scholarly journals HUGO Gene Nomenclature Committee (HGNC) recommendations for the designation of gene fusions

Leukemia ◽  
2021 ◽  
Author(s):  
Elspeth A. Bruford ◽  
Cristina R. Antonescu ◽  
Andrew J. Carroll ◽  
Arul Chinnaiyan ◽  
Ian A. Cree ◽  
...  

AbstractGene fusions have been discussed in the scientific literature since they were first detected in cancer cells in the early 1980s. There is currently no standardized way to denote the genes involved in fusions, but in the majority of publications the gene symbols in question are listed either separated by a hyphen (-) or by a forward slash (/). Both types of designation suffer from important shortcomings. HGNC has worked with the scientific community to determine a new, instantly recognizable and unique separator—a double colon (::)—to be used in the description of fusion genes, and advocates its usage in all databases and articles describing gene fusions.

2001 ◽  
Vol 109 (6) ◽  
pp. 678-680 ◽  
Author(s):  
Sue Povey ◽  
Ruth Lovering ◽  
Elspeth Bruford ◽  
Mathew Wright ◽  
Michael Lush ◽  
...  

2011 ◽  
Vol 39 (4) ◽  
pp. 977-983 ◽  
Author(s):  
Colin D. Bingle ◽  
Ruth L. Seal ◽  
C. Jeremy Craven

We present the BPIFAn/BPIFBn systematic nomenclature for the PLUNC (palate lung and nasal epithelium clone)/PSP (parotid secretory protein)/BSP30 (bovine salivary protein 30)/SMGB (submandibular gland protein B) family of proteins, based on an adaptation of the SPLUNCn (short PLUNCn)/LPLUNCn (large PLUNCn) nomenclature. The nomenclature is applied to a set of 102 sequences which we believe represent the current reliable data for BPIFA/BPIFB proteins across all species, including marsupials and birds. The nomenclature will be implemented by the HGNC (HUGO Gene Nomenclature Committee).


2016 ◽  
Author(s):  
Lynn Zentner ◽  
Gerhard Klimeck

Established in 2002, nanoHUB.org continues to attract a large community of users for computational tools and learning materials related to nanotechnology [1, 2]. Over the last 12 months, nanoHUB has engaged over 1.4 million visitors and 13,000 simulation users with over 5,000 items of content, making it a premier example of an established science gateway. The nanoHUB team tracks references to nanoHUB in the scientific literature and have found nearly 1,600 vetted citations to nanoHUB, with over 19,000 secondary citations to the primary papers, supporting the concept that nanoHUB enables quality research. nanoHUB is also used extensively for both informal and formal education [3,4], with automatic algorithms detecting use in 1,501 classrooms reaching nearly 30,000 students. During 14 years of operation, the nanoHUB team has had an opportunity to study the behaviors of its user base, evaluate mechanisms for success, and learn when and how to make adjustments to better serve the community and stakeholders. We have developed a set of success criteria for a science gateway such as nanoHUB, for attracting and growing an active community of users. Outstanding science content is necessary and that content must continue to expand or the gateway and community will grow stagnant. A large challenge is to incentivize a community to not only use the site, but more importantly, to contribute [5,6]. There is often a recruitment and conversion process that involves, first, attracting users, giving them reason to stay, use, and share increasingly complex content, and then go on to become content authors themselves. This process requires a good understanding of the user community and its needs as well as an active outreach program, led by a user-oriented content steward with a technical background sufficient to understand the work and needs of the community. A reliable infrastructure is a critical key to maintaining an active, participatory community. Using underlying HUBzero® technology, nanoHUB is able to leverage infrastructure developments from across a wide variety of hubs, and by utilizing platform support from the HUBzero team, access development and operational expertise from a team of 25 professionals that one scientific project would be hard-pressed to support on its own. nanoHUB has found that open assessment and presentation of stats and impact metrics not only inform development and outreach activities but also incentivize users and provide transparency to the scientific community at large.


