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CHEK2 protein, human

The CHEK2 protein is a serine/threonine-protein kinase that plays a crucial role in the DNA damage response pathway.
It is activated in response to DNA double-strand breaks and regulates cell cycle checkpoints, DNA repair, and apoptosis.
Mutations in the CHEK2 gene have been associated with an increased risk of several types of cancer, including breast, prostate, and thyroid cancer.
Researchers can use PubCompare.ai's AI-driven platform to optimize their CHEK2 research by locating the best protocols and products from literature, pre-prints, and patents through intelligent comparisons, enhancing reproducibility and research accuracy.

Most cited protocols related to «CHEK2 protein, human»

1
Ancestry Inference and Lung Cancer GWAS Meta-Analysis
Cited 80 times
FlashPCA30 (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 GWAS3 (link),4 (link),6 (link), including studies of IARC, MDACC, SLRI, ICR, Harvard, NCI, Germany and deCODE as described previously3 (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 previously4 (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 GCTA32 (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 above33 (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
2
Autosomal Dominant Cancer Variant Analysis
Cited 13 times
Our analysis focused on variants identified among 20 genes associated with autosomal dominant cancer-predisposition syndromes that involve maintenance of DNA integrity (Table 2). The pathogenicity of germline variants was determined according to established American College of Medical Genetics and Genomics and Association for Molecular Pathology consensus criteria and International Agency for Research on Cancer guidelines.24 (link),26 At least two independent expert reviewers evaluated all variants against published literature and public databases, including ClinVar and variant-specific databases, in addition to population frequency databases, including 1000 Genomes and the Exome Aggregation Consortium. Expected high-penetrance or moderate-penetrance variants classified as mutations that are pathogenic or likely to be pathogenic are reported here. Low-penetrance variants, such as CHEK2 p.I157T, were excluded.
Pritchard C.C., Mateo J., Walsh M.F., De Sarkar N., Abida W., Beltran H., Garofalo A., Gulati R., Carreira S., Eeles R., Elemento O., Rubin M.A., Robinson D., Lonigro R., Hussain M., Chinnaiyan A., Vinson J., Filipenko J., Garraway L., Taplin M.E., AlDubayan S., Han G.C., Beightol M., Morrissey C., Nghiem B., Cheng H.H., Montgomery B., Walsh T., Casadei S., Berger M., Zhang L., Zehir A., Vijai J., Scher H.I., Sawyers C., Schultz N., Kantoff P.W., Solit D., Robson M., Van Allen E.M., Offit K., de Bono J, & Nelson P.S. (2016). Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer. The New England journal of medicine, 375(5), 443-453.
Publication 2016
CHEK2 protein, human Exome Genes Genome Germ-Line Mutation Malignant Neoplasms Multiple Pterygium Syndrome, Autosomal Dominant Mutation Pathogenicity Susceptibility, Disease Syndrome
3
Selecting Breast Cancer Samples with Low TILs
Cited 13 times
To select for samples with relative low number of TILs, hierarchical clustering of expression data was used. This approach has largely been described in our previous work [18 ]. In short, the proportion of lymphocytic nuclei of 96 tumor samples was assessed on H&E-stained frozen sections. For subsequent mRNA analysis, the luminal and basal samples were processed separately. These samples were divided in two groups based on the TIL percentages (high and low TIL count, median split) on which subsequent ANOVA analysis was performed to find differentially expressed probe sets passing a FDR p-value <0.05. Finally, the overlapping probe-sets for the luminal and basal sample sets were determined to create the final mRNA immune signature, see Additional File 1. Following this approach, 14 out of a total of 17 CHEK2* 1100delC and 34 out of a total of 49 BRCAX breast cancers with relative low levels of this expression signature were selected for further supervised analyses.
Massink M.P., Kooi I.E., Martens J.W., Waisfisz Q, & Meijers-Heijboer H. (2015). Genomic profiling of CHEK2*1100delC-mutated breast carcinomas. BMC Cancer, 15, 877.
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Publication 2015
Breast Carcinoma Cell Nucleus CHEK2 protein, human Frozen Sections Lymphocyte Lymphocytes, Tumor-Infiltrating Neoplasms neuro-oncological ventral antigen 2, human RNA, Messenger
4
Germline DNA Sequencing for Cancer Predisposition
Cited 12 times
