Relevant bibliographies by topics / Genomic classification

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Academic literature on the topic 'Genomic classification'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Genomic classification"

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Murthy, H. N., S. C. Hiremath, and A. N. Pyati. "Genomic Classification in Guizotia (Asteraceae)." CYTOLOGIA 60, no. 1 (1995): 67–73. http://dx.doi.org/10.1508/cytologia.60.67.

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Akbani, Rehan, Kadir C. Akdemir, B. Arman Aksoy, Monique Albert, Adrian Ally, Samirkumar B. Amin, Harindra Arachchi, et al. "Genomic Classification of Cutaneous Melanoma." Cell 161, no. 7 (June 2015): 1681–96. http://dx.doi.org/10.1016/j.cell.2015.05.044.

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Doranga, Saroj, Rajeev Nepal, and Pratigya Timsina. "Automated Classification of Genetic Mutations in Cancer using Machine Learning." Scientific Researches in Academia 1, no. 1 (November 23, 2023): 108–23. http://dx.doi.org/10.3126/sra.v1i1.60140.

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Efforts to decipher the genomic data of cancer and its implications for treatment face challenges. Robust preclinical models reflecting human cancer's genomic diversity, along with comprehensive genetic and pharmacological annotations, can greatly aid in this endeavor. Large collections of cancer cell lines effectively capture the genomic diversity and provide valuable insights into the response to anti-cancer drugs. In this study, we demonstrate significant agreement and biological consistency between drug sensitivity measurements and their corresponding genomic predictors from two publicly available pharmacogenomics databases: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer. Despite ongoing efforts to identify cancer-related metabolic changes that may reveal vulnerabilities to targeted drugs, systematic evaluations of metabolism in relation to functional genomics features and associated dependencies are still uncommon. To gain further insights into the metabolic diversity of cancer, we analyzed 225 metabolites in 928 cell lines representing over 20 cancer types using liquid chromatography-mass spectrometry (LC-MS) in the Cancer Cell Line Encyclopedia (CCLE). The analysis revealed missing data for various features, with certain percentages exceeding 40%, leading to the removal of 12 features according to standard procedures. Further analysis revealed 25 unique chromosomes and 4 unique Variant_Types in the dataset. Model performance assessment showed an accuracy score of 96% using a logistic regression model.
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Faillot, Simon, Thomas Foulonneau, Mario Néou, Stéphanie Espiard, Simon Garinet, Anna Vaczlavik, Anne Jouinot, et al. "Genomic classification of benign adrenocortical lesions." Endocrine-Related Cancer 28, no. 1 (January 2021): 79–95. http://dx.doi.org/10.1530/erc-20-0128.

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Benign adrenal tumors cover a spectrum of lesions with distinct morphology and steroid secretion. Current classification is empirical. Beyond a few driver mutations, pathophysiology is not well understood. Here, a pangenomic characterization of benign adrenocortical tumors is proposed, aiming at unbiased classification and new pathophysiological insights. Benign adrenocortical tumors (n = 146) were analyzed by transcriptome, methylome, miRNome, chromosomal alterations and mutational status, using expression arrays, methylation arrays, miRNA sequencing, SNP arrays, and exome or targeted next-generation sequencing respectively. Pathological and hormonal data were collected for all tumors. Pangenomic analysis identifies four distinct molecular categories: (1) tumors responsible for overt Cushing, gathering distinct tumor types, sharing a common cAMP/PKA pathway activation by distinct mechanisms; (2) adenomas with mild autonomous cortisol excess and non-functioning adenomas, associated with beta-catenin mutations; (3) primary macronodular hyperplasia with ARMC5 mutations, showing an ovarian expression signature; (4) aldosterone-producing adrenocortical adenomas, apart from other benign tumors. Epigenetic alterations and steroidogenesis seem associated, including CpG island hypomethylation in tumors with no or mild cortisol secretion, miRNA patterns defining specific molecular groups, and direct regulation of steroidogenic enzyme expression by methylation. Chromosomal alterations and somatic mutations are subclonal, found in less than 2/3 of cells. New pathophysiological insights, including distinct molecular signatures supporting the difference between mild autonomous cortisol excess and overt Cushing, ARMC5 implication into the adreno-gonadal differentiation faith, and the subclonal nature of driver alterations in benign tumors, will orient future research. This first genomic classification provides a large amount of data as a starting point.
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Ornella, L., P. Pérez, E. Tapia, J. M. González-Camacho, J. Burgueño, X. Zhang, S. Singh, et al. "Genomic-enabled prediction with classification algorithms." Heredity 112, no. 6 (January 15, 2014): 616–26. http://dx.doi.org/10.1038/hdy.2013.144.

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Spino, Marissa, and Matija Snuderl. "Genomic Molecular Classification of CNS Malignancies." Advances In Anatomic Pathology 27, no. 1 (January 2020): 44–50. http://dx.doi.org/10.1097/pap.0000000000000254.

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Graur, Dan, Yichen Zheng, and Ricardo B. R. Azevedo. "An Evolutionary Classification of Genomic Function." Genome Biology and Evolution 7, no. 3 (January 28, 2015): 642–45. http://dx.doi.org/10.1093/gbe/evv021.

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Kundra, Ritika, Hongxin Zhang, Robert Sheridan, Sahussapont Joseph Sirintrapun, Avery Wang, Angelica Ochoa, Manda Wilson, et al. "OncoTree: A Cancer Classification System for Precision Oncology." JCO Clinical Cancer Informatics, no. 5 (March 2021): 221–30. http://dx.doi.org/10.1200/cci.20.00108.

