Identification Of Sequence Variants Research Articles

A Spike-Control Approach that Evaluates High Resolution Mass Spectrometry-Based Sequence Variant Analytical Method Performance for Therapeutic Proteins.

An amino acid sequence variant (SV) is defined as an unintended amino acid substitution in protein drug products. SVs contribute to product heterogeneity and can potentially impact product quality, safety, immunogenicity, and efficacy. The analysis of biotherapeutics for SVs is important throughout the product life cycle including clone selection, development of nutrient feed strategies, commercial manufacturing process, and post-approval changes to monitor product quality. The proposed analytical procedure for SVs consists of both qualitative (identification of SVs) and quantitative (quantitation of identified SVs) components. The complexities of SV analysis and the variety of current procedures highlight the need for a systematic approach for assessing the capability of these methodologies to reliably identify and quantitate SVs in biotherapeutics. We described here a "spike-control" approach for evaluating SV analytical procedure. The concept was adopted from quality control samples routinely used in analytical procedure validation. One FDA approved monoclonal antibody (mAb) was spiked with accurate amounts of highly homologous mAb to create mAb samples containing low yet accurate levels of "artificial" SVs. Spike-control samples were denatured, reduced, alkylated, digested and then analyzed by high resolution Orbitrap mass spectrometry. In silico analysis revealed four single amino acid differences between the two mAbs that could be used to represent SVs in the spike-control samples. All four "artificial" SVs were reliably identified by the current workflow. Analytical range (0.01% to 2%), accuracy and precision of identified SVs have also been evaluated. Overall, spike-control sample(s) helped to demonstrate that the SV analytical procedure (i.e., sample preparation, LC separation, mass spectrometry determinations and bioinformatic software) was fit for purpose and suitable for the identification and quantitation of SVs at a pre-determined threshold.

NGSpop: A desktop software that supports population studies by identifying sequence variations from next-generation sequencing data.

Next-generation sequencing (NGS) is widely used in all areas of genetic research, such as genetic disease diagnosis and breeding, and it can produce massive amounts of data. The identification of sequence variants is an important step when processing large NGS datasets; however, currently, the process is complicated, repetitive, and requires concentration, which can be taxing on the researcher. Therefore, to support researchers who are not familiar enough with bioinformatics to identify sequence variations regularly from large datasets, we have developed a fully automated desktop software, NGSpop. NGSpop includes functionalities for all the variant calling and visualization procedures used when processing NGS data, such as quality control, mapping, filtering details, and variant calling. In the variant calling step, the user can select the GATK or DeepVariant algorithm for variant calling. These algorithms can be executed using pre-set pipelines and options or customized with the user-specified options. NGSpop is implemented using JavaFX (version 1.8) and can thus be run on Unix-like operating systems such as Ubuntu Linux (version 16.04, 18.0.4). Although several pipelines and visualization tools are available for NGS data analysis, most integrated environments do not support batch processes; thus, variant detection cannot be automated for population-level studies. The NGSpop software developed in this study has an easy-to-use interface and helps in rapid analysis of multiple NGS data from population studies. According to a benchmark test, it effectively reduced the carbon footprint in bioinformatics analysis by expending the least central processing unit heat and power. Additionally, this software makes it possible to use the GATK and DeepVariant algorithms more flexibly and efficiently than other programs by allowing users to choose between the algorithms. As a limitation, NGSpop currently supports only the sequencing reads in fastq format produced by the Illumina platform. NGSpop is freely available at https://sourceforge.net/projects/ngspop/.

Analysis of Wilson disease mutations in copper binding domain of ATP7B gene.

Wilson’s disease (WD) is an autosomal recessive disorder, resulting from variations in ATP7B gene. Clinical heterogeneity, including neuropsychiatric and hepatic manifestations over a large range of age groups make diagnosis difficult. Most of WD patients suffer severe disabilities and even die. So, overall goal of proposed study is the genetic and clinical characterization of Wilson’s disease cases from Pakistani population. Clinical data was collected, and patients were investigated for variations in selected ATP7B exons using PCR based Sanger sequencing. Pathogenic effect predictions for detected variants were carried out using PROVEAN, MutationTaster2, and HSF software’s. Clinical heterogeneity was observed in patients including reduced serum ceruloplasmin, signs of chronic liver damage and raised 24 h urinary copper excretion. Mean age of onset was 11.3 years. Kayser-Fleischer rings were present in 75% of cases. About 82.5% patients belonged to inbred families. Patients having neurological disorder were above 12 years of age. Total ten variants in analyzed region of ATP7B gene, including a reported variation (p. L227Yfs*35) were found in patients. The study also identified 4 putative novel synonymous variants (c.251A>C, c.15T>A, c.6T>C, c.238C>T) and 5 reported polymorphisms (c.83C>A, c.39_40insCGGCG, p.V456L, c.39_40insCGCCG and c.1544-53A>C). Reliable understanding of clinical presentations and genotype-phenotype correlation provide insight to function and structure of ATP7B and may assist in disease prognosis and family counseling. The study revealed clinical presentation of Pakistani WD cases and identification of sequence variants in screened region of ATP7B.

