RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
RT @sborg40: Roughly there are “policing” genes (tumor suppression genes) working incessantly all the time protecting us against oncogenes…
RT @sborg40: Roughly there are “policing” genes (tumor suppression genes) working incessantly all the time protecting us against oncogenes…
RT @sborg40: Roughly there are “policing” genes (tumor suppression genes) working incessantly all the time protecting us against oncogenes…
RT @sborg40: Roughly there are “policing” genes (tumor suppression genes) working incessantly all the time protecting us against oncogenes…
Roughly there are “policing” genes (tumor suppression genes) working incessantly all the time protecting us against oncogenes producing cancer, which are occurring all the time but progressively increase with age. https://t.co/n5nKJGb7J4
RT @raphaels7: Wrong How do i know? Because @P_J_Buckhaults cannot predict the occurrence of cancers from mutations. Worse than that, he c…
Wrong How do i know? Because @P_J_Buckhaults cannot predict the occurrence of cancers from mutations. Worse than that, he can’t even characterize cancers according to their mutations Also, when we transplant cancer genomes into normal cells, no cancer. B
Ko zna zna. Konkretan odgovor na pitanje bez špekulacija i teorija zavjere.
Cancer
RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
This is why humanity should cure cancer. With the help of pfizer by creating cancer
RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
RT @P_J_Buckhaults: @stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function…
@stopvaccinating loss of function mutations in tumor suppressor genes (like APC, TP53, PTEN, RB1) and gain of function mutations in oncogenes (like KRAS, BRAF, ERBB2, PIK3CA). https://t.co/fHA3JIjvne . these mutations happen in normal cells as we age.
RT @eportacangen: A little bit of backstory: remember when, back in 2018, we were young, didn’t know what COVID was and TCGA was finishing?…
A little bit of backstory: remember when, back in 2018, we were young, didn’t know what COVID was and TCGA was finishing? We published this study, co-led between @HawkinsBailey, @ctokheim and yours truly, where we looked for driver genes and mutations htt
Using 26 computational tools to catalog driver genes and mutations, Bailey et al identify 299 driver genes with implications regarding cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations https:/
@DMarzese They were derived from this paper: https://t.co/9M5kmFSUtL
299 unique Landscape of Cancer Driver Genes obtained from a systematic approach and manual curation of previous TCGA marker papers with 26 out of 41,63% supported by -omics network tools not used in original significantly mutated gene SMG detection studies
RT @ctokheim: @sbarnettARK That being said. Simple models that use protein structure are surprisingly effective. See validation rates in th…
@sbarnettARK That being said. Simple models that use protein structure are surprisingly effective. See validation rates in this: https://t.co/CK3XfjlnIa
@anshulkundaje @hjpimentel @arjunrajlab @michaelhoffman I agree that the ideal setup is to integrate multiple tools. Actually, we've done that before for driver genes (https://t.co/zVPyejjJzR). But unfortunately integrating multiple can often be complex an
@sbarnettARK @junglainc @claraya @AlbertVilella And here how methods that use 3D structures are the best at separating oncogenic mutations from passenger ones in cancer driver genes https://t.co/2ylSvK4mGz
実際の使用例はこちら💁♂️ Bailey MH, et al. (2019) Comprehensive Characterization of Cancer Driver Genes and Mutations. Cells. https://t.co/EXYG32gKe8 https://t.co/PpPjqhU5K9
RT @ctokheim: @jgschraiber @cj_battey In cancer for somatic variant interpretation, the conclusion of coding variants as high effect size i…
@jgschraiber @cj_battey In cancer for somatic variant interpretation, the conclusion of coding variants as high effect size is completely justified. TCGA WES identifies many driver mutations (https://t.co/zVPyejjJzR), while ICGC WGS found only ~<10% rol
We and others have found that spatial clustered mutations in protein structure often have higher validation rates (~78%). https://t.co/CK3XfjlnIa 8/9
@ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller Sources for which genes are tumor suppressor genes include: 1) Cancer Genome Landscape review (https://t.co/mp5E1qjeEC); 2) Per cancer type annotation of the TCGA (Table S1, see https://t.co/zVPyejjJzR
RT @ctokheim: @ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller A couple of visual ways: 1) lollipop diagrams display the linear seque…
@ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller A couple of visual ways: 1) lollipop diagrams display the linear sequence (usually protein) of your mutation with all mutations in a cohort. 2) Closeness in 3D protein structure is a much better predic
RT @eportacangen: Cool! Un dels gràfics que vam fer pel projecte de l'Atles Genòmic del Càncer (TCGA en anglès) surt a un documental de la…
Cool! Un dels gràfics que vam fer pel projecte de l'Atles Genòmic del Càncer (TCGA en anglès) surt a un documental de la PBS sobre genòmica https://t.co/T4RFU7VPyq
RT @ctokheim: Wow! #TheGenePBS discusses cancer genetics and shows a 10 second cameo of a figure from one of my co-first author papers (htt…
RT @ctokheim: Wow! #TheGenePBS discusses cancer genetics and shows a 10 second cameo of a figure from one of my co-first author papers (htt…
RT @ctokheim: Wow! #TheGenePBS discusses cancer genetics and shows a 10 second cameo of a figure from one of my co-first author papers (htt…
RT @ctokheim: Wow! #TheGenePBS discusses cancer genetics and shows a 10 second cameo of a figure from one of my co-first author papers (htt…
Wow! #TheGenePBS discusses cancer genetics and shows a 10 second cameo of a figure from one of my co-first author papers (https://t.co/GuYrxOOky2, timestamp 54:45 & https://t.co/OOJTByJHiS, Figure 2). @KarchinRachel
RT @eportacangen: These results also align very well with our findings in TCGA: mutations in cancer genes look very different in tissues wh…
These results also align very well with our findings in TCGA: mutations in cancer genes look very different in tissues where they are "known" to be oncogenic compared to those where they are not (Figure 3) https://t.co/2ylSvK4mGz
RT @NCIDataSci: The Cancer Genome Atlas project used 26 computational tools to identify 299 driver genes, which are critical for #precision…
RT @eportacangen: @ksamocha @laurensvdwiel We found that, whenever they're available, protein structures are usually better to separate ben…
@ksamocha @laurensvdwiel We found that, whenever they're available, protein structures are usually better to separate benign (passenger) mutations from those that are more oncogenic (driver) https://t.co/2ylSvK4mGz
@AbbiesArmy Please invite the TCGA consortium. As of 2018 #DIPG is not one of the 33 cancers studied, nor is #H3K27M included in its "Comprehensive Characterization of Cancer Driver Genes and Mutations." https://t.co/PBaHSK7xnD Perhaps pediatric & adu
EK: Cancer driver genes get altered through copy number or mutations. #19w5128 https://t.co/flvdjcBhwy https://t.co/zrpCxc3UXP
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @mmw_lmw: #PanCancer Driver Genes and Mutations | 299 driver genes identified: Discovery and Validation | #OpenAccess | LiDing @WUSTL @C…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
Quin laboriós gràfic. M'imagino hores i hores, peça a peça. Meravellós.
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @eportacangen: Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenom…
Our paper was chosen as one of the 10 NCI's genomics favourite papers in 2018. Congrats everybody and thank you @NCIgenomics!
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @YungangXu: Comprehensive Characterization of Cancer Driver Genes and Mutations: Cell https://t.co/gs4rJgbeeQ
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @YungangXu: Comprehensive Characterization of Cancer Driver Genes and Mutations: Cell https://t.co/gs4rJgbeeQ
RT @YungangXu: Comprehensive Characterization of Cancer Driver Genes and Mutations: Cell https://t.co/gs4rJgbeeQ
Comprehensive Characterization of Cancer Driver Genes and Mutations: Cell https://t.co/gs4rJgbeeQ
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @mmw_lmw: #PanCancer Driver Genes and Mutations | 299 driver genes identified: Discovery and Validation | #OpenAccess | LiDing @WUSTL @C…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…
RT @NCIgenomics: #9: #PanCancer atlas pipeline of 26 computational tools identifies ~3400 mutations across 299 driver genes from #TCGA exom…