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Gene, BRCA2

Q: What is the BRCA2 gene and what is its role in the body?

The BRCA2 gene is a tumor suppressor gene located on chromosome 13q12-13. It encodes a large protein involved in DNA repair, cell cycle regulation, and transcriptional regulation. Germline mutations in BRCA2 are associated with an increased risk of breast, ovarian, pancreatic, and other cancers.

Gene, BRCA2: A tumor suppressor gene located on chromosome 13q12-13.
Germline mutations in BRCA2 are associated with an increased risk of breast, ovarian, pancreatic, and other cancers.
BRCA2 encodes a large protein invloved in DNA repair, cell cycle regulation, and transcriptional regulation.
PubCompare.ai's AI-powered tools help researchers optimize BRCA2 research protocols, locating the most effective methods from literature, preprints, and patents to advance gene research and improve patient outcomes.

Most cited protocols related to «Gene, BRCA2»

Ancestry Inference and Lung Cancer GWAS Meta-Analysis

FlashPCA^{30 (link)} was run for principal component analysis (PCA) to infer genetic ancestry by genotype. The regression model assumed an additive genetic model and included the first three eigenvalues from FlashPCA as covariates. For imputed data of smaller sample size, which was enrolled in our analysis later, we changed the method score to EM algorithm to accommodate smaller sample size.
We combined imputed genotypes from 14,803 cases and 12,262 controls from the OncoArray series with 14,436 cases and 44,188 controls samples undertaken by the previous lung cancer GWAS^{3 (link),4 (link),6 (link)}, including studies of IARC, MDACC, SLRI, ICR, Harvard, NCI, Germany and deCODE as described previously^{3 (link),4 (link),6 (link)}, and we ensured that there were no overlap between the ATBC, EAGLE and CARET studies included in both the previous GWAS and current OncoArray dataset by comparing the identity tags (IDs) of all study participants.
In addition to lung cancer, analyses by histological strata (adenocarcinoma, squamous cell carcinoma, small cell carcinoma (SCLC) and smoking status (Ever/Never) was assessed where data were available. Results from analyses defined by Ever and Never smoking strata did not identify any novel variants.
We conducted the fixed effects meta-analysis with the inverse variance weighting and random effects meta-analysis from the DerSimonian-Laird method³¹. All meta-analysis and calculations were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). As the same referent panel was used for all studies, all SNPs showed the same forward alignment profiles. We excluded poorly imputed SNPs defined by imputation quality R² < 0.3 or Info < 0.4 for each meta-analysis component and SNPs with a Minor allele frequency (MAF) >0.01 (except for CHEK2 rs17879961 and BRCA2 rs11571833 which we have validated extensively previously^{4 (link)}. We generated the index of heterogeneity(I²) and P-value of Cochran’s Q statistic to assess heterogeneity in meta-analyses and considered only variants with little evidence for heterogeneity in effect between the studies (P-value of Cochran’s Q statistic >0.05). SNPs were retained for study provided the average imputation R-square was at least 0.4. For SNPs in the 0.4–0.8 range that reached genome wide significance results were evaluated for consistency with neighboring SNPs to assure a reliable inference. Due to the smaller sample size and fewer sites contributing in the strata of Never Smokers and SCLC, we additionally required variants to be present in each of the meta-analysis components to be retained for these 2 stratified analyses.
Conditional analysis was undertaken using SNPTEST where individual level data was available and GCTA^{32 (link)} packages for the previous lung cancer GWAS, with the LD estimates obtained from individuals of European origin for the later. Results were combined using fixed effects inverse variance weighted meta-analysis as described above^{33 (link)}.