Germline DNA samples were subjected to dual bar-coded QIAseq (Qiagen) multiplex amplicon-based analysis of 746 target regions in 37 cancer-predisposition genes.17 (link) Libraries from 768 samples 6 were pooled and sequenced in each lane of a HiSeq 4000 system (Illumina). Genetic variants were identified with the use of the Genome Analysis Toolkit (GATK) Haplotype Caller tool and Var-Dict variant caller tool. High-quality sequence data (read depth of >20 times) were obtained for 99.3% of the target regions. Twenty-eight cancer predisposition genes including 12 established breast cancer–predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C, RAD51D, and TP53) and 16 candidate predisposition genes were evaluated (Table S3).18 (link)–30 (link) Loss-of-function variants and variants identified as “pathogenic” or “likely pathogenic” in the ClinVar database were classified as pathogenic variants (see the Supplementary Appendix).17 (link) Pathogenic variants in NF1 and TP53 were restricted to those with an alternate allele fraction (calculated as the number of alternate allele reads divided by the total number of reads at a specific genomic position) between 0.3 and 0.7 in an effort to exclude potential clonal hematopoiesis variants.31 (link)
Hu C., Hart S.N., Gnanaolivu R., Huang H., Lee K.Y., Na J., Gao C., Lilyquist J., Yadav S., Boddicker N.J., Samara R., Klebba J., Ambrosone C.B., Anton-Culver H., Auer P., Bandera E.V., Bernstein L., Bertrand K.A., Burnside E.S., Carter B.D., Eliassen H., Gapstur S.M., Gaudet M., Haiman C., Hodge J.M., Hunter D.J., Jacobs E.J., John E.M., Kooperberg C., Kurian A.W., Marchand L.L., Lindstroem S., Lindstrom T., Ma H., Neuhausen S., Newcomb P.A., O’Brien K.M., Olson J.E., Ong I.M., Pal T., Palmer J.R., Patel A.V., Reid S., Rosenberg L., Sandler D.P., Scott C., Tamimi R., Taylor J.A., Trentham-Dietz A., Vachon C.M., Weinberg C., Yao S., Ziogas A., Weitzel J.N., Goldgar D.E., Domchek S.M., Nathanson K.L., Kraft P., Polley E.C, & Couch FJ P.r.o.f. (2021). A Population-Based Study of Genes Previously Implicated in Breast Cancer. The New England journal of medicine, 384(5), 440-451.
Publication 2021
Alleles BRCA1 protein, human Breast CDH1 protein, human CHEK2 protein, human Gene, BRCA2 Gene, Cancer Genes Genetic Diversity Genome Germ Line Haplotypes Malignant Neoplasm of Breast PALB2 protein, human Pathogenicity PTEN protein, human RAD51C protein, human Susceptibility, Disease TP53 protein, human
5
Germline Variant Analysis in Cancer Predisposition Genes
Cited 12 times
We searched for pathogenic germline variants (SNVs, indels and copy number alterations) in a broad list of 152 germline predisposition genes previously curated61 (link), using GATK HaplotypeCaller55 output from each sample. For each variant identified, we assessed the genotype in the germline (HET or HOM), whether there was a second somatic hit in the tumour, and whether the wild type or the variant itself was lost by a copy number alteration. We observed that for the variants in many of the 152 predisposition genes that a loss of wild type in the tumour via LOH was lower than the average rate of LOH across the cohort and that fewer than 5% of observed variants had a second somatic hit in the same gene. Moreover, in many of these genes, the ALT variant was lost via LOH as frequently as the wild type, suggesting that a considerable portion of the 566 variants may be passengers. For our downstream analysis and driver catalogue, we therefore restricted our analysis to a more conservative ‘high confidence’ list including only the 25 cancer related genes in the ACMG secondary findings reporting guidelines (v.2.0)62 (link), together with four curated genes (CDKN2A, CHEK2, BAP1 and ATM), selected because these are the only additional genes from the larger list of 152 genes with a significantly increased proportion of called germline variants with loss of wild type in the tumour sample.
Priestley P., Baber J., Lolkema M.P., Steeghs N., de Bruijn E., Shale C., Duyvesteyn K., Haidari S., van Hoeck A., Onstenk W., Roepman P., Voda M., Bloemendal H.J., Tjan-Heijnen V.C., van Herpen C.M., Labots M., Witteveen P.O., Smit E.F., Sleijfer S., Voest E.E, & Cuppen E. (2019). Pan-cancer whole-genome analyses of metastatic solid tumours. Nature, 575(7781), 210-216.
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Publication 2019
CDKN2A Gene CHEK2 protein, human Copy Number Polymorphism Diploid Cell Gene, Cancer Genes Genes, vif Genetic Predisposition to Disease Genotype Germ-Line Mutation Germ Line INDEL Mutation Neoplasm Metastasis Neoplasms Neoplasms, Second Primary Oncogenes Pathogenicity Susceptibility, Disease

