Next-generation Sequencing Data Analysis Research Articles

TRNA-Derived Small RNA: A Novel Regulatory Small Non-Coding RNA.

Deep analysis of next-generation sequencing data unveils numerous small non-coding RNAs with distinct functions. Recently, fragments derived from tRNA, named as tRNA-derived small RNA (tsRNA), have attracted broad attention. There are mainly two types of tsRNAs, including tRNA-derived stress-induced RNA (tiRNA) and tRNA-derived fragment (tRF), which differ in the cleavage position of the precursor or mature tRNA transcript. Emerging evidence has shown that tsRNAs are not merely tRNA degradation debris but have been recognized to play regulatory roles in many specific physiological and pathological processes. In this review, we summarize the biogeneses of various tsRNAs, present the emerging concepts regarding functions and mechanisms of action of tsRNAs, highlight the potential application of tsRNAs in human diseases, and put forward the current problems and future research directions.

Yleaf: Software for Human Y-Chromosomal Haplogroup Inference from Next-Generation Sequencing Data.

Next-generation sequencing (NGS) technologies offer immense possibilities given the large genomic data they simultaneously deliver. The human Y-chromosome serves as good example how NGS benefits various applications in evolution, anthropology, genealogy, and forensics. Prior to NGS, the Y-chromosome phylogenetic tree consisted of a few hundred branches, based on NGS data, it now contains many thousands. The complexity of both, Y tree and NGS data provide challenges for haplogroup assignment. For effective analysis and interpretation of Y-chromosome NGS data, we present Yleaf, a publically available, automated, user-friendly software for high-resolution Y-chromosome haplogroup inference independently of library and sequencing methods.

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

An integrated clinical and genomic information system for cancer precision medicine

BackgroundIncreasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient’s genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly.ResultsHere, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data.ConclusionsOur CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.

WISARD: workbench for integrated superfast association studies for related datasets

BackgroundA Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies—from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used.ResultsWe present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts.ConclusionsComparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value.

The emerging clinical relevance of genomics in cancer medicine.

The combination of next-generation sequencing and advanced computational data analysis approaches has revolutionized our understanding of the genomic underpinnings of cancer development and progression. The coincident development of targeted small molecule and antibody-based therapies that target a cancer's genomic dependencies has fuelled the transition of genomic assays into clinical use in patients with cancer. Beyond the identification of individual targetable alterations, genomic methods can gauge mutational load, which might predict a therapeutic response to immune-checkpoint inhibitors or identify cancer-specific proteins that inform the design of personalized anticancer vaccines. Emerging clinical applications of cancer genomics include monitoring treatment responses and characterizing mechanisms of resistance. The increasing relevance of genomics to clinical cancer care also highlights several considerable challenges, including the need to promote equal access to genomic testing.

Haplotype-based allele mining in the Japan-MAGIC rice population

Multi-parent advanced generation inter-cross (MAGIC) lines have broader genetic variation than bi-parental recombinant inbred lines. Genome-wide association study (GWAS) using high number of DNA polymorphisms such as single-nucleotide polymorphisms (SNPs) is a popular tool for allele mining in MAGIC populations, in which the associations of phenotypes with SNPs are investigated; however, the effects of haplotypes from multiple founders on phenotypes are not considered. Here, we describe an improved method of allele mining using the newly developed Japan-MAGIC (JAM) population, which is derived from eight high-yielding rice cultivars in Japan. To obtain information on the haplotypes in the JAM lines, we predicted the haplotype blocks in the whole chromosomes using 16,345 SNPs identified via genotyping-by-sequencing analysis. Using haplotype-based GWAS, we clearly detected the loci controlling the glutinous endosperm and culm length traits. Information on the alleles of the eight founders, which was based on the effects of mutations revealed by the analysis of next-generation sequencing data, was used to narrow down the candidate genes and reveal the associations between alleles and phenotypes. The haplotype-based allele mining (HAM) proposed in this study is a promising approach to the detection of allelic variation in genes controlling agronomic traits in MAGIC populations.

NGSphy: phylogenomic simulation of next-generation sequencing data.

