Log In Sign up
The largest database of trusted experimental protocols
> Procedures > Molecular Biology Research Technique > RNA-Seq

RNA-Seq

RNA-Seq (RNA sequencing) is a powerful technique for comprehensive analysis of the transcriptome, enabling the identification and quantification of RNA molecules in a biological sample.
This high-throughput sequencing approach provides a detailed snapshot of the gene expression profile, revealing insights into cellular processes, disease mechanisms, and genetic variations.
RNA-Seq allows researchers to explore the complete transcriptional landscape, including coding and non-coding RNAs, alternative splicing events, and novel transcript discoveries.
With its high sensitivity and dynamic range, RNA-Seq has become an invaluable tool for a wide range of applications, from basic biological research to clinical diagnostics and personalized medicine.
By leveraging the latest advancements in sequencing technology and bioinformatics, researchers can optimize their RNA-Seq workflows and enhance the quality and reproducibility of their findings.

Most cited protocols related to «RNA-Seq»

1
Diagnostic and Differential Expression Analysis Plots
Cited 14121 times
Figure 3 shows example diagnostic plots. Panel (A) shows RNA-seq data from Pickrell et al. (9 (link)) that has been analysed as described by Law et al. (10 (link)). Panels (B) and (C) display the two-colour microarray quality control data set presented by Ritchie et al. (11 (link)). Panel (B) displays background corrected but non-normalized intensities from one typical array. Panel (C) was generated from a subset of 30 of the control arrays after print-tip loess normalization (12 ).
Figure 4 shows example DE summary plots. Panels (A) and (B) were generated using the two-colour microarray data from GEO series GSE2593. Intensities were background corrected and normalized as previously described (13 (link)). Panel (A) shows a volcano plot for the comparison of samples with RUNX1 over-expressed versus wild-type samples, while panel (B) shows a Venn diagram of differentially expressed probes for each of the three over-expressed genes versus wild-type. Probes with false discovery rate less than 0.05 were considered to be differentially expressed. Panel (C) uses RNA-seq data from GEO series GSE52870. The data were analysed as described in Figure 5 of Liu et al. (7 (link)).
Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W, & Smyth G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.
Publication 2015
Diagnosis Genes Microarray Analysis RNA-Seq RUNX1 protein, human
2
Flexible Genomic Comparisons with BEDTools
Cited 6476 times
Except for the novel paired-end functionality and support for alignments in BAM format, many of the genomic comparisons supported by BEDTools can be performed in one way or another with available web-based tools. However, BEDTools offers several important advantages. First, it can read data from standard input and write to standard output, which allows complex set operations to be performed by combining BEDTools operations with each other or with existing UNIX utilities. Second, most of the tools can distinguish DNA strands when searching for overlaps, which allows orientation to be considered when interpreting paired-end mapping or RNA-seq data. Third, the use of BEDTools mitigates the need to interact with local or public instances of the UCSC Genome Browser or Galaxy, which can be a major bottleneck when working with large genomics datasets. Finally, the speed and extensive functionality of BEDTools allow greater flexibility in defining and refining genomic comparisons. These features allow for diverse and complex comparisons to be made between ever-larger genomic datasets.
Quinlan A.R, & Hall I.M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842.
Publication 2010
Genome RNA-Seq
3
Enhancing RNA-Seq Read Alignment Accuracy
Cited 2594 times
Exon-spanning reads sometimes have very small anchors (defined here as 1–7 bp) in one of the exons. Correctly aligning these reads is extremely difficult because a 1- to 7-bp anchor will align to numerous locations, even in a local FM index. Arguably the most effective approach to align such short-anchored reads is to use splice site information to remove the introns computationally before alignment. We can identify and collect splice site locations when aligning reads with long anchors and then rerun HISAT for the short-anchored reads (Supplementary Fig. 9). This two-step approach is very similar to the two-step algorithm in TopHat2.
