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Illumina Sequencing

Illumina Sequencing is a powerful genomic analysis technique that utilizes massively parallel sequencing to generate high-throughput, high-accuracy DNA sequencing data.
This approach leverages innovative biochemistry and advanced imaging technologies to enable researchers to quickly and reliably sequence genetic samples, providing valuable insights into a wide range of biological systems and processes.
Illumina Sequencing is widely used across diverse fields, including genetics, oncology, microbiomics, and epigenetics, to drive groundbreaking discoveries and advance our understanding of the fundamental mechanisms of life.
With its exceptional performance, ease of use, and rapidly evolving capabilities, Illumina Sequencing has become an indispensable tool for modern biological research.

Most cited protocols related to «Illumina Sequencing»

1
Expanding Deep Learning Viral Datasets
To achieve high prediction accuracy, a deep learning algorithm needs a large amount of training data. Though a large number of training sequences were obtained from RefSeq, there is a potential to enlarge the training dataset by including viral sequences from metavirome sequencing data. Metavirome sequencing targets at sequencing mainly viruses by removing prokaryotic cells in samples using the physical 0.22 μm filters. Metavirome sequencing does not rely on culturing viruses in the lab, so it is able to capture both cultivated and uncultivated viruses, representing the true viral diversity. A few studies have used this technique to extract viruses and sequenced viral genomes in human gut and ocean samples [1 (link),2 (link),62 (link),63 (link)]. Normal et al. sequenced virome in the human gut sample from IBD patients using Illumina sequencing technology[1 (link)]. Reyes et al. studied viruses in fecal samples from Malawian twins with Severe Acute Malnutrition (SAM) using Roche 454 sequencing technology [2 (link)]. Minot et al. and Kim et al. investigated virome in healthy human gut using Roche 454 [11 (link),62 (link)]. For marine virome, the Tara Ocean Virome project collected the largest number of virome samples from both surface- and deep-ocean sites over the world [63 (link)].
We collected the metavirome samples from those studies and aimed to add more viral diversity, especially adding viruses not- or under-represented in RefSeq, to the training data. We were careful in quality control of the samples because it is likely that the sample can be contaminated by prokaryotic DNA, since the physical filters may not exclude small sized prokaryotic cells. The details of preparation of metavirome data and quality control can be found in Supplementary Materials and Supplementary Table S3. Up to 1.3 million of sequences were generated from the metavirome data, and they were combined with sequences derived from viral RefSeq before May 2015 for training. The same number of prokaryotic sequences were paired with the viral sequences in the enlarged dataset for training. The new model was evaluated and compared with the original model trained based on RefSeq only, using the test sequences from RefSeq after May 2015.
Ren J., Song K., Deng C., Ahlgren N.A., Fuhrman J.A., Li Y., Xie X., Poplin R, & Sun F. (2020). Identifying viruses from metagenomic data using deep learning. Quantitative biology (Beijing, China), 8(1), 64-77.
Publication 2020
Feces Genome, Human Homo sapiens Illumina Sequencing Marines Patients Physical Examination Prokaryotic Cells Quality of Health Care Severe Acute Malnutrition Twins Viral Genome Virome Virus
2
Forward-Primer Barcoded 16S Amplicon Sequencing
We first modified the 515f/806r PCR primer construct by bar coding the forward primer, rather than the reverse primer, with 960 unique, 12-base Golay bar codes (14 (link)). The addition of bar codes to the forward primer enables the user to produce amplicons spanning multiple 16S rRNA gene variable regions, such as the V4-V5 construct described here. Parada et al. (4 (link)) demonstrated improved phylogenetic resolution using the 515f-926r construct; additionally, the percentage of eukaryotic taxa amplified by this construct indicated that in marine or other non-host-associated environments, this construct may be a particularly attractive choice for producing amplicons from all three domains. The forward primer bar-coding schema can also facilitate the amplification of alternative taxa by allowing pairing of the 515f primer with various other reverse primers, taking full advantage of current longer-read Illumina sequencing technology. This approach is advantageous over that of popular dual-bar-coding schemes (15 (link), 16 (link)), which do not allow the flexibility that single-ended forward bar coding does, due to the necessity of a variety of bar codes for both the forward and reverse primers.
