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Опубліковано: 11 вересня 2023

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Статті в журналах з теми "EXOME SEQUENCING DATA"

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Liu, Pengfei, Linyan Meng, Elizabeth A. Normand, Fan Xia, Xiaofei Song, Andrew Ghazi, Jill Rosenfeld, et al. "Reanalysis of Clinical Exome Sequencing Data." New England Journal of Medicine 380, no. 25 (June 20, 2019): 2478–80. http://dx.doi.org/10.1056/nejmc1812033.

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Gonsalves, Stephen G., David Ng, Jennifer J. Johnston, Jamie K. Teer, Peter D. Stenson, David N. Cooper, James C. Mullikin, and Leslie G. Biesecker. "Using Exome Data to Identify Malignant Hyperthermia Susceptibility Mutations." Anesthesiology 119, no. 5 (November 1, 2013): 1043–53. http://dx.doi.org/10.1097/aln.0b013e3182a8a8e7.

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Abstract Background: Malignant hyperthermia susceptibility (MHS) is a life-threatening, inherited disorder of muscle calcium metabolism, triggered by anesthetics and depolarizing muscle relaxants. An unselected cohort was screened for MHS mutations using exome sequencing. The aim of this study was to pilot a strategy for the RYR1 and CACNA1S genes. Methods: Exome sequencing was performed on 870 volunteers not ascertained for MHS. Variants in RYR1 and CACNA1S were annotated using an algorithm that filtered results based on mutation type, frequency, and information in mutation databases. Variants were scored on a six-point pathogenicity scale. Medical histories and pedigrees were reviewed for malignant hyperthermia and related disorders. Results: The authors identified 70 RYR1 and 53 CACNA1S variants among 870 exomes. Sixty-three RYR1 and 41 CACNA1S variants passed the quality and frequency metrics but the authors excluded synonymous variants. In RYR1, the authors identified 65 missense mutations, one nonsense, two that affected splicing, and one non–frameshift indel. In CACNA1S, 48 missense, one frameshift deletion, one splicing, and one non–frameshift indel were identified. RYR1 variants predicted to be pathogenic for MHS were found in three participants without medical or family histories of MHS. Numerous variants, previously described as pathogenic in mutation databases, were reclassified by the authors as being of unknown pathogenicity. Conclusions: Exome sequencing can identify asymptomatic patients at risk for MHS, although the interpretation of exome variants can be challenging. The use of exome sequencing in unselected cohorts is an important tool to understand the prevalence and penetrance of MHS, a critical challenge for the field.
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Hatzis, C. "Bioinformatics analysis pipeline for exome sequencing data." AACR Education book 2014, no. 1 (April 4, 2014): 131–34. http://dx.doi.org/10.1158/aacr.edb-14-6406.

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De Filippo, MR, G. Giurato, C. Cantarella, F. Rizzo, F. Cirillo, and A. Weisz. "Development of pipeline for exome sequencing data analysis." EMBnet.journal 18, A (April 29, 2012): 98. http://dx.doi.org/10.14806/ej.18.a.438.

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Romanel, Alessandro, Tuo Zhang, Olivier Elemento, and Francesca Demichelis. "EthSEQ: ethnicity annotation from whole exome sequencing data." Bioinformatics 33, no. 15 (March 27, 2017): 2402–4. http://dx.doi.org/10.1093/bioinformatics/btx165.

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Meng, Jia, Xiaodong Cui, Manjeet K. Rao, Yidong Chen, and Yufei Huang. "Exome-based analysis for RNA epigenome sequencing data." Bioinformatics 29, no. 12 (April 14, 2013): 1565–67. http://dx.doi.org/10.1093/bioinformatics/btt171.

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Samuels, David C., Leng Han, Jiang Li, Sheng Quanghu, Travis A. Clark, Yu Shyr, and Yan Guo. "Finding the lost treasures in exome sequencing data." Trends in Genetics 29, no. 10 (October 2013): 593–99. http://dx.doi.org/10.1016/j.tig.2013.07.006.

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Vosberg, Sebastian, Luise Hartmann, Stephanie Schneider, Klaus H. Metzeler, Bianka Ksienzyk, Kathrin Bräundl, Martin Neumann, et al. "Detection of Chromosomal Aberrations in Acute Myeloid Leukemia By Copy Number Alteration Analysis of Exome Sequencing Data." Blood 126, no. 23 (December 3, 2015): 3859. http://dx.doi.org/10.1182/blood.v126.23.3859.3859.

