The data provided here are part of a Galaxy tutorial that analyzes ChIP-seq data from a study published by Wu et al., 2014 (DOI:10.1101/gr.164830.113). The goal of this study was to investigate "the dynamics of occupancy and the role in gene regulation of the transcription factor Tal1, a critical regulator of hematopoiesis, at multiple stages of hematopoietic differentiation." To this end, ChIP-seq experiments were performed in multiple mouse cell types including a G1E cell line and megakaryocytes, the two cell types represented here. The dataset contains biological replicate Tal1 ChIP-seq and input control experiments (*.fastqsanger files). Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to chromosome 19 and a subset of interesting genomic loci (ChIPseq_regions_of_interest_v4.bed) pulled from the Wu et al. publication. Also included is a gene annotation file (RefSeq_gene_annotations_mm10.bed) with gene names added for viewing in a genome browser.

The data provided here are part of a Galaxy Training Network tutorial that analyzes ChIP-seq data from a study published by Ross-Inness et al., 2012 (DOI:10.1038/nature10730) to identify the binding sites of the Estrogen receptor, a transcription factor known to be associated with different types of breast cancer.

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

Shown are the number of peaks called and the total number of bp covered by each peak set for H3K4me3, H3K36me3, and H3K9me3 using the original Sole-search program or the program which has been modified to identify broad regions covered by modified histones. Also shown in the increase in genome coverage (fold difference) that results when using the modified peak calling program. Both the original and the modified program can be accessed at http://chipseq.genomecenter.ucdavis.edu/cgi-bin/chipseq.cgi.

ChIP-seq (chromatin immunoprecipitation followed by sequencing) is commonly used to identify genome-wide protein-DNA interactions. However, ChIP-seq often gives a low yield, which is not ideal for quantitative outcomes. An alternative method to ChIP-seq is ChEC-seq (Chromatin endogenous cleavage with high-throughput sequencing). In this method, the endogenous TF (transcription factor) of interest is fused with MNase (micrococcal nuclease) that non-specifically cleaves DNA near binding sites. Compared to the original ChEC-seq method, the modified version requires far less amplification. Since MACS3 failed to identify peaks in data generated from the modified ChEC-seq method, a new peak finder has been developed specifically for it. There are three functions in the peak_finder/. callpeaks() is used to identify peaks from BAM files. goanalysis() is used to make GO (Gene Ontology) term plots from peaks. bedtomeme() is a wrapper function to perform MEME analysis in R after MEME Suite is installed locally. Methods ****EXCERPTED FROM BIORXIV PREPRINT; SEE PREPRINT OR PUBLISHED PAPER FOR REFERENCES AND DETAILS**** Yeast strains All yeast strains were derived from BY4741. A C-terminal micrococcal nuclease fusion was introduced to the protein of interest through transformation and homologous recombination of PCR-amplified DNA. Primers were designed with 50-bp of homology to the 3’ end of the coding sequence of interest. The 3xFLAG-MNase with a KanR marker was amplified from pGZ108 (Zentner et al., 2015) and transformed into BY4741 as previously described. Successful transformation was confirmed by immunoblotting and PCR, followed by sequencing. Lyophilized DNA oligonucleotides were resuspended in molecular-grade water to a concentration of 100 µM. For ligation, the following pair of oligonucleotides were annealed to produce the Y-adapter: Tn5ME-A (5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3’) and Y-Adapt-i5 R (5’-CTGTCTCTTATACACATCTTCATAGTAATCATC-3’). For Tn5 Tagmentation, the following i7 oligonucleotides were annealed: Tn5ME-B (5’ -GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’) and Tn5MErev, (5’-PO4-CTGTCTCTTATACACATCT-3’). Pairs of oligonucleotides were annealed as follows: 45 µl of each oligo (100 µM) was combined with 10 µl of 1 M Potassium Acetate, 300 mM HEPES, pH 7.5 in a 0.2 ml PCR tube. In a thermocycler, the mixture was heated to 95˚C for 4 minutes, cooled 1°C/minute until 50°C, incubated at 50°C for 5 minutes, and then cooled 1°C/minute until 4°C. Hybridized oligos were stored in 15 µl aliquots at -20˚C. Tn5 purification and adapter loading Tn5 E54K L372P was purified as previously described (Hennig et al., 2017). We found that Tn5 was sufficiently pure following purification on Ni2+-chromatography and we therefore omitted the final gel filtration step. Purified Tn5 was aliquoted and stored at -80°C. Optimal Tn5 activity was determined by cleaving genomic DNA and assessing fragmentation using the Femto Pulse (Figure S2d), and resulting DNA libraries were confirmed to be of appropriate length for Illumina Sequencing by TapeStation (Figure S2e). Tn5 was thawed on ice and 100 µl Tn5 was added to 10 µl i7 (45 µM) in a 1.7 ml tube and mixed by gently pipetting. The mixture was incubated at 23°C, mixing at 350 rpm for 45 minutes. Adapter-loaded Tn5 was stored at -20°C and used within 24 hours.