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PURPOSE Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.
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Kim, Jong-Won. "Diagnostic Classification and Genomic Analyses of Cancer." Laboratory Medicine Online 11, no. 4 (October 1, 2021): 223–29. http://dx.doi.org/10.47429/lmo.2021.11.4.223.

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Joly, Yann, Hilary Burton, Bartha Maria Knoppers, Ida Ngueng Feze, Tom Dent, Nora Pashayan, Susmita Chowdhury, et al. "Life insurance: genomic stratification and risk classification." European Journal of Human Genetics 22, no. 5 (October 16, 2013): 575–79. http://dx.doi.org/10.1038/ejhg.2013.228.

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Dissertations / Theses on the topic "Genomic classification"

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Pfaff, Florian [Verfasser]. "Expanding the virosphere : advanced genomic classification / Florian Pfaff." Greifswald : Universitätsbibliothek Greifswald, 2017. http://d-nb.info/114441251X/34.

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Sonnhammer, Erik Leonard Laage. "Classification of protein domain families for genomic sequence analysis." Thesis, Open University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336799.

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Stone, Thomas John. "Genomic classification and analysis of epilepsy-associated glioneuronal tumours." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037593/.

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INTRODUCTION: Glioneuronal tumours are a group of low-grade epilepsy-associated tumours with marked variability in their histological features, resulting in a lack of diagnostic consensus between institutions. This is confounded by a dearth of knowledge regarding their underlying biology, and subsequent lack of robust biologically informed diagnostic tools. This lack of understanding also impedes the development of novel and targeted treatment strategies. METHODS: I have undertaken a comprehensive molecular analysis of the most prevalent glioneuronal tumours: ganglioglioma and dysembryoplastic neuroepithelial tumours. I have used RNA sequencing and Illumina 450K methylation arrays to classify tumours in an unsupervised manner according to their genomic profiles. I then carried out in silico analyses on these datasets to identify genes, gene networks, and pathways that are differentially regulated between groups. Additionally, I have undertaken molecular assays to identify mutations that are specific to each group. Finally, I have used immunohistochemistry to assess a number of potential diagnostic markers revealed by expression profiling. RESULTS: Unsupervised clustering revealed glioneuronal tumours classify into two molecular groups (termed Group 1 and Group 2), which are only partially consistent with histological classification. Group 1 is defined by an astrocytic expression phenotype and an enrichment for BRAF-V600E mutations. Group 2 is defined by an oligodendrocyte precursor phenotype and an enrichment for FGFR1 mutations. A number of disease relevant networks and pathways are differentially regulated between these groups. Additionally, immunohistochemistry against Cyclin-D1 and PDGFRα can be used to distinguish tumour groups from one another. CONCLUSION: This is the first comprehensive genomic investigation of a large cohort of glioneuronal tumours without prior histological bias. I present data suggesting the current histological classification of these lesions is insufficient, and recommend a novel biologically informed strategy. My results also provide insight into the pathways underlying the development of these tumours. This information may assist in the development of novel treatment strategies.
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Hua, Jianping. "Topics in genomic image processing." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3244.

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The image processing methodologies that have been actively studied and developed now play a very significant role in the flourishing biotechnology research. This work studies, develops and implements several image processing techniques for M-FISH and cDNA microarray images. In particular, we focus on three important areas: M-FISH image compression, microarray image processing and expression-based classification. Two schemes, embedded M-FISH image coding (EMIC) and Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis, have been introduced for M-FISH image compression and microarray image processing, respectively. In the expression-based classification area, we investigate the relationship between optimal number of features and sample size, either analytically or through simulation, for various classifiers.
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Saluja, Sunil K. (Sunil Kumar) 1968. "A computational framework for the identification, cataloging, and classification of evolutionary conserved genomic DNA." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28590.

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Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2004.
Includes bibliographical references (leaves 27-29).
Evolutionarily conserved genomic regions (ecores) are understudied, and yet comprise a very large percentage of the Human Genome. Highly conserved human-mouse non-coding ecores, for example, are more abundant within the Human Genome than those regions, which are currently estimated to encode for proteins. Subsets of these ecores also exhibit conservation that extends across several species. These genomic regions have managed to survive millions of years of evolution despite the fact that they do not appear to directly encode for proteins. The survival of these regions compels us to investigate their potential function. Development of a computational framework for the classification and clustering of these regions may be the first step in understanding their function. The need for a standardized framework is underscored by the explosive growth in the number of publicly available, fully sequenced genomes, and the diverse set of methodologies used to generate cross-species alignments. This project describes the design and implementation of a system for the identification, classification and cataloguing of ecores across multiple species. A key feature of this system is its ability to quickly incorporate new genomes and assemblies as they become available. Additionally, this system provides investigators with a feature rich user interface, which facilitates the retrieval of ecores based on a wide range of parameters. The system returns a dynamically annotated list of evolutionarily conserved regions, which is used as input to several classification schemes, aimed at identifying families of ecores that share similar features, including depth of evolutionary conservation, position relative to known genes, sequence similarity,
(cont.) and content of transcription factor binding sites. Families of ecores have already been retrieved by the system and clustered using this feature space, and are currently awaiting biological validation.
by Sunil K. Saluja.
S.M.
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Sharma, Jason P. (Jason Poonam) 1979. "Classification performance of support vector machines on genomic data utilizing feature space selection techniques." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87830.

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Stagni, Camilla. "Genomic analysis in cutaneous melanoma: a tool for predictive biomarker identification and molecular classification." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426683.

