A flexible computational pipeline for research analyses of unsolved clinical exome cases.
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ABSTRACT:
SUBMITTER: Lassmann T
PROVIDER: S-EPMC7730424 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
A flexible computational pipeline for research analyses of unsolved clinical exome cases.
Lassmann Timo T Francis Richard W RW Weeks Alexia A Tang Dave D Jamieson Sarra E SE Broley Stephanie S Dawkins Hugh J S HJS Dreyer Lauren L Goldblatt Jack J Groza Tudor T Kamien Benjamin B Kiraly-Borri Cathy C McKenzie Fiona F Murphy Lesley L Pachter Nicholas N Pathak Gargi G Poulton Cathryn C Samanek Amanda A Skoss Rachel R Slee Jennie J Townshend Sharron S Ward Michelle M Baynam Gareth S GS Blackwell Jenefer M JM
NPJ genomic medicine 20201210 1
Exome sequencing has enabled molecular diagnoses for rare disease patients but often with initial diagnostic rates of ~25-30%. Here we develop a robust computational pipeline to rank variants for reassessment of unsolved rare disease patients. A comprehensive web-based patient report is generated in which all deleterious variants can be filtered by gene, variant characteristics, OMIM disease and Phenolyzer scores, and all are annotated with an ACMG classification and links to ClinVar. The pipeli ...[more]
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Project description:BackgroundGiven the rarity of most single-gene Mendelian disorders, concerted efforts of data exchange between clinical and scientific communities are critical to optimize molecular diagnosis and novel disease gene discovery.MethodsWe designed and implemented protocols for the study of cases for which a plausible molecular diagnosis was not achieved in a clinical genomics diagnostic laboratory (i.e. unsolved clinical exomes). Such cases were recruited to a research laboratory for further analyses, in order to potentially: (1) accelerate novel disease gene discovery; (2) increase the molecular diagnostic yield of whole exome sequencing (WES); and (3) gain insight into the genetic mechanisms of disease. Pilot project data included 74 families, consisting mostly of parent-offspring trios. Analyses performed on a research basis employed both WES from additional family members and complementary bioinformatics approaches and protocols.ResultsAnalysis of all possible modes of Mendelian inheritance, focusing on both single nucleotide variants (SNV) and copy number variant (CNV) alleles, yielded a likely contributory variant in 36% (27/74) of cases. If one includes candidate genes with variants identified within a single family, a potential contributory variant was identified in a total of ~51% (38/74) of cases enrolled in this pilot study. The molecular diagnosis was achieved in 30/63 trios (47.6%). Besides this, the analysis workflow yielded evidence for pathogenic variants in disease-associated genes in 4/6 singleton cases (66.6%), 1/1 multiplex family involving three affected siblings, and 3/4 (75%) quartet families. Both the analytical pipeline and the collaborative efforts between the diagnostic and research laboratories provided insights that allowed recent disease gene discoveries (PURA, TANGO2, EMC1, GNB5, ATAD3A, and MIPEP) and increased the number of novel genes, defined in this study as genes identified in more than one family (DHX30 and EBF3).ConclusionAn efficient genomics pipeline in which clinical sequencing in a diagnostic laboratory is followed by the detailed reanalysis of unsolved cases in a research environment, supplemented with WES data from additional family members, and subject to adjuvant bioinformatics analyses including relaxed variant filtering parameters in informatics pipelines, can enhance the molecular diagnostic yield and provide mechanistic insights into Mendelian disorders. Implementing these approaches requires collaborative clinical molecular diagnostic and research efforts.