EP356: Exome sequencing expands the sensitivity and specificity of identification of sequence variants and CNVs in phenotypic females with DSD

Disorders of sexual development (DSD) include a heterogeneous group of congenital conditions associated with atypical development of internal and external genital structures. Potential genetic causes of DSDs are chromosomal aneuploidies, copy number variants (CNVs) within open reading frames or promoter regions, and pathogenic variants in single genes. In cases of gonadal dysgenesis in 46,XY females, testis development is impaired and absent or reduced production of testosterone and Anti-Mullerian Hormone (AMH) can lead to the formation of female external genitalia or ambiguous genitalia.

Screening for autosomal recessive and X-linked conditions during pregnancy and preconception: a practice resource of the American College of Medical Genetics and Genomics (ACMG)

Carrier screening began 50 years ago with screening for conditions that have a high prevalence in defined racial/ethnic groups (e.g., Tay–Sachs disease in the Ashkenazi Jewish population; sickle cell disease in Black individuals). Cystic fibrosis was the first medical condition for which panethnic screening was recommended, followed by spinal muscular atrophy. Next-generation sequencing allows low cost and high throughput identification of sequence variants across many genes simultaneously. Since the phrase “expanded carrier screening” is nonspecific, there is a need to define carrier screening processes in a way that will allow equitable opportunity for patients to learn their reproductive risks using next-generation sequencing technology. An improved understanding of this risk allows patients to make informed reproductive decisions. Reproductive decision making is the established metric for clinical utility of population-based carrier screening. Furthermore, standardization of the screening approach will facilitate testing consistency. This practice resource reviews the current status of carrier screening, provides answers to some of the emerging questions, and recommends a consistent and equitable approach for offering carrier screening to all individuals during pregnancy or preconception.

Identification and functional characterisation of late blight resistance polymorphic genes in Russet Burbank potato cultivar.

In plants, the biosynthesis of the phenylpropanoid, flavonoid and fatty acid pathway monomers, polymers and conjugated metabolites play a vital role in disease resistance. These are generally deposited to reinforce cell walls to contain the pathogen to the site of infection. Identification of sequence variants in genes that biosynthesise these resistance metabolites can explain the mechanisms of disease resistance. The resistant and susceptible genotypes inoculated with Phytophthora infestans were RNA sequenced to identify the single nucleotide polymorphisms (SNPs) and insertion/deletion (InDel) variations. The SNPs/InDels were annotated and classified into different categories based on their effect on gene functions. In the selected 25 biosynthetic genes overlapping 39 transcripts, a total of 52 SNPs/InDels were identified in the protein-coding (CDS) regions. These were categorised as deleterious based on prediction of their effects on protein structure and function. The SNPs/InDels data obtained in this study can be used in genome editing to enhance late blight resistance in Russet Burbank and other potato cultivars.

Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data.

High-throughput sequencing technologies have rapidly developed during the past years and have become an essential tool in plant sciences. However, the analysis of genomic data remains challenging and relies mostly on the performance of automatic pipelines. Frequently applied pipelines involve the alignment of sequence reads against a reference sequence and the identification of sequence variants. Since most benchmarking studies of bioinformatics tools for this purpose have been conducted on human datasets, there is a lack of benchmarking studies in plant sciences. In this study, we evaluated the performance of 50 different variant calling pipelines, including five read mappers and ten variant callers, on six real plant datasets of the model organism Arabidopsis thaliana. Sets of variants were evaluated based on various parameters including sensitivity and specificity. We found that all investigated tools are suitable for analysis of NGS data in plant research. When looking at different performance metrics, BWA-MEM and Novoalign were the best mappers and GATK returned the best results in the variant calling step.

Curating the gnomAD database: Report of novel variants in the globin-coding genes and bioinformatics analysis.