More specifically, in the two-step HISATx2 method, we use the first run of HISAT (HISATx1) to generate a list of splice sites supported by reads with long anchors. In the second run we then use the splice sites to align reads with small anchors. For example, consider the unmapped read spanning exons e2 and e3 (the upper portion of Supplementary Fig. 9). The right part of the read will be mapped to exon e3 using the global search and extension operations, leaving a short, 3-bp segment unmapped. We then check the splice sites found in the first run of HISAT to find any splice sites near this partial alignment. In this example, we find a splice site supported by a read spanning exons e2 and e3 with long anchors in each exon. On the basis of this information, we directly compare the 3 bp of the read and the corresponding genomic sequence in exon e2. If it matches, we combine the 3-bp alignment with the alignment of the rest of the read. This ‘junction extension’ procedure that makes use of previously identified splice sites is represented by brown arrows in the figure.
As we show in our experiments on simulated reads, this two-step strategy produces accurate alignment of reads with anchors as small as 1 bp (see Results). Although HISATx2 has considerably better sensitivity, it takes twice as long to run as HISATx1. As an alternative, we developed a hybrid method, HISAT, which has sensitivity almost equal to that of HISATx2 but with the speed of HISATx1. HISAT collects splice sites as it processes the reads, similarly to the first run of HISATx2. However, as it is processing, it uses the splice sites collected thus far to align short-anchored reads. In the vast majority of cases, it can align even the shortest anchors because it has seen the associated splice sites earlier. This result follows from the observation that most splice sites can be discovered within the first few million reads, and most RNA-seq data sets contain tens of millions of reads. As our results show, HISAT provides alignment sensitivity that very nearly matches the two-step HISATx2 algorithm, with a run time nearly as fast as the one-step HISAT method.
The hybrid approach is also effective in aligning reads spanning more than two exons, which are more likely to have small anchors. The alignment sensitivity for such reads increases from 53% using HISATx1 to 95% using HISAT (Supplementary Fig. 2).
Kim D., Langmead B, & Salzberg S.L. (2015). HISAT: a fast spliced aligner with low memory requirements. Nature methods, 12(4), 357-360.
Publication 2015
Exons Genome Hybrids Hypersensitivity Introns RNA-Seq Toxic Epidermal Necrolysis Vision
4
Developing GEPIA: A Comprehensive Cancer Gene Expression Profiling Tool
Cited 2360 times
The GEPIA website is freely available to all users. It is built by the HTML5 and JavaScript libraries, including jQuery (http://jquery.com), Bootstrap (http://getbootstrap.com/) for the client-side user interface. The server-side and interactive data processing are carried out by PHP scripts (version 7.0.13). The web site automatically adjusts the look and feel according to different browsers and devices, ranging from desktop computers to tablets and smart phones. There is no login requirement for accessing any features in GEPIA.
To solve the imbalance between the tumor and normal data which can cause inefficiency in various differential analyses, we download the TCGA and GTEx gene expression data that are re-computed from raw RNA-Seq data by the UCSC Xena project based on a uniform pipeline (Figure 1). We consult with medical experts to determine the most appropriate sample grouping for tumor-normal comparisons. The datasets are stored in a MySQL relational database (version 5.7.17).
The GEPIA web server features are divided into seven major tabs: General, Differential Genes, Expression DIY, Survival, Similar Genes, Correlation and PCA, which provides key interactive functions corresponding to differential expression analysis, customizable profiling plotting, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis (Figure 2).
All plotting features in GEPIA are developed using R (version 3.3.2) and Perl (version 5.22.1) programs. The GEPIA outputs consist of plots and tables. Static visualizations are rendered as Portable Document Format (PDF), Scalable Vector Graphics (SVG) and Portable Network Graphics (PNG) images. The rotatable 3D plots are built by the plotly.js library (https://plot.ly/). Tables are generated by the DataTables (https://www.datatables.net/) JavaScript library, allowing for data querying and selection.
Tang Z., Li C., Kang B., Gao G., Li C, & Zhang Z. (2017). GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45(Web Server issue), W98-W102.