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Abstract Exome sequencing is widely used and established to detect tumor-specific sequence variants such as point mutations and small insertions/deletions. Beyond single nucleotide resolution, sequencing data can also be used to identify changes in sequence coverage between samples enabling the detection of copy number alterations (CNAs). Somatic CNAs represent gain or loss of genomic material in tumor cells like aneuploidies (e.g. monosomies and trisomies), duplications, or deletions. In order to test the feasibility of somatic CNA detection from exome data, we analyzed 13 acute myeloid leukemia (AML) patients with known cytogenetic alterations detected at diagnosis (n=8) and/or at relapse (n=11). Corresponding remission exomes from all patients were available as germline controls resulting in 19 comparisons of paired leukemia and remission exome data sets. Exome sequencing was performed on a HiSeq 2500 instrument (Illumina) with mean target coverage of >100x. Exons with divergent coverage were detected using a linear regression model on mean exon coverage, and CNAs were called by an exact segmentation algorithm (Rigaill et al. 2012, Bioinformatics). For all samples, cytogenetic information was available either form routine chromosomal analysis or fluorescent in situ hybridization (FISH). Blast count were known for all but one AML sample (n=19). Copy number-neutral cytogenetic alterations such as balanced translocations were excluded from the comparative analysis. By CNA-analysis of exomes we were able to detect chromosomal aberrations consistent with routine cytogenetics in 18 out of 19 (95%) AML samples. In particular, we confirmed 2 out of 2 monosomies (both -7), and 9 out of 10 trisomies (+4, n=1; +8, n=8; +21, n=1), e.g. trisomy 8 in figure 1A. Partial amplifications or deletions of chromosomes were confirmed in 10 out of 10 AML samples (dup(1q), n=3; dup(8q), n=1; del(5q), n=3; del(17p), n=1; del(20q), n=2), e.g. del(5q) in figure 1B. In the one case with inconsistent findings of chromosomal aberrations between exome and cytogenetic data there was a small subclone harboring the alteration described in only 4 out of 21 metaphases (19%). To assess the specificity of our CNA approach, we analyzed the exomes of 44 cytogenetically normal (CN) AML samples. Here we did not detect any CNAs larger than 5 Mb in the vast majority of these samples (43/44, 98%), only one large CNA was detected indicating a trisomy 8. Estimates of the clone size were highly correlated between CNA-analysis of exomes and the parameters from cytogenetics and cytomorphology (p=0.0076, Fisher's exact test, Figure 1C). In CNA-analysis of exomes, we defined the clone size based on the coverage ratio: . Clone size estimation by cytogenetics and cytomorphology was performed by calculating the mean of blast count and abnormal metaphase/interphase count. Of note, clones estimated by CNA-analysis of exomes tended to be slightly larger. This may result from purification by Ficoll gradient centrifugation prior to DNA extraction for sequencing and/or the fact that the fraction of cells analyzed by cytogenetics does not represent the true size of the malignant clone accurately because of differences in the mitotic index between normal and malignant cells. Overall, there was a high correlation between our CNA analysis of exome sequencing data and routine cytogenetics including limitations in the detection of small subclones. Our results confirm that high throughput sequencing is a versatile, valuable, and robust method to detect chromosomal changes resulting in copy number alterations in AML with high specificity and sensitivity (98% and 95%, respectively). Figure 1. (A) Detection of trisomy 8 with an estimated clone size of 100% (B) Detection of deletion on chromosome 5q with an estimated clone size of 90% (C) Correlation of clone size estimation by routine diagnostics and exome sequencing (p=0.0076) Figure 1. (A) Detection of trisomy 8 with an estimated clone size of 100%. / (B) Detection of deletion on chromosome 5q with an estimated clone size of 90%. / (C) Correlation of clone size estimation by routine diagnostics and exome sequencing (p=0.0076) Figure 2. Figure 2. Disclosures No relevant conflicts of interest to declare.
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Hintzsche, Jennifer D., William A. Robinson, and Aik Choon Tan. "A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data." International Journal of Genomics 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/7983236.

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Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.
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Valdés-Mas, Rafael, Silvia Bea, Diana A. Puente, Carlos López-Otín, and Xose S. Puente. "Estimation of Copy Number Alterations from Exome Sequencing Data." PLoS ONE 7, no. 12 (December 19, 2012): e51422. http://dx.doi.org/10.1371/journal.pone.0051422.

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Більше джерел

Дисертації з теми "EXOME SEQUENCING DATA"

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Sigurgeirsson, Benjamín. "Analysis of RNA and DNA sequencing data : Improved bioinformatics applications." Doctoral thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184158.

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Massively parallel sequencing has rapidly revolutionized DNA and RNA research. Sample preparations are steadfastly advancing, sequencing costs have plummeted and throughput is ever growing. This progress has resulted in exponential growth in data generation with a corresponding demand for bioinformatic solutions. This thesis addresses methodological aspects of this sequencing revolution and applies it to selected biological topics. Papers I and II are technical in nature and concern sample preparation and data anal- ysis of RNA sequencing data. Paper I is focused on RNA degradation and paper II on generating strand specific RNA-seq libraries. Paper III and IV deal with current biological issues. In paper III, whole exomes of cancer patients undergoing chemotherapy are sequenced and their genetic variants associ- ated to their toxicity induced adverse drug reactions. In paper IV a comprehensive view of the gene expression of the endometrium is assessed from two time points of the menstrual cycle. Together these papers show relevant aspects of contemporary sequencing technologies and how it can be applied to diverse biological topics.

QC 20160329

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Zhang, Lu, and 张璐. "Identification and prioritization of single nucleotide variation for Mendelian disorders from whole exome sequencing data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521905.

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With the completion of human genome sequencing project and the rapid development of sequencing technologies, our capacity in tackling with genetic and genomic changes that underlie human diseases has never been greater. The recent successes in identifying disease causal single nucleotide variations (SNVs) for Mendelian disorders using whole exome sequencing may bring us one step further to understand the pathogenesis of Mendelian diseases. However, many hurdles need to be overcome before the promises can become widespread reality. In this study, we investigated various strategies and designed a toolkit named PriSNV for SNV identification and prioritization, respectively. The SNV identification pipeline including read alignment, PCR duplication removal, indel realignment, base quality score recalibration, SNV and genotype calling was examined by simulation and real sequencing data. By incorporating sequencing errors and small indels, most of the read alignment software can achieve satisfied results. Nonetheless, the reads with medium size and large indels are prone to be wrongly mapped to the reference genome due to the limitation of gap opening strategies of available read alignment software. In addition, although mapping quality can only reflect certain information of the mapping error rate, it is still important to be adopted to filter out obvious read alignment errors. The PCR duplication removal, indel realignment and base quality score recalibration have proven to be necessary and can substantially reduce the false positive SNV calls. Based on the same quality criterion, Varscan performs as the most sensitive software for SNV calling, unfortunately at mean time the false positive calls are enriched in its result. In order to prioritize the small subset of functionally important variants from tens of thousands of variants in whole human exome, we developed a toolkit called PriSNV, a systematic prioritization pipeline that makes use of information on variant quality, gene candidacy based on the number of novel nonsynonymous mutations in a gene, gene functional annotation, known involvement in the disease or relevant pathways, and location in linkage regions. Prediction of functional impact of the coding variants is also used to aid the search for causal mutations in Mendelian disorders. For the patient affected by Chron's disease, the candidate genes can be substantially reduced from 9615 to 3 by the gene selection strategies implemented in PriSNV. In general, our results for SNV identification can help the biologists to realize the limitation of available software and shed light on the development of new strategies for accurately identifying SNV calls in the future. PriSNV, the software we developed for SNV prioritization, can provide significant help to biologists in prioritizing SNV calls in a systematic way and reducing search space for further analysis and experimental verification.
published_or_final_version
Paediatrics and Adolescent Medicine
Master
Master of Philosophy
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Carraro, Marco. "Development of bioinformatics tools to predict disease predisposition from Next Generation Sequencing (NGS) data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426807.