Massive parallel sequencing technologies are facilitating the faster identification of sequence variants with the consequent capability of untangling the molecular bases of many human genetic syndromes. However, it is not always easy to understand the impact of novel variants, especially for missense changes, which can lead to a spectrum of phenotypes. This study presents a custom-designed multistep methodology to evaluate the impact of novel variants aggregated in the genome aggregation database for the HBB, HBA2, and HBA1 genes, by testing and improving its performance with a dataset of previously described alterations affecting those same genes. This approach scored high sensitivity and specificity values and showed an overall better performance than sequence-derived predictors, highlighting the importance of protein conformation and interaction specific analyses in curating variant databases. This study also describes the strengths and limitations of these structural studies and allows identifying residues in the globin chains more prone to tolerate substitutions.

Vi-HMM: a novel HMM-based method for sequence variant identification in short-read data

BackgroundAccurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in next-generation sequencing (NGS) applications. Existing methods for calling these variants often make simplified assumptions of positional independence and fail to leverage the dependence between genotypes at nearby loci that is caused by linkage disequilibrium (LD).Results and conclusionWe propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short-read data. This method allows transitions between hidden states (defined as “SNP,” “Ins,” “Del,” and “Match”) of adjacent genomic bases and determines an optimal hidden state path by using the Viterbi algorithm. The inferred hidden state path provides a direct solution to the identification of SNPs and INDELs. Simulation studies show that, under various sequencing depths, vi-HMM outperforms commonly used variant calling methods in terms of sensitivity and F1 score. When applied to the real data, vi-HMM demonstrates higher accuracy in calling SNPs and INDELs.

Identification of sequence variants associated with severe microtia-astresia by targeted sequencing

BackgroundMicrotia-atresia is characterized by abnormalities of the auricle (microtia) and aplasia or hypoplasia of the external auditory canal, often associated with middle ear abnormalities. To date, no causal genetic mutations or genes have been identified in microtia-atresia patients.MethodsWe designed a panel of 131 genes associated with external/middle or inner ear deformity. Targeted genomic capturing combined with next-generation sequencing (NGS) was utilized to screen for mutations in 40 severe microtia-atresia patients. Mutations detected by NGS were filtered and validated. And then mutations were divided into three categories—rare or novel variants, low-frequency variants and common variants—based on their frequency in the public database. The rare or novel mutations were prioritized by pathogenicity analysis. For the low-frequency variants and common variants, we used association studies to explore risk factors of severe microtia-atresia.ResultsSixty-five rare heterozygous mutations of 42 genes were identified in 27 (67.5%) severe microtia-atresia patients. Association studies to determine genes that were potentially pathogenic found that PLEC, USH2A, FREM2, DCHS1, GLI3, POMT1 and GBA genes were significantly associated with severe microtia-atresia. Of these, DCHS1 was strongly suggested to cause severe microtia-atresia as it was identified by both low-frequency and common variants association studies. A rare mutation (c.481C > T, p.R161C) in DCHS1 identified in one individual may be deleterious and may cause severe microtia-atresia.ConclusionWe identified several genes that were significantly associated with severe microtia-atresia. The findings provide new insights into genetic background of external ear deformities.

Mitochondrial and chloroplast genomes provide insights into the evolutionary origins of quinoa (Chenopodium quinoa Willd.)

Quinoa has recently gained international attention because of its nutritious seeds, prompting the expansion of its cultivation into new areas in which it was not originally selected as a crop. Improving quinoa production in these areas will benefit from the introduction of advantageous traits from free-living relatives that are native to these, or similar, environments. As part of an ongoing effort to characterize the primary and secondary germplasm pools for quinoa, we report the complete mitochondrial and chloroplast genome sequences of quinoa accession PI 614886 and the identification of sequence variants in additional accessions from quinoa and related species. This is the first reported mitochondrial genome assembly in the genus Chenopodium. Inference of phylogenetic relationships among Chenopodium species based on mitochondrial and chloroplast variants supports the hypotheses that 1) the A-genome ancestor was the cytoplasmic donor in the original tetraploidization event, and 2) highland and coastal quinoas were independently domesticated.

SeqVItA: Sequence Variant Identification and Annotation Platform for Next Generation Sequencing Data.