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Project 1. Identification of molecular signatures associated with response to MAPK inhibitors. BRAF V600-mutated melanoma benefits from MAPK inhibitors-based therapy. Yet, the onset of resistance impacts long-term efficacy and can even be immediate. In this study, we examined the genetic alterations characterizing melanoma progression to identify predictive factors of response to MAPK inhibitors (MAPKi). Specifically, we evaluated BRAF copy number variation (CNV), BRAF mutant (BRAFmut) allele frequency, PTEN loss or mutations and TERT promoter mutations in pre-treatment melanoma specimens from MAPKi-treated patients (pts) and we analyzed their association with progression free survival (PFS). We also applied a comprehensive unbiased approach, using genome-wide CNV analysis, to identify additional genomic aberrations potentially associated with response to therapy. We found that 65% pts displayed BRAF gains, often supported by chromosome 7 polysomy. In addition, we observed that 64% pts had a balanced BRAF mutant/wild-type allele ratio, while 14% and 23% pts had low and high BRAFmut allele frequency, respectively. Notably, a significantly higher risk of progression was observed in pts with a diploid BRAF status vs. those with BRAF gains (HR = 2.86; 95% CI 1.29‒6.35; p = 0.01) and in pts with low vs. those with a balanced BRAFmut allele percentage (HR = 4.54, 95% CI 1.33‒15.53; p = 0.016). We identified PTEN gene mutations affecting the catalitic and C2 domains in 27% pts. Moreover, we observed a complete PTEN loss in 42% pts, partial loss in 35% pts and no loss in 23% pts. Of note, we found PTEN loss also in pre-treatment samples from pts with long PFS. Sequencing of TERT promoter gene disclosed mutations in 78% pts. The -124C>T and the -146C>T mutations were equally frequent (36%) while the -138-139CC>TT was present only in 5% pts. Fifty-one % pts carried also the neighboring polymorphism rs2853669, which reportedly counteracts the activating effect of the above-mentioned mutations on TERT expression. Upon stratification of the TERT promoter mutant cohort based on presence/absence of the polymorphism, TERTmutant/SNPcarrier pts showed a trend toward better PFS (median PFS 11.5 mo., 95% CI 3.12‒19.88) compared to TERTmutant/SNPnon-carrier pts (median PFS 7 mo., 95% CI 4.27‒9.72). When stratifying based on mutation type, the -146C>T mutation correlated with shorter PFS (median PFS 5.45 mo., 95% CI 2.80‒9.20) compared to the -124C>T one (median PFS 15.2 mo., 95% CI 5.57‒). Genome-wide CNV analysis pointed at chr3p24, chr3p21.2 and chr17p13.1, which are differently alterated between pts with long and short time to disease progression, as regions of potential interest to identify new genes involved in therapeutic resistance. Our data suggest that quantitative analysis of the BRAF gene and sequencing of the TERT promoter gene could be useful to select the melanoma pts who are most likely to benefit from MAPKi therapy. In addition, chromosome 3 and 17 could be regions that warrant further investigation. Conversely, because PTEN loss was present in pre-treatment samples from pts with both short and long PFS, the assessment of PTEN gene status does not seem to provide information about patient responsiveness to treatment. Project 2. Research of molecular biomarkers to classify the acral melanoma. Acral lentiginous melanoma (ALM) is a rare subtype of cutaneous melanoma with specific morphological, epidemiological, and genetic features. Since the genomic landscape of ALM is still incompletely described, we used whole genome CNV analysis to characterize ALM and detail the genomic signatures that differentiate ALM from non-acral melanoma (NAM). We observed that the most strikingly different copy number aberrations were a higher frequency of losses of chromosome 16q24.2-16q24.3 in ALM than in NAM (64.7% vs. 10%) and a lower frequency of gains of chromosome 7q21.2-7q33 in ALM than in NAM (26.5% vs.79.5%). We observed also that ALM more often (than NAM) harbored clusters of breakpoints and isochromosomes. Moreover, in ALM we identified focal amplification of TERT, CCND1, MDM2 and MITF. In NAM, instead, we found only two focal amplifications, involving BRAF and MITF. Focal homozygous copy losses affected especially the CDKN2A and PTEN genes, both in ALM and in NAM, even though they were more frequent in the latter group. In keeping with previous observations that led to classify ALM as a distinct molecular subtype of melanoma, we observed a peculiar genomic landscape in ALM (vs. NAM). Our study provides insights into the molecular characteristics of ALM, which is key to full elucidation of its pathogenesis.
Progetto 1: identificazione di signatures molecolari associate alla risposta al trattamento con inibitori del MAPK pathway. I melanomi portatori di una mutazione nel codone V600 del gene BRAF rispondono agli inibitori del MAPK pathway, ma l’efficacia a lungo termine di questa terapia è limitata dallo sviluppo di resistenza, talvolta immediata. In questo studio, abbiamo esaminato le alterazioni molecolari caratterizzanti la progressione del melanoma al fine di identificare fattori predittivi di risposta/resistenza ai MAPK-inibitori. Nello specifico, su una serie di campioni pretrattamento di pazienti affetti da melanoma, trattati con MAPK-inibitori, abbiamo valutato numero di copie del gene BRAF e percentuale di allele V600-mutato, delezione e mutazioni di PTEN, alterazioni del promotore di TERT, e ne abbiamo analizzato l’associazione con la risposta dei pazienti alla terapia. Inoltre, abbiamo determinato il copy number variation dell’intero genoma dei nostri campioni per individuare ulteriori aberrazioni non note potenzialmente associate con la risposta alla terapia. Abbiamo identificato un numero aumentato di copie (gain) del gene BRAF, spesso dovuto a polisomia del cromosoma 7, nel 65% dei pazienti; l’allele mutato è stato trovato in una percentuale compresa tra il 35% e il 65% nel 64% dei pazienti, inferiore al 35% nel 14% dei pazienti e superiore al 65% nel 23% dei pazienti. Dall’analisi di sopravvivenza, è risultato che i pazienti con BRAF diploide o una percentuale di allele mutato inferiore al 35% presentano un più alto rischio di progressione rispetto a coloro che presentano gain di BRAF (HR=2.86; 95% CI 1.29-6.35; p=0.01) o tra il 35% e il 65% di allele mutato (HR=4.54,CI 1.33-15.53; p=0.016), rispettivamente. L’analisi di PTEN ha rivelato la presenza di mutazioni nel 27% dei pazienti, localizzate a livello dei domini catalitico e C2 della proteina codificata; inoltre, il 42% dei casi valutati mostrava una delezione completa del gene, il 35% una delezione parziale, mentre nel 23% dei pazienti non è stata individuata alcuna aberrazione di PTEN. Da notare, delezioni di PTEN sono emerse sia nei casi di melanoma resistente alla terapia, che in quelli che avevano risposto a lungo. Il sequenziamento del promotore del gene TERT ha permesso l’identificazione di mutazioni nel 78% dei pazienti. Le mutazioni -124C>T e -146C>T mostravano la stessa frequenza (36%) nella nostra coorte, mentre la -138-139CC>TT è stata individuata solo nel 5% dei casi. Il 51% dei pazienti presentava inoltre lo SNP rs2853669, noto per contrastare l’effetto attivante delle mutazioni sull’espressione di TERT. Stratificando la coorte di pazienti mutati in base alla presenza/assenza del polimorfismo, i pazienti TERT mutati/SNP carriers mostravano un trend verso una migliore PFS (PFS mediana 11.5 mesi, 95% CI 3.12-19.88) rispetto ai TERT mutati/SNP non-carriers (PFS mediana 7 mesi, 95% CI 4.27-9.72). La mutazione -146C>T, inoltre, correlava con PFS più breve (PFS mediana 5.45 mesi, 95% CI 2.80-9.20) rispetto alla -124C>T (PFS mediana 15.2 mesi, 95% CI 5.57-). Dall’analisi del copy number variation (CNV) sull’intero genoma, le regioni chr3p24, chr3p21.2 e chr17p13.1 hanno mostrato pattern di alterazioni diverse in pazienti responsivi vs. non-responsivi alle terapie; risultano pertanto regioni di potenziale interesse per l’individuazione di nuovi geni coinvolti nella resistenza alla terapia. I nostri dati suggeriscono dunque che l’analisi quantitativa del gene BRAF e il sequenziamento del promotore di TERT costituiscono un utile strumento di selezione dei pazienti con maggiore probabilità di rispondere alla terapia con MAPK-inibitori, contrariamente alla valutazione dello status di PTEN. L’analisi genome-wide, invece, indica di approfondire lo studio dei cromosomi 3 e 17. Progetto 2: ricerca di marcatori biomolecolari per la classificazione del melanoma acrale. Il melanoma acrale lentigginoso è un raro sottotipo di melanoma cutaneo con specifiche caratteristiche morfologiche, epidemiologiche e genetiche. Poiché il genoma del melanoma acrale non è ancora stato pienamente caratterizzato, ne abbiamo analizzato il CNV per individuare quei caratteri genomici peculiari che lo differenziano dal melanoma non acrale. La nostra analisi genome-wide ha evidenziato una maggiore frequenza di delezioni della regione 16q24.2-16q24.3, gains meno frequenti nella regione 7q21.2-7q33, una più accentuata frammentazione genomica e numerosi isocromosomi come caratteri che distinguono il melanoma acrale dal non acrale. Abbiamo inoltre identificato amplificazioni focali nei geni TERT, CCND1, MDM2 e MITF, più rare nei non acrali, laddove interessavano altri geni, come BRAF e MITF. Delezioni focali sono state individuate soprattutto nei geni CDKN2A e PTEN in entrambi i sottotipi di melanoma, anche se più frequenti nei non acrali. I nostri dati, in accordo con il classificare il melanoma acrale come tipo distinto di melanoma, hanno consentito di delinearne alcune delle peculiarità genomiche, chiave per elucidarne anche la patogenesi.
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McConechy, Melissa. "PPP2R1A mutations in gynaecologic cancers: functional characterization and use in the genomic classification of tumours." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52829.