Publication 2017
cDNA Library Cloning Vectors Feelings Gene Expression Genes Medical Devices Neoplasms Patients RNA-Seq
5
Comparative Evaluation of RNA-seq and Microarray
Cited 2200 times
Genome-wide expression was measured in liver and kidney using RNA-seq on the Illumina GA I and hybridization of the same samples to Affymetrix HG-U133 Plus 2.0 arrays. The sample preparation and data analysis was designed to maximize the similarity between the microarray and RNA-seq experiments (see Marioni et al. [21 (link)]). Differential expression between kidney and liver was determined using an empirical Bayes modified t-statistic on the microarray platform and P-values for DE were downloaded from their website. For the RNA-seq experiment, the data were normalized using TMM normalization [27 ] and a negative binomial exact test was used to determine DE [16 (link)]. To test the GOseq method, we used the genes called DE from the microarray experiment to calculate the significance of over-representation of each GO category using the standard GO analysis methods. We also calculated P-values for each GO category being over-represented among genes that were DE in the RNA-seq data, using both the GOseq and hypergeometric methods. GOseq's ability to outperform the hypergeometric method, as measured by its ability to reproduce the results of the microarray GO analysis, was quantified by calculating a P-value for the difference in the two methods being due to chance. To do this, a NULL was chosen under which both methods were equally likely to correctly recover each microarray GO category, with this likelihood given by a binomial distribution.
Young M.D., Wakefield M.J., Smyth G.K, & Oshlack A. (2010). Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology, 11(2), R14.
Publication 2010
Crossbreeding Genes Genome Kidney Liver Microarray Analysis RNA-Seq

Most recents protocols related to «RNA-Seq»

1
Validation of Differentially Expressed Genes in Chaetoceros socialis
Six DEGs were validated through a real-time qPCR analysis (Table S5). Three DEGs were randomly chosen, in addition to the most downregulated high-affinity nitrate transporter (NTR2:6) and one NADH-nitrate reductase, which are related to nitrate uptake, and a silicon efflux transporter (LSI3) related to the deposition of silicon in spore valves. Two genotyped strains of C. socialis, namely APC12 and MCA6 were used for this purpose: the former strain is the one used for the transcriptome experiment, while MCA6 is a freshly established strain isolated at station LTER-MC in the Gulf of Naples and for which the D1–D3 region of the nuclear-encoded large subunit ribosomal DNA (partial 28S rDNA) has been sequenced as in [70 ] to confirm its identity.
Triplicate cultures of both strains were maintained in control and low N media, with the same nutrient concentrations used for the RNA-seq experiment. Cells were harvested on day 2 in the control, when the percentage of spores was zero, and on day 3 in the treatments, when the percentage of spores was ~ 33 and ~ 38% for APC12 and MCA6, respectively, corresponding to the ones recorded at T3 of the transcriptome experiment. RNA extraction and purification were performed as illustrated above. Total RNA was reverse-transcribed using the QuantiTect® Reverse Transcription Kit (Qiagen, Venlo, Limburgo, Nederlands).
RTqPCR amplification was performed with cDNA diluted 1:10, in a 10 µl reaction containing each primer at a final concentration of 1 µM and Fast SYBR Green Master mix with ROX (Applied Biosystems) using a ViiA™ 7 Real-Time PCR System (Applied Biosystems by Life Technologies, Carlsbad, CA, USA) and the following cycling parameters: 95 °C for 20 s, 40 cycles at 95 °C for 1 s, 60 °C for 20 s, 95 °C for 15 s, 60 °C 1 min, and a gradient from 60 °C to 95 °C for 15 min. Raw results were processed using the ViiA™ 7 Software and exported into Microsoft Excel for further analyses. The reference gene used was the tubulin gamma chain (TUB G) designed using sequence information from the transcriptome and the software Primer3Plus v.2.4.2 ([71 (link)]). The sequences for the forward and reverse primers are 5’- TGCAGAGTTTGGTCGATGAG -3’and 5’-GGAAGCCAAAGAGTCTGCTG-3’, respectively, yielding a PCR product of 197 bp (Table S5). Primers for all other tested DEGs were designed using the same approach. log2(FC)s were obtained with the Relative Expression Software Tool-Multiple Condition Solver (REST-MCS) ([72 (link)]). A pairwise fixed reallocation randomisation test has been used to identify statistically significant results (P ≤ 0.05).