The current trend in clinical data analysis is to understand how individuals respond to therapies and drug interactions based on their genetic makeup. This has led to a paradigm shift in healthcare; caring for patients is now 99% information and 1% intervention. Reducing costs of next generation sequencing (NGS) technologies has made it possible to take genetic profiling to the clinical setting. This requires not just fast and accurate algorithms for variant detection, but also a knowledge-base for variant annotation and prioritization to facilitate tailored therapeutics based on an individual's genetic profile. Here we show that it is possible to provide a fast and easy access to all possible information about a variant and its impact on the gene, its protein product, associated pathways and drug-variant interactions by integrating previously reported knowledge from various databases. With this objective, we have developed a pipeline, Sequence Variants Identification and Annotation (SeqVItA) that provides end-to-end solution for small sequence variants detection, annotation and prioritization on a single platform. Parallelization of the variant detection step and with numerous resources incorporated to infer functional impact, clinical relevance and drug-variant associations, SeqVItA will benefit the clinical and research communities alike. Its open-source platform and modular framework allows for easy customization of the workflow depending on the data type (single, paired, or pooled samples), variant type (germline and somatic), and variant annotation and prioritization. Performance comparison of SeqVItA on simulated data and detection, interpretation and analysis of somatic variants on real data (24 liver cancer patients) is carried out. We demonstrate the efficacy of annotation module in facilitating personalized medicine based on patient's mutational landscape. SeqVItA is freely available at https://bioinf.iiit.ac.in/seqvita.

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes.

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 × 10-8) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.

In vivo and In vitro methods to identify DNA sequence variants that alter RNA Splicing.

Identification of sequence variants that create or eliminate splice sites has proven to be a significant challenge and represents one of many roadblocks in the clinical interpretation of rare genetic variation. Current methods of identifying splice altering sequence variants exist, however, these are limited by an imperfect understanding of splice signals and cumbersome functional assays. We have recently developed a computational tool that prioritizes putative splice-altering sequence variants, and a moderate-throughput minigene assay that confirms the variants which alter splicing. This bioinformatic strategy represents a substantial increase in accuracy and efficiency of historical in vitro splicing assays. In this unit we give detailed instructions on how to organize, run, and interpret various features of this protocol. We expect that splice-altering variants revealed through this protocol can be reliably carried forward for further clinical and biological analyses.

A robust targeted sequencing approach for low input and variable quality DNA from clinical samples

Next-generation deep sequencing of gene panels is being adopted as a diagnostic test to identify actionable mutations in cancer patient samples. However, clinical samples, such as formalin-fixed, paraffin-embedded specimens, frequently provide low quantities of degraded, poor quality DNA. To overcome these issues, many sequencing assays rely on extensive PCR amplification leading to an accumulation of bias and artifacts. Thus, there is a need for a targeted sequencing assay that performs well with DNA of low quality and quantity without relying on extensive PCR amplification. We evaluate the performance of a targeted sequencing assay based on Oligonucleotide Selective Sequencing, which permits the enrichment of genes and regions of interest and the identification of sequence variants from low amounts of damaged DNA. This assay utilizes a repair process adapted to clinical FFPE samples, followed by adaptor ligation to single stranded DNA and a primer-based capture technique. Our approach generates sequence libraries of high fidelity with reduced reliance on extensive PCR amplification—this facilitates the accurate assessment of copy number alterations in addition to delivering accurate single nucleotide variant and insertion/deletion detection. We apply this method to capture and sequence the exons of a panel of 130 cancer-related genes, from which we obtain high read coverage uniformity across the targeted regions at starting input DNA amounts as low as 10 ng per sample. We demonstrate the performance using a series of reference DNA samples, and by identifying sequence variants in DNA from matched clinical samples originating from different tissue types.

Characterization of a Human Induced Pluripotent Stem Cell-Derived Cardiomyocyte Model for the Study of Variant Pathogenicity: Validation of a KCNJ2 Mutation.