McKay J.D., Hung R.J., Han Y., Zong X., Carreras-Torres R., Christiani D.C., Caporaso N.E., Johansson M., Xiao X., Li Y., Byun J., Dunning A., Pooley K.A., Qian D.C., Ji X., Liu G., Timofeeva M.N., Bojesen S.E., Wu X., Le Marchand L., Albanes D., Bickeböller H., Aldrich M.C., Bush W.S., Tardon A., Rennert G., Teare M.D., Field J.K., Kiemeney L.A., Lazarus P., Haugen A., Lam S., Schabath M.B., Andrew A.S., Shen H., Hong Y.C., Yuan J.M., Bertazzi P.A., Pesatori A.C., Ye Y., Diao N., Su L., Zhang R., Brhane Y., Leighl N., Johansen J.S., Mellemgaard A., Saliba W., Haiman C.A., Wilkens L.R., Fernandez-Somoano A., Fernandez-Tardon G., van der Heijden H.F., Kim J.H., Dai J., Hu Z., Davies M.P., Marcus M.W., Brunnström H., Manjer J., Melander O., Muller D.C., Overvad K., Trichopoulou A., Tumino R., Doherty J.A., Barnett M.P., Chen C., Goodman G.E., Cox A., Taylor F., Woll P., Brüske I., Wichmann H.E., Manz J., Muley T.R., Risch A., Rosenberger A., Grankvist K., Johansson M., Shepherd F.A., Tsao M.S., Arnold S.M., Haura E.B., Bolca C., Holcatova I., Janout V., Kontic M., Lissowska J., Mukeria A., Ognjanovic S., Orlowski T.M., Scelo G., Swiatkowska B., Zaridze D., Bakke P., Skaug V., Zienolddiny S., Duell E.J., Butler L.M., Koh W.P., Gao Y.T., Houlston R.S., McLaughlin J., Stevens V.L., Joubert P., Lamontagne M., Nickle D.C., Obeidat M., Timens W., Zhu B., Song L., Kachuri L., Artigas M.S., Tobin M.D., Wain L.V., Rafnar T., Thorgeirsson T.E., Reginsson G.W., Stefansson K., Hancock D.B., Bierut L.J., Spitz M.R., Gaddis N.C., Lutz S.M., Gu F., Johnson E.O., Kamal A., Pikielny C., Zhu D., Lindströem S., Jiang X., Tyndale R.F., Chenevix-Trench G., Beesley J., Bossé Y., Chanock S., Brennan P., Landi M.T, & Amos C.I. (2017). Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nature genetics, 49(7), 1126-1132.

Publication 2017

Adenocarcinoma Carcinoma, Small Cell CHEK2 protein, human Eagle Europeans Gene, BRCA2 Genetic Heterogeneity Genome Genome-Wide Association Study Genotype Lung Cancer Single Nucleotide Polymorphism Squamous Cell Carcinoma

Comprehensive Gene Expression Datasets

Three publicly available datasets were employed to represent a broad range of gene expression studies performed in practice. The first dataset consists of gene expression measurements for 6,216 genes in 112 segregants of a cross between two isogenic strains of yeast, as well as genotypes across 3,312 markers [10 (link),21 (link)]. The second dataset consists of gene expression for 3,226 genes in seven BRCA1 and eight BRCA2 mutation–positive tumor samples [22 (link)]; several genes with apparent outliers were removed as described [23 (link)] for a total of 3,170 genes. The third dataset consists of gene expression measurements in kidney samples from normal kidney tissue obtained at nephrectomy from 133 patients [7 ]; the 34,061 genes analyzed in [8 (link)] were also analyzed here. Seventy-four of the tissue samples were obtained from the cortex and 59 from the medulla. Details of the protocol for each study appear in the corresponding references. All expression data were analyzed on the log scale.

Leek J.T, & Storey J.D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics, 3(9), e161.

Publication 2007

Cortex, Cerebral Gene, BRCA1 Gene, BRCA2 Gene Expression Genes Genotype Kidney Medulla Oblongata Mutation Neoplasms Nephrectomy Patients Saccharomyces cerevisiae Strains Tissues

Genome-wide Breast Cancer Associations

We attempted to identify all associations previously reported from genome-wide or candidate analysis at a significance level P<5x10^-8 for overall breast cancer, ER-negative or ER-positive breast cancer, in BRCA1 or BRCA2 carriers, or in meta-analyses of these categories. Where multiple studies reported associations in the same region, we used the first reported association unless later studies identified a variant that was clearly more strongly associated. We only included one SNP per 500kb interval, unless joint analysis provided clear evidence (P<5x10^-8) of more than one independent signal. For the analysis of credible risk variants (CRVs), we restricted attention to regions where the most significant signal had a P-value<10^-7 in Europeans (77 regions). To avoid complications with defining CRVs for secondary signals, we considered only the primary signal and defined CRVs as those whose P-value was within two orders of magnitude of the most significant P-value.

