: Tricks and Twists from a High Throughput Exome Workflow

Next generation sequencing (NGS) has been a great success and is now a standard method of research in the life sciences. With this technology, dozens of whole genomes or hundreds of exomes can be sequenced in rather short time, producing huge amounts of data. Complex bioinformatics analyses are required to turn these data into scientific findings. In order to run these analyses fast, automated workflows implemented on high performance computers are state of the art. While providing sufficient compute power and storage to meet the NGS data challenge, high performance computing (HPC) systems require special care when utilized for high throughput processing. This is especially true if the HPC system is shared by different users. Here, stability, robustness and maintainability are as important for automated workflows as speed and throughput. To achieve all of these aims, dedicated solutions have to be developed. In this paper, we present the tricks and twists that we utilized in the implementation of our exome data processing workflow. It may serve as a guideline for other high throughput data analysis projects using a similar infrastructure. The code implementing our solutions is provided in the supporting information files.     

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor–immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.     

Progress in Detecting Genetic Alterations and Their Association with Human Disease

The completion of the Human Genome Project provided a reference sequence to which researchers could compare sequences from individual patients in the hope of identifying disease-causing mutations. However, this still necessitated candidate gene testing or a very limited screen of multiple genes using Sanger sequencing. With the advent of high-throughput Sanger sequencing, it became possible to screen hundreds of patients for alterations in hundreds of genes. This process was time consuming and limited to a few locations/institutions that had the space to house tens of sequencing equipment. The development of next generation sequencing revolutionized the process. It is now feasible to sequence the entire exome of multiple individuals in about 10 days. However, this meant that a massive amount of data needed to be filtered to identify the relevant alteration. This is presently the rate-limiting step in providing a convincing association between a genetic alteration and a human disorder.     

Next generation molecular diagnosis of mitochondrial disorders

Mitochondrial disorders are by far the most genetically heterogeneous group of diseases, involving two genomes, the 16.6 kb mitochondrial genome and ~ 1500 genes encoded in the nuclear genome. For maternally inherited mitochondrial DNA disorders, a complete molecular diagnosis requires several different methods for the detection and quantification of mtDNA point mutations and large deletions. For mitochondrial disorders caused by autosomal recessive, dominant, and X-linked nuclear genes, the diagnosis has relied on clinical, biochemical, and molecular studies to point to a group of candidate genes followed by stepwise Sanger sequencing of the candidate genes one-by-one. The development of Next Generation Sequencing (NGS) has revolutionized the diagnostic approach. Using massively parallel sequencing (MPS) analysis of the entire mitochondrial genome, mtDNA point mutations and deletions can be detected and quantified in one single step. The NGS approach also allows simultaneous analyses of a group of genes or the whole exome, thus, the mutations in causative gene(s) can be identified in one-step. New approaches make genetic analyses much faster and more efficient. Huge amounts of sequencing data produced by the new technologies brought new challenges to bioinformatics, analytical pipelines, and interpretation of numerous novel variants. This article reviews the clinical utility of next generation sequencing for the molecular diagnoses of complex dual genome mitochondrial disorders.     

Sequencing our way towards understanding global eukaryotic biodiversity

Microscopic eukaryotes are abundant, diverse and fill critical ecological roles across every ecosystem on Earth, yet there is a well-recognized gap in understanding of their global biodiversity. Fundamental advances in DNA sequencing and bioinformatics now allow accurate en masse biodiversity assessments of microscopic eukaryotes from environmental samples. Despite a promising outlook, the field of eukaryotic marker gene surveys faces significant challenges: how to generate data that are most useful to the community, especially in the face of evolving sequencing technologies and bioinformatics pipelines, and how to incorporate an expanding number of target genes.     

Analysis of Downs syndrome with molecular techniques for future diagnoses

Down syndrome (DS) is a genetic disorder appeared due to the presence of trisomy in chromosome 21 in the G-group of the acrocentric region. DS is also known as non-Mendelian inheritance, due to the lack of Mendel’s laws. The disorder in children is identified through clinical symptoms and chromosomal analysis and till now there are no biochemical and molecular analyses. Presently, whole exome sequencing (WES) has largely contributed in identifying the new disease-causing genes and represented a significant breakthrough in the field of human genetics and this technique uses high throughput sequencing technologies to determine the arrangement of DNA base pairs specifying the protein coding regions of an individual’s genome. Apart from this next generation sequencing and whole genome sequencing also contribute for identifying the disease marker. From this review, the suggestion was to perform the WES is DS children to identify the marker region.     