Long-QT syndrome is a potentially fatal condition for which 30% of patients are without a genetically confirmed diagnosis. Rapid identification of causal mutations is thus a priority to avoid at-risk situations that can lead to fatal cardiac events. Massively parallel sequencing technologies are useful for the identification of sequence variants; however, electrophysiological testing of newly identified variants is crucial to demonstrate causality. Long-QT syndrome could, therefore, benefit from having a standardized platform for functional characterization of candidate variants in the physiological context of human cardiomyocytes. Using a variant in Kir2.1 (Gly52Val) revealed by whole-exome sequencing in a patient presenting with symptoms of long-QT syndrome as a proof of principle, we demonstrated that commercially available human induced pluripotent stem cell-derived cardiomyocytes are a powerful model for screening variants involved in genetic cardiac diseases. Immunohistochemistry experiments and whole-cell current recordings in human embryonic kidney cells expressing the wild-type or the mutant Kir2.1 demonstrated that Kir2.1-52V alters channel cellular trafficking and fails to form a functional channel. Using human induced pluripotent stem cell-derived cardiomyocytes, we not only confirmed these results but also further demonstrated that Kir2.1-52V is associated with a dramatic prolongation of action potential duration with evidence of arrhythmic activity, parameters which could not have been studied using human embryonic kidney cells. Our study confirms the pathogenicity of Kir2.1-52V in 1 patient with long-QT syndrome and also supports the use of isogenic human induced pluripotent stem cell-derived cardiomyocytes as a physiologically relevant model for the screening of variants of unknown function.

ISVASE: identification of sequence variant associated with splicing event using RNA-seq data

BackgroundExon recognition and splicing precisely and efficiently by spliceosome is the key to generate mature mRNAs. About one third or a half of disease-related mutations affect RNA splicing. Software PVAAS has been developed to identify variants associated with aberrant splicing by directly using RNA-seq data. However, it bases on the assumption that annotated splicing site is normal splicing, which is not true in fact.ResultsWe develop the ISVASE, a tool for specifically identifying sequence variants associated with splicing events (SVASE) by using RNA-seq data. Comparing with PVAAS, our tool has several advantages, such as multi-pass stringent rule-dependent filters and statistical filters, only using split-reads, independent sequence variant identification in each part of splicing (junction), sequence variant detection for both of known and novel splicing event, additional exon-exon junction shift event detection if known splicing events provided, splicing signal evaluation, known DNA mutation and/or RNA editing data supported, higher precision and consistency, and short running time. Using a realistic RNA-seq dataset, we performed a case study to illustrate the functionality and effectiveness of our method. Moreover, the output of SVASEs can be used for downstream analysis such as splicing regulatory element study and sequence variant functional analysis.ConclusionsISVASE is useful for researchers interested in sequence variants (DNA mutation and/or RNA editing) associated with splicing events. The package is freely available at https://sourceforge.net/projects/isvase/.

Identification of sequence variants influencing immunoglobulin levels.

Immunoglobulins are the effector molecules of the adaptive humoral immune system. In a genome-wide association study of 19,219 individuals, we found 38 new variants and replicated 5 known variants associating with IgA, IgG or IgM levels or with composite immunoglobulin traits, accounted for by 32 loci. Variants at these loci also affect the risk of autoimmune diseases and blood malignancies and influence blood cell development. Notable associations include a rare variant at RUNX3 decreasing IgA levels by shifting isoform proportions (rs188468174[C>T]: P = 8.3 × 10-55, β = -0.90 s.d.), a rare in-frame deletion in FCGR2B abolishing IgG binding to the encoded receptor (p.Asn106del: P = 4.2 × 10-8, β = 1.03 s.d.), four IGH locus variants influencing class switching, and ten new associations with the HLA region. Our results provide new insight into the regulation of humoral immunity.

Identification of Sequence Variants within Experimentally Validated Protein Interaction Sites Provides New Insights into Molecular Mechanisms of Disease Development.

Protein interactions (PI) underlie complex biological processes. Protein interaction partners include DNA, RNA, ions, small chemical compounds, and proteins (protein-protein interactions; PPI). Analysis of sequence variants within regions corresponding to experimentally validated PI sites presents novel opportunities for understanding of complex diseases. Such information has not been systematically collected due to the fact that datasets are dispersed throughout databases and publications. Sequence variants and PI regions were obtained from the UniProt database. The location of the variants was compared to start and end positions of each PPI. Associations of sequence variants with phenotype were obtained from databases including COSMIC, GAD, PharmGKB, and dbSNP. We developed a catalogue of 603 sequence variants located within regions corresponding to experimentally validated PI sites, mostly PPI regions. These sequence variants were previously associated with risk for cancer, reproduction, ageing, renal, and immune system diseases. The developed catalogue connects information from different research papers and databases, represents a new layer of information and enables designing new hypotheses. It provides a baseline for prioritization of sequence variants, which may affect protein function and binding sites. The study contributes to the development of the proteogenomics field and provides new insights for understanding molecular mechanisms underlying disease development.

Tiered Human Integrated Sequence Search Databases for Shotgun Proteomics.

The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances-a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/ .