• antiphospholipid syndrome
• biomarkers
• diagnosis
• next-generation sequencing
• prognosis

• National Natural Science Foundation of China

• SRISTI BIRAC

• COVID-19
• NGS
• SARS-CoV-2
• benchmarking
• genome assemblers
• viruses

• Benchmarking
• COVID-19/epidemiology
• Genome, Viral
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Pandemics
• SARS-CoV-2/genetics
• Sequence Analysis, DNA/methods
• Software

Computational biology provides software tools for testing and making inferences about biological data. In the face of increasing volumes of data, heuristic methods that trade software speed for accuracy may be employed. We have studied these trade-offs using the results of a large number of independent software benchmarks, and evaluated whether external factors, including speed, author reputation, journal impact, recency and developer efforts, are indicative of accurate software.

We find that software speed, author reputation, journal impact, number of citations and age are unreliable predictors of software accuracy. This is unfortunate because these are frequently cited reasons for selecting software tools. However, GitHub-derived statistics and high version numbers show that accurate bioinformatic software tools are generally the product of many improvements over time. We also find an excess of slow and inaccurate bioinformatic software tools, and this is consistent across many sub-disciplines. There are few tools that are middle-of-road in terms of accuracy and speed trade-offs.

Our findings indicate that accurate bioinformatic software is primarily the product of long-term commitments to software development. In addition, we hypothesise that bioinformatics software suffers from publication bias. Software that is intermediate in terms of both speed and accuracy may be difficult to publish—possibly due to author, editor and reviewer practises. This leaves an unfortunate hole in the literature, as ideal tools may fall into this gap. High accuracy tools are not always useful if they are slow, while high speed is not useful if the results are also inaccurate.

The online version contains supplementary material available at (10.1186/s13059-022-02625-x).

• alignment
• ancient DNA
• benchmarking
• reference bias

• Chromosomes
• high-throughput nucleotide sequencing
• sequence analysis

Human microRNAs (miRNAs) have diverse functions in biology, and play a role in nearly every biological process. Here we report that miR-520d-5p (520d-5p) causes undifferentiated cancer cells to adopt benign or normal status in vivo in immunodeficient mice via demethylation and P53 upregulation. Further we found that 520-5p causes normal cells to elongate cellular lifetime and mesenchymal stem cell-like status with CD105 positivity. We hypothesized that ectopic 520d-5p expression reduced mutations in undifferentiated type of hepatoma (HLF) cells through synergistic modulation of methylation-related enzymatic expression.

To examine whether there were any changes in mutation status in cells treated with 520d-5p, we performed next generation sequencing (NGS) in HLF cells and human iPSC-derivative cells in pre-mesenchymal stem cell status. We analyzed the data using both genome-wide and individual gene function approaches.

520d-5p induced a shift towards a wild type or non-malignant phenotype, which was regulated by nucleotide mutations in both HLF cells and iPSCs. Further, 520d-5p reduced mutation levels in both the whole genome and genomic fragment assemblies.

Cancer cell genomic mutations cannot be repaired in most contexts. However, these findings suggest that applied development of 520d-5p would allow new approaches to cancer research and improve the quality of iPSCs used in regenerative medicine.

The online version of this article (10.1186/s12885-019-5786-y) contains supplementary material, which is available to authorized users.

• Genomic conversion
• Hsa-miR-520d-5p
• Mutation
• Progenitor MSC
• iPSC

In addition to the rapid advancement in Next-Generation Sequencing (NGS) technology, clinical panel sequencing is being used increasingly in clinical studies and tests. However, tools that are used in NGS data analysis have not been comparatively evaluated in performance for panel sequencing. This study aimed to evaluate the tools used in the alignment process, the first procedure in bioinformatics analysis, by comparing tools that have been widely used with ones that have been introduced recently. With the accumulated panel sequencing data, detected variant lists were cataloged and inserted into simulated reads produced from the reference genome (h19). The amount of unmapped reads and misaligned reads, mapping quality distribution, and runtime were measured as standards for comparison. As the most widely used tools, Bowtie2 and BWA–MEM each showed explicit performance with AUC of 0.9984 and 0.9970 respectively. Kart, maintaining superior runtime and less number of misaligned read, also similarly possessed high level of AUC (0.9723). Such selection and optimization method of tools appropriate for panel sequencing can be utilized for fields requiring error minimization, such as clinical application and liquid biopsy studies.

