Allprep dna rna mini kit

Name: Allprep dna rna mini kit
Brand: Qiagen
SKU: ZyLhCZIBPBHhf-iFePAt
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The AllPrep DNA/RNA Mini Kit is a laboratory product designed to simultaneously purify high-quality genomic DNA and total RNA from a single biological sample. The kit utilizes a spin-column-based method to ensure efficient extraction and separation of both DNA and RNA components.

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The AllPrep DNA/RNA Mini Kit is an active product commercially available from Qiagen through authorized distributors. Typical prices for the kit range from $797.00 to $976.33.

For a more eco-friendly option, Qiagen offers the QIAwave DNA/RNA Mini Kit, which uses 52% less plastic and 58% less cardboard compared to the standard kit.

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3 137 protocols using «allprep dna rna mini kit»

Quantitative RT-PCR Analysis of Gene Expression

2025

RNA was isolated from whole-cell lysates using an AllPrep DNA/RNA Mini Kit (Qiagen), according to the manufacturer’s recommendations. The RNA that was harvested was treated with an on-column DNase during the isolation procedure to ensure that no contaminating DNA was present. RNA was then reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) in a Bio-Rad T100 Thermal Cycler. The cDNA was then pre-amplified using the TaqMan PreAmp Master Mix Kit (Thermo Fisher Scientific) in the Bio-Rad T100 Thermal Cycler, according to the manufacturer’s instructions. Next, quantitative real-time PCR was performed in an Applied Biosystems StepOnePlus Real-Time PCR machine (Thermo Fisher Scientific) using the TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific) and predesigned TaqMan Gene Expression Assays (Thermo Fisher Scientific) for MT-RNR2 (Hs02596860_s1) and ACTB (Hs99999903_m1), according to the manufacturer’s instructions.

Wilson K., Holjencin C., Lee H., Annamalai B., Ishii M., Gilbert J.L., Jakymiw A, & Rohrer B. (2025). Development of a cell-penetrating peptide-based nanocomplex for long-term delivery of intact mitochondrial DNA into epithelial cells. Molecular Therapy. Nucleic Acids, 36(1), 102449.

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Simultaneous DNA and RNA Extraction

2025

Genomic DNA and total RNA were isolated simultaneously from matched sets of tumor and clinically normal tonsil/base of tongue biopsies from each OPC patient using the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) as per manufacturer’s instructions. Expression of specific transcripts was analyzed by qRT-PCR as described below.

Papayannakos C.J., Israr M., DeVoti J.A., Lam F., Arazi A., Frank D.K., Kamdar D.P., Pereira L.M., Seetharamu N., Steinberg B.M, & Bonagura V.R. (2025). Oropharyngeal carcinomas induce circulating monocytes to express a TAM-like pro-tumor expression profile that suppresses T-cell proliferation. Frontiers in Immunology, 16, 1539780.

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Genome-wide DNA Methylation Profiling of Tumor Samples

2025

Tumor DNA was extracted from fresh-frozen tissue samples using the Qiagen AllPrep DNA/RNA Mini Kit (see above). Genome-wide DNA methylation profiling was performed using Infinium HumanMethylation EPIC Kit according to the manufacturer’s instructions (Illumina, San Diego, CA, USA)⁹⁵. DNA methylation-based classification was performed using the Heidelberg Brain Tumor Methylation Classifier v12 (www.molecularneuropathology.org)¹¹. The minfi Bioconductor package v.1.24.0 was used to calculate raw signal intensities from IDAT files, normalization was performed for each sample individually and batch effects were corrected using the removeBatchEffect function of the limma package v3.34.5^{96 ,97}. Both methylated and unmethylated signals were corrected individually. Subsequently, beta values were calculated, CpG probes for downstream analysis selected and unsupervised nonlinear dimension reduction was calculated using the 1-variance weighted Pearson correlation between the samples to construct a distant matrix¹¹. This matrix was used as input for t-SNE visualization¹¹. Copy number variation analysis was performed using the conumee package v1.12.0 and copy number changes were evaluated by inspecting the respective plots¹¹.