Lessons learnt on the analysis of large sequence data in animal genomics

The ’omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human ’omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next‐generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large‐scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry—the software may crash or stop—and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.     

IonNeurONet – Deutsches Netzwerk für neurologische und ophthalmologische Ionenkanalerkrankungen

IonNeurONet is a collaborative research project involving the Universities of Tübingen and Ulm, and CeGaT GmbH, Tübingen, that is funded by the German Federal Ministry of Education and Research. It aims to unravel the pathomechanisms of rare neurological and ophthalmological ion channel disorders, providing the basis for the development of novel therapies. Establishment of a clinical network will provide nation-wide care for these rare and often unrecognized disorders. A quick and efficient genetic diagnostic tool based on next generation sequencing (NGS) has already been developed. It will regularly be updated with novel disease genes identified by whole exome sequencing. Other subprojects focus on detailed functional physiological studies in heterologous expression systems, muscle and nerve cells. The project establishes platforms for genetic and bioinformatics analyses, automated and deep functional studies, channel trafficking, and induced pluripotent stem cells.     

Directed next generation sequencing for phylogenetics: An example using Decapoda (Crustacea)

We propose a method using next generation sequencing technology for phylogenetics. The method is PCR based, requires little training beyond basic lab skills and is both cost and time effective. With this method we generated data for and produced a phylogeny of Decapoda that demonstrates this method's potential, the quality of the data, and the ability for the method to fit or even replace current Sanger based methods of generating DNA data for phylogenetic reconstruction. Finally, we discuss advantages and current challenges of the directed next generation sequencing approach.     

Bridging genomics technology and biology

A report on the UK Genome Science Meeting, held at the University of Nottingham, UK, 2–4 September 2013.This year’s newly named UK Genome Science Meeting was the fourth edition of what was previously known as the UK Next Generation Sequencing Meeting. The renaming reflects technological developments that continue to redefine the meaning of next generation sequencing, and the fact that high-throughput sequencing is now a common tool in the scientific community. Indeed, an enormous diversity of topics was presented at this compact 3-day meeting, ranging from evolving technologies and bioinformatics, through to evolutionary genomics, metagenomics and clinical applications, amongst others. The meeting succeeded in portraying how, in relatively few years, new sequencing technologies have revolutionized the way we do basic research in just about every subject area, and are quickly making their way through translational research, and into the clinic and field.     

From barcodes to genomes: extending the concept of DNA barcoding

DNA barcoding has had a major impact on biodiversity science. The elegant simplicity of establishing massive scale databases for a few barcode loci is continuing to change our understanding of species diversity patterns, and continues to enhance human abilities to distinguish among species. Capitalizing on the developments of next generation sequencing technologies and decreasing costs of genome sequencing, there is now the opportunity for the DNA barcoding concept to be extended to new kinds of genomic data. We illustrate the benefits and capacity to do this, and also note the constraints and barriers to overcome before it is truly scalable. We advocate a twin track approach: (i) continuation and acceleration of global efforts to build the DNA barcode reference library of life on earth using standard DNA barcodes and (ii) active development and application of extended DNA barcodes using genome skimming to augment the standard barcoding approach.     

Issues in bioinformatics benchmarking: the case study of multiple sequence alignment

The post-genomic era presents many new challenges for the field of bioinformatics. Novel computational approaches are now being developed to handle the large, complex and noisy datasets produced by high throughput technologies. Objective evaluation of these methods is essential (i) to assure high quality, (ii) to identify strong and weak points of the algorithms, (iii) to measure the improvements introduced by new methods and (iv) to enable non-specialists to choose an appropriate tool. Here, we discuss the development of formal benchmarks, designed to represent the current problems encountered in the bioinformatics field. We consider several criteria for building good benchmarks and the advantages to be gained when they are used intelligently. To illustrate these principles, we present a more detailed discussion of benchmarks for multiple alignments of protein sequences. As in many other domains, significant progress has been achieved in the multiple alignment field and the datasets have become progressively more challenging as the existing algorithms have evolved. Finally, we propose directions for future developments that will ensure that the bioinformatics benchmarks correspond to the challenges posed by the high throughput data.     

Deep sequencing of small RNAs in plants: applied bioinformatics

Small RNAs, including microRNA and short-interfering RNAs, play important roles in plants. In recent years, developments in sequencing technology have enabled the large-scale discovery of sRNAs in various cells, tissues and developmental stages and in response to various stresses. This review describes the bioinformatics challenges to analysing these large datasets of short-RNA sequences and some of the solutions to those challenges.