Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine

Using ncRNAs as Tools in Cancer Diagnosis and Treatment-The Way towards Personalized Medicine to Improve Patients' Health

Although the first discovery of a non-coding RNA (ncRNA) dates back to 1958, only in recent years has the complexity of the transcriptome started to be elucidated. However, its components are still under investigation and their identification is one of the challenges that scientists are presently facing. In addition, their function is still far from being fully understood. The non-coding portion of the genome is indeed the largest, both quantitatively and qualitatively. A large fraction of these ncRNAs have a regulatory role either in coding mRNAs or in other ncRNAs, creating an intracellular network of crossed interactions (competing endogenous RNA networks, or ceRNET) that fine-tune the gene expression in both health and disease. The alteration of the equilibrium among such interactions can be enough to cause a transition from health to disease, but the opposite is equally true, leading to the possibility of intervening based on these mechanisms to cure human conditions. In this review, we summarize the present knowledge on these mechanisms, illustrating how they can be used for disease treatment, the current challenges and pitfalls, and the roles of environmental and lifestyle-related contributing factors, in addition to the ethical, legal, and social issues arising from their (improper) use.

Comparison of library construction kits for mRNA sequencing in the Illumina platform

BACKGROUND

The emergence of next-generation sequencing (NGS) technologies has made a tremendous contribution to the deciphering and significance of transcriptome analysis in biological fields. Since the advent of NGS technology in 2007, Illumina, Inc. has provided one of the most widely used sequencing platforms for NGS analysis.

OBJECTIVE

Although reagents and protocols provided by Illumina are adequately performed in transcriptome sequencing, recently, alternative reagents and protocols which are relatively cost effective are accessible. However, the kits derived from various manufacturers have advantages and disadvantages when researchers carry out the transcriptome library construction.

METHODS

We compared them using a variety of protocols to produce Illumina-compatible libraries based on transcriptome. Three different mRNA sequencing kits were selected for this study: TruSeq^® RNA Sample Preparation V2 (Illumina, Inc., USA), Universal Plus mRNA-Seq (NuGEN, Ltd., UK), and NEBNext^® Ultra™ Directional RNA Library Prep Kit for Illumina^® (New England BioLabs, Ltd., USA). We compared them focusing on cost, experimental time, and data output.

RESULTS

The quality and quantity of sequencing data obtained through the NGS technique were strongly influenced by the type of the sequencing library kits. It suggests that for transcriptome studies, researchers should select a suitable library construction kit according to the goal and resources of experiments.

CONCLUSION

The present work will help researchers to choose the right sequencing library construction kit for transcriptome analyses.

Similarities and differences between variants called with human reference genome HG19 or HG38

Background

Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed.

Methods

We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs.

Results

The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected).

Conclusion

A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2620-0) contains supplementary material, which is available to authorized users.

Detection of novel germline mutations in six breast cancer predisposition genes by targeted next-generation sequencing

In this study, a customized amplicon-based target sequencing panel was designed to enrich the whole exon regions of six genes associated with the risk of breast cancer. Targeted next-generation sequencing (NGS) was performed for 146 breast cancer patients (BC), 71 healthy women with a family history of breast cancer (high risk), and 55 healthy women without a family history of cancer (control). Sixteen possible disease-causing mutations on four genes were identified in 20 samples. The percentages of possible disease-causing mutation carriers in the BC group (8.9%) and in the high-risk group (8.5%) were higher than that in the control group (1.8%). The BRCA1 possible disease-causing mutation group had a higher prevalence in family history and triple-negative breast cancer, while the BRCA2 possible disease-causing mutation group was younger and more likely to develop axillary lymph node metastasis (P < 0.05). Among the 146 patients, 47 with a family history of breast cancer were also sequenced with another 14 moderate-risk genes. Three additional possible disease-causing mutations were found on PALB2, CHEK2, and PMS2 genes, respectively. The results demonstrate that the six-gene targeted NGS panel may provide an approach to assess the genetic risk of breast cancer and predict the clinical prognosis of breast cancer patients.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide a means for assessing concerns regarding SNP array-based GWAS findings as well as for realistically bounding expectations for next generation sequencing (NGS)-based GWAS. We calculated and compared base composition, transitions to transversions ratio, minor allele frequency and heterozygous rate for SNPs from HapMap and 1KGP for the 622 common individuals. We analysed the genotype discordance between HapMap and 1KGP to assess consistency in the SNPs from the two references. In 1KGP, 90.58% of 36,817,799 SNPs detected were not measured in HapMap. More SNPs with minor allele frequencies less than 0.01 were found in 1KGP than HapMap. The two references have low disc ordance (generally smaller than 0.02) in genotypes of common SNPs, with most discordance from heterozygous SNPs. Our study demonstrated that SNP array-based GWAS findings were reliable and useful, although only a small portion of genetic variances were explained. NGS can detect not only common but also rare variants, supporting the expectation that NGS-based GWAS will be able to incorporate a much larger portion of genetic variance than SNP arrays-based GWAS.

Genomic Discoveries and Personalized Medicine in Neurological Diseases

In the past decades, we have witnessed dramatic changes in clinical diagnoses and treatments due to the revolutions of genomics and personalized medicine. Undoubtedly we also met many challenges when we use those advanced technologies in drug discovery and development. In this review, we describe when genomic information is applied in personal healthcare in general. We illustrate some case examples of genomic discoveries and promising personalized medicine applications in the area of neurological disease particular. Available data suggest that individual genomics can be applied to better treat patients in the near future.