Michailidou K., Lindström S., Dennis J., Beesley J., Hui S., Kar S., Lemaçon A., Soucy P., Glubb D., Rostamianfar A., Bolla M.K., Wang Q., Tyrer J., Dicks E., Lee A., Wang Z., Allen J., Keeman R., Eilber U., French J.D., Chen X.Q., Fachal L., McCue K., McCart Reed A.E., Ghoussaini M., Carroll J., Jiang X., Finucane H., Adams M., Adank M.A., Ahsan H., Aittomäki K., Anton-Culver H., Antonenkova N.N., Arndt V., Aronson K.J., Arun B., Auer P.L., Bacot F., Barrdahl M., Baynes C., Beckmann M.W., Behrens S., Benitez J., Bermisheva M., Bernstein L., Blomqvist C., Bogdanova N.V., Bojesen S.E., Bonanni B., Børresen-Dale A.L., Brand J.S., Brauch H., Brennan P., Brenner H., Brinton L., Broberg P., Brock I.W., Broeks A., Brooks-Wilson A., Brucker S.Y., Brüning T., Burwinkel B., Butterbach K., Cai Q., Cai H., Caldés T., Canzian F., Carracedo A., Carter B.D., Castelao J.E., Chan T.L., Cheng T.Y., Chia K.S., Choi J.Y., Christiansen H., Clarke C.L., Collée M., Conroy D.M., Cordina-Duverger E., Cornelissen S., Cox D.G., Cox A., Cross S.S., Cunningham J.M., Czene K., Daly M.B., Devilee P., Doheny K.F., Dörk T., dos-Santos-Silva I., Dumont M., Durcan L., Dwek M., Eccles D.M., Ekici A.B., Eliassen A.H., Ellberg C., Elvira M., Engel C., Eriksson M., Fasching P.A., Figueroa J., Flesch-Janys D., Fletcher O., Flyger H., Fritschi L., Gaborieau V., Gabrielson M., Gago-Dominguez M., Gao Y.T., Gapstur S.M., García-Sáenz J.A., Gaudet M.M., Georgoulias V., Giles G.G., Glendon G., Goldberg M.S., Goldgar D.E., González-Neira A., Grenaker Alnæs G.I., Grip M., Gronwald J., Grundy A., Guénel P., Haeberle L., Hahnen E., Haiman C.A., Håkansson N., Hamann U., Hamel N., Hankinson S., Harrington P., Hart S.N., Hartikainen J.M., Hartman M., Hein A., Heyworth J., Hicks B., Hillemanns P., Ho D.N., Hollestelle A., Hooning M.J., Hoover R.N., Hopper J.L., Hou M.F., Hsiung C.N., Huang G., Humphreys K., Ishiguro J., Ito H., Iwasaki M., Iwata H., Jakubowska A., Janni W., John E.M., Johnson N., Jones K., Jones M., Jukkola-Vuorinen A., Kaaks R., Kabisch M., Kaczmarek K., Kang D., Kasuga Y., Kerin M.J., Khan S., Khusnutdinova E., Kiiski J.I., Kim S.W., Knight J.A., Kosma V.M., Kristensen V.N., Krüger U., Kwong A., Lambrechts D., Marchand L.L., Lee E., Lee M.H., Lee J.W., Lee C.N., Lejbkowicz F., Li J., Lilyquist J., Lindblom A., Lissowska J., Lo W.Y., Loibl S., Long J., Lophatananon A., Lubinski J., Luccarini C., Lux M.P., Ma E.S., MacInnis R.J., Maishman T., Makalic E., Malone K.E., Kostovska I.M., Mannermaa A., Manoukian S., Manson J.E., Margolin S., Mariapun S., Martinez M.E., Matsuo K., Mavroudis D., McKay J., McLean C., Meijers-Heijboer H., Meindl A., Menéndez P., Menon U., Meyer J., Miao H., Miller N., Mohd Taib N.A., Muir K., Mulligan A.M., Mulot C., Neuhausen S.L., Nevanlinna H., Neven P., Nielsen S.F., Noh D.Y., Nordestgaard B.G., Norman A., Olopade O.I., Olson J.E., Olsson H., Olswold C., Orr N., Pankratz V.S., Park S.K., Park-Simon T.W., Lloyd R., Perez J.I., Peterlongo P., Peto J., Phillips K.A., Pinchev M., Plaseska-Karanfilska D., Prentice R., Presneau N., Prokofieva D., Pugh E., Pylkäs K., Rack B., Radice P., Rahman N., Rennert G., Rennert H.S., Rhenius V., Romero A., Romm J., Ruddy K.J., Rüdiger T., Rudolph A., Ruebner M., Rutgers E.J., Saloustros E., Sandler D.P., Sangrajrang S., Sawyer E.J., Schmidt D.F., Schmutzler R.K., Schneeweiss A., Schoemaker M.J., Schumacher F., Schürmann P., Scott R.J., Scott C., Seal S., Seynaeve C., Shah M., Sharma P., Shen C.Y., Sheng G., Sherman M.E., Shrubsole M.J., Shu X.O., Smeets A., Sohn C., Southey M.C., Spinelli J.J., Stegmaier C., Stewart-Brown S., Stone J., Stram D.O., Surowy H., Swerdlow A., Tamimi R., Taylor J.A., Tengström M., Teo S.H., Terry M.B., Tessier D.C., Thanasitthichai S., Thöne K., Tollenaar R.A., Tomlinson I., Tong L., Torres D., Truong T., Tseng C.C., Tsugane S., Ulmer H.U., Ursin G., Untch M., Vachon C., van Asperen C.J., Van Den Berg D., van den Ouweland A.M., van der Kolk L., van der Luijt R.B., Vincent D., Vollenweider J., Waisfisz Q., Wang-Gohrke S., Weinberg C.R., Wendt C., Whittemore A.S., Wildiers H., Willett W., Winqvist R., Wolk A., Wu A.H., Xia L., Yamaji T., Yang X.R., Yip C.H., Yoo K.Y., Yu J.C., Zheng W., Zheng Y., Zhu B., Ziogas A., Ziv E., Lakhani S.R., Antoniou A.C., Droit A., Andrulis I.L., Amos C.I., Couch F.J., Pharoah P.D., Chang-Claude J., Hall P., Hunter D.J., Milne R.L., García-Closas M., Schmidt M.K., Chanock S.J., Dunning A.M., Edwards S.L., Bader G.D., Chenevix-Trench G., Simard J., Kraft P, & Easton D.F. (2017). Association analysis identifies 65 new breast cancer risk loci. Nature, 551(7678), 92-94.