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The sequencing of the human genome has opened up completely new avenues in research and the notion of personalized medicine has become common. DNA Sequencing technology has evolved by several orders of magnitude, coming into the range of $1,000 for a complete human genome. The promise of identifying genetic variants that influence our lifestyles and make us susceptible to diseases is now becoming reality. However, genome interpretation remains one the most challenging problems of modern biology. The focus of my PhD project is the development of bioinformatics tools to predict diseases predisposition from sequencing data. Several of these methods have been tested in the context of the Critical Assessment of Genome Interpretation (CAGI), always achieving good prediction performances. During my PhD project I faced the complete spectrum of challenges to be address in order to translate the sequencing revolution into clinical practice. One of the biggest problem when dealing with sequencing data is the interpretation of variants pathogenic effect. Dozens of bioinformatics tools have been created to separate mutations that could be involved in a pathogenic phenotype from neutral variants. In this context the problem of benchmarking is critical, as prediction performance are usually tested on different sets of variants, making the comparison among these tools impossible. To address this problem I performed a blinded comparison of pathogenicity predictors in the context of CAGI, realizing the most complete performance assessment among all the iterations of this collaborative experiment. Another challenge that needs to be address to realize the personalized medicine revolution is the phenotype prediction. During my PhD I had the opportunity to develop several methods for the complex phenotype prediction from targeted enrichment and exome sequencing data. In this context challenges like misinterpretation or overinterpretation of variants pathogenicity have emerged, like in the case of phenotype prediction from the Hopkins Clinical Panel. In addition, other complementary issues of phenotype predictions, like the possible presence of incidental findings have to be considered. Ad hoc prediction strategies have been defined while facing with different kinds of sequencing data. A clear example is the case of Crohn’s disease risk prediction. Always in the context of the CAGI experiment, three iterations of this prediction challenge have been run so far. Analysis of datasets revealed how population structure and bias in data preparation and sequencing could affect prediction performance, leading to inflated results. For this reason a completely new prediction strategy has been defined for the last edition of the Crohn’s disease challenge, exploiting data from Genome Wide Association Studies and Protein Protein Interaction network, to address the problem of missing heritability. Good prediction performance have been achieved, especially for individuals with an extreme predicted risk score. Last, my work has been focused on the prediction of a health related trait: the blood group phenotype. The accuracy of serological tests is very poor for minor blood groups or weak phenotypes. Blood groups incompatibilities can be harmful for critical individuals like oncohematological patients. BOOGIE exploits haplotype tables, and the nearest neighbor algorithm to identify the correct phenotype of a patient. The accuracy of our method has been tested in ABO and RhD systems achieving good results. In addition, our analyses paved the way for a further increase in performance, moving towards a prediction system that in the future could become a real alternative to wet lab experiments.
Il completamento del progetto genoma umano ha aperto numerosi nuovi orizzonti di ricerca. Tra questi, la possibilità di conoscere le basi genetiche che rendono ogni individuo suscettibile alle diverse malattie ha aperto la strada ad una nuova rivoluzione: l’avvento della medicina personalizzata. Le tecnologie di sequenziamento del DNA hanno subito una notevole evoluzione, ed oggi il prezzo per sequenziare un genoma è ormai prossimo alla soglia psicologica dei $ 1 000. La promessa di identificare varianti genetiche che influenzano il nostro stile di vita e che ci rendono suscettibili alle malattie sta quindi diventando realtà. Tuttavia, molto lavoro è ancora necessario perché questo nuovo tipo di medicina possa trasformarsi in realtà. In particolare la sfida oggi non è più data dalla generazione dei dati di sequenziamento, ma è rappresentata invece dalla loro interpretazione. L'obiettivo del mio progetto di dottorato è lo sviluppo di metodi bioinformatici per predire la predisposizione a patologie, a partire da dati di sequenziamento. Molti di questi metodi sono stati testati nel contesto del Critical Assessment of Genome Interpretation (CAGI), una competizione internazionale focalizzata nel definire lo stato dell’arte per l’interpretazione del genoma, ottenendo sempre buoni risultati. Durante il mio progetto di dottorato ho avuto l'opportunità di affrontare l’intero spettro delle sfide che devono essere gestite per tradurre le nuove capacità di sequenziamento del genoma in pratica clinica. Uno dei problemi principali che si devono gestire quando si ha a che fare con dati di sequenziamento è l'interpretazione della patogenicità delle mutazioni. Decine di predittori sono stati creati per separare varianti neutrali dalle mutazioni che possono essere causa di un fenotipo patologico. In questo contesto il problema del benchmarking è fondamentale, in quanto le prestazioni di questi tool sono di solito testate su diversi dataset di varianti, rendendo impossibile un confronto di performance. Per affrontare questo problema, una comparazione dell’accuratezza di questi predittori è stata effettuata su un set di mutazioni con fenotipo ignoto nel contesto del CAGI, realizzando la valutazione per predittori di patogenicità più completa tra tutte le edizioni di questo esperimento collaborativo. La previsione di fenotipi a partire da dati di sequenziamento è un'altra sfida che deve essere affrontata per realizzare le promesse della medicina personalizzata. Durante il mio dottorato ho avuto l'opportunità di sviluppare diversi predittori per fenotipi complessi utilizzando dati provenienti da pannelli genici ed esomi. In questo contesto sono stati affrontati problemi come errori di interpretazione o la sovra interpretazione della patogenicità della varianti, come nel caso della sfida focalizzata sulla predizione di fenotipi a partire dall’Hopkins Clinical Panel. Sono inoltre emersi altri problemi complementari alla previsione di fenotipo, come per esempio la possibile presenza di risultati accidentali. Specifiche strategie di predizione sono state definite lavorando con diversi tipi di dati di sequenziamento. Un esempio è dato dal morbo di Crohn. Tre edizioni del CAGI hanno proposto la sfida di identificare individui sani o affetti da questa patologia infiammatoria utilizzando unicamente dati di sequenziamento dell’esoma. L'analisi dei dataset ha rivelato come la presenza di struttura di popolazione e problemi nella preparazione e sequenziamento degli esomi abbiano compromesso le predizioni per questo fenotipo, generando una sovrastima delle performance di predizione. Tenendo in considerazione questo dato è stata definita una strategia di predizione completamente nuova per questo fenotipo, testata in occasione dell'ultima edizione del CAGI. Dati provenienti da studi di associazione GWAS e l’analisi delle reti di interazione proteica sono stati utilizzati per definire liste di geni coinvolti nell’insorgenza della malattia. Buone performance di predizione sono state ottenute in particolare per gli individui a cui era stata assegnata una elevata probabilità di essere affetti. In ultima istanza, il mio lavoro è stato focalizzato sulla predizione di gruppi sanguigni, sempre a partire da dati di sequenziamento. L'accuratezza dei test sierologici, infatti, è ridotta in caso di gruppi di sangue minori o fenotipi deboli. Incompatibilità per tali gruppi sanguigni possono essere critiche per alcune classi di individui, come nel caso dei pazienti oncoematologici. La nostra strategia di predizione ha sfruttato i dati genotipici per geni che codificano per gruppi sanguigni, presenti in database dedicati, e il principio di nearest neighbour per effettuare le predizioni. L’accuratezza del nostro metodo è stata testata sui sistemi ABO e RhD ottenendo buone performance di predizione. Inoltre le nostre analisi hanno aperto la strada ad un ulteriore aumento delle prestazioni per questo tool.
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Fewings, Eleanor Rose. "The use of whole exome sequencing data to identify candidate genes involved in cancer and benign tumour predisposition." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/285963.