| S-EPMC5361813 | biostudies-literature
Project description:Objective: In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR.Materials and methods: Largely data-driven, FIDDLE systematically transforms structured EHR data into feature vectors, limiting the number of decisions a user must make while incorporating good practices from the literature. To demonstrate its utility and flexibility, we conducted a proof-of-concept experiment in which we applied FIDDLE to 2 publicly available EHR data sets collected from intensive care units: MIMIC-III and the eICU Collaborative Research Database. We trained different ML models to predict 3 clinically important outcomes: in-hospital mortality, acute respiratory failure, and shock. We evaluated models using the area under the receiver operating characteristics curve (AUROC), and compared it to several baselines.Results: Across tasks, FIDDLE extracted 2,528 to 7,403 features from MIMIC-III and eICU, respectively. On all tasks, FIDDLE-based models achieved good discriminative performance, with AUROCs of 0.757-0.886, comparable to the performance of MIMIC-Extract, a preprocessing pipeline designed specifically for MIMIC-III. Furthermore, our results showed that FIDDLE is generalizable across different prediction times, ML algorithms, and data sets, while being relatively robust to different settings of user-defined arguments.Conclusions: FIDDLE, an open-source preprocessing pipeline, facilitates applying ML to structured EHR data. By accelerating and standardizing labor-intensive preprocessing, FIDDLE can help stimulate progress in building clinically useful ML tools for EHR data.
| S-EPMC7727385 | biostudies-literature
Project description:BackgroundWhole-exome sequencing (WES) is a popular next-generation sequencing technology used by numerous laboratories with various levels of statistical and analytical expertise. Centralized databases, such as the Sequence Read Archive and the European Nucleotide Archive, allow data to be reanalyzed by independent labs to confirm results and derive additional insights. Access to new and shared data highlights the necessity for software that both lowers the statistical and analytical expertise required to generate results and promotes reproducible methodology among laboratories.FindingsWe have developed fastq2vcf, a pipeline that automates the genomic variant calling process using multiple callers. Fastq2vcf offers improved flexibility, efficiency, and reproducibility by seamlessly integrating several leading sequencing analysis tools. It outputs not only the annotated variant call set for each caller, but also the consensus variant call set shared by different callers. Furthermore, it can be customized and extended easily.ConclusionsOur software tool automatically generates executable command lines for a variety of tools required for analyzing WES data. It is also highly configurable and provides users with complete control of the processing procedure, making it easy to submit and track jobs in both single workstation and parallelized computing environments. By using this pipeline, WES analysis can be easily reproduced.
| S-EPMC4376134 | biostudies-literature
Project description:We present an easy-to-use, open-source Optimised Exome analysis tool, OpEx (http://icr.ac.uk/opex) that accurately detects small-scale variation, including indels, to clinical standards. We evaluated OpEx performance with an experimentally validated dataset (the ICR142 NGS validation series), a large 1000 exome dataset (the ICR1000 UK exome series), and a clinical proband-parent trio dataset. The performance of OpEx for high-quality base substitutions and short indels in both small and large datasets is excellent, with overall sensitivity of 95%, specificity of 97% and low false detection rate (FDR) of 3%. Depending on the individual performance requirements the OpEx output allows one to optimise the inevitable trade-offs between sensitivity and specificity. For example, in the clinical setting one could permit a higher FDR and lower specificity to maximise sensitivity. In contexts where experimental validation is not possible, minimising the FDR and improving specificity may be a preferable trade-off for slightly lower sensitivity. OpEx is simple to install and use; the whole pipeline is run from a single command. OpEx is therefore well suited to the increasing research and clinical laboratories undertaking exome sequencing, particularly those without in-house dedicated bioinformatics expertise.