We added pad regions to increase the melting temperature (Tm) of the sequencing primers to approximately 66°C (calculated using OligoAnalyzer 3.1) based upon Illumina’s technical guidelines for amplicon primers (https://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf). The modified PCR and sequencing primers, including the Smith and Peay (2014) modified ITS sequencing primers and the bar-coded primer constructs (see Table S1 in the supplemental material) (http://www.earthmicrobiome.org/emp-standard-protocols/16s/; New Illumina HiSeq 16S primer sequences) were screened for dimers and the secondary structure by using Primer Prospector (17 (link)). Each construct was filtered using the check_primer_barcode_dimers script with a score_threshold of −20.0. Predicted taxonomic coverage was generated by scoring each of the primers (analyze_primers script with default settings) against the SILVA 111 97% OTUs database (18 (link)), available for download at http://www.arb-silva.de/download/archive/qiime/. Predicted taxonomic coverage for each primer pair was then generated from the scored hits with the taxa_coverage script, allowing a single non-3′-mismatch for the primers with a score_threshold parameter of 0.4. Graphs for overall domain-level and phylum-level taxa were generated from the text output of taxa coverage, which was split by domain and sorted by sequence counts per phylum from the SILVA 111 database (see Fig. S1 in the supplemental material).
Walters W., Hyde E.R., Berg-Lyons D., Ackermann G., Humphrey G., Parada A., Gilbert J.A., Jansson J.K., Caporaso J.G., Fuhrman J.A., Apprill A, & Knight R. (2015). Improved Bacterial 16S rRNA Gene (V4 and V4-5) and Fungal Internal Transcribed Spacer Marker Gene Primers for Microbial Community Surveys. mSystems, 1(1), e00009-15.
Publication 2015
DNA Library Eukaryota Illumina Sequencing Marines Metagenome Multiple Birth Offspring Oligonucleotide Primers RNA, Ribosomal, 16S
3
Tomato Transcriptome Dynamics During Fruit Set
To investigate the tomato transcriptome dynamics of fruit set, RNA were isolated from flower buds (Bud) and flowers at anthesis (Ant) and post-anthesis (Pos) stages. For each stage, cDNA libraries were generated from three biological replicates and subjected to Illumina mRNA-Seq technology sequencing. Then, after mapping reads to the tomato genome sequence, we obtained a table of raw counts with 34675 rows (genes) and 9 columns (3 stages and 3 replicates per stage). These technical procedures are described in Maza et al. (2013 (link)). In this paper, for sake of simplicity, the matrix (34675 × 9) containing raw counts is denoted by X.
, & Maza E. (2016). In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design. Frontiers in Genetics, 7, 164.
Publication 2016
Biopharmaceuticals cDNA Library Flowers Fruit Genes Illumina Sequencing Lycopersicon esculentum RNA, Messenger Transcriptome
4
Genome Assembly of Marine Mammals
Whole genome shotgun sequences were generated using an Illumina HiSeq platform, from DNA libraries of the killer whale, walrus, manatee and bottlenose dolphin. The dolphin had previously been Sanger sequenced at 2× coverage and library and sequencing protocols have been previous described22 . The dolphin assembly was produced by assembling the ~2.5× Sanger data with ~ 3.5× Roche 454 FLX fragment data and ~30× Illumina HiSeq data. The Sanger and 454 data were combined with the Atlas assembler and then Atlas-Link23 and Atlas-GapFill24 were used to add the Illumina data and improve the scaffolds and fill intra-scaffold gaps.
The de-novo assemblies were produced using methods similar to those used in the Assemblathon II comparison. An initial assembly was generated using AllPath-LG with default parameters and MIN_CONTIG=300 and all sequence data except the 500 bp insert data. The assembled scaffolds from the initial assembly were further extended using Atlas-Link based upon the linking information provided from the 3 kb and 8 kb libraries. Atlas-GapFill was then used to fill gaps within scaffolds by locally assembling the reads associated with each gap. For the killer whale and walrus respectively, these reads were assembled into draft genomes with contig N50 sizes of 70.3 kb and 90.0 kb, and scaffold N50 sizes of 12.7 Mb and 2.6 Mb (Supplementary Table 1). The assemblies of 2,249 Mb and 2,300 Mb cover approximately 85% and 95% of the estimated 2,373 Mb killer whale and 2,400 Mb walrus genomes respectively. The improved dolphin assembly contig N50 is 11.9 kb and the scaffold N50 is 115 kb. The total assembled size of the genome is 2.33 Gb (2.55 Gb with gaps) and covers ~95.3% of the genome.
