| 31. |
- Shah, MY, et al.
(författare)
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Cancer-associated rs6983267 SNP and its accompanying long noncoding RNA CCAT2 induce myeloid malignancies via unique SNP-specific RNA mutations
- 2018
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Ingår i: Genome research. - : Cold Spring Harbor Laboratory. - 1549-5469 .- 1088-9051. ; 28:4, s. 432-447
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Tidskriftsartikel (refereegranskat)abstract
- The cancer-risk-associated rs6983267 single nucleotide polymorphism (SNP) and the accompanying long noncoding RNA CCAT2 in the highly amplified 8q24.21 region have been implicated in cancer predisposition, although causality has not been established. Here, using allele-specific CCAT2 transgenic mice, we demonstrate that CCAT2 overexpression leads to spontaneous myeloid malignancies. We further identified that CCAT2 is overexpressed in bone marrow and peripheral blood of myelodysplastic/myeloproliferative neoplasms (MDS/MPN) patients. CCAT2 induces global deregulation of gene expression by down-regulating EZH2 in vitro and in vivo in an allele-specific manner. We also identified a novel non-APOBEC, non-ADAR, RNA editing at the SNP locus in MDS/MPN patients and CCAT2-transgenic mice. The RNA transcribed from the SNP locus in malignant hematopoietic cells have different allelic composition from the corresponding genomic DNA, a phenomenon rarely observed in normal cells. Our findings provide fundamental insights into the functional role of rs6983267 SNP and CCAT2 in myeloid malignancies.
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| 32. |
- Ardestani, Shahrzad, et al.
(författare)
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B2SHARE : An open eScience data sharing platform
- 2015
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Ingår i: Proceedings - 11th IEEE International Conference on eScience. - : IEEE. ; , s. 448-453
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Konferensbidrag (refereegranskat)abstract
- Scientific data sharing is becoming an essential service for data driven science and can significantly improve the scientific process by making reliable, and trustworthy data available. Thereby reducing redundant work, and providing insights on related research and recent advancements. For data sharing services to be useful in the scientific process, they need to fulfill a number of requirements that cover not only discovery, and access to data. But to ensure the integrity, and reliability of published data as well. B2SHARE, developed by the EUDAT project, provides such a data sharing service to scientific communities. For communities that wish to download, install and maintain their own service, it is also available as software. B2SHARE is developed with a focus on user-friendliness, reliability, and trustworthiness, and can be customized for different organizations and use-cases. In this paper we discuss the design, architecture, and implementation of B2SHARE. We show its usefulness in the scientific process with some case studies in the biodiversity field.
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| 33. |
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| 34. |
- Belov, Vladimir, et al.
(författare)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
-
Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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| 35. |
- Haelewaters, D., et al.
(författare)
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Fungal Systematics and Evolution: FUSE 6
- 2020
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Ingår i: Sydowia. - 0082-0598. ; 72, s. 171-296
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Fungal Systematics and Evolution (FUSE) is one of the journal series to address the“fusion”between morphological data and molecular phylogenetic data and to describe new fungal taxa and interesting observations. This paper is the 6th contribution in the FUSE series—presenting one new genus, twelve new species, twelve new country records, and three new combinations. The new genus is: Pseudozeugandromyces (Laboulbeniomycetes, Laboulbeniales). The new species are: Albatrellopsis flettioides from Pakistan, Aureoboletus garciae from Mexico, Entoloma canadense from Canada, E. frigidum from Sweden, E. porphyroleucum from Vietnam, Erythrophylloporus flammans from Vietnam, Marasmiellus boreoorientalis from Kamchatka Peninsula in the Russian Far East, Marasmiellus longistipes from Pakistan, Pseudozeugandromyces tachypori on Tachyporus pusillus (Coleoptera, Staphylinidae) from Belgium, Robillarda sohagensis from Egypt, Trechispora hondurensis from Honduras, and Tricholoma kena- nii from Turkey. The new records are: Arthrorhynchus eucampsipodae on Eucampsipoda africanum (Diptera, Nycteribiidae) from Rwanda and South Africa, and on Nycteribia vexata (Diptera, Nycteribiidae) from Bulgaria; A. nycteribiae on Eucampsipoda africanum from South Africa, on Penicillidia conspicua (Diptera, Nycteribiidae) from Bulgaria (the first undoubtful country record), and on Penicillidia pachymela from Tanzania; Calvatia lilacina from Pakistan; Entoloma shangdongense from Pakistan; Erysiphe quercicola on Ziziphus jujuba (Rosales, Rhamnaceae) and E. urticae on Urtica dioica (Rosales, Urticaceae) from Paki- stan; Fanniomyces ceratophorus on Fannia canicularis (Diptera, Faniidae) from the Netherlands; Marasmiellus biformis and M. subnudus from Pakistan; Morchella anatolica from Turkey; Ophiocordyceps ditmarii on Vespula vulgaris (Hymenoptera, Vespidae) from Austria; and Parvacoccum pini on Pinus cembra (Pinales, Pinaceae) from Austria. The new combinations are: Ap pendiculina gregaria, A. scaptomyzae, and Marasmiellus rodhallii. Analysis of an LSU dataset of Arthrorhynchus including isolates of A. eucampsipodae from Eucampsipoda africanum and Nycteribia spp. hosts, revealed that this taxon is a complex of multiple species segregated by host genus. Analysis of an SSU–LSU dataset of Laboulbeniomycetes sequences revealed support for the recognition of four monophyletic genera within Stigmatomyces sensu lato: Appendiculina, Fanniomyces, Gloeandromyces, and Stigmatomyces sensu stricto. Finally, phylogenetic analyses of Rhytismataceae based on ITS–LSU ribosomal DNA resulted in a close relationship of Parvacoccum pini with Coccomyces strobi.
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| 36. |
- Lensink, Marc F., et al.
(författare)
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Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
- 2023
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Ingår i: Proteins. - : WILEY. - 0887-3585 .- 1097-0134.
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Tidskriftsartikel (refereegranskat)abstract
- We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average similar to 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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| 37. |
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| 38. |
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