Most recents protocols related to «CHEK2 protein, human»

1
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.
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
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
2
Retrospective Analysis of Genomic Profiles in Oncology

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Publication 2023
BLOOD BRCA1 protein, human CHEK1 protein, human CHEK2 protein, human Circulating Tumor DNA Clinical Laboratory Services DNA Library FANCL protein, human Gene, BRCA2 Genes Genome Genome, Human Mutation Neoplasms PALB2 protein, human Patients Punctures RAD51C protein, human RAD54L protein, human Specimen Collections, Blood Tissues
3
Comparative Analysis of Cancer Genomics
De-identified genomic sequencing data from The Cancer Genome Atlas and other large-scale, cancer-specific sequencing studies were accessed and queried online through the cBioPortal for Cancer Genomics at https://www.cbioportal.org/ (11 (link), 12 (link)). Patient cohorts were primarily selected from the TCGA pan-cancer atlas, and the accession numbers for each tumor-specific analysis were recorded (Supplementary Methods). Somatic alterations included were loss-of-function single nucleotide variants (SNVs), indels, and copy number variants (CNVs) classified as pathogenic or likely pathogenic. Statistical analysis to compare deleterious alteration counts of ATM, BRCA1, and CHEK2 within each tumor type was performed with Microsoft Excel version 16.64 (Microsoft, WA, USA) using the Z-test for independent proportions. A p-value equal to or less than 0.05 was considered significant.
Borja N.A., Silva-Smith R., Huang M., Parekh D.J., Sussman D, & Tekin M. (2023). Atypical ATMs: Broadening the phenotypic spectrum of ATM-associated hereditary cancer. Frontiers in Oncology, 13, 1068110.
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Publication 2023
BRCA1 protein, human CHEK2 protein, human Copy Number Polymorphism Diploid Cell Genome INDEL Mutation Malignant Neoplasms Neoplasms Nucleotides Pathogenicity Patients
4
Genetic Interactions in Cancer Subtypes
Pathogenic alterations in the relevant cancer types accessed through cBioPortal as above were jointly analyzed to determine the relationship between variants in ATM, BRCA1, CHEK2, and TP53. A Log2 odds ratio was used to calculate how strongly the presence or absence of alterations in one gene was associated with the presence or absence of alterations in a second gene within the selected samples. A q-value derived from the Benjamini-Hochberg FDR correction procedure equal to or less than 0.05 was considered significant.
Borja N.A., Silva-Smith R., Huang M., Parekh D.J., Sussman D, & Tekin M. (2023). Atypical ATMs: Broadening the phenotypic spectrum of ATM-associated hereditary cancer. Frontiers in Oncology, 13, 1068110.
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Publication 2023
BRCA1 protein, human CHEK2 protein, human Genes Malignant Neoplasms Pathogenicity TP53 protein, human
5
Analyzing CHEK2 c.1100delC in Breast Cancer
Multiple imputation, performed using R package MICE (version 3.13.0), was used to handle missing values in clinical and pathological variables. Details are given in the Supplementary Methods and Table S2. Descriptive statistics are shown as mean ± standard deviation (SD) or median and interquartile range (IQR). We used Pearson’s χ2 test for categorical data and Kruskal-Wallis test for continuous data to calculate differences in patients’ characteristics. The primary study outcomes were time to CBC and BCSS (time to death due to BC).
Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of treatment given for the first primary BC (radiotherapy and/or type of systemic treatment) and CHEK2 c.1100delC status with time to CBC were estimated via Cox regression models allowing for delayed entry, stratified by country and adjusted for age at first primary BC diagnosis, tumor size, nodal status, grade and ER status. Since ER status is known to violate the proportionality hazards assumption and because the majority of CHEK2 c.1100delC carriers develop ER-positive BC, we performed an additional main analysis restricted to patients diagnosed with a first primary ER-positive BC. We assumed that patients with unknown CBC status did not develop a CBC during follow-up, and that for CBC cases with unknown time from first primary BC to CBC diagnosis, CBC occurrence was at last available follow-up.
Time at risk started either three months after first primary BC diagnosis or at study entry if entry was more than three months after first primary BC diagnosis, and ended at time of CBC, death or last follow-up, whichever came first. We tested for potential differential association of adjuvant and/or neo-adjuvant therapy on CBC risk according to CHEK2 c.1100delC status by including an interaction term between treatment (radiotherapy or systemic treatment) variable and CHEK2 c.1100delC status in the model. CBC risk analyses were stratified by two follow-up time intervals: i) the first 5 years after BC diagnosis and ii) starting 5 years after BC diagnosis.
To gain further insight into the relation between CHEK2 c.1100delC status, treatment given for the first primary BC, CBC risk and death, we used a multi-state model in the framework of the Cox model, with diagnosis of the first primary BC as initial state, diagnosis of CBC as intermediate (transient) state, and death due to BC, death due to other causes, and death due to unknown causes as absorbing states (Fig. 2), as specified in the Supplementary Methods.
The main CBC risk and multi-state analyses were performed on imputed datasets. Complete-case analyses (excluding study subjects with missing values in any of the variables included in the models) were performed as sensitivity analyses. Additional analyses were restricted to: a) patients diagnosed with first primary BC from 2000 onwards to reduce heterogeneity in treatment regimens; b) patients diagnosed at age 40 or younger to see if the association with radiotherapy was stronger in this subgroup, as reported previously in the general BC population13 (link).
Morra A., Schreurs M.A., Andrulis I.L., Anton-Culver H., Augustinsson A., Beckmann M.W., Behrens S., Bojesen S.E., Bolla M.K., Brauch H., Broeks A., Buys S.S., Camp N.J., Castelao J.E., Cessna M.H., Chang-Claude J, & Chung W.K. (2023). Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer-specific survival. Research Square.
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Publication Preprint 2023
Breast-Conserving Surgery CHEK2 protein, human Genetic Heterogeneity Hypersensitivity Mus Neoplasms Patients Pharmaceutical Adjuvants Radiotherapy Transients Treatment Protocols Youth