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Endometrial carcinoma is the most common gynaecological cancer in developed countries. The current endometrial pathologic classification system lacks reproducibility, which has hampered the development of new treatments for these cancers. The PP2A phosphatase complexes are responsible for regulating many cellular pathways, and may play a role in the deregulation of endometrial cancer-associated pathways. In this thesis, the role of PPP2R1A mutations in the subtype-specific classification of gynaecological tumours was investigated. Additionally, mutational profiles will be used to improve the classification of the subtypes of endometrial carcinomas. Lastly, the functional effect of mutant PPP2R1A on PP2A-subunit protein interactions will be determined, in the context of endometrial cancer cell lines. Next-generation and Sanger sequencing was used to determine the presence of mutations in endometrial and ovarian carcinomas. PPP2R1A isogenic endometrial-specific cell lines were generated using somatic cell gene knockout by homologous recombination. Co-immunoprecipitation and mass spectrometry was used to determine effects of the PPP2R1A W257L mutation on its ability to interact with PP2A subunits. Subtype-specific somatic PPP2R1A mutations were identified in endometrial serous carcinomas. Low-grade endometrial endometrioid carcinomas were defined by mutations in the genes: ARID1A, PTEN, PIK3CA, CTNNB1, and KRAS, whereas high-grade endometrioid also harbor TP53 mutations. Endometrial serous carcinomas harbor mutations in PPP2R1A, FBXW7, PIK3CA and TP53. Consequently, the molecular profiles proved useful in assisting classification of tumours with overlapping morphological features that cause irreproducibility in diagnoses. Proteomic analysis of isogenic cell lines determined that the PPP2R1A W257L mutation disrupts interaction with PPP2R5C and PPP2R5D subunits. In addition, PPP2R1A mutated protein caused an increased interaction with the endogenous PP2A inhibitor SET/I2PP2A. The integration of mutational profiles and other genomic features will be used to improve clinical and pathological classification in endometrial tumours that are difficult to diagnose. PPP2R1A mutations are likely playing a role in the transformation of gynaecological carcinoma, by disrupting PP2A subunit interactions with tumour suppressor functions. Increased interaction of mutant PPP2R1A with SET/I2PP2A adds another layer of complexity to the tumour suppressive role of PP2A. In the future, targeting the PP2A complex with novel therapeutics could provide an alternative method for treating gyneacological cancers with poor outcomes.
Medicine, Faculty of
Pathology and Laboratory Medicine, Department of
Graduate
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Marisa, Laetitia. "Classification et caractérisation des cancers colorectaux par approches omiques." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066235/document.