2021 ◽  
Author(s):  
Michael Christian Leitner ◽  
Frank Daumann ◽  
Florian Follert ◽  
Fabio Richlan

The phenomenon of home advantage (or home bias) is well-analyzed in the scientific literature and is traditionally an interdisciplinary topic. Current theorizing views the fans as a crucial factor influencing the outcome of a football (a.k.a. soccer) game, as the crowd influences the behavior of the players and officials involved in the game through social pressure. So far, the phenomenon has been difficult to study because, although there have always been single matches where the spectators were excluded, this never happened globally to all teams within a league or even across leagues. From an empirical perspective, the situation with COVID-19 governmental measures, especially the ban of fans from stadiums all over the world, can be interpreted as a “natural experiment” and analyzed accordingly. Thus, several studies examined the influence of supporters by comparing matches before the COVID-19 restrictions with so-called ghost games during the pandemic. To synthesize the existing knowledge after over a year of ghost games and to offer the scientific community and other stakeholders an overview regarding the numerous studies, we provide a systematic literature review that summarizes the main findings of empirical studies and discusses the results accordingly. Our findings - based on 16 studies - indicate that ghost games have a considerable impact on the phenomenon of home advantage. No study found an increased home advantage in ghost games. Rather, our results show that 13 (from 16 included) analyzed studies conclude – based on their individually analyzed data – a more or less significant decrease of home advantage in ghost games. We conclude that our findings are highly relevant from a both socio-economic and behavioral perspective and highlight the indirect and direct influence of spectators and fans on football. Our results have – besides for the scientific community – a high importance for sports and team managers, media executives, fan representatives and other responsible.


Author(s):  
Luc Schneider

This contribution tries to assess how the Web is changing the ways in which scientific knowledge is produced, distributed and evaluated, in particular how it is transforming the conventional conception of scientific authorship. After having properly introduced the notions of copyright, public domain and (e-)commons, I will critically assess James Boyle's (2003, 2008) thesis that copyright and scientific (e-) commons are antagonistic, but I will mostly agree with the related claim by Stevan Harnad (2001a,b, 2008) that copyright has become an obstacle to the accessibility of scientific works. I will even go further and argue that Open Access schemes not only solve the problem of the availability of scientific literature, but may also help to tackle the uncontrolled multiplication of scientific publications, since these publishing schemes are based on free public licenses allowing for (acknowledged) re-use of texts. However, the scientific community does not seem to be prepared yet to move towards an Open Source model of authorship, probably due to concerns related to attributing credit and responsability for the expressed hypotheses and results. Some strategies and tools that may encourage a change of academic mentality in favour of a conception of scientific authorship modelled on the Open Source paradigm are discussed.


Author(s):  
Anderson Rossanez ◽  
Julio Cesar dos Reis ◽  
Ricardo da Silva Torres ◽  
Hélène de Ribaupierre

Abstract Background Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. Methods In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. Results The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. Conclusions We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.


2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 2019-2019 ◽  
Author(s):  
Deepa Suresh Subramaniam ◽  
Joanne Xiu ◽  
Shwetal Mehta ◽  
Zoran Gatalica ◽  
Jeffrey Swensen ◽  
...  