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The development of whole exome sequencing has transformed the study of disease predisposition. The sequencing of both large disease sets and smaller rare disease families enables the identification of new predisposition variants and potentially provide clinical insight into disease management. There is no standard protocol for analysing exome sequencing data. Outside of extremely large sequencing studies including thousands of individuals, statistical approaches are often underpowered to detect rare disease associated variants. Aggregation of variants into functionally related regions, including genes, gene clusters, and pathways, allows for the detection of biological processes that, when interrupted, may impact disease risk. In silico functional studies can also be utilised to further understand how variants disrupt biological processes and identify genotype-phenotype relationships. This study describes the exploration of sequencing datasets from cancers and benign tumour diseases including: i) hereditary diffuse gastric cancer, ii) sweat duct proliferation tumours, iii) adrenocortical carcinoma, and iv) breast cancer. Each set underwent germline whole exome sequencing followed by additional tumour or targeted sequencing to identify associated predisposition genes. Variants within a cluster of risk genes that are involved in double strand break repair were identified as associated with hereditary diffuse gastric cancer risk via gene ontology enrichment analysis. This cluster included PALB2 within which, using externally collated data, loss of function variants were identified as significantly associated with hereditary diffuse gastric cancer risk. Germline protein-affecting variants in the myosin gene MYH9 were identified in all individuals with a rare sweat duct proliferative syndrome, suggesting a role for MYH9 in skin development, regulation and tumorigenesis. These MYH9 variants were analysed in silico to identify a genotype-phenotype relationship between the clinical presentation and variants in the ATP binding pocket of the protein. Tumour matched normal sequence data from adrenocortical carcinoma cases was used to elucidate the role of Lynch syndrome genes in disease pathogenesis. Within the breast cancer set, candidate genes were selected to undergo targeted sequencing in a larger set of cases to further explore their role in breast cancer risk. Risk associated genes identified within this study may ultimately aid in diagnosis and management of disease. This thesis has also generated multiple novel tools and sequencing analysis techniques that may be of use for further studies by aiding in the prioritisation of candidate variants. The described techniques will provide support to researchers working on rare, statistically underpowered datasets and to provide standard analysis pipelines for a range of dataset sizes and types, including familial data and unrelated individuals.
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Chennen, Kirsley. "Maladies rares et "Big Data" : solutions bioinformatiques vers une analyse guidée par les connaissances : applications aux ciliopathies." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAJ076/document.

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Au cours de la dernière décennie, la recherche biomédicale et la pratique médicale ont été révolutionné par l'ère post-génomique et l'émergence des « Big Data » en biologie. Il existe toutefois, le cas particulier des maladies rares caractérisées par la rareté, allant de l’effectif des patients jusqu'aux connaissances sur le domaine. Néanmoins, les maladies rares représentent un réel intérêt, car les connaissances fondamentales accumulées en temps que modèle d'études et les solutions thérapeutique qui en découlent peuvent également bénéficier à des maladies plus communes. Cette thèse porte sur le développement de nouvelles solutions bioinformatiques, intégrant des données Big Data et des approches guidées par la connaissance pour améliorer l'étude des maladies rares. En particulier, mon travail a permis (i) la création de PubAthena, un outil de criblage de la littérature pour la recommandation de nouvelles publications pertinentes, (ii) le développement d'un outil pour l'analyse de données exomique, VarScrut, qui combine des connaissance multiniveaux pour améliorer le taux de résolution
Over the last decade, biomedical research and medical practice have been revolutionized by the post-genomic era and the emergence of Big Data in biology. The field of rare diseases, are characterized by scarcity from the patient to the domain knowledge. Nevertheless, rare diseases represent a real interest as the fundamental knowledge accumulated as well as the developed therapeutic solutions can also benefit to common underlying disorders. This thesis focuses on the development of new bioinformatics solutions, integrating Big Data and Big Data associated approaches to improve the study of rare diseases. In particular, my work resulted in (i) the creation of PubAthena, a tool for the recommendation of relevant literature updates, (ii) the development of a tool for the analysis of exome datasets, VarScrut, which combines multi-level knowledge to improve the resolution rate
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Chakrabortty, Sharmistha. "SNPs and Indels Analysis in Human Genome using Computer Simulation and Sequencing Data." University of Toledo Health Science Campus / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=mco1501726874739045.