Velz J., Freudenmann L.K., Medici G., Dubbelaar M., Mohme M., Ghasemi D.R., Scheid J., Kowalewski D.J., Patterson A.B., Zeitlberger A.M., Lamszus K., Westphal M., Eyrich M., Messing-Jünger M., Röhrig A., Reinhard H., Beccaria K., Craveiro R.B., Frey B.M., Sill M., Nahnsen S., Gauder M., Kapolou K., Silginer M., Weiss T., Wirsching H.G., Roth P., Grotzer M., Krayenbühl N., Bozinov O., Regli L., Rammensee H.G., Rushing E.J., Sahm F., Walz J.S., Weller M, & Neidert M.C. (2025). Mapping naturally presented T cell antigens in medulloblastoma based on integrative multi-omics. Nature Communications, 16, 1364.

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Genomic DNA Extraction from Brain, Saliva, and Blood

2025

Genomic DNA was extracted from fresh frozen brain tissue using a Qiagen AllPrep DNA/RNA Mini Kit (Valencia, CA, USA, no. 80204) according to the manufacturer’s instructions. Saliva and blood samples were extracted using NucleoBond CB20 and CB100 respectively (Macherey-Nagel, Düren, Germany, no. 740507 and no. 740508).

Coleman M., Wang M., Snell P., Lee W.S., D'Arcy C., Mignone C., Pope K., Gillies G., Maixner W., Wray A., Harvey A.S., Simons C., Leventer R.J., Stephenson S.E., Lockhart P.J, & Howell K.B. (2025). The genetic landscape and classification of infantile epileptic spasms syndrome requiring surgery due to suspected focal brain malformations. Brain Communications, 7(1), fcaf034.

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Bulk RNA Sequencing for Comprehensive Transcriptome Analysis

2025

Bulk RNA‐Sequencing: RNA extraction was completed by the Qiagen AllPrep DNA/RNA Mini kit (Qiagen, Hilden, Germany) according to manufacturer specifications. RNA‐sequencing was performed at the Johns Hopkins Experimental and Computational Genomics Core (ECGC). All RNA samples passed the quality control procedure using the Bioanalyzer Total RNA Pico kit (Agilent, Santa Clara CA). The libraries for RNA sequencing were constructed by TruSeq Stranded Total RNA with Ribo‐Zero (Illumina, San Diego CA), which was monitored using the Bioanalyzer High Sensitivity kit (Agilent, Santa Clara CA). The libraries were sequenced on an Illumina Novaseq 6000 (Illumina, San Diego CA) instrument using 150bp paired‐end dual indexed reads to a target depth of ≈100000 000 reads per sample with Q30 > 92%. Reads were aligned to the reference genome utilizing illumine package bcl2fastq. Differential expression analysis and statistical testing were performed using DESeq2 software. The biological functions were investigated via KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) comparing the expression of different genes (DGE).

Yin H., Chen Q., Gao S., Shoucair S., Xie Y., Habib J.R., He T., Gan W., Wang J., Zhang L., Xu H., Shi C., He J., Wang W., Jin Y., Goggins M.G., Liu L., Lou W., Wu W., Yu J, & Pu N. (2025). The Crosstalk with CXCL10‐Rich Tumor‐Associated Mast Cells Fuels Pancreatic Cancer Progression and Immune Escape. Advanced Science, 12(14), 2417724.

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Top 5 most cited protocols using «allprep dna rna mini kit»