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Le cancer du côlon (CC) est l'un des cancers les plus fréquents et les plus mortels en France et dans le monde. Près de la moitié des patients décèdent dans les 5 ans suivant le diagnostic. La classification clinique en stade histologique et la classification moléculaire selon les formes d'instabilité du génome (l'instabilité des microsatellites (MSI), l'instabilité chromosomique (CIN) et l'hyperméthylation des promoteurs (CIMP)) ne suffisent pas à définir des entités homogènes du point de vue moléculaire et à prédire de manière efficace la récidive. Pour améliorer la prise en charge des patients, il apparaît indispensable de mieux appréhender la diversité de la maladie afin de trouver des marqueurs pronostiques et prédictifs efficaces. Mon travail de thèse a donc été d'étudier la diversité des CC à l'échelle moléculaire par l'utilisation d'approches omiques sur une large cohorte de patients. Il a abouti à l'établissement d'une classification transcriptomique robuste de ce cancer dans son ensemble, validée sur des données indépendantes, et à la caractérisation fine de chacun des sous-types. Six sous-types ont ainsi été définis présentant des caractéristiques clinico-pathologiques, des altérations moléculaires de l'ADN, des enrichissements de signatures liées aux lésions et cellules d'origines, des voies de signalisation dérégulées et des survies bien distinctes. Les résultats de ce travail ont été confortés par un travail de classification consensus mis en place avec un consortium de travail international auquel j'ai participé. Ces résultats ont permis de confirmer que le cancer colorectal n'est pas une maladie homogène. Ils ouvrent de nouvelles perspectives pour l'établissement de signatures pronostiques et la recherche de cibles pour de nouveaux traitements ainsi que pour l'évaluation de la réponse au traitement au sein d'essais cliniques
Colon cancer (CC) is one of the most frequent and most deadly cancer in France and worldwide. Nearly half of patients die within 5 years after diagnosis. Clinical stage based on histological features and molecular classification based genomic instabilities (microsatellite instability (MSI), chromosomal instability (CIN) and hypermethylation of the promoters (ICPM)) are not sufficient to define homogeneous molecular entities and to predict recurrence effectively. To improve patient care, it is essential to better understand the diversity of the disease so that effective prognostic and predictive markers could be found. My PhD work has been focused on studying the diversity of CC at the molecular level through the use of omics approaches on a large cohort of tumor samples. It led to the establishment of a robust transcriptomic classification of these cancers, validated on independent data sets, and to a detailed characterization of each of the subtypes. Six subtypes have been defined and were associated with distinct clinicopathological characteristics and molecular alterations, specific enrichments of supervised gene expression signatures related to cell and lesions of origin, specific deregulated signaling pathways and distinct survival. The results of this work have been strengthened by a consensus classification defined by an international consortium working group in which I've been involved. These results confirm that colorectal cancer is an heterogeneous disease. They provide a renewed framework to develop prognostic signatures, discover new treatment targets, identify new therapeutic strategies and assess response to treatment in clinical trials
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Pages, Mélanie. "Integrative genomic, epigenetic, radiologic and histological characterization of pediatric glioneuronal tumors." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCB217.

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The large-scale genomic studies performed recently has enabled the objective identification of numerous novel genomic alterations and highlighted that pediatric brain tumors often harbor quiet cancer genomes, with a single driver genomic alteration. This characteristic is of special interest in the current context of precision medicine development. Low-grade glioneuronal tumor group is highly heterogeneous and remains particularly challenging since it includes a broad spectrum of tumors, often poorly discriminated by their histopathological features and not completely molecularly characterized. We used targeted methods (IHC, FISH, targeted sequencing), and large scale genomic and epigenetic methodologies to perform an integrative analysis to further characterized papillary glioneuronal tumors (PGNT), midline gangliogliomas and dysembryoplastic neuroepithelial tumors (DNT). We demonstrated that PGNT is a distinct entity characterized by a PRKCA fusion. We highlighted that H3 K27M mutation can occur in association with BRAF V600E mutation in midline grade I glioneuronal tumors, showing that despite the presence of H3 K27M mutations, these cases should not be graded and treated as grade IV tumors because they have a better spontaneous outcome than classic diffuse midline H3 K27M-mutant glioma. The DNT study enable us 1) to specify that non-specific DNT corresponds to a clinico-histological tumor group encompassing diverse molecularly distinct entities and 2) to demonstrate that specific DNTs can be progressive tumors and harbored a distinct DNA methylation profile. Diagnosis and genomic profiling that can guide precision medicine require tissue acquisition by neurosurgical procedures that are often difficult or not possible. We validated a sample collection procedure and we developed methodologies to detect circulating tumor DNA (ctDNA) in CSF, plasma and urine to identify clinically relevant genomic alterations from a cohort of 235 pediatric patients with brain tumors. We optimized a method to process ctDNA and performed ultra-low pass whole genome sequencing (ULP-WGS) using unique molecular identifiers, confirming we can reliably construct sequencing libraries from CSF-, plasma- and urine-derived ctDNA. ULP-WGS has also been used to assess sequencing library quality, copy number variations (CNVs) and tumor fraction. The vast majority of samples undergoing ULPWGS exhibited no CNVs, consistent with either absence in the tumor or low levels of tumorderived cfDNA. To distinguish between these, we developed a hybrid capture sequencing panel allowing identification of specific mutations and fusions more common in pediatric brain tumors
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Books on the topic "Genomic classification"