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Bertoldi, Loris. "Bioinformatics for personal genomics: development and application of bioinformatic procedures for the analysis of genomic data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421950.

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In the last decade, the huge decreasing of sequencing cost due to the development of high-throughput technologies completely changed the way for approaching the genetic problems. In particular, whole exome and whole genome sequencing are contributing to the extraordinary progress in the study of human variants opening up new perspectives in personalized medicine. Being a relatively new and fast developing field, appropriate tools and specialized knowledge are required for an efficient data production and analysis. In line with the times, in 2014, the University of Padua funded the BioInfoGen Strategic Project with the goal of developing technology and expertise in bioinformatics and molecular biology applied to personal genomics. The aim of my PhD was to contribute to this challenge by implementing a series of innovative tools and by applying them for investigating and possibly solving the case studies included into the project. I firstly developed an automated pipeline for dealing with Illumina data, able to sequentially perform each step necessary for passing from raw reads to somatic or germline variant detection. The system performance has been tested by means of internal controls and by its application on a cohort of patients affected by gastric cancer, obtaining interesting results. Once variants are called, they have to be annotated in order to define their properties such as the position at transcript and protein level, the impact on protein sequence, the pathogenicity and more. As most of the publicly available annotators were affected by systematic errors causing a low consistency in the final annotation, I implemented VarPred, a new tool for variant annotation, which guarantees the best accuracy (>99%) compared to the state-of-the-art programs, showing also good processing times. To make easy the use of VarPred, I equipped it with an intuitive web interface, that allows not only a graphical result evaluation, but also a simple filtration strategy. Furthermore, for a valuable user-driven prioritization of human genetic variations, I developed QueryOR, a web platform suitable for searching among known candidate genes as well as for finding novel gene-disease associations. QueryOR combines several innovative features that make it comprehensive, flexible and easy to use. The prioritization is achieved by a global positive selection process that promotes the emergence of the most reliable variants, rather than filtering out those not satisfying the applied criteria. QueryOR has been used to analyze the two case studies framed within the BioInfoGen project. In particular, it allowed to detect causative variants in patients affected by lysosomal storage diseases, highlighting also the efficacy of the designed sequencing panel. On the other hand, QueryOR simplified the recognition of LRP2 gene as possible candidate to explain such subjects with a Dent disease-like phenotype, but with no mutation in the previously identified disease-associated genes, CLCN5 and OCRL. As final corollary, an extensive analysis over recurrent exome variants was performed, showing that their origin can be mainly explained by inaccuracies in the reference genome, including misassembled regions and uncorrected bases, rather than by platform specific errors.
Nell’ultimo decennio, l’enorme diminuzione del costo del sequenziamento dovuto allo sviluppo di tecnologie ad alto rendimento ha completamente rivoluzionato il modo di approcciare i problemi genetici. In particolare, il sequenziamento dell’intero esoma e dell’intero genoma stanno contribuendo ad un progresso straordinario nello studio delle varianti genetiche umane, aprendo nuove prospettive nella medicina personalizzata. Essendo un campo relativamente nuovo e in rapido sviluppo, strumenti appropriati e conoscenze specializzate sono richieste per un’efficiente produzione e analisi dei dati. Per rimanere al passo con i tempi, nel 2014, l’Università degli Studi di Padova ha finanziato il progetto strategico BioInfoGen con l’obiettivo di sviluppare tecnologie e competenze nella bioinformatica e nella biologia molecolare applicate alla genomica personalizzata. Lo scopo del mio dottorato è stato quello di contribuire a questa sfida, implementando una serie di strumenti innovativi, al fine di applicarli per investigare e possibilmente risolvere i casi studio inclusi all’interno del progetto. Inizialmente ho sviluppato una pipeline per analizzare i dati Illumina, capace di eseguire in sequenza tutti i processi necessari per passare dai dati grezzi alla scoperta delle varianti sia germinali che somatiche. Le prestazioni del sistema sono state testate mediante controlli interni e tramite la sua applicazione su un gruppo di pazienti affetti da tumore gastrico, ottenendo risultati interessanti. Dopo essere state chiamate, le varianti devono essere annotate al fine di definire alcune loro proprietà come la posizione a livello del trascritto e della proteina, l’impatto sulla sequenza proteica, la patogenicità, ecc. Poiché la maggior parte degli annotatori disponibili presentavano errori sistematici che causavano una bassa coerenza nell’annotazione finale, ho implementato VarPred, un nuovo strumento per l’annotazione delle varianti, che garantisce la migliore accuratezza (>99%) comparato con lo stato dell’arte, mostrando allo stesso tempo buoni tempi di esecuzione. Per facilitare l’utilizzo di VarPred, ho sviluppato un’interfaccia web molto intuitiva, che permette non solo la visualizzazione grafica dei risultati, ma anche una semplice strategia di filtraggio. Inoltre, per un’efficace prioritizzazione mediata dall’utente delle varianti umane, ho sviluppato QueryOR, una piattaforma web adatta alla ricerca all’interno dei geni causativi, ma utile anche per trovare nuove associazioni gene-malattia. QueryOR combina svariate caratteristiche innovative che lo rendono comprensivo, flessibile e facile da usare. La prioritizzazione è raggiunta tramite un processo di selezione positiva che fa emergere le varianti maggiormente significative, piuttosto che filtrare quelle che non soddisfano i criteri imposti. QueryOR è stato usato per analizzare i due casi studio inclusi all’interno del progetto BioInfoGen. In particolare, ha permesso di scoprire le varianti causative dei pazienti affetti da malattie da accumulo lisosomiale, evidenziando inoltre l’efficacia del pannello di sequenziamento sviluppato. Dall’altro lato invece QueryOR ha semplificato l’individuazione del gene LRP2 come possibile candidato per spiegare i soggetti con un fenotipo simile alla malattia di Dent, ma senza alcuna mutazione nei due geni precedentemente descritti come causativi, CLCN5 e OCRL. Come corollario finale, è stata effettuata un’analisi estensiva su varianti esomiche ricorrenti, mostrando come la loro origine possa essere principalmente spiegata da imprecisioni nel genoma di riferimento, tra cui regioni mal assemblate e basi non corrette, piuttosto che da errori piattaforma-specifici.
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8

Hsieh, PingHsun. "Model-Based Population Genetics in Indigenous Humans: Inferences of Demographic History, Adaptive Selection, and African Archaic Admixture using Whole-Genome/Exome Sequencing Data." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612540.