Nucleic Acids Extraction and Microarray Analysis

Cited 44 times

Nucleic acids were extracted from tonsil specimens (Supplementary Fig. 3b) and PBMCs (patients 1 to 3 in Supplementary Fig. 4c) using AllPrep DNA/RNA Mini kits (Qiagen). For FL specimens (Fig. 2i, Fig. 3c), total RNA and genomic DNA were prepared and stored using TRIzol and RNeasy Midi Kits (Qiagen). Sufficient nucleic acid was confirmed for 80% of archival FL specimens after quality control assessment of a subset of these patients. Total RNA from FL samples was linearly amplified (3′ IVT Express, Affymetrix) prior to microarray hybridization. For all above samples, total cellular RNA (at least 300 ng) was assessed for yield (NanoDrop 2000, Thermo Scientific), and quality (2100 Bioanalyzer, Agilent), and cRNA was hybridized to HGU133 Plus 2.0 microarrays (Affymetrix) according to the manufacturer’s protocol.
Two additional cohorts of PBMCs were analyzed in this study (Fig. 3a,b). For the first cohort (n = 20 subjects; Fig. 3a), PBMCs (~1×10⁶ viable cells per mL) were collected in 1 mL TRIzol (Invitrogen) and stored at −80 °C until use. Total RNA was isolated according to the TRIzol protocol (Invitrogen). Total RNA yield was assessed using the Thermo Scientific NanoDrop 1000 micro-volume spectrophotometer (absorbance at 260 nm and the ratio of 260/280 and 260/230). RNA integrity was assessed using a Bioanalyzer NANO Lab-on-a-Chip instrument (Agilent). Biotinylated, amplified antisense complementary RNA (cRNA) targets were prepared from 200 to 250 ng of total RNA using the Illumina RNA amplification kit (Life Technologies), and 750 ng of labeled cRNA was hybridized overnight to Human HT-12 V4 BeadChip arrays (Illumina). The arrays were then washed, blocked, stained and scanned on an Illumina BeadStation 500 following the manufacturer’s protocols. GenomeStudio software version 1.9.0 (Illumina) was used to generate signal intensity values from the scans. For the second cohort (Fig. 3b), PBMCs (1.4×10⁶ to 4.0×10⁶ cells per mL) from six healthy male adults were isolated and prepared as described in Supplementary Note and then frozen at −80 °C until use. Total cellular RNA ≥300 ng) was isolated from these six subjects along with viably preserved PBMCs from patient 4 (Supplementary Fig. 4c) using RNeasy Mini Kit (Qiagen) and assessed for yield (NanoDrop 2000, Thermo Scientific), and quality (2100 Bioanalyzer, Agilent). Total RNA was linearly amplified (3′ IVT Express, Affymetrix), and cRNA was hybridized to HGU133A microarrays (Affymetrix) according to the manufacturer’s protocol.

Newman A.M., Liu C.L., Green M.R., Gentles A.J., Feng W., Xu Y., Hoang C.D., Diehn M, & Alizadeh A.A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature methods, 12(5), 453-457.

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Corresponding organizations : Stanford University, California Institute for Regenerative Medicine, Center for Systems Biology

Rectal Transcriptome Profiling of IBD Patients

Cited 19 times

RNA was isolated from rectal biopsies obtained during diagnostic colonoscopy using the Qiagen AllPrep RNA/DNA Mini Kit. PolyA-RNA selection, fragmentation, cDNA synthesis, adaptor ligation, TruSeq RNA sample library preparation (Illumina, San Diego, CA), and paired-end 75 bp sequencing was performed^{9 (link)}. An additional validation of the baseline rectal gene expression at diagnosis utilized the independent RISK cohort of treatment-naïve pediatric patients (55 non-IBD controls, 43 UC patients, and 92 CD patients with rectal inflammation) and single-end 75 bp mRNA sequencing^{9 (link)}. Reads were quantified by kallisto^{51 (link)}, using Gencode v24 as the reference genome and transcripts per million (TPM) as an output. We included 14,085 protein-coding mRNA genes with TPM above 1 in 20% of the samples in our downstream analysis. Only samples for which the gene expression (Y encoded genes and XIST)-determined gender matched the clinical-reported gender were included in the analyses (we excluded only one sample with unmatched gender). Four other PROTECT samples were excluded due to poor read quality. A total of 226 RNAseq samples with mean read depth of ~47 M (14 M std. deviation) were stratified into specific clinical subgroups including Ctl (n = 20), and UC (n = 206), and were substratified based on disease severity, and on histologic findings. Differentially expressed genes were determined in GeneSpring® software with fold change differences (FC) ≥ 1.5 and using the Benjamini–Hochberg false discovery rate correction (FDR, 0.001) for all analyses except for the corticosteroid response genes that was calculated out of the 712 severity genes with FDR < 0.05. Unsupervised hierarchical clustering using Euclidean distance metric and Ward’s linkage rule was used to test for groups of rectal biopsies with similar patterns of gene expression. ToppGene^{6 (link)} and ToppCluster^{7 (link)} software were used to test for functional annotation enrichment analyses of immune cell types, pathways, phenotype, immune cell-type enrichments, and biologic functions. Visualization of the network was obtained using Cytoscape.v3.0.2^{52 (link)}.
For validation of the association between baseline gene expression and outcome, we also generated independent Lexogen QuantSeq 3′ mRNAseq libraries^{19 (link)} and single-end 100 bp sequencing for 134 participants who also had Illumina mRNAseq data (the Discovery Cohort) and for 50 participants who did not have Illumina mRNAseq data (the independent Validation cohort, Table 1). PCA was performed to summarize variation in gene expression between patients, and principal components (PC) values were extracted for downstream analyses. We considered several central gene expression pathways PC1 preidentified by the previous differential expression analyses and functional annotation enrichment analyses of the core 5296 UC genes, the 712 severity genes, and the 115 corticosteroid response gene signature for the model building and associations with the microbial composition as described below. PROTECT (GSE109142) and RISK (GSE117993) rectal mRNAseq data sets were deposited into GEO.