1

Fenaux, Robert. The classification of Appendicularia (Tunicata): History and current state. [Monaco: Institut océanographique], 1993.

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Genome clustering: From linguistic models to classification of genetic texts. Berlin: Springer, 2010.

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Maes, Dominiek, Marina Sibila, and Maria Pieters, eds. Mycoplasmas in swine. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249941.0000.

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Abstract This book contains 14 chapters that discuss the genetics, epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma infections in pigs. Chapter 1 discusses the phylogenetics and classification of Mycoplasma species in pigs; Chapter 2 describes the genomic diversity and antigenic variation of Mycoplasma hyopneumoniae strains; Chapter 3 discusses the pathogenesis, virulence factor and pathogenicity of Mycoplasma hyopneumoniae; Chapter 4 discusses the molecular epidemiology, risk factors, transmission and prevalence of Mycoplasma hyopneumoniae, Chapter 5 discusses the clinical signs and gross lesions of Mycoplasma hyopneumoniae infection; Chapter 6 discusses immune responses against Mycoplasma infections; Chapter 7 describes the interactions of Mycoplasma hyopneumoniae with other pathogens and their economic impact; Chapter 8 discusses the diagnosis of Mycoplasma hyopneumoniae infection and its associated diseases; Chapter 9 describes the general control measures against Mycoplasma hyopneumoniae infections; Chapter 10 describes the selection and efficacy of antimicrobials against Mycoplasma hyopneumoniae infections; Chapter 11 discusses the development and efficacy of vaccines against Mycoplasma hyopneumoniae; Chapter 12 describes the eradication of Mycoplasma hyopneumoniae in pig herds; Chapter 13 describes the epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma hyorhinis and Mycoplasma hyosynoviae in pig herds and Chapter 14 discusses the epidemiology, prevalence, transmission, pathogenesis, clinical signs, diagnosis, treatment, prevention, control and economic impact of Mycoplasma suis infection in pigs.
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1963-, Feng Zhi, and Long Ming, eds. Viral genomes: Diversity, properties, and parameters. Hauppauge, NY: Nova Science Publishers, 2009.

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Viruses and the environment. 2nd ed. London ; New York: Chapman and Hall, 1995.

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1954-, Heiner Monika, and SpringerLink (Online service), eds. Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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János, Varga, and Samson Robert A, eds. Aspergillus in the genomic era. Wageningen, Netherlands: Wageningen Academic Publishers, 2008.

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Matsui, Shigeyuki, and Hisashi Noma. Estimation and Selection in High-Dimensional Genomic Studies: Multiple Testing, Gene Ranking, and Classification. Springer, 2020.

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Sherman, Mark E., Melissa A. Troester, Katherine A. Hoadley, and William F. Anderson. Morphological and Molecular Classification of Human Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0003.

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Accurate and reproducible classification of tumors is essential for clinical management, cancer surveillance, and studies of pathogenesis and etiology. Tumor classification has historically been based on the primary anatomic site or organ in which the tumor occurs and on its morphologic and histologic phenotype. While pathologic criteria are useful in predicting the average behavior of a group of tumors, histopathology alone cannot accurately predict the prognosis and treatment response of individual cancers. Traditional measures such as tumor stage and grade do not take into account molecular events that influence tumor aggressiveness or changes in the tumor composition during treatment. This chapter provides a primer on approaches that use pathology and molecular biology to classify and subclassify cancers. It describes the features of carcinomas, sarcomas, and malignant neoplasms of the immune system and blood, as well as various high-throughput genomic platforms that characterize the molecular profile of tumors.
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Bolshoy, Alexander, Zeev Volkovich, and Valery Kirzhner. Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer, 2010.

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Book chapters on the topic "Genomic classification"

1

Braga-Neto, Ulisses M., Emre Arslan, Upamanyu Banerjee, and Arghavan Bahadorinejad. "Bayesian Classification of Genomic Big Data." In Signal Processing and Machine Learning for Biomedical Big Data, 411–27. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351061223-20.

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La Rosa, Massimo, Antonino Fiannaca, Riccardo Rizzo, and Alfonso Urso. "Genomic Sequence Classification Using Probabilistic Topic Modeling." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 49–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09042-9_4.

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Li, Jingyi Jessica, and Xin Tong. "Genomic Applications of the Neyman–Pearson Classification Paradigm." In Big Data Analytics in Genomics, 145–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41279-5_4.

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Ahmad Dar, Mayasar, and Deepmala Sharma. "Revisiting the Genomics and Genetic Codes Using Walsh-Hadamard Spectrum Analysis." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 106–13. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_11.