Chromatin endogenous cleavage detailed protocol Chromatin digestion 1. Grow cells in 10ml overnight at 30°C, 200 rpm. 2. Dilute cultures into 50ml media to OD600 ~ 0.1. 3. When cultures reach OD600 = 0.5 - 0.8, harvest 25 ODs (i.e. 50ml if the OD600 = 0.5) of cells by centrifugation at 2500 x g for 1 minute. 4. Resuspend cells in 1 ml Buffer A and transfer to a 1.5 ml tube. 5. Pellet cells by centrifugation at 2500 x g for 1 minute, remove supernatant. 6. Wash cells 2 x 1 ml Buffer A, removing supernatant. 7. Resuspend cells in 600 µl Buffer A + 0.1% Digitonin. 8. Transfer tube to a 30°C heat block and incubate for 5 minutes. 9. Add 5 µl of 333 mM CaCl2, mix by inverting, incubate at 30°C for the appropriate cleavge time (determined empirically for each protein). 10. To stop the reaction, remove 200 µl cells and combine with 200 µl 2x Stop Buffer. 11. Add 8 µl Proteinase K (20 µg/µl) and mix. 12. Incubate at 50°C, agitating 800 rpm for 30 minutes in a thermomixer. 13. Remove samples from thermomixer and cool at room temperature for 5 minutes. 14. Add 400 µl Phenol-Chloroform-Isoamyl Alcohol (25:24:1), pH 7.8, mix. 15. Centrifuge at 24,000 x g, 5 minutes. 16. Transfer aqueous phase to a phase-lock tube. 17. Add 200 µl Phenol-Chloroform-Isoamyl Alcohol (25:24:1), pH 7.8. 18. Invert 10x to mix. 19. Centrifuge at 24,000 x g for 5 minutes. 20. Transfer aqueous phase to a tube containing 1 ml 100% Ethanol. 21. Add 2 µl of linear acrylamide (5 µg/µl). 22. Invert 10x to mix. 23. Incubate at -80°C for 30 minutes. 24. Centrifuge at 24,000 x g, 4°C for 10 minutes. 25. Pour off supernatant. 26. Wash DNA pellet in 1 ml of 70% ethanol. 27. Centrifuge at 24,000 x g for 1 minute. 28. Pour off supernatant. Collect residual ethanol by centrifugation and remove by pipetting. 29. Dry DNA pellet until all ethanol had evaporated. 30. Add 58 µl of 10 mM Tris-HCl, pH 8.5 to DNA pellet. 31. Incubate overnight at room temperature. 32. Incubate at 37°C for 30 minutes. 33. Add 2 µl RNase A (10 µg/µl) to DNA. 34. Incubate at 37°C for 30 minutes. 35. Evaluate molecular weight of DNA by gel electrophoresis, 0.8% agarose or TapeStation. 36. Quantify DNA concentration with the Qubit double-stranded DNA, Broad Range Assay. 37. Stored DNA at 4°C until library preparation was performed (up to a month), then stored at -20°C.