Pelusi A., Ambrosino L., Miralto M., Chiusano M.L., Rogato A., Ferrante M.I, & Montresor M. (2023). Gene expression during the formation of resting spores induced by nitrogen starvation in the marine diatom Chaetoceros socialis. BMC Genomics, 24, 106.
Publication 2023
Cells DNA, Complementary DNA, Ribosomal Fast Green Gamma Rays Genes Membrane Transport Proteins NADH-Nitrate Reductase Nitrates Nitrate Transporter Nutrients Oligonucleotide Primers Reverse Transcription Ribosome Subunits, Large RNA-Seq Silicon Spores Strains Transcriptome Tubulin
2
Transcriptome Analysis of Brassica Seed Development
The raw data of 144 seed coats RNA-seq data of six Brassica species, B. rapa (Parkland-R), B. oleracea (Chinese Kale-O), B. nigra (CR2748-N), B. napus (DH12075-P), B. juncea (AC Vulcan-J), B. carinata (C901163-C) with eight developmental stages (Unfertilized ovule integuments (UO; no embryo), 1- to 2-cell zygote stage (S1), 4- to 8-cell stage (S2, 8-cell stage shown), 16- to 64-cell stage (S3, globular stage shown), heart stage(S4), torpedo stage(S5), bent stage(S6), and mature (S7) stage of seed formation) were collected from Gene Expression Omnibus under accession no. GSE153257. Low-quality reads were removed from the raw reads using Cutadapt and Trimmomatic software to get clean reads [39 , 2 ]. Clean reads were mapped to the corresponding reference genome using HISAT2 software [51 (link)]. Gene expression levels of each gene were calculated using StringTie and Ballgown software [51 (link)]. The read counts of each gene were calculated using the htseq-count function in htseq software [1 (link)]. The R package DEseq2 (v1.16.1) was used to identify the differentially expressed genes (DEGs) between leaves of different colors based on the following criteria: padj < 0.05 & log2FoldChange > 2 [5 (link)].
Chen D., Chen H., Dai G., Zhang H., Liu Y., Shen W., Zhu B., Cui C, & Tan C. (2023). Genome-wide identification and expression analysis of the anthocyanin-related genes during seed coat development in six Brassica species. BMC Genomics, 24, 103.
Publication 2023
Brassica Cells Chinese Embryo Eye Gene Expression Genes Heart Kale Ovule RNA-Seq Substantia Nigra System, Integumentary Torpedo Zygote
3
Co-expression Network Analysis of Anthocyanin Genes
The RNA-seq data were used to perform co-expression network analysis using R language (v4.2.1). In order to calculate the adjacent order function formed by the gene network and the difference coefficients of different nodes, the TOM similarity algorithm calculates the co-expression correlation matrix to express the gene correlation in the network. The correlation network diagram was drawn by extracting the non-weight coefficients (weight) of anthocyanin-related genes in the matrix. STRING software (https://version-11-5.string-db.org/) was used to reveal a co-expression plot [33 (link), 70 (link)].
Chen D., Chen H., Dai G., Zhang H., Liu Y., Shen W., Zhu B., Cui C, & Tan C. (2023). Genome-wide identification and expression analysis of the anthocyanin-related genes during seed coat development in six Brassica species. BMC Genomics, 24, 103.
Publication 2023
Anthocyanins Gene Regulatory Networks Genes RNA-Seq
4
Chromogranin A and Synaptophysin Expression in Colorectal Cancer
We downloaded a total of 568 CRC and 44 normal tissues from TCGA (https://portal.gdc.cancer.gov). The data included RNA-seq profiles and clinicopathological information. We further compared the expression of CgA and SYP between normal and malignant samples. The overall survival and progression-free survival of patients expressing high and low levels of the two markers were also acquired by applying the Kaplan–Meier method (“limma,” “survival,” and “survminer” packages in R software).
Chen Y., Liang Y., Cao L., Dong X, & Sun D. (2023). Neuroendocrine differentiation: a risk fellow in colorectal cancer. World Journal of Surgical Oncology, 21, 89.