Advances in sequencing technologies have made it feasible to obtain massive datasets for phylogenomic inference, often consisting of large numbers of loci from multiple species and individuals. The phylogenomic analysis of next-generation sequencing (NGS) data requires a complex computational pipeline where multiple technical and methodological decisions are necessary that can influence the final tree obtained, like those related to coverage, assembly, mapping, variant calling and/or phasing. To assess the influence of these variables we introduce NGSphy, an open-source tool for the simulation of Illumina reads/read counts obtained from haploid/diploid individual genomes with thousands of independent gene families evolving under a common species tree. In order to resemble real NGS experiments, NGSphy includes multiple options to model sequencing coverage (depth) heterogeneity across species, individuals and loci, including off-target or uncaptured loci. For comprehensive simulations covering multiple evolutionary scenarios, parameter values for the different replicates can be sampled from user-defined statistical distributions. Source code, full documentation and tutorials including a 'Getting started' guide are available at http://github.com/merlyescalona/ngsphy. Supplementary data are available at Bioinformatics online.

Seqfam: A python package for analysis of Next Generation Sequencing DNA data in families

This article introduces seqfam, a python package which is primarily designed for analysing next generation sequencing (NGS) DNA data from families with known pedigree information in order to identify rare variants that are potentially causal of a disease/trait of interest. It uses the popular and versatile Pandas library, and can be straightforwardly integrated into existing analysis code/pipelines. Seqfam can be used to verify pedigree information, to perform Monte Carlo gene dropping, to undertake regression-based gene burden testing, and to identify variants which segregate by affection status in families via user-defined pattern of occurrence rules. Additionally, it can generate scripts for running analyses in a “MapReduce pattern” on a computer cluster, something which is usually desirable in NGS data analysis and indeed “big data” analysis in general. This article summarises how seqfam’s main user functions work and motivates their use. It also provides explanatory context for example scripts and data included in the package which demonstrate use cases. With respect to verifying pedigree information, software exists for efficiently calculating kinship coefficients, so seqfam performs the necessary extra steps of mapping pedigrees and kinship coefficients to expected and observed degrees of relationship respectively. Gene dropping and the application of variant pattern of occurrence rules in families can provide evidence for a variant being causal. The authors are unaware of other software which performs these tasks in familial cohorts, so seqfam fulfils this need. Gene burden rather than single marker tests are often used to detect rare causal variants due to greater power. Seqfam may be an attractive alternative to existing gene burden testing software due to its flexibility, particularly in grouping and aggregating variants.

Yleaf: Software for Human Y-Chromosomal Haplogroup Inference from Next-Generation Sequencing Data.

Next-generation sequencing (NGS) technologies offer immense possibilities given the large genomic data they simultaneously deliver. The human Y-chromosome serves as good example how NGS benefits various applications in evolution, anthropology, genealogy, and forensics. Prior to NGS, the Y-chromosome phylogenetic tree consisted of a few hundred branches, based on NGS data, it now contains many thousands. The complexity of both, Y tree and NGS data provide challenges for haplogroup assignment. For effective analysis and interpretation of Y-chromosome NGS data, we present Yleaf, a publically available, automated, user-friendly software for high-resolution Y-chromosome haplogroup inference independently of library and sequencing methods.

A novel algorithm for determining the level of mosaicism in preimplantation genetic screening (PGS) with next-generation sequencing (NGS)

Introduction: Filtering PCR-artifacts, normalization and aberrations calling are common tasks implemented in any software designed for analyzing NGS data. However, most of the algorithms have been developed for post-natal diagnosis, where NGS has recently become a useful but not fully compatible approach for PGS. Previous methods based on whole-genome sequencing (WGS) require high-depth coverage on the whole genome scale and are not cost-efficient. Moreover, they do not determine precisely the level of mosaicism, because this is very uncommon in post-natal diagnosis. In order to accurately identify genome-wide aneuploidies by NGS, we implemented an algorithm that can identify the percentage of aneuploidy for embryo-biopsies. This algorithm is able to improve confidence-value and reduce the risk of misdiagnosis. Less reads are required enabling high-level multiplexing.

Comparison of Burrows-Wheeler Transform-Based Mapping Algorithms Used in High-Throughput Whole-Genome Sequencing: Application to Illumina Data for Livestock Genomes.

Ongoing developments and cost decreases in next-generation sequencing (NGS) technologies have led to an increase in their application, which has greatly enhanced the fields of genetics and genomics. Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. Efficient alignment of the reads onto the reference genome with high accuracy is very important because it determines the global quality of downstream analyses. In this study, we evaluate the performance of three Burrows-Wheeler transform-based mappers, BWA, Bowtie2, and HISAT2, in the context of paired-end Illumina whole-genome sequencing of livestock, using simulated sequence data sets with varying sequence read lengths, insert sizes, and levels of genomic coverage, as well as five real data sets. The mappers were evaluated based on two criteria, computational resource/time requirements and robustness of mapping. Our results show that BWA and Bowtie2 tend to be more robust than HISAT2, while HISAT2 was significantly faster and used less memory than both BWA and Bowtie2. We conclude that there is not a single mapper that is ideal in all scenarios but rather the choice of alignment tool should be driven by the application and sequencing technology.