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Reconstructing the origins and evolutionary journey of humans is a central piece of biology. Complementary to archeology, population genetics studying genetic variation among individuals in extant populations has made considerable progress in understanding the evolution of our species. Particularly, studies in indigenous humans provide valuable insights on the prehistory of humans because their life history closely resembles that of our ancestors. Despite these efforts, it can be difficult to disentangle population genetic inferences because of the interplay among evolutionary forces, including mutation, recombination, selection, and demographic processes. To date, few studies have adopted a comprehensive framework to jointly account for these confounding effects. The shortage of such an approach inspired this dissertation work, which centered on the development of model-based analysis and demonstrated its importance in population genetic inferences. Indigenous African Pygmy hunter-gatherers have been long studied because of interest in their short stature, foraging subsistence strategy in rainforests, and long-term socio-economic relationship with nearby farmers. I proposed detailed demographic models using genomes from seven Western African Pygmies and nine Western African farmers (Appendix A). Statistical evidence was shown for a much deeper divergence than previously thought and for asymmetric migrations with a larger contribution from the farmers to Pygmies. The model-based analyses revealed significant adaption signals in the Pygmies for genes involved in muscle development, bone synthesis, immunity, reproduction, etc. I also showed that the proposed model-based approach is robust to the confounding effects of evolutionary forces (Appendix A). Contrary to the low-latitude African homeland of humans, the indigenous Siberians are long-term survivors inhabiting one of the coldest places on Earth. Leveraging whole exome sequencing data from two Siberian populations, I presented demographic models for these North Asian dwellers that include divergence, isolation, and gene flow (Appendix B). The best-fit models suggested a closer genetic affinity of these Siberians to East Asians than to Europeans. Using the model-based framework, seven NCBI BioSystems gene sets showed significance for polygenic selection in these Siberians. Interestingly, many of these candidate gene sets are heavily related to diet, indicating possible adaptations to special dietary requirements in these populations in cold, resource-limited environments. Finally, I moved beyond studying the history of extant humans to explore the origins of our species in Africa (Appendix C). Specifically, with statistical analyses using genomes only from extant Africans, I rejected the null model of no archaic admixture in Africa and in turn gave the first whole-genome evidence for interbreeding among human species in Africa. Using extensive simulation analyses under various archaic admixture models, the results suggest recurrent admixture between the ancestors of archaic and modern Africans, with evidence that at least one such event occurred in the last 30,000 years in Africa.
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9

Nambot, Sophie. "Exploration pangénomique des anomalies du développement de causes rares." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCI012.