Publication 2023
Malignant Neoplasms Patients RNA-Seq Tissues
5
Transcriptional Profiling of Endocrine Cells
RNA-seq libraries were prepared from 100 FACS-sorted cells/sample obtained from the pancreases of reporter Isl1CKO-Ai14 mutant (n = 5 samples) and reporter control-Ai14 (n = 6) from E14.5 embryos; and Isl1CKO-Ai14 mutant (n = 6) and reporter control-Ai14 (n = 5) from P9 mice. Each sample contained 100 tdTomato+ endocrine cells. Following the manufacturer's instructions, the NEB Next single-cell low input RNA library prep kit for Illumina was used for cDNA synthesis, amplification, and library generation [67 (link)] at the Gene Core Facility (Institute of Biotechnology CAS, Czechia). Fragment Analyzer assessed the quality of cDNA libraries. The libraries were sequenced on an Illumina NextSeq 500 next-generation sequencer. NextSeq 500/550 High Output kit 75 cycles (Illumina #200,024,906) were processed at the Genomics and Bioinformatics Core Facility (Institute of Molecular Genetics CAS, Czechia). RNA-Seq reads in FASTQ files were mapped to the mouse genome using STAR [version 2.7.0c [68 (link)]] GRCm38 primary assembly and annotation version M8. The raw data of RNA sequencing were processed with a standard pipeline. Using cutadapt v1.18 [69 (link)], the number of reads (minimum, 32 million; maximum, 73 million) was trimmed by Illumina sequencing adaptor and of bases with reading quality lower than 20, subsequently reads shorter than 20 bp were filtered out TrimmomaticPE version 0.36 [70 (link)].
Ribosomal RNA and reads mapping to UniVec database were filtered out using bowtie v1.2.2. with parameters -S -n 1 and SortMeRNA [71 (link)]. A count table was generated by Rsubread v2.0.1 package using default parameters without counting multi mapping reads. The raw RNA-seq data were deposited at GEO: (https://www.ncbi.nlm.nih.gov/geo/).
DESeq2 [v1.26.0 [72 (link)]] default parameters were used to normalize data and compare the different groups. Genes were then filtered using the criteria of an adjusted P-value Padj < 0.05, and a base mean ≥ 50, and Fold change > 1.5 for upregulated genes and < 0.5 for downregulated genes for both E14.5 and P9 data to identify differentially expressed genes between Isl1CKO and control endocrine cells. The enrichment of the functional categories and functional annotation clustering of the differentially expressed genes was performed using g: Profiler [73 (link)] using version e104_eg51_p15_3922dba with g: SCS multiple testing correction methods applying a significance threshold of 0.05. Transcription factor (TF) enrichment analysis (TFEA) [41 (link)] was used to identify the enrichment of TF target genes in our set of differentially expressed genes. The top seven enriched TFs are listed (Additional file 6: Dataset S1c).
Bohuslavova R., Fabriciova V., Lebrón-Mora L., Malfatti J., Smolik O., Valihrach L., Benesova S., Zucha D., Berkova Z., Saudek F., Evans S.M, & Pavlinkova G. (2023). ISL1 controls pancreatic alpha cell fate and beta cell maturation. Cell & Bioscience, 13, 53.
Publication 2023
Anabolism cDNA Library DNA, Complementary DNA Library Embryo Endocrine Cells Gene Annotation Genes Mus Pancreas Ribosomal RNA RNA-Seq tdTomato Transcription, Genetic Transcription Factor

Top products related to «RNA-Seq»

1
Trizol reagent by Thermo Fisher Scientific
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
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.
2
Hiseq 2500 by Illumina
Sourced in United States, China, Germany, United Kingdom, Canada, Switzerland, Sweden, Japan, Australia, France, India, Hong Kong, Spain, Cameroon, Austria, Denmark, Italy, Singapore, Brazil, Finland, Norway, Netherlands, Belgium, Israel
The HiSeq 2500 is a high-throughput DNA sequencing system designed for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data with speed and accuracy.
3
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.
4
Agilent 2100 bioanalyzer by Agilent Technologies
Sourced in United States, Germany, Canada, China, France, United Kingdom, Japan, Netherlands, Italy, Spain, Australia, Belgium, Denmark, Switzerland, Singapore, Sweden, Ireland, Lithuania, Austria, Poland, Morocco, Hong Kong, India
The Agilent 2100 Bioanalyzer is a lab instrument that provides automated analysis of DNA, RNA, and protein samples. It uses microfluidic technology to separate and detect these biomolecules with high sensitivity and resolution.