Refining the Results of a Classical SELEX Experiment by Expanding the Sequence Data Set of an Aptamer Pool Selected for Protein A.

New, as yet undiscovered aptamers for Protein A were identified by applying next generation sequencing (NGS) to a previously selected aptamer pool. This pool was obtained in a classical SELEX (Systematic Evolution of Ligands by EXponential enrichment) experiment using the FluMag-SELEX procedure followed by cloning and Sanger sequencing. PA#2/8 was identified as the only Protein A-binding aptamer from the Sanger sequence pool, and was shown to be able to bind intact cells of Staphylococcus aureus. In this study, we show the extension of the SELEX results by re-sequencing of the same aptamer pool using a medium throughput NGS approach and data analysis. Both data pools were compared. They confirm the selection of a highly complex and heterogeneous oligonucleotide pool and show consistently a high content of orphans as well as a similar relative frequency of certain sequence groups. But in contrast to the Sanger data pool, the NGS pool was clearly dominated by one sequence group containing the known Protein A-binding aptamer PA#2/8 as the most frequent sequence in this group. In addition, we found two new sequence groups in the NGS pool represented by PA-C10 and PA-C8, respectively, which also have high specificity for Protein A. Comparative affinity studies reveal differences between the aptamers and confirm that PA#2/8 remains the most potent sequence within the selected aptamer pool reaching affinities in the low nanomolar range of KD = 20 ± 1 nM.

Targeted Next-Generation Sequencing Reveals Novel RP1 Mutations in Autosomal Recessive Retinitis Pigmentosa.

Retinitis pigmentosa (RP) is a group of rare inherited retinal dystrophies that result in a progressive loss of vision. Molecular diagnosis of RP is difficult due to its phenotypic and genetic heterogeneities. To investigate causative genetic mutations in a collection of RP cases: one Indian and two Chinese families with autosomal-recessive RP and two sporadic patients with RP. A total of 163 genes, which have previously been found to be involved in inherited retinal disorders, were selected for targeted next-generation sequencing (NGS). Stringent NGS data analyses followed by confirmation using Sanger sequencing and segregation analyses were applied to evaluate all identified pathogenic mutations. Four novel frameshift mutations and two compound heterozygous mutations were identified in RP1. In addition, all mutations were found to co-segregate with the disease in the three familial cases; none of the mutations were detected in control samples. This study expands the mutational spectrums of RP1 for RP.

MutScan: fast detection and visualization of target mutations by scanning FASTQ data

BackgroundSome types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data.ResultWe developed MutScan to address problems of sensitive detection and efficient validation for target mutations. MutScan involves highly optimized string-searching algorithms, which can scan input FASTQ files to grab all reads that support target mutations. The collected supporting reads for each target mutation will be piled up and visualized using web technologies such as HTML and JavaScript. Algorithms such as rolling hash and bloom filter are applied to accelerate scanning and make MutScan applicable to detect or visualize target mutations in a very fast way.ConclusionMutScan is a tool for the detection and visualization of target mutations by only scanning FASTQ raw data directly. Compared to conventional pipelines, this offers a very high performance, executing about 20 times faster, and offering maximal sensitivity since it can grab mutations with even one single supporting read. MutScan visualizes detected mutations by generating interactive pile-ups using web technologies. These can serve to validate target mutations, thus avoiding false positives. Furthermore, MutScan can visualize all mutation records in a VCF file to HTML pages for cloud-friendly VCF validation. MutScan is an open source tool available at GitHub: https://github.com/OpenGene/MutScan

Multiple Integration and Data Annotation Study (MIDAS): improving next-generation sequencing data analysis by genotype-phenotype correlations