Haberman Y., Karns R., Dexheimer P.J., Schirmer M., Somekh J., Jurickova I., Braun T., Novak E., Bauman L., Collins M.H., Mo A., Rosen M.J., Bonkowski E., Gotman N., Marquis A., Nistel M., Rufo P.A., Baker S.S., Sauer C.G., Markowitz J., Pfefferkorn M.D., Rosh J.R., Boyle B.M., Mack D.R., Baldassano R.N., Shah S., Leleiko N.S., Heyman M.B., Grifiths A.M., Patel A.S., Noe J.D., Aronow B.J., Kugathasan S., Walters T.D., Gibson G., Thomas S.D., Mollen K., Shen-Orr S., Huttenhower C., Xavier R.J., Hyams J.S, & Denson L.A. (2019). Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response. Nature Communications, 10, 38.

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Corresponding organizations : Cincinnati Children's Hospital Medical Center, University of Cincinnati, Broad Institute, Harvard University, Technion – Israel Institute of Technology, Tel Aviv University, Sheba Medical Center, Children's Hospital of Pittsburgh, Georgia Institute of Technology, The Coordinating Center, Boston Children's Hospital, Women & Children's Hospital of Buffalo, Emory University, Cohen Children's Medical Center, Riley Hospital for Children, Goryeb Children's Hospital, Nationwide Children's Hospital, Children's Hospital of Eastern Ontario, Children's Hospital of Philadelphia, Hasbro Children's Hospital, University of California, San Francisco, Hospital for Sick Children, The University of Texas Southwestern Medical Center, Medical College of Wisconsin, RTI International, Massachusetts General Hospital, Connecticut Children's Medical Center

Transcriptome Profiling of Melanoma Cell Lines

Cited 12 times

RNA extraction was performed using AllPrep DNA/RNA Mini kit from Qiagen in 53 human melanoma cell lines. Libraries were prepared using the Illumina TruSeq RNA sample preparation kit per the manufacturer’s instructions. RNA sequencing was performed using 50 bp paired end sequencing on the Illumina HiSeq 2000 platform. Paired end 50 bp reads generated from the melanoma cell line RNA sequencing were mapped using HISAT2 to the Homo sapiens hg38 genome build and raw counts were quantified using HTSeq. Both the 53 melanoma cell line panel and TCGA raw expected counts were analyzed similarly to reduce technical variability from data processing. Cell line and TCGA raw counts were normalized to FPKM values using conditional quantile normalization (CQN) to adjust for gene length and GC content (Hansen et al., 2012 (link)). FPKM values were next transformed in log2 space with an offset of 1. For the vemurafenib treated samples, RNASeq was performed using 50 bp single end sequencing and mapped the Homo sapiens NCBI build 37.2 reference genome using TopHat2 v2.0.9 (Kim et al., 2013 (link)) and normalized to fragments per kilobase of exon per million fragments mapped (FPKM) using Cufflinks v2.2.1 and the geometric library size normalization method (Trapnell et al., 2012 (link)).

Tsoi J., Robert L., Paraiso K., Galvan C., Sheu K.M., Lay J., Wong D.J., Atefi M., Shirazi R., Wang X., Braas D., Grasso C.S., Palaskas N., Ribas A, & Graeber T.G. (2018). Multi-stage differentiation defines melanoma subtypes with differential vulnerability to drug-induced iron-dependent oxidative stress. Cancer cell, 33(5), 890-904.e5.