Sequencing and assembly of the manatee varied slightly from the other marine mammals: the manatee’s DNA was sequenced to 90× total coverage by Illumina sequencing technology comprising 45× coverage of 180 bp fragment libraries, 42× coverage of 3 kb sheared jumping libraries, 2× coverage of 6–14 kb sheared jumping libraries, and 1× coverage of Fosill jumping libraries (PMID: 22800726). The sequence was then assembled using ALLPATHS-LG (PMID: 21187386). The draft assembly is 3.10 Gb in size and is composed of 2.77 Gb of sequence plus gaps between contigs. The manatee genome assembly has a contig N50 size of 37.8 kb, a scaffold N50 size of 14.4 Mb, and quality metrics comparable to other Illumina genome assemblies.
Foote A.D., Liu Y., Thomas G.W., Vinař T., Alföldi J., Deng J., Dugan S., van Elk C.E., Hunter M.E., Joshi V., Khan Z., Kovar C., Lee S.L., Lindblad-Toh K., Mancia A., Nielsen R., Qin X., Qu J., Raney B.J., Vijay N., Wolf J.B., Hahn M.W., Muzny D.M., Worley K.C., Gilbert M.T, & Gibbs R.A. (2015). Convergent evolution of the genomes of marine mammals. Nature genetics, 47(3), 272-275.
Publication 2015
DNA Library Dolphins Genome Illumina Sequencing Mammals Marines Odobenus rosmarus Orcinus orca Trichechus Tursiops truncatus
5
Genome Sequencing and SNP Calling in Candida albicans
Genomic DNA was extracted from the colonies using the phenol chloroform protocol previously described in ref. 15 (link) or the QiaAmp DNA Mini Kit (Qiagen). The genomes were sequenced either at the Biomics Pole—Genomic Platform of Institut Pasteur, the Department of Genetics at Stanford University or the Sequencing facility of the University of Exeter (see Supplementary Data 1 for details) using the Illumina sequencing technology. Paired-end reads of 100–125 bp were obtained. Reads have been deposited at the NCBI Sequence Read Archive under BioProject ID PRJNA432884.
Each set of paired-end reads was mapped against the C. albicans reference genome SC5314 haplotype A or haplotype B53 (link) downloaded from the Candida Genome Database54 (link) (version A22 06-m01) using the Burrows–Wheeler Alignment tool, BWA version 0.7.755 (link), with the BWA-MEM algorithm, specifically designed for sequences ranging from 70 bp to 1 Mb and recommended for high-quality queries. SAMtools version 1.256 (link) and Picard tools version 1.94 (http://broadinstitute.github.io/picard) were then used to filter, sort and convert SAM files.
SNPs were called using Genome Analysis Toolkit version 3.1–157 (link)–59 , according to the GATK Best Practices. SNPs and indels were filtered using these following parameters: VariantFiltration, QD < 2.0, LowQD, ReadPosRankSum < −8.0, LowRankSum, FS > 60.0, HightFS, MQRankSum < −12.5, MQRankSum, MQ < 40.0, LowMQ, HaplotypeScore > 13.0, HaploScore. Coverages were also calculated using the Genome Analysis Toolkit.
We created two tables encompassing all 182 isolates from VCF files using custom scripts. One encompassed 264,999 confident SNPs across the 182 isolates containing no missing data. Besides passing GATK’s filters, we also checked for read depth (it had to be between 0.5 and 1.5 of the mean genome coverage), heterozygous positions should have an allelic ratio of number of alternative allele reads/total number of reads comprised between 15 and 85% and homozygous positions should have an allelic ratio of number of alternative allele reads/total number of reads >98% (Supplementary Data 2). The second table encompassed 589,255 SNPs where some of the new filters described above could be not respected and we created a code to have information of which filter did not pass:—for wrong allelic ratio of reference/alternative allele for heterozygous positions,++ for wrong allelic ratio of reference/alternative allele for homozygous positions, ## for a read depth not between 0.5 and 1.5 of the mean genome coverage; some positions could have several filters which did not pass: a combination of—and ## gave && and ++and ## gave ** (Supplementary Data 3).