Publication 2017

Attention BRCA1 protein, human Europeans Gene, BRCA2 Genome Joints Malignant Neoplasm of Breast

Genome-wide Breast Cancer Associations

Publication 2017

Attention BRCA1 protein, human Europeans Gene, BRCA2 Genome Joints Malignant Neoplasm of Breast

Benchmarking Mutation Effect Predictors

To standardize the procedure of compiling mutations that can be employed for the benchmarking of mutation effect predictors, mutations affecting six bona fide oncogenes (BRAF, KIT, PIK3CA, KRAS, EGFR, and ERRB2), whose mutations preferentially affect kinase domains, six recently described cancer genes (DICER1, ESR1, IDH1, IDH2, MYOD1, and SF3B1), whose mutations do not affect kinase domains, and three bona fide TSGs (TP53, BRCA1, and BRCA2) were retrieved from the TCGA Pan-Cancer dataset by Kandoth et al. [36 (link)] and from studies functionally testing mutations affecting these genes (Additional file 2). In addition, for TSGs, specific databases were employed; for TP53, the IARC database [29 (link),37 ], and for BRCA1 and BRCA2, the Universal Mutation Database (UMD) [28 (link),38 ,39 ]. This mining exercise resulted in the identification of 3,706 mutations, of which 3,591 were SNVs (Table 1, Additional file 2). Given that some mutation effect prediction algorithms (that is, PolyPhen-2, MutationTaster, CanDrA, and Condel) do not process dinucleotide or trinucleotide missense mutations, and to have the same number of mutations successfully analyzed by each predictor, we have only included SNVs for the purpose of creating a mutation dataset to benchmark mutation effect predictors. SNVs were also annotated based on their presence in the COSMIC dataset v68 [26 (link)].