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Titre : Exploration pangénomique des anomalies du développement de causes raresMots clés : anomalies du développement, séquençage d’exome, paratage de données, phénotypage inverseLes anomalies du développement sont un groupe de maladies hétérogènes, tant sur le plan clinique que moléculaire. Elles comprennent plus de 3.000 maladies monogéniques, mais seulement un tiers d’entre elles ont actuellement une cause moléculaire connue. Bien que les progrès des techniques de séquençage aient permis d’identifier des centaines de nouveaux gènes ces dernières années, de nombreux patients restent encore sans diagnostic. La grande hétérogénéité génétique de ces pathologies met à l’épreuve la démarche diagnostique classique comprenant une expertise clinique, une étude pan-génomique par puce à ADN et/ou l’analyse ciblée de gènes connus et, depuis peu, le séquençage haut débit d’exome ciblé sur les gènes associés à une pathologie humaine. En attendant que le séquençage du génome soit économiquement plus accessible et l’interprétaion des ses données mieux appréhendée pour une utilisation diagnostique, nous avons choisi d’explorer de nouvelles stratégies afin d’optimiser le séquençage d’exome dans l’identification de nouvelles bases moléculaires.Le premier article a pour objectif de démontrer la faisabilité et l’efficacité de la réanalyse annuelle des données de séquençage d’exome négatif dans un cadre diagnostique. Les patients éligibles à l’étude présentaient une anomalie du développement sans cause moléculaire établie après une démarche diagnostique classique incluant une analyse chromosomique sur puce à ADN et une analyse d’exome diagnostique. Cette première étude a permis de réaliser un nombre significatif de diagnostics supplémentaires, mais aussi d’identifier des variations candidates pour lesquelles nous avons utilisé le partage international de données et l’approche de phénotypage inverse pour établir des corrélations phénotype-génotype et des cohortes de réplication génotypique et/ou phénotypique. Ces stratégies nous ont permis de remplir les critères ACMG nécessaires pour établir la pathogénicité de ces variations.Fort de cette expérience et souhaitant aller plus loin dans l’identification de nouvelles bases moléculaires pour nos patients, nous avons poursuivi cet effort de réanalyse dans un cadre de recherche. Ce travail fait l’objet du second article de cette thèse et a conduit à l’identification de 17 nouveaux gènes d’anomalies du développement. Le partage de données a conduit à l’élaboration de nombreuses collaborations internationales et de plusieurs études fonctionnelles par des équipes spécialisées.L’application de ces outils dans une forme syndromique de déficience intellectuelle ultra-rare est illustrée à travers le troisieme article. Suite à un effort collaboratif important, nous avons pu décrire de manière précise le phénotype de 25 patients jamais rapportés dans la littérature porteurs de variations pathogènes au sein du gène TBR1, gène candidat dans les troubles du spectre autistique associés à une déficience intellectuelle.Ces différents travaux démontrent l’efficacité de stratégies innovantes dans l’identification de nouvelles bases moléculaires chez les patients atteints d’anomalies du développement, à savoir la réanalyse des données d’exome, le phénotypage inverse et le partage international de données. Pour les patients et leur famille, cela permet de comprendre l’origine de leur pathologie, de mettre fin à l’errance diagnostique, de préciser le pronostic et l’évolution développementale probable, et la mise en place d’une prise en charge adaptés. Il est aussi indispensable pour fournir un conseil génétique fiable, et éventuellement proposer un diagnostic prénatal voire pré-implantatoire. Pour les généticiens, cela permet la compréhension de nouveaux processus physiopathologiques, l’élaboration de nouveaux tests diagnostiques et la découverte de nouvelles cibles thérapeutiques
Title : Genome-wide exploration of congenital anomalies of rare causesKey words : congenital anomalies, exome sequencing, data-sharing, reverse phenotypingCongenital anomalies are a group of diseases that are both clinically and molecularly heterogeneous. They include more than 3,000 monogenic diseases, but only a third of them have a known molecular cause. Although advances in sequencing techniques have identified hundreds of new genes in recent years, many patients remain undiagnosed. The vast genetic heterogeneity of these conditions challenges the conventional diagnostic approach that typically includes clinical expertise, a pan-genomic microarray study and/or targeted analysis of known genes and, recently, exome sequencing targeting the genes already associated with human disease. Until genome sequencing becomes more affordable and the interpretation of its data for diagnostic use is better perceived, we have chosen to explore new strategies to optimize the identification of new molecular bases through exome sequencing.The first article aimed to demonstrate the feasibility and effectiveness of annual reanalysis of negative exome sequencing data in a diagnostic setting. Patients eligible for the study had developmental anomalies, but no molecular cause was established after a standard diagnostic procedure including DNA chromosome analysis and diagnostic exome analysis. This first study yielded a significant number of additional diagnoses, but also identified candidate variants for which we used international data-sharing and reverse phenotyping to establish cohorts of genotypic and/or phenotypic replication and genotype-phenotype correlations. These strategies allowed us to meet the ACMG criteria necessary to establish the pathogenicity of these variants.With this experience, and because we wished to go further in identifying new molecular bases for our patients, we continued the reanalysis project within a research framework. This was the focus of the second article of this thesis. The reanalysis project led to the identification of 17 new genes associated with congenital anomalies. Data-sharing has led to the development of numerous international collaborations and functional studies carried out by specialized teams.The third article illustrated the application of these tools in a syndromic form of ultra-rare intellectual disability. Following a considerable collaborative effort, we were able to accurately describe the phenotype of 25 unreported patients in the literature with pathogenic variants in the TBR1 gene, a candidate gene in autism spectrum disorders associated to intellectual disability.These various studies demonstrate how innovative strategies can be effective for identifying new molecular bases in patients with congenital anomalies. These strategies include exome data reanalysis, reverse phenotyping, and international data-sharing. For patients and their families, knowing the molecular basis of the disease makes it possible to understand the origin of the condition and to put an end to diagnostic wandering. In addition, they are able to learn more about the prognosis and developmental progression, and they can obtain appropriate care management. This information is also essential for reliable genetic counseling, and may offer the possibility of prenatal or even pre-implantation diagnosis. These new diagnoses also give geneticists a chance to understand new physiopathological processes, to develop new diagnostic tests and even to discover new therapeutic targets
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10

GIOVANNETTI, AGNESE. "Analysis of non-coding DNA from whole exome sequencing data." Doctoral thesis, 2019. http://hdl.handle.net/11573/1234470.

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Next Generation Sequencing technologies have completely changed the way to study molecular bases underlying Rare Genetic Diseases (RGDs). Currently, sequencing of the exonic portion of the human genome – the exome (1%) – performed through Whole Exome Sequencing (WES) experiments represents the most used approach to discover molecular mechanisms underlying RGDs. To date, several tools have been developed to analyse and interpret data generated from WES. However, due to both technical and experimental limitations, its diagnostic rate is ~20-30%. In this context, we evaluated whether WES data contain information on non-coding sequences, focusing on microRNAs (miRNAs). Comparative analysis of capture design and experimental coverage allowed to disclose that in WES data reside information related to miRNA sequences that are efficiently captured by most exome enrichment kits. We therefore analysed WES of a cohort of 259 individuals, including patients affected by several genetic diseases and their unaffected relatives, searching for variants in miRNAs and performing functional annotation. Sanger sequence validation confirms the reliable call of variants mapping in miRNA sequences. To date, no dedicated tool is available to properly retrieve and analyse miRNAs from WES and WGS data. We therefore developed a tool, “AnnomiR”, that allows to systematically analyse miRNA variants and miRNAs, providing functional annotation retrieved from several databases. This tool can be integrated in a standard workflow of analysis for WES and WGS data. WES data contain a great amount of information that is generally discarded by commonly used workflow of analysis and that should be considered, as it could help in the comprehension of molecular mechanisms underlying RGDs. In this context, systematic study of miRNAs could help elucidating their role as disease-causative and phenotypic modifiers in a wide spectrum of human diseases, allowing to achieve a better characterisation of variability of the human genome related to these non-coding sequences.
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Частини книг з теми "EXOME SEQUENCING DATA"

1

Wang, Xinkun. "Genotyping and Variation Discovery by Whole Genome/Exome Sequencing." In Next-Generation Sequencing Data Analysis, 215–36. 2nd ed. New York: CRC Press, 2023. http://dx.doi.org/10.1201/9780429329180-13.

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2

Ulintz, Peter J., Weisheng Wu, and Chris M. Gates. "Bioinformatics Analysis of Whole Exome Sequencing Data." In Methods in Molecular Biology, 277–318. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8876-1_21.

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3

Spinelli, Roberta, Rocco Piazza, Alessandra Pirola, Simona Valletta, Roberta Rostagno, Angela Mogavero, Manuela Marega, Hima Raman, and Carlo Gambacorti-Passerini. "Whole-Exome Sequencing Data – Identifying Somatic Mutations." In Springer Handbook of Bio-/Neuroinformatics, 419–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-30574-0_25.