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AbstractWalsh-Hadamard spectrum is widely used in the field of science and technology like classification of cancer cells, image processing, speech processing, signal and image compression etc. In this paper, a genomic analysis using Walsh-Hadamard spectrum and cross-correlation has been done. Transformation of genetic code using Walsh-Hadamard spectrum has been given. We redefine the Walsh-Hadamard spectrum in genomics and analyse the origin of mRNA features by using this spectra. Finally, using Walsh-Hadamard spectrum the overall energy of the mRNA sequence has been evaluated.
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Badescu, Dunarel, Abdoulaye Baniré Diallo, and Vladimir Makarenkov. "Identification of Specific Genomic Regions Responsible for the Invasivity of Neisseria Meningitidis." In Studies in Classification, Data Analysis, and Knowledge Organization, 491–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_53.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Reproducing Kernel Hilbert Spaces Regression and Classification Methods." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 251–336. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_8.

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AbstractThe fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning. Key elements for the construction of RKHS regression methods are provided, the kernel trick is explained in some detail, and the main kernel functions for building kernels are provided. This chapter explains some loss functions under a fixed model framework with examples of Gaussian, binary, and categorical response variables. We illustrate the use of mixed models with kernels by providing examples for continuous response variables. Practical issues for tuning the kernels are illustrated. We expand the RKHS regression methods under a Bayesian framework with practical examples applied to continuous and categorical response variables and by including in the predictor the main effects of environments, genotypes, and the genotype ×environment interaction. We show examples of multi-trait RKHS regression methods for continuous response variables. Finally, some practical issues of kernel compression methods are provided which are important for reducing the computation cost of implementing conventional RKHS methods.
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Stiglic, Gregor, Juan J. Rodriguez, and Peter Kokol. "Rotation of Random Forests for Genomic and Proteomic Classification Problems." In Advances in Experimental Medicine and Biology, 211–21. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7046-6_21.

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Karetla, Girija Rani, Daniel R. Catchpoole, and Quang Vinh Nguyen. "Hybrid Framework for Genomic Data Classification Using Deep Learning: QDeep_SVM." In Algorithms for Intelligent Systems, 451–63. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1620-7_36.

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Patel, Nisha B., and Paul A. Lawson. "The Strength of Chemotaxonomy." In Trends in the systematics of bacteria and fungi, 141–67. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789244984.0141.

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Abstract This book chapter discusses the history and development of chemotaxonomic methods, with examples of the application to different taxa, and with extensive reference to primary literature and reviews. The application of in silico methods utilizing information from the genome and future directions will also be discussed. The delineation of higher taxa at the family level and above may especially be aided by chemotaxonomic criteria, as demonstrated in published minimum standards. Although chemotaxonomic methods have been enormously important in the past with identification and classification schemes, it remains to be seen in what form they will be utilized in the genomic era, and in the suite of methods available in the era of omics.
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Tahiri, Nadia, and Aleksandr Koshkarov. "New Metrics for Classifying Phylogenetic Trees Using K-means and the Symmetric Difference Metric." In Studies in Classification, Data Analysis, and Knowledge Organization, 383–91. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_41.

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AbstractThe k-means method can be adapted to any type of metric space and is sometimes linked to the median procedures. This is the case for symmetric difference metric (or Robinson and Foulds) distance in phylogeny, where it can lead to median trees as well as to Euclidean Embedding. We show how a specific version of the popular k-means clustering algorithm, based on interesting properties of the Robinson and Foulds topological distance, can be used to partition a given set of trees into one (when the data is homogeneous) or several (when the data is heterogeneous) cluster(s) of trees. We have adapted the popular cluster validity indices of Silhouette, and Gap to tree clustering with k-means. In this article, we will show results of this new approach on a real dataset (aminoacyl-tRNA synthetases). The new version of phylogenetic tree clustering makes the new method well suited for the analysis of large genomic datasets.
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Conference papers on the topic "Genomic classification"

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Akhtar, Mahmood, Eliathamby Ambikairajah, and Julien Epps. "GMM-Based Classification of Genomic Sequences." In 2007 15th International Conference on Digital Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icdsp.2007.4288529.

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Watson, Ian R., Chang-Jiun Wu, Lihua Zou, Jeffrey E. Gershenwald, and Lynda Chin. "Abstract 2972: Genomic classification of cutaneous melanoma." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-2972.

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Bowtell, David D. L. "Abstract IA02: Genomic classification of ovarian cancer." In Abstracts: AACR Special Conference: Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; October 17-20, 2015; Orlando, FL. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1557-3265.ovca15-ia02.

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Akhtar, Mahmood, Eliathamby Ambikairajah, and Julien Epps. "Comprehensive autoregressive modeling for classification of genomic sequences." In 2007 6th International Conference on Information, Communications & Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icics.2007.4449750.

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King, Stuart, Yanni Sun, James Cole, and Sakti Pramanik. "BLAST Tree: Fast Filtering for Genomic Sequence Classification." In 2010 IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2010. http://dx.doi.org/10.1109/bibe.2010.74.

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Pahadia, Mayank, Akash Srivastava, Divyang Srivastava, and Nagamma Patil. "Classification of multi-genomic data using MapReduce paradigm." In 2015 International Conference on Computing, Communication & Automation (ICCCA). IEEE, 2015. http://dx.doi.org/10.1109/ccaa.2015.7148460.

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Ruusuvuori, Pekka, Olli Yli-Harja, Chao Sima, and Edward Dougherty. "Classification of quantized small sample data." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353172.

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Gadia, V., and G. Rosen. "A text-mining approach for classification of genomic fragments." In 2008 IEEE International Conference on Bioinformatics and Biomedcine Workshops. IEEE, 2008. http://dx.doi.org/10.1109/bibmw.2008.4686216.

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Mahapatra, Aritra, and Jayanta Mukherjee. "GenFooT: Genomic Footprint of mitochondrial sequence for Taxonomy classification." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313475.