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Corresponding organizations : University of California, Los Angeles, Institute for Molecular Medicine, California NanoSystems Institute

Unbiased B-cell Library Preparation for MiSeq

Cited 11 times

B-cell libraries for Illumina MiSeq sequencing were prepared, as follows. Total RNA was extracted from 5 to 10 million PBMCs using the AllPrep DNA/RNA Mini Kit (Qiagen). In an effort to generate unbiased B-cell libraries, cDNA synthesis was subsequently performed using the Takara Clontech SMARTer RACE cDNA Amplification Kit using primers with specificity to IgG, IgM, IgK and IgL. The subsequent RACE-ready cDNA was diluted in Tricine-EDTA according to the manufacturer's recommended protocol. First-round Ig-encoding sequence amplification was performed using AccuPrime Pfx Supermix (Invitrogen, Waltham, MA, USA), containing gene-specific primers (120 nm) and 1 × concentration of Takara/Clontech 10 × Universal primer mix. Amplicons were purified using FlashGels (Lonza, Allendale, NJ, USA) and used as templates for second-round PCR amplification. A second-round PCR amplification (10 cycles) was performed in order to add MiSeq adapter sequences to both ends of the amplicon. After re-purification, a final 5-cycle amplification was performed by adding P5 and P7 index sequences for Illumina sequencing. Purified, indexed libraries were quantitated using the KAPA library quantification kit (Kapa Biosystems, Wilmington, MA, USA) performed on an Applied Biosystems 7500 Fast real-time PCR machine (Applied Biosystems, Foster City, CA, USA).

Vigdorovich V., Oliver B.G., Carbonetti S., Dambrauskas N., Lange M.D., Yacoob C., Leahy W., Callahan J., Stamatatos L, & Sather D.N. (2016). Repertoire comparison of the B-cell receptor-encoding loci in humans and rhesus macaques by next-generation sequencing. Clinical & Translational Immunology, 5(7), e93-.

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Corresponding organizations : Molina Center for Energy and the Environment, Center for Infectious Disease Research, Harry K. Dupree Stuttgart National Aquaculture Research Center, Fred Hutch Cancer Center

Comprehensive Tumor RNA-Seq Analysis Pipeline

Cited 7 times

We obtained a total of 66 tumor samples from which we extracted RNA using the AllPrep DNA/RNA Mini Kit (Qiagen) and mirVana miRNA Isolation Kit (Ambion). Poly-A selection was used for library construction, and samples were sequenced using the Illumina HiSeq 2500 platform with a read length of 2 × 100 at the UCLA Technology Center for Genomics and Bioinformatics. Raw FASTQ files were aligned to the hg19 genome using HISAT2 version 2.0.4 (ref.^{46 (link)}) with the default parameters, and counted with HTSeq version 0.6.1 (ref.^{47 (link)}) with the intersection-nonempty mode (ambiguous reads were counted if fully overlapping). Raw counts were then normalized to fragments per kilobase of transcript per million mapped reads (FPKM). Two tumor biopsies were excluded from the analysis due to discordancy with previous IHC analysis (Supplementary Fig. 1a–c). Four tumor biopsies were excluded based on the expression of KRT15 and KRT5 (Supplementary Fig. 1d,e). A total of 60 tumor biopsies were considered for transcriptomic analysis. RNA-Seq-based cell deconvolution of tissue-infiltrating and stromal populations was performed using MCP-counter^{20 (link)} with the default settings, and immune cell infiltration was defined using the upper and lower quartile scores for each of the obtained immune cell populations. Differential gene expression was performed based on the negative binomial distribution with the DESeq2 package^{48 (link)} using the default settings (Wald significance test). Principal component analyses were also performed, using the DESeq2 package^{47 (link)}, on prior normalization of raw reads using the variance-stabilizing transformation (vst) function. To identify enriched signaling pathways, we utilized GSEA with the following gene sets: C2 Curated Gene Sets and C5 Gene Ontology Gene Sets^{49 (link)}. Pan-cancer correlation analysis between PAK4 expression and immune cell infiltration (calculated using MCP-counter as described above) was performed using gene expression data from 32 tumor types from the TCGA Research Network (http://cancergenome.nih.gov/).

Abril-Rodriguez G., Torrejon D.Y., Liu W., Zaretsky J.M., Nowicki T.S., Tsoi J., Puig-Saus C., Baselga-Carretero I., Medina E., Quist M.J., Garcia A.J., Senapedis W., Baloglu E., Kalbasi A., Cheung-Lau G., Berent-Maoz B., Comin-Anduix B., Hu-Lieskovan S., Wang C.Y., Grasso C.S, & Ribas A. (2019). PAK4 inhibition improves PD-1 blockade immunotherapy. Nature cancer, 1(1), 46-58.

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Corresponding organizations : University of California, Los Angeles

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