Buffer A 15 mM Tris-HCl, pH 7.5 80 mM KCl 0.1 mM EGTA 1.0 mM PMSF 0.5 mM Spermidine 0.2 mM Spermine -Add 1 EDTA-Free Protease Inhibitor Tab per 50 ml Buffer A (Roche; Sigma # 11873580001)

2x Stop Buffer 400 mM NaCl 20 mM EDTA 4 mM EGTA

Bioinformatic analysis Quality Control, Trimming, and Mapping Read quality and sequencer performance was evaluated with FASTQC. Reads were adapter and quality trimmed with Trimmomatic (Bolger et al., 2014) using single-end settings. Bases at either end of a read were trimmed if base-call quality was less than 30, and only reads of length ≥25 bp were retained. Trimmed reads were mapped to the Saccharomyces cerevisiae genome (Engel et al., 2013), version R64-4-1 with Bowtie2 (Langmead and Salzberg, 2012)and mapped reads with a MAPQ <10 were removed with Samtools (Li et al., 2009). DoubleChEC identification of high-confidence TF binding sites For peak calling analysis, BAM files for three or more biological replicates of the TF-MNase and soluble MNase were read and trimmed to the first base pair. Unnormalized counts and normalized counts per million (CPMn) were tallied for each base pair in the yeast genome and the average CPMn values among replicates were calculated for each position. Next, mean CPMn values were smoothed using a sliding window of 3 and a step size of 2. Windows with CPMn values less than three times the genome average were filtered out. After this filtering, local maxima (windows with values greater than their immediate neighbors) were identified. Unnormalized reads were smoothed, retaining positions that were identified as local maxima, and inputted them in DESeq2 (version 1.36.0) to identify windows with values significantly higher than those in the soluble MNase control. Only TF-MNase peaks with a greater log2-fold change of 1.7 and an adjusted p-value less than 0.0001 over soluble MNase were retained. Finally, the peaks were filtered again to identify doublet peaks that are between 15bp and 50bp apart, which were merged to single peaks. GO term plot A list of genes whose 700bp upstream regions overlap with peaks identified by the peak finder was input to enrichGO (Wu et al., 2021) to generate GO term plots based on biological functions. The 10 most significant GO terms with adjusted p-values less than 0.05 were plotted. MEME analyses The MEME Suite (version 5.5.1) was installed onto the local computer and two custom wrapper functions were written in R for the local bed2fasta and meme programs. These functions were then used to convert bed files, generated from peak calling, into FASTA files. These FASTA files were subsequently to generate motif logos. Both bed2fasta and meme programs were run using their default parameter values.

ChIP-Atlas is the database and its web interface to provide the result of analysis processed from the entire ChIP-Seq data archived in Sequence Read Archive. We have curated metadata described by original data submitter to enable further data analysis. See details here: https://github.com/inutano/chip-atlas/wiki

Datasets for Galaxy Training on ChIP-SEQ analysis. Raw files can be downloaded from SRA project SRP051214

RNA-seq is a sensitive and accurate technique to compare steady state levels of RNA between different cellular states. However, as it does not provide an account of transcriptional activity per se, other technologies are needed to more precisely determine acute transcriptional responses. Here, we have developed an easy, sensitive and accurate novel method, iRNA-seq, for genome-wide assessment of transcriptional activity based on analysis of intron coverage from total RNA-seq data. To test our method, we have performed total RNA-seq and RNA polymerase II (RNAPII) ChIP-seq profiling of the acute transcriptional response of human adipocytes to TNFα treatment and analyzed these using iRNA-seq in addition to different publically availbale dataset. Comparison of the results derived from iRNA-seq analyses with results derived using current methods for genome-wide determination of transcriptional activity, i.e. Global Run-On (GRO)-seq and RNA polymerase II (RNAPII) ChIP-seq, demonstrate that iRNA-seq provides very similar results in terms of number of regulated genes and their fold change. However, unlike the current methods that are all very labor-intensive and demanding in terms of sample material and technologies, iRNA-seq is cheap and easy and requires very little sample material. In conclusion, iRNA-seq offers an attractive novel alternative to current methods for determination of changes in transcriptional activity at a genome-wide level. Genome-wide assesment of the acute transcriptional response to TNFa in human SGBS adiposytes using total RNA-seq data end RNAPII ChIP-seq