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Guarracino, M. R., C. Cifarelli, O. Seref, and P. M. Pardalos. "A parallel classification method for genomic and proteomic problems." In 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06). IEEE, 2006. http://dx.doi.org/10.1109/aina.2006.47.

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Reports on the topic "Genomic classification"

1

Kamalakaran, Sitharthan, and Josh Dubnau. A Strategy to Rapidly Re-Sequence the NF1 Genomic Loci Using Microarrays and Bioinformatics for Molecular Classification of the Disease. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada478099.

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McCarthy, Noel, Eileen Taylor, Martin Maiden, Alison Cody, Melissa Jansen van Rensburg, Margaret Varga, Sophie Hedges, et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, July 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.

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This human campylobacteriosis sentinel surveillance project was based at two sites in Oxfordshire and North East England chosen (i) to be representative of the English population on the Office for National Statistics urban-rural classification and (ii) to provide continuity with genetic surveillance started in Oxfordshire in October 2003. Between October 2015 and September 2018 epidemiological questionnaires and genome sequencing of isolates from human cases was accompanied by sampling and genome sequencing of isolates from possible food animal sources. The principal aim was to estimate the contributions of the main sources of human infection and to identify any changes over time. An extension to the project focussed on antimicrobial resistance in study isolates and older archived isolates. These older isolates were from earlier years at the Oxfordshire site and the earliest available coherent set of isolates from the national archive at Public Health England (1997/8). The aim of this additional work was to analyse the emergence of the antimicrobial resistance that is now present among human isolates and to describe and compare antimicrobial resistance in recent food animal isolates. Having identified the presence of bias in population genetic attribution, and that this was not addressed in the published literature, this study developed an approach to adjust for bias in population genetic attribution, and an alternative approach to attribution using sentinel types. Using these approaches the study estimated that approximately 70% of Campylobacter jejuni and just under 50% of C. coli infection in our sample was linked to the chicken source and that this was relatively stable over time. Ruminants were identified as the second most common source for C. jejuni and the most common for C. coli where there was also some evidence for pig as a source although less common than ruminant or chicken. These genomic attributions of themselves make no inference on routes of transmission. However, those infected with isolates genetically typical of chicken origin were substantially more likely to have eaten chicken than those infected with ruminant types. Consumption of lamb’s liver was very strongly associated with infection by a strain genetically typical of a ruminant source. These findings support consumption of these foods as being important in the transmission of these infections and highlight a potentially important role for lamb’s liver consumption as a source of Campylobacter infection. Antimicrobial resistance was predicted from genomic data using a pipeline validated by Public Health England and using BIGSdb software. In C. jejuni this showed a nine-fold increase in resistance to fluoroquinolones from 1997 to 2018. Tetracycline resistance was also common, with higher initial resistance (1997) and less substantial change over time. Resistance to aminoglycosides or macrolides remained low in human cases across all time periods. Among C. jejuni food animal isolates, fluoroquinolone resistance was common among isolates from chicken and substantially less common among ruminants, ducks or pigs. Tetracycline resistance was common across chicken, duck and pig but lower among ruminant origin isolates. In C. coli resistance to all four antimicrobial classes rose from low levels in 1997. The fluoroquinolone rise appears to have levelled off earlier and among animals, levels are high in duck as well as chicken isolates, although based on small sample sizes, macrolide and aminoglycoside resistance, was substantially higher than for C. jejuni among humans and highest among pig origin isolates. Tetracycline resistance is high in isolates from pigs and the very small sample from ducks. Antibiotic use following diagnosis was relatively high (43.4%) among respondents in the human surveillance study. Moreover, it varied substantially across sites and was highest among non-elderly adults compared to older adults or children suggesting opportunities for improved antimicrobial stewardship. The study also found evidence for stable lineages over time across human and source animal species as well as some tighter genomic clusters that may represent outbreaks. The genomic dataset will allow extensive further work beyond the specific goals of the study. This has been made accessible on the web, with access supported by data visualisation tools.
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.
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Ahlgren, Per, Tobias Jeppsson, Esa Stenberg, and Erik Berg. A bibliometric analysis of battery research with the BATTERY 2030+ roadmap as point of departure. Uppsala universitet, 2022. http://dx.doi.org/10.33063/diva-473454.

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In this bibliometric study, we analyze the six battery research subfields identified in the BATTERY 2030+ roadmap: Battery Interface Genome, Materials Acceleration Platform, Recyclability, Smart functionalities: Self-healing, Smart functionalities: Sensing, and Manufacturability. In addition, we analyze the entire research field related to BATTERY 2030+ as a whole, using two operationalizations. We (a) evaluate the European standing in the subfields/the BATTERY 2030+ field in comparison to the rest of the world, and (b) identify strongholds of the subfields/the BATTERY 2030+ field across Europe. For each subfield and the field as a whole, we used seed articles, i.e. articles listed in the BATTERY 2030+ roadmap or cited by such articles, in order to generate additional, similar articles located in an algorithmically obtained classification system. The output of the analysis is publication volumes, field normalized citation impact values with comparisons between country/country aggregates and between organizations, co-publishing networks between countries and organizations, and keyword co-occurrence networks. For the results related to (a), the performance of EU & associated (countries) is similar to China and the aggregate Japan-South Korea-Singapore and well below North America regarding citation impact and with respect to the field as a whole. Exceptions are, however, the subfields Battery Interface Genome and Recyclability. For the results related to (b), there is a large variability in the EU & associated organizations regarding volume in the different subfields. For citation impact, examples of high-performing EU & associated organizations are ETH Zurich and Max Planck Society for the Advancement of Science.
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