| S-EPMC4971567 | biostudies-literature
Project description:Objective:Reaching a genetic diagnosis of mitochondrial disorders (MDs) is challenging due to their broad phenotypic and genotypic heterogeneity. However, there is growing evidence that the use of whole exome sequencing (WES) for diagnosing patients with a clinical suspicion of an MD is effective (39-60%). We aimed to study the effectiveness of WES in clinical practice in Estonia, in patients with an unsolved, but suspected MD. We also show our first results of mtDNA analysis obtained from standard WES reads. Methods:Retrospective cases were selected from a database of 181 patients whose fibroblast cell cultures had been stored from 2003 to 2013. Prospective cases were selected during the period of 2014-2016 from patients referred to a clinical geneticist in whom an MD was suspected. We scored each patient according to the mitochondrial disease criteria (MDC) (Morava et al., 2006) after re-evaluation of their clinical data, and then performed WES analysis. Results:A total of 28 patients were selected to the study group. A disease-causing variant was found in 16 patients (57%) using WES. An MD was diagnosed in four patients (14%), with variants in the SLC25A4, POLG, SPATA5, and NDUFB11 genes. Other variants found were associated with a neuromuscular disease (SMN1, MYH2, and LMNA genes), neurodegenerative disorder (TSPOAP1, CACNA1A, ALS2, and SCN2A genes), multisystemic disease (EPG5, NKX1-2, ATRX, and ABCC6 genes), and one in an isolated cardiomyopathy causing gene (MYBPC3). The mtDNA point mutation was found in the MT-ATP6 gene of one patient upon mtDNA analysis. Conclusions:The diagnostic yield of WES in our cohort was 57%, proving to be a very good effectiveness. However, MDs were found in only 14% of the patients. We suggest WES analysis as a first-tier method in clinical genetic practice for children with any multisystem, neurological, and/or neuromuscular problem, as nuclear DNA variants are more common in children with MDs; a large number of patients harbor disease-causing variants in genes other than the mitochondria-related ones, and the clinical presentation might not always point towards an MD. We have also successfully conducted analysis of mtDNA from standard WES reads, providing further evidence that this method could be routinely used in the future.
| S-EPMC6043467 | biostudies-literature
Project description:The clinical relevance of the bidirectional cross-talk between heart and kidney is increasingly recognized. However, the optimal approach to the management of kidney dysfunction in heart failure remains unclear. The purpose of this article is to outline the most plausible pathophysiologic theories that attempt to explain the renal impairment in acute and chronic heart failure, and to review the current treatment strategies for these situations.
| S-EPMC3108192 | biostudies-other
Project description:The use of 3C-based methods has revealed the importance of the 3D organization of the chromatin for key aspects of genome biology. However, the different caveats of the variants of 3C techniques have limited their scope and the range of scientific fields that could benefit from these approaches. To address these limitations, we present 4Cin, a method to generate 3D models and derive virtual Hi-C (vHi-C) heat maps of genomic loci based on 4C-seq or any kind of 4C-seq-like data, such as those derived from NG Capture-C. 3D genome organization is determined by integrative consideration of the spatial distances derived from as few as four 4C-seq experiments. The 3D models obtained from 4C-seq data, together with their associated vHi-C maps, allow the inference of all chromosomal contacts within a given genomic region, facilitating the identification of Topological Associating Domains (TAD) boundaries. Thus, 4Cin offers a much cheaper, accessible and versatile alternative to other available techniques while providing a comprehensive 3D topological profiling. By studying TAD modifications in genomic structural variants associated to disease phenotypes and performing cross-species evolutionary comparisons of 3D chromatin structures in a quantitative manner, we demonstrate the broad potential and novel range of applications of our method.
| S-EPMC5862518 | biostudies-literature
Project description:BackgroundWerner syndrome (WS) is an autosomal recessive progeroid syndrome caused by variants in WRN. The International Registry of Werner Syndrome has identified biallelic pathogenic variants in 179/188 cases of classical WS. In the remaining nine cases, only one heterozygous pathogenic variant has been identified.MethodsTargeted long-read sequencing (T-LRS) on an Oxford Nanopore platform was used to search for a second pathogenic variant in WRN. Previously, T-LRS was successfully used to identify missing variants and analyse complex rearrangements.ResultsWe identified a second pathogenic variant in eight of nine unsolved WS cases. In five cases, T-LRS identified intronic splice variants that were confirmed by either RT-PCR or exon trapping to affect splicing; in one case, T-LRS identified a 339 kbp deletion, and in two cases, pathogenic missense variants. Phasing of long reads predicted all newly identified variants were on a different haplotype than the previously known variant. Finally, in one case, RT-PCR previously identified skipping of exon 20; however, T-LRS did not detect a pathogenic DNA sequence variant.ConclusionT-LRS is an effective method for identifying missing pathogenic variants. Although limitations with computational prediction algorithms can hinder the interpretation of variants, T-LRS is particularly effective in identifying intronic variants.
| S-EPMC9613861 | biostudies-literature
Project description:Background Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. Findings zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution. Conclusions zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data.
2018-06-22 | GSE99822 | GEO
Project description:Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.
| S-EPMC3617196 | biostudies-literature