Ropars J., Maufrais C., Diogo D., Marcet-Houben M., Perin A., Sertour N., Mosca K., Permal E., Laval G., Bouchier C., Ma L., Schwartz K., Voelz K., May R.C., Poulain J., Battail C., Wincker P., Borman A.M., Chowdhary A., Fan S., Kim S.H., Le Pape P., Romeo O., Shin J.H., Gabaldon T., Sherlock G., Bougnoux M.E, & d’Enfert C. (2018). Gene flow contributes to diversification of the major fungal pathogen Candida albicans. Nature Communications, 9, 2253.
Publication 2018
Alleles BP 100 Candida Chloroform Genome Haplotypes Heterozygote Homozygote Illumina Sequencing INDEL Mutation Phenols Self Confidence Single Nucleotide Polymorphism

Most recents protocols related to «Illumina Sequencing»

1
Transcriptome and Methylome Analysis
RNA libraries were prepared by the FASTERIS Company (https://www.fasteris.com). Total RNA was polyA purified and libraries were prepared for illumina NextSeq sequencing technologies. For RNAseq analysis, two biological replicates per mutant were used (M23 and M25). In addition, two biological replicates of a Pt1 8.6 line was sequenced in the same run as a control (total of 6 samples). Bisulfite libraries and treatments were performed by the FASTERIS Company and DNA was sequenced on an Illumina NextSeq instrument. 150 bp paired-end reads were generated with 30X coverage. A 5mC map was also generated in the reference Pt1 8.6 line as a control.
Hoguin A., Yang F., Groisillier A., Bowler C., Genovesio A., Ait-Mohamed O., Vieira F.R, & Tirichine L. (2023). The model diatom Phaeodactylum tricornutum provides insights into the diversity and function of microeukaryotic DNA methyltransferases. Communications Biology, 6, 253.
Publication 2023
Biopharmaceuticals hydrogen sulfite Illumina Sequencing Poly A
2
Optimized ddRAD Sequencing for Amphibian Genomes
To determine appropriate restriction enzyme combinations and insert size for ddRAD protocols, 500 ng of DNA for two samples from each clade (R. chiricahuensis and E. anthonyi) was double digested with four enzyme pairs: SphI + EcoRI, EcoRI + MspI, SphI + MluCl, and SphI + MspI (New England BioLabs), cleaned using handmade Serapure beads (see Rohland & Reich, 2012 ), and sent to the University of Texas at Austin Genomics Sequencing and Analysis Facility (GSAF) for fragment visualization using an Agilent 2100 Bioanalyzer (Agilent) and standard 2100 Expert Software. We selected the SphI + MluCl enzyme combination for both Epipedobates and Rana because they sheared reasonable subsets of the genomes (~1%) at a size range amenable to Illumina sequencing technology (~300 nt). Based on our Bioanalyzer results, we aimed to recover 0.98% of the genome in Epipedobates (size selection window: 275–325 nt; x¯ = 291 nt) and 1.21% of the genome in Rana (size selection window: 300–350 nt; x¯ = 314 nt). We estimated the genome size of Epipedobates as 9GB, based on the upper limit for the dendrobatid Oophaga (Rogers et al., 2018 (link)), and 6GB for Rana catesbeiana following Hammond et al. (2017 ). To target a coverage depth of 20×, we requested 7.27 and 5.55 million paired‐end reads (2 × 150 paired‐end reads) per sample for Epipedobates and Rana, respectively (Table 1; see also Supporting Information). Preliminary data now suggest that Epipedobates genomes are closer to 6GB in size (R. D. Tarvin, unpublished data), which would imply that fewer reads could have been requested. Library preparation was performed following Peterson et al. (2012 (link)), using handmade Sera‐mag Speedbeads for all but the final bead clean‐up step (in which Dynabeads were used). DNA was quantified using PicoGreen dsDNA quantitation, DNA was standardized, and size selection was accomplished using a Pippin Prep machine (using a 2% cassette). Pooled libraries (total concentrations of 0.92 ng/μL for Epipedobates and 1.91 ng/μL for Rana) were then sequenced at the GSAF on an Illumina HiSeq 4000.
Chambers E.A., Tarvin R.D., Santos J.C., Ron S.R., Betancourth‐Cundar M., Hillis D.M., Matz M.V, & Cannatella D.C. (2023). 2b or not 2b? 2bRAD is an effective alternative to ddRAD for phylogenomics. Ecology and Evolution, 13(3), e9842.