Martelotto L.G., Ng C.K., De Filippo M.R., Zhang Y., Piscuoglio S., Lim R.S., Shen R., Norton L., Reis-Filho J.S, & Weigelt B. (2014). Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome Biology, 15(10), 484.

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Publication 2014

BRAF protein, human BRCA1 protein, human Cosmic composite resin DICER1 protein, human Dinucleoside Phosphates EGFR protein, human Gene, BRCA2 Gene, Cancer Genes IDH2, human K-ras Genes Malignant Neoplasms Missense Mutation Mutation MYOD1 protein, human Oncogenes Phosphotransferases PIK3CA protein, human TP53 protein, human

Most recents protocols related to «Gene, BRCA2»

PARG Inhibitor Effects on Colony Formation

Not available on PMC !

Example 4

Colony formation assay was performed using HCC1937 (BRCA1-deficient breast cancer cells), HCC1937 BRCA1 (BRCA1-reconstituted HCC1937 cells) cells, PEO-1 (BRCA2-deficient ovarian cancer cells), and PEO-4 (BRCA2-reconstituted PEO-1 cells) with indicated concentrations of PARG inhibitor (#34).

Colony formation assay: HCC1937, HCC1937-BRCA1, PEO-1 or PEO-4 (˜1000 cells) were seeded into six-well plates and then treated by various doses of PARG inhibitor (#34). After 14-21 days of culture, the viable cells were fixed by methanol and stained with crystal violet. The number of colonies (>50 cells for each colony) was calculated.

US11858879B2. PARG inhibitors and method of use thereof (2024-01-02). City of Hope [US]. Inventors: Xiaochun Yu [US], Shih-Hsun Chen [US], Yate-Ching Yuan [US], Hongzhi Li [US], David Horne [US].

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Patent 2024

Biological Assay BRCA1 protein, human Cells Gene, BRCA2 Malignant Neoplasm of Breast Malignant Neoplasms Methanol Ovarian Cancer Violet, Gentian

Breast Cancer Diagnostic Accuracy

After screening the related studies in the databases, all included diagnostic trials needed to meet the following inclusion and exclusion criteria. Inclusion criteria were as follows: the study population were cases with a confirmed diagnosis of breast cancer; diagnostic methods used were mammography or MRI; the gold standard for diagnosis of breast cancer was pathological examination; Include high-risk factors: BRCA1 or BRCA2 mutation carriers, personal or family history of breast or ovarian cancer, history of prior chest radiation. Exclusion criteria were as follows: case report or review study type; patients had obvious symptoms of breast cancer during screening; and duplicated publication or data.

Ding W., Fan Z., Xu Y., Wei C., Li Z., Lin Y., Zhu J, & Ruan G. (2023). Magnetic resonance imaging in screening women at high risk of breast cancer: A meta-analysis. Medicine, 102(10), e33146.

Publication 2023

BRCA1 protein, human Breast Breast Carcinoma Chest Diagnosis Gene, BRCA2 Gold Malignant Neoplasm of Breast Mammography Mutation Ovarian Cancer Patients Radiotherapy