5
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.
6
Trizol by Thermo Fisher Scientific
Sourced in United States, Germany, China, Japan, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Belgium, Denmark, Netherlands, India, Ireland, Lithuania, Singapore, Sweden, Norway, Austria, Brazil, Argentina, Hungary, Sao Tome and Principe, New Zealand, Hong Kong, Cameroon, Philippines
TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.
7
Nextseq 500 by Illumina
Sourced in United States, Germany, China, United Kingdom, Australia, France, Italy, Canada, Japan, Austria, India, Spain, Switzerland, Cameroon, Netherlands, Czechia, Sweden, Denmark
The NextSeq 500 is a high-throughput sequencing system designed for a wide range of applications, including gene expression analysis, targeted resequencing, and small RNA discovery. The system utilizes reversible terminator-based sequencing technology to generate high-quality, accurate DNA sequence data.
8
Novaseq 6000 by Illumina
Sourced in United States, China, United Kingdom, Japan, Germany, Canada, Hong Kong, Australia, France, Italy, Switzerland, Sweden, India, Denmark, Singapore, Spain, Cameroon, Belgium, Netherlands, Czechia
The NovaSeq 6000 is a high-throughput sequencing system designed for large-scale genomic projects. It utilizes Illumina's sequencing by synthesis (SBS) technology to generate high-quality sequencing data. The NovaSeq 6000 can process multiple samples simultaneously and is capable of producing up to 6 Tb of data per run, making it suitable for a wide range of applications, including whole-genome sequencing, exome sequencing, and RNA sequencing.
9
2100 bioanalyzer by Agilent Technologies
Sourced in United States, Germany, Canada, United Kingdom, France, China, Japan, Spain, Ireland, Switzerland, Singapore, Italy, Australia, Belgium, Denmark, Hong Kong, Netherlands, India
The 2100 Bioanalyzer is a lab equipment product from Agilent Technologies. It is a microfluidic platform designed for the analysis of DNA, RNA, and proteins. The 2100 Bioanalyzer utilizes a lab-on-a-chip technology to perform automated electrophoretic separations and detection.
10
Hiseq 4000 by Illumina
Sourced in United States, China, United Kingdom, Hong Kong, France, Canada, Germany, Switzerland, India, Norway, Japan, Sweden, Cameroon, Italy
The HiSeq 4000 is a high-throughput sequencing system designed for generating large volumes of DNA sequence data. It utilizes Illumina's proven sequencing-by-synthesis technology to produce accurate and reliable results. The HiSeq 4000 has the capability to generate up to 1.5 terabytes of data per run, making it suitable for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis.

More about "RNA-Seq"

RNA-sequencing (RNA-Seq) is a powerful high-throughput technique for comprehensive analysis of the transcriptome, enabling the identification and quantification of RNA molecules in a biological sample.
This advanced sequencing approach provides a detailed snapshot of the gene expression profile, revealing valuable insights into cellular processes, disease mechanisms, and genetic variations.
RNA-Seq allows researchers to explore the complete transcriptional landscape, including coding and non-coding RNAs, alternative splicing events, and novel transcript discoveries.
With its high sensitivity and dynamic range, RNA-Seq has become an invaluable tool for a wide range of applications, from basic biological research to clinical diagnostics and personalized medicine.
Researchers can optimize their RNA-Seq workflows by leveraging the latest advancements in sequencing technology, such as the HiSeq 2500, HiSeq 2000, NextSeq 500, and NovaSeq 6000 systems, as well as bioinformatics tools.
The Agilent 2100 Bioanalyzer and RNeasy Mini Kit are commonly used for sample preparation and quality control, while TRIzol reagent is a widely adopted method for RNA extraction.
By utilizing AI-driven platforms like PubComapre.ai, researchers can identify the most reproducible and accurate research methods, enhancing the quality and reliability of their RNA-Seq projects.
This comprehensive approach to RNA-Seq workflow optimization ensures that researchers can make the most of this powerful technique and uncover valuable insights from their transcriptome data.