Publication 2023
austin Deoxyribonuclease EcoRI DNA, Double-Stranded DNA Library DNA Restriction Enzymes Enzymes Genome Illumina Sequencing PicoGreen Rana Rana catesbeiana Serum
3
Batch Correction Strategies for Differential Gene Expression
The samples were collected and processed across six batches over 2.5 years. Although the key methodology was the same (sequencing vendor, library preparation kit, and Illumina sequencing technology), some procedures changed over time, including the total number of reads sequenced, the switch to a dual DNA/RNA extraction protocol, and the inclusion of samples with lower tumor fractions on the slides (Supplementary Table S1). Several batch correction approaches were explored by adding covariates to the DESeq2 (18 (link)) differential expression analysis (Supplementary Fig. S1) for the following key contrasts in this study: (A) CR versus PD at 3 months in the PRE samples and (B) D11 versus PRE. In both of those contrasts, using no batch correction yielded the highest number of differentially expressed genes. Adding a categorical covariate for six batches reduced the number of differential genes by ≥50% in the D11 versus PRE contrast. Batch 5 was all PRE samples and Batch 6 was mostly D11, so when they were treated as separate batches, the number of differential genes fell dramatically. Combining Batch 5 and Batch 6 preserved many of the D11 versus PRE differences. Several other covariates in the differential expression model were tested (e.g., percentage of tumor on slide, DV200, batches), and the resulting fold-changes highly overlapped with the simple batch model (Supplementary Fig. S2). Given the above, all further analysis was performed using five batches (Batches 1–4 with 5 and 6 combined) as a covariate.
For sample level analyses, such as single sample gene set enrichment analysis (ssGSEA) scores and gene plot figures, we used the expression values adjusted by vst (DESeq2 R package; ref. 18 (link)), followed by removeBatchEffect (limma R package; ref. 19 (link)) for the five batch groups using the STAR counts as inputs. When performing differential expression analysis between two groups of samples, we used DESeq2 with the raw gene-level counts from the STAR output, with the five batch groups as a covariate. Supplementary Figure S3 shows the comparison of the fold-changes between response groups of the PRE samples.
Olson N.E., Ragan S.P., Reiss D.J., Thorpe J., Kim Y., Abramson J.S., McCoy C., Newhall K.J, & Fox B.A. (2023). Exploration of Tumor Biopsy Gene Signatures to Understand the Role of the Tumor Microenvironment in Outcomes to Lisocabtagene Maraleucel. Molecular Cancer Therapeutics, 22(3), 406-418.
Publication 2023
cDNA Library Genes Illumina Sequencing Neoplasms
4
Worm Genomic DNA Extraction and Sequencing
Genomic DNA was collected from ∼100 mg of worm tissue using the Qiagen blood and tissue kit (13323) following the manufacturer's recommendations. DNA was eluted with 10 mM Tris–HCl (pH 8.0). Samples were quality-checked to ensure a minimum quantity of 1500 ng and a 260/280 ratio of 1.8 before submitting for sequencing. Paired-end short-read WGS were obtained for all strains with PCR-free library preparation protocol and NovaSeq 6000 Illumina sequencing technology.
Maroilley T., Flibotte S., Jean F., Rodrigues Alves Barbosa V., Galbraith A., Chida A.R., Cotra F., Li X., Oncea L., Edgley M., Moerman D, & Tarailo-Graovac M. (2023). Genome sequencing of C. elegans balancer strains reveals previously unappreciated complex genomic rearrangements. Genome Research, 33(1), 154-167.
Publication 2023
BLOOD DNA Library Genome Helminths Illumina Sequencing Strains Tissues Tromethamine
5
RNA Extraction and Transcriptome Analysis of E. coli
Total RNA was isolated from E. coli strains that were grown for 48 hr with 0.6 g/L malate as carbon sources and H2 supply. Total RNA was extracted using Trizol® Reagent (Invitrogen, Carlsbad, CA, USA) according to the instruction manual. Purified RNA was quantified at an optical density (O.D.) of 260 nm using a ND-1000 spectrophotometer (Nanodrop Technology, Waltham, MA, USA) and qualitated using a Bioanalyzer 2100 (Agilent Technology, Santa Clara, CA, USA) with the RNA 6000 LabChip kit (Agilent Technology, Wilmington, DE, USA). All RNA sample preparation procedures were carried out according to the Illumina’s official protocol. The SureSelect XT HS2 mRNA Library Preparation kit (Agilent, Santa Clara, CA, USA) was used for library construction, followed by AMPure XP beads’ (Beckman Coulter, Brea, CA, USA) size selection. The sequence was determined using Illumina’s sequencing-by-synthesis (SBS) technology (Illumina, San Diego, CA, USA). Sequencing data (FASTQ reads) were generated using Welgene Biotech’s pipeline based on Illumina’s basecalling program bcl2fastq v2.20. Differential expression analysis was performed using StringTie (StringTir v2.1.4) and DEseq (DEseq v1.39.0) or DEseq2 (DEseq2 v1.28.1) with genome bias detection/correction and Welgene Biotech’s in-house pipeline.