• Alignment tool
• CancerSCAN
• Clinical panel sequencing
• Next-Generation Sequencing
• Read mapping

• (Cytosine-5) methylation
• 5-methylcytosine
• Bisulfite conversion
• Data analysis
• Differential methylation
• High-throughput sequencing
• Modification
• RNA
• RNA methylation
• RNA modification
• RNA-BSseq
• Sodium bisulfite
• Transcription
• m5C

• 5-Methylcytosine
• Base Composition
• Computational Biology/methods
• High-Throughput Nucleotide Sequencing
• Methylation
• Quality Control
• RNA/chemistry
• RNA/genetics
• Sequence Analysis, RNA/methods
• Software
• Transcriptome

• Candidate gene identification
• Colorectal cancer
• Genetic predisposition
• Next generation sequencing

• Animals
• Colorectal Neoplasms/genetics
• Genetic Predisposition to Disease/genetics
• Humans

• ZonMw
• KWF Kankerbestrijding (NL)
• KWF Kankerbestrijding

• MLTK
• centronuclear myopathy
• congenital myopathy
• exome
• kinase

• Genetic diversity
• Haplotype reconstruction
• Next-generation sequencing
• Viral quasispecies

• Animals
• Female
• Genome
• Genomics/methods
• High-Throughput Nucleotide Sequencing/methods
• Male
• Molecular Sequence Annotation
• Phylogeny
• Rats
• Rats, Inbred Strains/genetics
• Reference Standards
• Sequence Analysis, DNA/methods

• Seventh Framework Programme

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will encourage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions.

• Beta vulgaris
• CenH3
• Centromere
• ChIP-Seq
• H3K9me2
• Heterochromatin
• Repeats

• Beta vulgaris/genetics
• Beta vulgaris/metabolism
• Centromere/metabolism
• Chromatin/genetics
• Chromatin/metabolism
• Chromatin Immunoprecipitation
• Heterochromatin/genetics
• Heterochromatin/metabolism
• Histones/metabolism
• Lysine/metabolism
• Plant Proteins/genetics
• Plant Proteins/metabolism
• Sequence Analysis, DNA

• Bundesministerium für Bildung und Forschung
• Czech Science Foundation
• Akademie Věd České Republiky

Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.

• mutation annotation
• pathway analysis
• sequence alignment
• single-nucleotide polymorphism
• template preparation

In this paper we propose a method and discuss its computational implementation as an integrated tool for the analysis of viral genetic diversity on data generated by high-throughput sequencing. The main motivation for this work is to better understand the genetic diversity of viruses with high rates of nucleotide substitution, as HIV-1 and Influenza. Most methods for viral diversity estimation proposed so far are intended to take benefit of the longer reads produced by some next-generation sequencing platforms in order to estimate a population of haplotypes which represent the diversity of the original population. The method proposed here is custom-made to take advantage of the very low error rate and extremely deep coverage per site, which are the main features of some neglected technologies that have not received much attention due to the short length of its reads, which precludes haplotype estimation. This approach allowed us to avoid some hard problems related to haplotype reconstruction (need of long reads, preliminary error filtering and assembly).

We propose to measure genetic diversity of a viral population through a family of multinomial probability distributions indexed by the sites of the virus genome, each one representing the distribution of nucleic bases per site. Moreover, the implementation of the method focuses on two main optimization strategies: a read mapping/alignment procedure that aims at the recovery of the maximum possible number of short-reads; the inference of the multinomial parameters in a Bayesian framework with smoothed Dirichlet estimation. The Bayesian approach provides conditional probability distributions for the multinomial parameters allowing one to take into account the prior information of the control experiment and providing a natural way to separate signal from noise, since it automatically furnishes Bayesian confidence intervals and thus avoids the drawbacks of preliminary error filtering.