Alignment of Short Reads: A Crucial Step for Application of Next-Generation Sequencing Data in Precision Medicine

Precision medicine or personalized medicine has been proposed as a modernized and promising medical strategy. Genetic variants of patients are the key information for implementation of precision medicine. Next-generation sequencing (NGS) is an emerging technology for deciphering genetic variants. Alignment of raw reads to a reference genome is one of the key steps in NGS data analysis. Many algorithms have been developed for alignment of short read sequences since 2008. Users have to make a decision on which alignment algorithm to use in their studies. Selection of the right alignment algorithm determines not only the alignment algorithm but also the set of suitable parameters to be used by the algorithm. Understanding these algorithms helps in selecting the appropriate alignment algorithm for different applications in precision medicine. Here, we review current available algorithms and their major strategies such as seed-and-extend and q-gram filter. We also discuss the challenges in current alignment algorithms, including alignment in multiple repeated regions, long reads alignment and alignment facilitated with known genetic variants.

The long tail and rare disease research: the impact of next-generation sequencing for rare Mendelian disorders

There are an estimated 6000-8000 rare Mendelian diseases that collectively affect 30 million individuals in the United States. The low incidence and prevalence of these diseases present significant challenges to improving diagnostics and treatments. Next-generation sequencing (NGS) technologies have revolutionized research of rare diseases. This article will first comment on the effectiveness of NGS through the lens of long-tailed economics. We then provide an overview of recent developments and challenges of NGS-based research on rare diseases. As the quality of NGS studies improve and the cost of sequencing decreases, NGS will continue to make a significant impact on the study of rare diseases moving forward.

An integrative computational approach for prioritization of genomic variants

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.

Quality control metrics improve repeatability and reproducibility of single-nucleotide variants derived from whole-genome sequencing

Although many quality control (QC) methods have been developed to improve the quality of single-nucleotide variants (SNVs) in SNV-calling, QC methods for use subsequent to single-nucleotide polymorphism-calling have not been reported. We developed five QC metrics to improve the quality of SNVs using the whole-genome-sequencing data of a monozygotic twin pair from the Korean Personal Genome Project. The QC metrics improved both repeatability between the monozygotic twin pair and reproducibility between SNV-calling pipelines. We demonstrated the QC metrics improve reproducibility of SNVs derived from not only whole-genome-sequencing data but also whole-exome-sequencing data. The QC metrics are calculated based on the reference genome used in the alignment without accessing the raw and intermediate data or knowing the SNV-calling details. Therefore, the QC metrics can be easily adopted in downstream association analysis.

Whole genome sequencing of 35 individuals provides insights into the genetic architecture of Korean population

Background

Due to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population.

Methods

In this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively. The consensus SNVs obtained from the two SNV pipelines were used to represent the SNVs of the Korean population. We compared these SNVs to those from 17 other populations provided by the HapMap consortium and the 1000 Genomes Project (1KGP) and identified SNVs that were only present in the Korean population. We studied the mutation spectrum and analyzed the genes of non-synonymous SNVs only detected in the Korean population.

Results

We detected a total of 8,555,726 SNVs in the 35 Korean individuals and identified 1,213,613 SNVs detected in at least one Korean individual (SNV-1) and 12,640 in all of 35 Korean individuals (SNV-35) but not in 17 other populations. In contrast with the SNVs common to other populations in HapMap and 1KGP, the Korean only SNVs had high percentages of non-silent variants, emphasizing the unique roles of these Korean only SNVs in the Korean population. Specifically, we identified 8,361 non-synonymous Korean only SNVs, of which 58 SNVs existed in all 35 Korean individuals. The 5,754 genes of non-synonymous Korean only SNVs were highly enriched in some metabolic pathways. We found adhesion is the top disease term associated with SNV-1 and Nelson syndrome is the only disease term associated with SNV-35. We found that a significant number of Korean only SNVs are in genes that are associated with the drug term of adenosine.

Conclusion

We identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms. The results expand our knowledge of the genetic architecture of the Korean population, which will benefit the implementation of personalized medicine for the Korean population.

High-Throughput Sequencing, a VersatileWeapon to Support Genome-Based Diagnosis in Infectious Diseases: Applications to Clinical Bacteriology

The recent progresses of high-throughput sequencing (HTS) technologies enable easy and cost-reduced access to whole genome sequencing (WGS) or re-sequencing. HTS associated with adapted, automatic and fast bioinformatics solutions for sequencing applications promises an accurate and timely identification and characterization of pathogenic agents. Many studies have demonstrated that data obtained from HTS analysis have allowed genome-based diagnosis, which has been consistent with phenotypic observations. These proofs of concept are probably the first steps toward the future of clinical microbiology. From concept to routine use, many parameters need to be considered to promote HTS as a powerful tool to help physicians and clinicians in microbiological investigations. This review highlights the milestones to be completed toward this purpose.

Production and analytic bioinformatics for next-generation DNA sequencing

The bioinformatics requirements within the clinical environment are very specific, and analytic techniques need to be fit for purpose, robust, and predictable. At the same time, the bewildering amount of information produced during these analyses needs to be carefully managed, used and interpreted correctly. The challenge for clinical laboratories now is to implement production analytical processes that are capable of handling different experimental approaches on current equipment, as well as to incorporate ways for these systems to evolve to take account of developments likely to make impacts in the near future. This is complicated by the many options available at each of the critical processing steps and a clear method needs to be developed to assemble appropriate pipelines. Here, I discuss the issues relevant to the development of an informatics pipeline that meets these criteria that should allow individual laboratories to assess their proposed strategies.