Brucella abortus ChipSeq analysis of BvrR

Some datasets for the SAOD (Statistical Analysis of Omics Data) course (Aix-Marseille Université, D. Puthier). The Homo_sapiens.GRCh38.110.chr.tsv was produced using the following command: gtftk retrieve -r 110 gtftk convert_ensembl -i Homo_sapiens.GRCh38.110.chr.gtf.gz | gtftk nb_exons | gtftk feature_size -t mature_rna | gtftk feature_size -t transcript -k tx_genomic_size | gtftk exon_sizes | gtftk intron_sizes | gtftk select_by_key -t | gtftk tabulate -k '*' -u -x > Homo_sapiens.GRCh38.110.chr.tsv

The chromatin immunoprecipitation sequencing (ChIP-seq) market is experiencing robust growth, driven by the increasing adoption of next-generation sequencing (NGS) technologies in genomics research and drug discovery. The market's expansion is fueled by the rising prevalence of chronic diseases requiring advanced diagnostic tools, a growing understanding of epigenetic mechanisms in disease pathogenesis, and the increasing availability of sophisticated bioinformatics tools for data analysis. Applications in hospital settings and diagnostic centers are leading the market, owing to the demand for precise and efficient disease diagnosis and personalized medicine approaches. Within the types of ChIP-seq, DNase-Seq and FAIRE-Seq are prominent, offering diverse approaches to studying chromatin structure and gene regulation. The competitive landscape is marked by the presence of established players like Thermo Fisher Scientific, Bio-Rad, and Merck, alongside specialized companies such as Creative Diagnostics and Profacgen, contributing to a dynamic and innovative market. While a precise market size is not provided, considering the growth potential within genomics and the established presence of key players, a reasonable estimate for the 2025 market size could be in the range of $500 million, growing at a conservative CAGR of 10-15% over the forecast period (2025-2033). This growth trajectory is expected to continue, fuelled by ongoing technological advancements and expanding research activities. The key restraints on market growth might include the high cost of ChIP-seq, the need for specialized expertise in sample preparation and data analysis, and the potential for variations in results depending on the chosen method and protocol. However, these limitations are being progressively addressed through technological improvements, the development of user-friendly software, and standardization efforts within the field. The future will likely witness greater accessibility of ChIP-seq technology, coupled with more affordable and automated solutions, leading to wider adoption in various research and clinical settings. The geographical distribution shows strong growth in North America and Europe, driven by strong research infrastructure and funding in these regions. However, emerging markets are poised for significant expansion, as research capabilities and healthcare spending increase globally.

Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

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NGS-Based Rna-Seq Market Size 2024-2028

The NGS-based RNA-seq market size is forecast to increase by USD 6.66 billion, at a CAGR of 20.52% between 2023 and 2028.

The market is witnessing significant growth, driven by the increased adoption of next-generation sequencing (NGS) methods for RNA-Seq analysis. The advanced capabilities of NGS techniques, such as high-throughput, cost-effectiveness, and improved accuracy, have made them the preferred choice for researchers and clinicians in various fields, including genomics, transcriptomics, and personalized medicine. However, the market faces challenges, primarily from the lack of clinical validation on direct-to-consumer genetic tests. As the use of NGS technology in consumer applications expands, ensuring the accuracy and reliability of results becomes crucial.
The absence of standardized protocols and regulatory oversight in this area poses a significant challenge to market growth and trust. Companies seeking to capitalize on market opportunities must focus on addressing these challenges through collaborations, partnerships, and investments in research and development to ensure the clinical validity and reliability of their NGS-based RNA-Seq offerings.

What will be the Size of the NGS-based RNA-Seq market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in NGS technology and its applications across various sectors. Spatial transcriptomics, a novel approach to studying gene expression in its spatial context, is gaining traction in disease research and precision medicine. Splice junction detection, a critical component of RNA-seq data analysis, enhances the accuracy of gene expression profiling and differential gene expression studies. Cloud computing plays a pivotal role in handling the massive amounts of data generated by NGS platforms, enabling real-time data analysis and storage. Enrichment analysis, gene ontology, and pathway analysis facilitate the interpretation of RNA-seq data, while data normalization and quality control ensure the reliability of results.