The methods described in this paper have been implemented as an integrated tool called Tanden (Tool for Analysis of Diversity in Viral Populations) and successfully tested on samples obtained from HIV-1 strain NL4-3 (group M, subtype B) cultivations on primary human cell cultures in many distinct viral propagation conditions. Tanden is written in C# (Microsoft), runs on the Windows operating system, and can be downloaded from: http://tanden.url.ph/.

• Bayesian inference
• Dirichlet distribution
• Viral diversity

• Germ cell transplantation
• Illumina
• RNA-Sequencing
• Reproduction
• Southern bluefin tuna
• Spermatogonia

• Animals
• Female
• Gene Expression Regulation, Developmental
• Germ Cells/growth & development
• Male
• Ovary/growth & development
• Reproduction
• Testis/growth & development
• Transcriptome
• Tuna/genetics
• Tuna/growth & development

• Australian Seafood Cooperative Research Centre
• Fisheries Research and Development Corporation
• Australian Research Council

Advances in high-throughput sequencing (HTS) have allowed researchers to obtain large amounts of biological sequence information at speeds and costs unimaginable only a decade ago. Phylogenetics, and the study of evolution in general, is quickly migrating towards using HTS to generate larger and more complex molecular datasets. In this paper, we present a method that utilizes microfluidic PCR and HTS to generate large amounts of sequence data suitable for phylogenetic analyses. The approach uses the Fluidigm Access Array System (Fluidigm, San Francisco, CA, USA) and two sets of PCR primers to simultaneously amplify 48 target regions across 48 samples, incorporating sample-specific barcodes and HTS adapters (2,304 unique amplicons per Access Array). The final product is a pooled set of amplicons ready to be sequenced, and thus, there is no need to construct separate, costly genomic libraries for each sample. Further, we present a bioinformatics pipeline to process the raw HTS reads to either generate consensus sequences (with or without ambiguities) for every locus in every sample or—more importantly—recover the separate alleles from heterozygous target regions in each sample. This is important because it adds allelic information that is well suited for coalescent-based phylogenetic analyses that are becoming very common in conservation and evolutionary biology. To test our approach and bioinformatics pipeline, we sequenced 576 samples across 96 target regions belonging to the South American clade of the genus Bartsia L. in the plant family Orobanchaceae. After sequencing cleanup and alignment, the experiment resulted in ~25,300bp across 486 samples for a set of 48 primer pairs targeting the plastome, and ~13,500bp for 363 samples for a set of primers targeting regions in the nuclear genome. Finally, we constructed a combined concatenated matrix from all 96 primer combinations, resulting in a combined aligned length of ~40,500bp for 349 samples.

• Cell Nucleus/genetics
• DNA Primers/metabolism
• DNA, Chloroplast/genetics
• Flowers/genetics
• Genetic Variation
• Genome, Plant
• Geography
• High-Throughput Nucleotide Sequencing/methods
• Microfluidics
• Orobanchaceae/genetics
• Phylogeny
• Polymerase Chain Reaction/methods
• Reproducibility of Results
• Species Specificity

• P20 RR016448 NCRR NIH HHS
• P20 RR016454 NCRR NIH HHS
• P20RR016454 NCRR NIH HHS
• P20RR16448 NCRR NIH HHS

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

• RNA-seq
• alternative splicing
• co-expression network
• data preprocessing
• differential gene expression
• pathway analysis
• systems biology
• variants detection

Application of next-generation sequencing (NGS) methods for transcriptome analysis (RNA-seq) has become increasingly accessible in recent years and are of great interest to many biological disciplines including, eg, evolutionary biology, ecology, biomedicine, and computational biology. Although virtually any research group can now obtain RNA-seq data, only a few have the bioinformatics knowledge and computation facilities required for transcriptome analysis. Here, we present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform offering a web-based interface that generates the outputs commonly used in de novo RNA-seq analysis and comparative transcriptomics. TRUFA provides a comprehensive service that allows performing dynamically raw read cleaning, transcript assembly, annotation, and expression quantification. Due to the computationally intensive nature of such analyses, TRUFA is highly parallelized and benefits from accessing high-performance computing resources. The complete TRUFA pipeline was validated using four previously published transcriptomic data sets. TRUFA’s results for the example datasets showed globally similar results when comparing with the original studies, and performed particularly better when analyzing the green tea dataset. The platform permits analyzing RNA-seq data in a fast, robust, and user-friendly manner. Accounts on TRUFA are provided freely upon request at https://trufa.ifca.es.