Western Blotting for DNA Damage Signaling

The standard western blotting protocol was conducted to detect the levels of indicated proteins as described previously (Yuan et al, 2017 (link); Li et al, 2021 (link)). Antibody against GAPDH (AF0006) was from Beyotime (Shanghai, China). Antibodies against γ‐H2AX (#2577), CDT1 (#8064), Chk1 (#2360), p‐Chk1(S317, #2344), p‐Chk2 (Thr68, #2661), caspase‐3 (#9662), caspase‐7 (#12827), caspase 9 (#9502), PARP (#9542), Bak (#12105), BID (#2002), Puma (#4976), Noxa (#14766), Bcl‐XL (#2764), XIAP (#14334), MRE11 (#4895), c‐IAP1 (#7943), c‐IAP2 (#3130), STAT1 (#14994), p‐STAT1 (Tyr701, #9167), p‐STAT1 (Ser727, #8826), STAT3 (#9139), p‐STAT3 (Ser727, #9145), STING (#13647), p‐STING (#19781), TBK1 (#3504), p‐TBK1 (#5483), IRF3 (#11904), p‐IRF3 (Ser386, #37829), p38 (#9212), p‐p38 (#9211), ERK1/2 (#9102), p‐ERK1/2 (#4370), MEK1/2 (#4694), p‐MEK1/2 (#9154), JNK (#9252), Axin2 (#2151), KU70 (#4588) and KU80 (#2753) were from Cell Signaling Technology. Antibodies against RPA32 (sc‐271578), Bax (sc‐493), MCL1 (sc‐819), PTIP (sc‐367459), Chk2 (sc‐9604), PARP2 (sc‐30622), XRCC1 (sc‐11429), XRCC3 (sc‐271714), MLH1 (sc‐581), MSH2 (sc‐494), TNKS1/2 (sc‐365897), p‐JNK (sc‐6254), PARP1 (sc‐7150) and PAR [pADPr (10H) (sc‐56198)] were from Santa Cruz Biotechnology (Santa Cruz, CA). Antibody against p‐RPA32 (PLA0071) was from Sigma (Shanghai, China). Antibody against RAD51 (ab63801), CTIP (ab70163), MCRS1/MSP58 (ab247013) and PALB2 (ab202970) were from Abcam. Antibody against MAD2L2/REV7 (BD‐612266) was from BD Biosciences. Antibody against BRCA1 (OP92) and BRCA2 (OP95) were from Millipore. Goat anti‐mouse IgG horseradish peroxidase antibody was provided by Merk/Calbiochem (Darmstadt, Germany). All of the primary antibodies, except for GAPDH, were used after 1:1,000 dilution, and GAPDH primary antibody was used after 1:5,000 dilution. Second antibodies were used following 1:2,000 dilution.

Wang L., Wang P., Chen X., Yang H., Song S., Song Z., Jia L., Chen H., Bao X., Guo N., Huan X., Xi Y., Shen Y., Yang X., Su Y., Sun Y., Gao Y., Chen Y., Ding J., Lang J., Miao Z., Zhang A, & He J. (2023). Thioparib inhibits homologous recombination repair, activates the type I IFN response, and overcomes olaparib resistance. EMBO Molecular Medicine, 15(3), e16235.

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Publication 2023

Antibodies Antibodies, Anti-Idiotypic AXIN2 protein, human BIRC2 protein, human BIRC3 protein, human BRCA1 protein, human Caspase-7 Caspase 3 Caspase 9 GAPDH protein, human Gene, BRCA2 Goat IGG-horseradish peroxidase Immunoglobulins IRF3 protein, human MAP2K1 protein, human MCL1 protein, human Mitogen-Activated Protein Kinase 3 MLH1 protein, human Mus PALB2 protein, human PARP1 protein, human PARP2 protein, human Proteins Puma RBBP8 protein, human STAT1 protein, human STAT3 protein, human TBK1 protein, human Technique, Dilution XRCC1 protein, human XRCC3 protein, human Xrcc6 protein, human

Generating Full-Length Structures of Large Proteins

Model structures for 24 of the 26 proteins encoded by the genes under study were deposited in the webserver of AlphaFold-EBI structure database (https://alphafold.ebi.ac.uk). The structures of ATM and BRCA2 were not included in the webserver due to their larger size than 2700 amino acids (aas) but in the proteome collections. Thus, ATM and BRCA2 structures were collected from the human proteome collection (UP000005640). The structures of both proteins were predicted in sequential rounds resulting in overlapping partial structures that were labeled as F1, F2, … Fn accordingly. For instance, BRCA2 structure (3,418 aas) was predicted in twelve sequential rounds resulting in twelve overlapping structures (F1, F2, … , F12). These partial BRCA2 structures were 1,400 aas in length and had at least 1,200 aa-long overlaps with the structures preceding them in the series. The F1 structure of BRCA2 covered the amino acids between the 1st and 1400th positions, while the F2 structure covered the region encompassing the residues from the 201th to 1600th positions resulting in an overlapping prediction for the region between 201-1,400. The last prediction (F12) was 1218-aa in length and covered the final region between the positions of 2201 and 3,418. The same scheme applying to the ATM structure (3,056 aas) resulted in 10 overlapping structures. To get the full-length structures, the structures of F1, F6, and F12 for BRCA2 and F1, F5 and F10 for ATM were utilized which showed at least 200 aas overlap with each other. The overlaps were used to structurally align two sequential structures with each other and then one of the overlaps was removed. Then the separate chains were linked to each other by amide bonds generating the full-length structure for both ATM and BRCA2. Superimposition, overlap removal and model joining were performed by Chimera UCSF (Pettersen et al., 2004 (link)). During model joining, the confidence scores for AF2 predictions (pLDDT) were kept in the B-factor column of the pdb file.