Cheng Y.C., Huang W.H., Lo S.C., Huang E., Chiang E.P., Huang C.C, & Yang Y.T. (2023). Conversion of Escherichia coli into Mixotrophic CO2 Assimilation with Malate and Hydrogen Based on Recombinant Expression of 2-Oxoglutarate:Ferredoxin Oxidoreductase Using Adaptive Laboratory Evolution. Microorganisms, 11(2), 253.
Publication 2023
Anabolism Carbon DNA Library Escherichia coli Genome Illumina Sequencing malate RNA, Messenger Strains trizol

Top products related to «Illumina Sequencing»

1
Sequencing technology by Illumina
Sourced in China, United States
Illumina sequencing technology is a DNA sequencing platform that utilizes a method called sequencing by synthesis. The technology enables the determination of the precise order of nucleotides within a DNA molecule, providing a comprehensive view of genetic information.
2
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.
3
Hiseq sequencing technology by Illumina
Sourced in United States
The HiSeq sequencing technology is a high-throughput DNA sequencing platform developed by Illumina. It is designed to generate large volumes of sequencing data with high accuracy. The HiSeq system utilizes a proprietary sequencing-by-synthesis chemistry and imaging technology to perform massively parallel sequencing of DNA samples.
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Miseq sequencing technology by Illumina
Sourced in United States, France, United Kingdom
The MiSeq is a desktop sequencing system designed for targeted resequencing, amplicon sequencing, and small genome sequencing. It utilizes Illumina's sequencing-by-synthesis technology to generate high-quality sequencing data.
5
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.
6
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.
7
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.
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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.
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Miseq technology by Illumina
Sourced in United States, France, China, Germany, Canada, United Kingdom, Japan
The MiSeq technology is a desktop DNA sequencing system designed for targeted sequencing, small genome sequencing, and amplicon-based studies. It utilizes Illumina's sequencing-by-synthesis chemistry to generate high-quality sequencing data. The MiSeq system is capable of generating up to 15 gigabases of sequencing data per run with read lengths of up to 2x300 base pairs.
10
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.

More about "Illumina Sequencing"

Illumina sequencing, also known as next-generation sequencing (NGS) or massively parallel sequencing, is a powerful genomic analysis technique that revolutionized the field of DNA sequencing.
This innovative approach leverages advanced biochemistry and imaging technologies to rapidly and accurately sequence genetic samples, providing valuable insights into a wide range of biological systems and processes.
Illumina sequencing platforms, such as the HiSeq 2000, HiSeq 2500, HiSeq 4000, and MiSeq, utilize a sequencing-by-synthesis (SBS) chemistry to generate high-throughput, high-quality sequencing data.
These platforms are widely used across diverse fields, including genetics, oncology, microbiomics, and epigenetics, to drive groundbreaking discoveries and enhance our understanding of the fundamental mechanisms of life.
One of the key advantages of Illumina sequencing is its exceptional performance and ease of use.
The Agilent 2100 Bioanalyzer, a complementary tool, is often used to assess the quality and quantity of DNA samples prior to sequencing, ensuring optimal results.
Additionally, the TRIzol reagent is a commonly used RNA extraction method that can be seamlessly integrated into Illumina sequencing workflows.
With its rapidly evolving capabilities and widespread adoption, Illumina sequencing has become an indispensable tool for modern biological research.
Researchers can leverage the power of Illumina sequencing to maximize the accuracy and reproducibility of their studies, leading to more reliable and impactful findings.
By incorporating Illumina sequencing into their research, scientists can uncover novel insights and advance our understanding of the complex mechanisms that govern biological systems.