• RNA-seq
• annotation
• de novo assembly
• expression quantification
• read cleaning
• transcriptomics

• Bacterial community
• Composition
• Individual variability
• Yak rumen

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals.

• CNV
• GPU
• HTS
• parallel
• read-depth

Bioinformatics software quality assurance is essential in genomic medicine. Systematic verification and validation of bioinformatics software is difficult because it is often not possible to obtain a realistic "gold standard" for systematic evaluation. Here we apply a technique that originates from the software testing literature, namely Metamorphic Testing (MT), to systematically test three widely used short-read sequence alignment programs.

MT alleviates the problems associated with the lack of gold standard by checking that the results from multiple executions of a program satisfy a set of expected or desirable properties that can be derived from the software specification or user expectations. We tested BWA, Bowtie and Bowtie2 using simulated data and one HapMap dataset. It is interesting to observe that multiple executions of the same aligner using slightly modified input FASTQ sequence file, such as after randomly re-ordering of the reads, may affect alignment results. Furthermore, we found that the list of variant calls can be affected unless strict quality control is applied during variant calling.

Thorough testing of bioinformatics software is important in delivering clinical genomic medicine. This paper demonstrates a different framework to test a program that involves checking its properties, thus greatly expanding the number and repertoire of test cases we can apply in practice.

• bioinformatics
• conservation genomics
• genome assembly
• next generation sequencing
• vertebrates
• whole - genome sequencing.

• ensemble genotyping
• false positive
• incidental finding
• logistic regression
• whole genome sequencing

• Animals
• Cytosine
• DNA Methylation
• Humans
• Sequence Analysis, DNA/methods
• Sulfites/chemistry

RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order to achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of “biological quality” are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system (CNS) of a Drosophila transgenic strain with neuronal-specific RNAi of an ubiquitous gene was profiled using RNA-seq. After observing the existence of an unusual variance in our dataset, we showed that the expression profile of a small panel of marker genes, including GAL4 under control of a tissue specific driver, can identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We further analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we showed that trimming 5′ ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of systematic technical errors.

• RNA-seq
• brain
• central nervous system
• drosophila
• shRNA transgenic strain

Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome.

G-SNPM is a tool that maps a short sequence representative of a SNP against a reference DNA sequence in order to find the physical position of the SNP in that sequence. In G-SNPM each SNP is mapped on its related chromosome by means of an automatic three-stage pipeline. In the first stage, G-SNPM uses the GPU-based short-read mapping tool SOAP3-dp to parallel align on a reference chromosome its related sequences representative of a SNP. In the second stage G-SNPM uses another short-read mapping tool to remap the sequences unaligned or ambiguously aligned by SOAP3-dp (in this stage SHRiMP2 is used, which exploits specialized vector computing hardware to speed-up the dynamic programming algorithm of Smith-Waterman). In the last stage, G-SNPM analyzes the alignments obtained by SOAP3-dp and SHRiMP2 to identify the absolute position of each SNP.

To assess G-SNPM, we used it to remap the SNPs of some commercial chips. Experimental results shown that G-SNPM has been able to remap without ambiguity almost all SNPs. Based on modern GPUs, G-SNPM provides fast mappings without worsening the accuracy of the results. G-SNPM can be used to deal with specialized Genome Wide Association Studies (GWAS), as well as in annotation tasks that require to update the SNP mapping probes.