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4

Biradar, Shivaleela, K. M. Kiran Kumar, M. Naveen Kumar, and R. L. Babu. "Identification of Clinical Variants Present in Skin Melanoma Using Exome Sequencing Data." In Learning and Analytics in Intelligent Systems, 85–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46943-6_10.

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5

Liu, Chang, and Xiao Yang. "Using Exome and Amplicon-Based Sequencing Data for High-Resolution HLA Typing with ATHLATES." In Methods in Molecular Biology, 203–13. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8546-3_14.

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Spinelli, Roberta, Rocco Piazza, Alessandra Pirola, Simona Valletta, Roberta Rostagno, Angela Mogavero, Manuela Marega, Hima Raman, and Carlo Gambacorti-Passerini. "A Bioinformatics Procedure to Identify and Annotate Somatic Mutations in Whole-Exome Sequencing Data." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 73–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35686-5_7.

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7

Oliveira, Jorge, Rute Pereira, Rosário Santos, and Mário Sousa. "Evaluating Runs of Homozygosity in Exome Sequencing Data - Utility in Disease Inheritance Model Selection and Variant Filtering." In Biomedical Engineering Systems and Technologies, 268–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94806-5_15.

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Sathyanarayanan, Anita, Srikanth Manda, Mukta Poojary, and Shivashankar H. Nagaraj. "Exome Sequencing Data Analysis." In Encyclopedia of Bioinformatics and Computational Biology, 164–75. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-809633-8.20094-0.

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9

"Exome Sequencing: Genome Sequencing Focusing on Exonic Regions." In Next-Generation Sequencing and Sequence Data Analysis, edited by Kuo Chiu, 84–87. BENTHAM SCIENCE PUBLISHERS, 2015. http://dx.doi.org/10.2174/9781681080925115010013.

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Acharya, Anu, Shibichakravarthy Kannan, Brajendra Kumar, Jasmine Khurana, Sushma Patil, and Geethanjali Tanikella. "Impact of Human Exome Sequencing on Clinical Research." In Healthcare Ethics and Training, 603–24. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2237-9.ch027.

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Recent advances in human exome sequencing and the associated advantages have made it a technology of choice in various domains. The savings in time, cost and data storage compared with whole genome sequencing make this technology a potential game changer in clinical research settings. Recent advances in NGS have made it feasible to use exome sequencing in clinical research for identifying novel and rare variants that can lead to change in protein structure and function which may finally culminate into a totally different phenotype. If whole exome is not desired the same technology can be used for studying target exonic regions to investigate causative genes for a specific phenotype associated with disease. Exome sequencing has emerged as an effective and efficient tool for the translational and clinical research. There is a demand for systematically storing variant information in large databanks. Meaningful information from the exome-seq data can be combined with other data. This can be correlated with clinical findings within a clinical trial setting for a better study outcome.
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Тези доповідей конференцій з теми "EXOME SEQUENCING DATA"

1

Kim, Daeyoon, Sung-Soo Yoon, Youngil Koh, Su Yeon Lee, Hongseok Yun, Sunghoon Cho, and Hyung-Lae Kim. "Abstract 2582: KIRnome: KIR genotyping for whole genome/exome sequencing data." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-2582.

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2

Lonigro, Robert J., Catherine Grasso, Yi-Mi Wu, Michael Quist, Xiaojun Jing, Rohit Mehra, Javed Siddiqui, Xuhong Cao, Scott Tomlins, and Arul Chinnaiyan. "Abstract LB-262: Estimation of tumor content from exome sequencing data." In Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1538-7445.am2011-lb-262.

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3

Cui, Xiaodong, Jia Meng, Manjeet K. Rao, Yidong Chen, and Ufei Huang. "An HMM-based Exome Peak-finding package for RNA epigenome sequencing data." In 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2013. http://dx.doi.org/10.1109/gensips.2013.6735940.

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4

Gorman, Kathleen Mary, Judith Conroy, Eva Forman, Sally A. Lynch, Nicholas M. Allen, Amre Shahwan, Brian Lynch, Sean Ennis, and Mary King. "OC48 Re-interrogation of whole exome sequencing data in developmental epileptic encephalopathies." In Faculty of Paediatrics of the Royal College of Physicians of Ireland, 9th Europaediatrics Congress, 13–15 June, Dublin, Ireland 2019. BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health, 2019. http://dx.doi.org/10.1136/archdischild-2019-epa.45.

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D'Antonio, Mattia, Paolo D'Onorio De Meo, Tiziana Castrignano, Giovanni Erbacci, Matteo Pallocca, and Graziano Pesole. "ODESSA: A high performance analysis pipeline for Ultra Deep targeted Exome Sequencing data." In 2014 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2014. http://dx.doi.org/10.1109/hpcsim.2014.6903743.

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6

Gu, Namjin, Yong-Min Kim, and Ryan W. Kim. "Detection of a fusion gene using soft-clipping reads in exome-sequencing data." In BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808719.2811420.

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7

Kong, Jinhwa, Jaemoon Shin, Jungim Won, Jeehee Yoon, and Unjoo Lee. "An efficient noise reduction method for copy number variations detection from whole exome sequencing data." In 2016 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2016. http://dx.doi.org/10.1109/hpcsim.2016.7568451.

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8

Duan, Junbo, Mingxi Wan, Hong-Wen Deng, and Yu-Ping Wang. "Modeling exome sequencing data with generalized Gaussian distribution with application to copy number variation detection." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732619.

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9

Tan, Renjie, Jixuan Wang, Xiaoliang Wu, Guoqiang Wan, Rongjie Wang, Rui Ma, Zhijie Han, et al. "ERDS-pe: A paired hidden Markov model for copy number variant detection from whole-exome sequencing data." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822508.

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10

Hollis, Robert L., John Thomson, Barbara Stanley, Alison M. Meynert, Michael Churchman, Tzyvia Rye, C. Simon Herrington, and Charlie Gourley. "Abstract 4161: Integrated analysis of whole exome sequencing and hormone receptor expression data in endometrioid ovarian cancer." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4161.

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