Precision medicine and personalized therapy are key applications of RNA-seq, with single-cell RNA-seq offering unprecedented insights into the complexities of gene expression at the single-cell level. Read alignment and variant calling are essential steps in RNA-seq data analysis, while bioinformatics pipelines and RNA-seq software streamline the process. NGS technology is revolutionizing drug discovery by enabling the identification of biomarkers and gene fusion detection in various diseases, including cancer and neurological disorders. RNA-seq is also finding applications in infectious diseases, microbiome analysis, environmental monitoring, agricultural genomics, and forensic science. Sequencing costs are decreasing, making RNA-seq more accessible to researchers and clinicians.

The ongoing development of sequencing platforms, library preparation, and sample preparation kits continues to drive innovation in the field. The dynamic nature of the market ensures that it remains a vibrant and evolving field, with ongoing research and development in areas such as data visualization, clinical trials, and sequencing depth.

How is this NGS-based RNA-Seq industry segmented?

The NGS-based RNA-seq industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Acamedic and research centers
  Clinical research
  Pharma companies
  Hospitals


Technology

  Sequencing by synthesis
  Ion semiconductor sequencing
  Single-molecule real-time sequencing
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Singapore


  Rest of World (ROW)

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By End-user Insights

The acamedic and research centers segment is estimated to witness significant growth during the forecast period.

The global next-generation sequencing (NGS) market for RNA sequencing (RNA-Seq) is primarily driven by academic and research institutions, including those from universities, research institutes, government entities, biotechnology organizations, and pharmaceutical companies. These institutions utilize NGS technology for various research applications, such as whole-genome sequencing, epigenetics, and emerging fields like agrigenomics and animal research, to enhance crop yield and nutritional composition. NGS-based RNA-Seq plays a pivotal role in translational research, with significant investments from both private and public organizations fueling its growth. The technology is instrumental in disease research, enabling the identification

The ChIP Sequencing (ChIP-seq) service market is experiencing robust growth, driven by the increasing adoption of next-generation sequencing (NGS) technologies in life sciences research and drug discovery. The market, currently estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This substantial growth is fueled by several key factors. The rising prevalence of chronic diseases necessitates extensive genomic research, increasing the demand for ChIP-seq services to understand gene regulation and epigenetic modifications. Furthermore, advancements in NGS technology, leading to increased throughput, reduced costs, and improved data analysis, are significantly boosting market expansion. The growing adoption of personalized medicine further contributes to the market's growth trajectory, as ChIP-seq plays a critical role in identifying potential drug targets and predicting individual responses to therapies. Key players in this competitive landscape include Illumina, BGI, Thermo Fisher Scientific, and Eurofins Genomics, constantly innovating and expanding their service offerings to cater to the growing demand. The market segmentation reflects the diversity of applications and customer needs. Academic research institutions represent a significant segment, driven by the need for understanding fundamental biological processes. Pharmaceutical and biotechnology companies constitute another major segment, leveraging ChIP-seq for drug development and target identification. While regulatory hurdles and high initial investment costs present some restraints, the long-term potential benefits outweigh these challenges, ensuring the sustained growth of the ChIP Sequencing service market. This growth is expected to be further propelled by ongoing technological advancements and the increasing integration of ChIP-seq with other genomic techniques. Geographic expansion, particularly in emerging economies with growing research infrastructure, also presents significant opportunities for market expansion.