• RNA-Seq
• accuracy estimation
• bank vole
• pool
• transcriptome

• Algorithms
• Computer Simulation
• Genetic Variation/genetics
• Genome, Viral
• Genomics/methods
• HIV-1/genetics
• Hepatitis B virus/genetics
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Phylogeny
• Sequence Analysis, DNA/methods
• Software
• Virus Assembly

• UL1 RR02989 NCRR NIH HHS
• MC_PC_13042 Medical Research Council
• R01 NS063897-01A2 NINDS NIH HHS
• R01 NS063897 NINDS NIH HHS
• MR/K006665/1 Medical Research Council

Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a “gold-standard” method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.

• Computational Biology/methods
• Exome/genetics
• Genome, Human/genetics
• Genotype
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Polymorphism, Single Nucleotide/genetics
• Sensitivity and Specificity
• Software

• RC2 DA028909 NIDA NIH HHS
• R01 DA012690 NIDA NIH HHS
• R01 DA012849 NIDA NIH HHS
• DA18432 NIDA NIH HHS
• KL2 RR024138 NCRR NIH HHS
• R01 DA018432 NIDA NIH HHS
• K24 MH064122 NIMH NIH HHS
• R01 AA011330 NIAAA NIH HHS
• R01 AA017535 NIAAA NIH HHS
• DA12849 NIDA NIH HHS
• DA028909 NIDA NIH HHS
• AA11330 NIAAA NIH HHS
• DA030976 NIDA NIH HHS
• DA24758 NIDA NIH HHS
• K01 DA024758 NIDA NIH HHS
• R01 DA030976 NIDA NIH HHS
• AA017535 NIAAA NIH HHS
• DA12690 NIDA NIH HHS
• MH64122 NIMH NIH HHS

Recent advances in next-generation sequencing technologies have transformed the genetics study of human diseases; this is an era of unprecedented productivity. Exome sequencing, the targeted sequencing of the protein-coding portion of the human genome, has been shown to be a powerful and cost-effective method for detection of disease variants underlying Mendelian disorders. Increasing effort has been made in the interest of the identification of rare variants associated with complex traits in sequencing studies. Here we provided an overview of the application fields for exome sequencing in human diseases. We describe a general framework of computation and bioinformatics for handling sequencing data. We then demonstrate data quality and agreement between exome sequencing and exome microarray (chip) genotypes using data collected on the same set of subjects in a genetic study of panic disorder. Our results show that, in sequencing data, the data quality was generally higher for variants within the exonic target regions, compared to that outside the target regions, due to the target enrichment. We also compared genotype concordance for variant calls obtained by exome sequencing vs. exome genotyping microarrays. The overall consistency rate was >99.83% and the heterozygous consistency rate was >97.55%. The two platforms share a large amount of agreement over low frequency variants in the exonic regions, while exome sequencing provides much more information on variants not included on exome genotyping microarrays. The results demonstrate that exome sequencing data are of high quality and can be used to investigate the role of rare coding variants in human diseases.

• Mendelian diseases
• complex traits
• exome arrays
• exome sequencing
• whole-genome sequencing

• next-generation sequencing
• cancer genome analysis

• Genome
• Humans
• Molecular Diagnostic Techniques
• Mutation
• Neoplasms/diagnosis
• Neoplasms/genetics
• Precision Medicine
• Sequence Analysis, DNA

• 090532 Wellcome Trust
• 16459 Cancer Research UK

• CNV
• ENCODE
• Encyclopedia of DNA Elements
• GWAS
• HGP
• Human Genome Project
• InDel
• NGS
• SNP
• WES
• WGS
• copy number variation
• genome-wide association study
• genotype-phenotype relationships
• insertion/deletion variant
• medical genomics
• next-generation sequencing
• single-nucleotide polymorphism
• variant burden
• variant calling
• whole-exome sequencing
• whole-genome sequencing

• Hantavirus
• Orthopoxvirus
• SNP
• TaqMan/PCR assays
• Viral genotyping
• Western/Eastern/Venezuelan encephalitis viruses

• next-generation sequencing
• genomics
• epigenomics
• transcriptomics
• cardiomyopathy
• heart failure