AbstractNext-generation sequencing (NGS) technologies in clinical diagnostics open vast opportunities through the ability to sequence all genes simultaneously at a cost and speed that is superior to traditional sequencing approaches. On the other hand, the practical implementation of NGS in routine diagnostics involves a variety of challenges, which need to be overcome. Among these are the generation, analysis and storage of large amounts of data, strict control of sequencing performance, validation of results, interpretation of detected variants and reporting. Here, we outline the Multiple Integration and Data Annotation Study, an approach for data integration in clinical diagnostics based on genotype-phenotype correlations. MIDAS aims to accelerate NGS data analysis and to enhance the validity of the results by computer-based variant prioritization using the clinical data of the patient. In this context, we present the MIDAS case reports of one patient with intellectual disability caused by a novel de novo loss-of-function variant in theGATAD2Bgene [NM_020699.3: c.1426G>T (p.Glu476*)] identified by trio whole-exome sequencing, as well as two cardiac disease patients with severe phenotype and multiple variants in genes linked to cardiac arrhythmogenic disorders analyzed with multi-gene panel sequencing. Based on the data collected in the MIDAS cohort, the MIDAS software will be tested and optimized. Moreover, the MIDAS software concept can be extended modularly to include further data resources for improved data handling and interpretation in the broad field of diagnostics.

A Powerful Gene-Based Test Accommodating Common and Low-Frequency Variants to Detect Both Main Effects and Gene-Gene Interaction Effects in Case-Control Studies.

Next-generation sequencing (NGS) has been widely used in genetic association studies to identify both common and rare variants associated with complex diseases. Various statistical association tests have been developed to analyze NGS data; however, most focus on identifying the marginal effects of a set of genetic variants on the disease. Only a few association tests for NGS data analysis have considered the interaction effects between genes. We developed three powerful gene-based gene-gene interaction tests for testing both the main effects and the interaction effects of common, low-frequency, and common with low-frequency variant pairs between two genes (the IGOF tests) in case-control studies using NGS data. We performed a comprehensive simulation study to verify that the proposed tests had appropriate type I error rates and significantly higher power than did other interaction tests for analyzing NGS data. The tests were applied to a whole-exome sequencing dataset for autism spectrum disorder (ASD) and the significant results were evaluated in another independent ASD cohort. The IGOF tests were implemented in C++ and are available at http://igof.sourceforge.net.

Computational analysis of next generation sequencing data and its applications in clinical oncology

Next generation sequencing (NGS) has made great strides in sequencing technology as it enables sequencing of genes in a high throughput manner with low cost. Various NGS platforms such as Illumina, Roche, ABI/SOLiD are used for wet-lab analysis of NGS data and computational tools such as BWA, Bowtie, Galaxy, SanGeniX are used for dry-lab analysis of NGS data. One of the important aspects of NGS data is its usage in early disease diagnosis especially in cancer which was earlier not possible with conventional sequencing technologies such as Sanger sequencing, NGS can identify all those mutations which cannot be identified using conventional sequencing technologies as researchers can now sequence the whole genome, exome or transcriptome. Exome sequencing is preferred, as a higher number of mutations are found to exist in the exome part of genes. The present comprehensive review encompasses the complete NGS data analysis workflow that includes alignment of NGS reads, identification and annotation of mutations and visualization, discussion of software tools for variant identification and annotation, evaluation of structural variation in NGS data, and study of different DNA sequencing technologies. In the field of clinical oncology, NGS has already proven its usefulness, and the mortality rate has been reduced, as now doctors can suggest a proper treatment to their patients by checking the complete genomic profile. However, data storage and the complexity in interpreting enormous amounts of data obtained with NGS still remain a computational challenge to researchers, as for each sample, the number of different and very large analysis files are generated directly from the raw sequence read file to the final result file. NGS resultant data is very complex, and its interpretation requires expert bioinformatics assistance, as a large number of mutations are identified from samples, but to differentiate clinically significant mutations among them with appropriate use of validation methods is a challenging task. This review is intended to provide researchers with a complete overview of NGS along with knowledge of how the tools will be employed, and insight into identification and interpretation of cancer mutations for clinical diagnostics.

BATCH-GE: Analysis of NGS Data for Genome Editing Assessment.

Due to its simple nature, the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technique is massively used nowadays to modify genomic loci in a wide range of model systems. The possibility to interrogate gene function on a genome-wide scale is revolutionizing fundamental life sciences and will lead to new clinical breakthroughs. Its strength is even more pronounced when it is used in tandem with next-generation sequencing (NGS). The high throughput and low cost cause NGS to be the method of choice for exploring CRISPR-Cas9 experimental results. To analyze the NGS reads from genome editing experiments only few bioinformatics tools are available. BATCH-GE is a flexible and easy-to-use tool, which is especially useful for dealing with large amounts of data. It detects and reports indel mutations and other precise genome editing events and calculates the corresponding mutagenesis efficiencies for a large number of samples in parallel.