Large sets of genomic regions are generated by the initial analysis of various genome-wide sequencing data, such as ChIP-seq and ATAC-seq experiments. Gene set enrichment (GSE) methods are commonly employed to determine the pathways associated with them. Given the pathways and other gene sets (e.g., GO terms) of significance, it is of great interest to know the extent to which each is driven by binding near transcription start sites (TSS) or near enhancers. Currently, no tool performs such an analysis. Here, we present a method that addresses this question to complement GSE methods for genomic regions. Specifically, the new method tests whether the genomic regions in a gene set are significantly closer to a TSS (or to an enhancer) than expected by chance given the total list of genomic regions, using a non-parametric test. Combining the results from a GSE test with our novel method provides additional information regarding the mode of regulation of each pathway, and additional evidence that the pathway is truly enriched. We illustrate our new method with a large set of ENCODE ChIP-seq data, using the chipenrich Bioconductor package. The results show that our method is a powerful complementary approach to help researchers interpret large sets of genomic regions.

Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).

The supporting data include:

• Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline
• Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline
• VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines

This study describes the epigenetic profiling of H3K4me2 and Pol II in growth stage of Tetrahymena thermophila. ChIP-Seq analysis of Pol II and H3K4me2 occupancy.

RNA-seq (RNA sequencing) uses high-throughput (HTS) data to reveal the presence and quantity of RNA in a biological sample at a given moment in time. In the training available at http://galaxyproject.github.io/RNA-Seq/tutorials/ref_based, we introduce the bioinformatics methods to analyze RNA-seq data using a reference genome. The toy datasets were extracted from the study of Brooks et al. 2011.

Two strains were analyzed by ChIP-seq to determine the genomic binding sites for the Aspergillus fumigatus transcription factor FfmA. A wild-type strain (AfS35) was subjected to ChIP-seq analysis using an antibody directed against the wild-type version of the FfmA protein. An strain containing an epitope-tagged version (FLAG-ffmA) of the ffmA gene under control of a doxycycline off (dox off) promoter was also analyzed by ChIP-seq. In this latter case, an anti-FLAG antibody was used. The negative controls in both cases consisted of a ChIP-seq reaction with the respective primary antibody omitted. ChIP-Seq

The global DNA sequencing chip market is experiencing robust growth, driven by advancements in genomics research, personalized medicine, and agricultural biotechnology. While precise market size figures for 2025 aren't provided, a logical estimation, considering typical market growth in this sector and the provided study period (2019-2033) with a base year of 2025, suggests a market value of approximately $5 billion USD in 2025. This significant valuation reflects the increasing adoption of DNA sequencing chips across diverse applications, including diagnostics, drug discovery, and agricultural improvements. The market is segmented by technology (ChIP-chip, ChIP-seq, and others), application (agriculture, forensics, medicine, and others), and geography. The high CAGR (let's assume a conservative estimate of 15% based on industry trends) indicates a promising outlook for continued expansion through 2033. Key drivers include decreasing sequencing costs, increasing throughput, and improved data analysis capabilities. The rising prevalence of chronic diseases, coupled with the expanding demand for personalized medicine, further fuels market growth. However, factors such as high initial investment costs for equipment and the need for skilled personnel could act as restraints. Major players like Illumina, Thermo Fisher Scientific, and Roche dominate the landscape, continually innovating and expanding their product offerings to meet the growing demand. The projected market expansion is fueled by several factors. The decreasing cost of sequencing technology makes it increasingly accessible to researchers and healthcare providers, leading to wider adoption. Furthermore, improvements in data analysis tools enable faster and more accurate interpretation of genomic data, enhancing the value proposition of DNA sequencing chips. The continuous development of new applications in various sectors, including agriculture (crop improvement and disease resistance), forensics (crime scene investigation and paternity testing), and medicine (cancer diagnostics and personalized treatment plans), significantly contributes to the overall market growth. Regional variations are expected, with North America and Europe currently holding significant market share due to established research infrastructure and higher healthcare spending. However, the Asia-Pacific region is anticipated to show substantial growth in the coming years, driven by increasing investments in genomics research and healthcare infrastructure in countries like China and India. This report provides a detailed analysis of the global DNA sequencing chip market, projecting robust growth fueled by advancements in technology and increasing applications across diverse sectors. We delve into market dynamics, competitive landscapes, and future trends, providing actionable insights for stakeholders. The global market is estimated to be valued at $2.5 billion in 2024, with projections exceeding $5 billion by 2030.