2019 Background: Fusions involving oncogenes have been reported in gliomas and may serve as novel therapeutic targets. We aim to use RNA-sequencing to interrogate a large cohort of gliomas for targetable genetic fusions. Methods: Gliomas were profiled using the ArcherDx FusionPlex Assay at a CLIA-certified lab (Caris Life Sciences) and 52 gene targets were analyzed. Fusions with preserved kinase domains were investigated. Results: Among 404 gliomas tested, 39 (9.7%) presented potentially targetable fusions, of which 24/226 (11%) of glioblastoma (GBM), 5/42 (12%) of anaplastic astrocytoma (AA), 2/25 (8%) of grade II astrocytoma and 3 of 7 (43%) of pilocytic astrocytoma (PA) harbored targetable fusions. In GBMs, 1 of 15 (6.7%) IDH-mutated tumors had a fusion while 22 of 175 (12.6%) IDH-wild type tumors had fusions. 46 oligodendroglial tumors were profiled and no fusions were seen, which was lower than frequency of fusions in astrocytic tumors (34/300, p = 0.0236). The most frequent fusions seen involved FGFR3 (N = 12), including 10 FGFR3-TACC3 (1 AA, 6 GBM and 3 glioma NOS); 1 FGFR3-NBR1 (AA) and 1 FGFR3-BRAP (GBM). 11 fusions involving MET were seen, 10 in GBM and 1 in AA. The most common MET fusion was PTPRZ1-MET (1 in AA and 4 in GBM), followed by ST7-MET (N = 3, GBM), CAPZA2-Met (N = 2, GBM) and TPR-MET (N = 1, GBM). 8 NTRK fusions were seen; 1 involving NTRK1 (BCAN-NTRK1, PA), 6 NTRK2 (1 NOS1AP-NTRK2 in AA; GKAP1-NTRK2, KCTD8-NTRK2, TBC1D2-NTRK2 and SOSTM1-NTRK2, 1 each in GBM and 1 VCAN-NTRK2 in grade II astrocytoma) and 1 NTRK3 (EML4-NTRK3 in GBM). EGFR fusions (2 EGFR-SEPT14 and 1 EGFR-VWC2) were seen in 3 GBMs, BRAF in 3 (1 KIAA1549-BRAF, 1 LOC100093631-BRAF in PA and 1 ZSCAN23-BRAF in glioma NOS) and PDGFRA (RAB3IP-PDGFRA, in GBM) in 1. C11orf95-RELA fusions were seen in 2 of 3 grade III ependymomas but not in the 2 grade II ependymomas. Conclusions: We report targetable fusion genes involving NTRK, MET, EGFR, FGFR3, BRAF and PDGFRA including novel fusions that haven’t been previously described in gliomas (e.g., EGFR-VWC2; FGFR3-NBR1). Fusions were seen in over 10% of astrocytic tumors, while none was seen oligodendrogliomas. Identification of such kinase-associated fusion transcripts may allow us to exploit therapeutic opportunities with targeted therapies in gliomas.


2002 ◽  
Vol 13 (12) ◽  
pp. 4111-4113 ◽  
Author(s):  
Ian G. Macara ◽  
Richard Baldarelli ◽  
Christine M. Field ◽  
Michael Glotzer ◽  
Yasuhide Hayashi ◽  
...  

There are 10 known mammalian septin genes, some of which produce multiple splice variants. The current nomenclature for the genes and gene products is very confusing, with several different names having been given to the same gene product and distinct names given to splice variants of the same gene. Moreover, some names are based on those of yeast or Drosophilaseptins that are not the closest homologues. Therefore, we suggest that the mammalian septin field adopt a common nomenclature system, based on that adopted by the Mouse Genomic Nomenclature Committee and accepted by the Human Genome Organization Gene Nomenclature Committee. The human and mouse septin genes will be namedSEPT1–SEPT10 and Sept1–Sept10, respectively. Splice variants will be designated by an underscore followed by a lowercase “v” and a number, e.g., SEPT4_v1.


2019 ◽  
Author(s):  
Xiaokang Lyu ◽  
Yuepei Xu ◽  
Xiaofan Zhao ◽  
Xi-Nian Zuo ◽  
Hu Chuan-Peng

P-value and confidence intervals (CIs) are the most widely used statistical indices in scientific literature. Several surveys revealed that these two indices are generally misunderstood. However, existing surveys on this subject fall under psychology and biomedical research, and data from other disciplines are rare. Moreover, the confidence of researchers when constructing judgments remains unclear. To fill this research gap, we survey 1,479 researchers and students from different fields in China. Results reveal that for significant (p < .05, CI doesn’t include 0) and non-significant (p > .05, CI includes 0) conditions, most respondents, regardless of academic degrees, research fields, and stages of career, could not interpret p-value and CI accurately. Moreover, the majority of them are confident about their (inaccurate) judgments (see osf.io/mcu9q/ for raw data, materials, and supplementary analyses). Therefore, misinterpretations of p-value and CIs prevail in the whole scientific community, thus the need for statistical training in science.


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