Keskin Karakoyun H., Yüksel Ş.K., Amanoglu I., Naserikhojasteh L., Yeşilyurt A., Yakıcıer C., Timuçin E, & Akyerli C.B. (2023). Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer. Frontiers in Genetics, 14, 1052383.

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Publication 2023

3'-(1-butylphosphoryl)adenosine Amides Amino Acids Chimera Gene, BRCA2 Genes Homo sapiens Prognosis Proteins Proteome

Hereditary Cancer Gene Variant Screening

Blood samples were collected in EDTA containing tubes. Genomic DNA was isolated with QIAamp DNA Mini QIAcube kit (QIAGEN, Germany) according to the manufacturer’s instructions. DNA concentrations were measured with the QubitTM Fluorometric Quantitation system (Thermo Fisher Scientific) using Qubit HS DNA Assay kit (Thermo Scientific, US). DNA libraries were obtained using the BRCA Hereditary Cancer MASTR Plus, Multiplicom (Agilent, United States) kit. Variant screening on 26 risk carrying genes for hereditary cancers like breast, ovarian and colorectal cancer (ABRAXAS1, ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MEN1, MLH1, MRE11, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53, and XRCC2) has been performed by this kit which contained five multiplex PCR primer pools. 10 ng of DNA per primer pool was used for multiplex PCR amplification, followed by barcode ligation and purification with Agentcourt AMPureXP reagent (Beckman Coulter, Beverly, MA, United States). Quantity and quality of prepared libraries were assessed by QubitTM Fluorometric Quantitation system (Thermo Fisher Scientific). For library preparation 4 ng DNA was used. After libraries were prepared, sequence analysis was performed with Illumina MiSeq instrument using MiSeq Reagent v3 kit (Illumina, US). All sequencing data were submitted to Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra/PRJNA895859).
Bioinformatics analysis was performed using the software Sophia Genetics DDM (Sophia Genetics v4.2). GRCh37/hg19 was used as the reference genome. During variant calling, a minimum sequence coverage depth and variant fraction parameters were set to 30x and 20%, respectively. Variants were classified according to the ACMG Guidelines (Richards et al., 2015 (link)) using databases of ClinVar (Landrum et al., 2014 (link)), BRCAExchange, OMIM^®, dbSNP (v.155), gnomAD (v2.1.1), in silico pathogenicity classifiers of MutationTaster (Schwarz et al., 2010 (link)), SIFT (Ng and Henikoff, 2003 (link)), PolyPhen-2 (Adzhubei et al., 2013 ), REVEL (Ioannidis et al., 2016 (link)). All variants with minor allele frequency (MAF2) of less than 1% in gnomAD database were considered.

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Publication 2023

Biological Assay BLOOD BRCA1 protein, human Breast Cancer, Familial CDH1 protein, human CHEK2 protein, human Colorectal Carcinoma DNA Library Edetic Acid Fluorometry Gene, BRCA2 Genetic Diversity Genome Ligation Malignant Neoplasms MLH1 protein, human MSH6 protein, human Multiple Endocrine Neoplasia Type 1 Multiplex Polymerase Chain Reaction MUTYH protein, human Oligonucleotide Primers Ovary PALB2 protein, human Pathogenicity PMS2 protein, human PTEN protein, human Rad50 protein, human RAD51C protein, human STK11 protein, human TACSTD1 protein, human TP53 protein, human XRCC2 protein, human

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Trizol reagent by Thermo Fisher Scientific

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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.

Brca2 by Santa Cruz Biotechnology

Sourced in United States

BRCA2 is a gene that provides instructions for making a protein involved in repairing damaged DNA. This protein plays a critical role in the repair process by facilitating the proper movement and positioning of other proteins necessary for DNA repair.

Rneasy mini kit by Qiagen

Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, Canada, France, China, Australia, Italy, Switzerland, Sweden, Belgium, Denmark, India, Jamaica, Singapore, Poland, Lithuania, Brazil, New Zealand, Austria, Hong Kong, Portugal, Romania, Cameroon, Norway

The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.