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The SALSA® MLPA® P045 BRCA2/CHEK2 probemix is a laboratory tool designed for the detection of copy number variations in the BRCA2 and CHEK2 genes. It utilizes the Multiplex Ligation-dependent Probe Amplification (MLPA) technique to simultaneously analyze multiple genomic regions. The probemix contains probes targeting specific sequences within these genes.
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Chek2 by Cell Signaling Technology
CHEK2 is a protein kinase that functions as a cell cycle checkpoint regulator. It plays a role in DNA damage response pathways and is involved in the phosphorylation of various substrates that regulate cell cycle progression and DNA repair.

More about "CHEK2 protein, human"

The CHEK2 gene encodes the Checkpoint kinase 2 (CHEK2) protein, a crucial player in the DNA damage response pathway.
This serine/threonine-protein kinase is activated in response to double-strand DNA breaks, regulating cell cycle checkpoints, DNA repair, and apoptosis.
Mutations in the CHEK2 gene have been linked to an increased risk of several cancers, including breast, prostate, and thyroid.
Researchers can optimize their CHEK2 studies by leveraging PubCompare.ai's AI-powered platform.
This tool helps locate the best protocols and products from literature, pre-prints, and patents through intelligent comparisons, enhancing reproducibility and research accuracy.
The platform's AI-driven approach is particularly useful for CHEK2 research, as it can help identify the most reliable and effective experimental methods.
Beyond CHEK2, the MiSeq and HiSeq 2000/2500 platforms from Illumina are commonly used for high-throughput DNA sequencing in genomic research.
Lipofectamine 2000, a transfection reagent, is often employed for cell line experiments involving gene expression and knockdown.
Cyclin B1 and β-actin are commonly used as marker proteins in cell cycle and cytoskeletal studies, respectively.
TaqMan assays and the BigDye Terminator v3.1 Cycle Sequencing Kit are popular tools for gene expression analysis and DNA sequencing, respectively.
The SALSA® MLPA® P045 BRCA2/CHEK2 probemix is a valuable resource for detecting mutations in the CHEK2 and BRCA2 genes.
By incorporating these related terms and techniques, researchers can enhance the comprehensiveness and relevance of their CHEK2 studies, ultimately advancing our understanding of this crucial DNA damage response protein and its implications for human health and disease.