Fetal bovine serum (fbs) by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal

Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

Miseq platform by Illumina

Sourced in United States, China, Germany, United Kingdom, Spain, Australia, Italy, Canada, Switzerland, France, Cameroon, India, Japan, Belgium, Ireland, Israel, Norway, Finland, Netherlands, Sweden, Singapore, Portugal, Poland, Czechia, Hong Kong, Brazil

The MiSeq platform is a benchtop sequencing system designed for targeted, amplicon-based sequencing applications. The system uses Illumina's proprietary sequencing-by-synthesis technology to generate sequencing data. The MiSeq platform is capable of generating up to 15 gigabases of sequencing data per run.

Hiseq 2000 by Illumina

Sourced in United States, China, Germany, United Kingdom, Hong Kong, Canada, Switzerland, Australia, France, Japan, Italy, Sweden, Denmark, Cameroon, Spain, India, Netherlands, Belgium, Norway, Singapore, Brazil

The HiSeq 2000 is a high-throughput DNA sequencing system designed by Illumina. It utilizes sequencing-by-synthesis technology to generate large volumes of sequence data. The HiSeq 2000 is capable of producing up to 600 gigabases of sequence data per run.

Bigdye terminator v3.1 cycle sequencing kit by Thermo Fisher Scientific

Sourced in United States, Japan, Germany, United Kingdom, China, Canada, Australia, France, Poland, Lithuania, Italy, Malaysia, Thailand, Switzerland, Denmark, Argentina, Norway, Netherlands, Singapore

The BigDye Terminator v3.1 Cycle Sequencing Kit is a reagent kit used for DNA sequencing. It contains the necessary components, including fluorescently labeled dideoxynucleotides, to perform the Sanger sequencing method.

What is the BRCA2 gene and what is its role in the body?

How can PubCompare.ai help with BRCA2 research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoit critical insights. The platform's AI-driven analysis can hlighlight key differences in protocol effectiveness for BRCA2 research, enabling researchers to choose the best option for reproducibility and accuracy. This helps identify the most effective protocols from literature, preprints, and patents to advance BRCA2 gene research and improve patient outcomes.

What are some common variations or types of the BRCA2 gene?

There are numerous known variants of the BRCA2 gene, some of which are associated with different cancer risks and prognoses. Some of the more common BRCA2 mutations include the 6174delT, 3036delACAA, and 3337C>T variants. Researchers can use PubCompare.ai to quickly compare study protocols targeting specific BRCA2 mutations and identify the most effective approaches for their research.

How can BRCA2 research be applied in practical settings?

BRCA2 research has important applications in both clinical and research settings. In the clinic, genetic testing for BRCA2 mutations can help identify individuals at high risk for certain cancers, allowing for enhanced screening and preventative measures. In the research lab, studies on BRCA2 function and its role in DNA repair pathways can lead to the development of new targeted therapies. PubCompare.ai empowers researchers to optimize their BRCA2 protocols and accelerate discoveries in this critical area of gene research.

More about "Gene, BRCA2"

BRCA2, a critical tumor suppressor gene, is associated with an increased risk of various cancers, including breast, ovarian, and pancreatic.
This large, complex gene plays a crucial role in DNA repair, cell cycle regulation, and transcriptional control.
Researchers studying BRCA2 often utilize specialized techniques and reagents to investigate its function and impact on disease development.
Key tools and methods used in BRCA2 research include Lipofectamine RNAiMAX and Lipofectamine 2000 for gene silencing and transfection, the RAD51 protein which interacts with BRCA2 in DNA repair processes, and TRIzol reagent and the RNeasy Mini Kit for RNA extraction and purification.
Genetic analysis of BRCA2 frequently involves next-generation sequencing platforms like the MiSeq and HiSeq 2000, as well as the BigDye Terminator v3.1 Cycle Sequencing Kit for Sanger sequencing.
By utilizing these specialized techniques and resources, researchers can optimize their BRCA2 studies, leading to a better understanding of this critical gene's function and its role in cancer development.
This knowledge can then be applied to improve patient outcomes and advance the field of gene research.
PubCompare.ai's AI-powered tools can help researchers identify the most effective protocols and products for their BRCA2 studies, streamlining the research process and driving progress in this important area of study.