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

description:

The Environmental Scanning and Program Characteristic (ESPC) Database is in a Microsoft (MS) Access format and contains Medicaid and CHIP data, for the 50 states and District of Columbia. Specifically, the ESPC database predominantly houses information on Medicaid and CHIP program characteristics as well as selected environmental factors that are available through other publicly available databases. This database contains data from 2005 onward, and was last updated in calendar year 2013. Program characteristics include data elements such as eligibility criteria, the presence of waiver programs, managed care penetration, benefit coverage, reimbursement levels, and expenditures.

The Environmental Scanning and Program Characteristic (ESPC) Database is in a Microsoft (MS) Access format and contains Medicaid and CHIP data, for the 50 states and District of Columbia. Specifically, the ESPC database predominantly houses information on Medicaid and CHIP program characteristics as well as selected environmental factors that are available through other publicly available databases. This database contains data from 2005 onward, and was last updated in calendar year 2013. Program characteristics include data elements such as eligibility criteria, the presence of waiver programs, managed care penetration, benefit coverage, reimbursement levels, and expenditures.

Table of HN-scores and ChIP-seq scores (MACS2 score) for each gene. The genes listed in this data are the only human and mouse gene symbols that can be converted.The ChIP-seq score is retrieved from the ChIP-Atlas database (http://chip-atlas.org) (accessed on February 2023). Using the "Target Genes" feature, data were obtained for HSF1, HSF2, and PPARGC1A.

All states (including the District of Columbia) are required to provide data to The Centers for Medicare & Medicaid Services (CMS) on a range of Medicaid and Children’s Health Insurance Program (CHIP) indicators related to key application, eligibility, enrollment and call center processes. These data reflect enrollment activity for all populations receiving comprehensive Medicaid and CHIP benefits in all states, as well as state program performance. States submit this data via the Performance Indicator dataset. Further information about this dataset is available at: https://www.medicaid.gov/medicaid/national-medicaid-chip-program-information/medicaid-chip-enrollment-data/performance-indicator-technical-assistance/index.html.

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.

All states (including the District of Columbia) provide data to the Centers for Medicare & Medicaid Services (CMS) on a range of Medicaid and Children’s Health Insurance Program (CHIP) eligibility and enrollment metrics. These data reflect state-reported information on Medicaid and CHIP eligibility renewals initiated and scheduled for completion during the reporting period. In addition to reporting the outcomes of renewals at the end of each reporting period, states also provide an update on renewals that were reported pending as of the end of a reporting period. For more information on these data, see Sections II and III of the Eligibility Processing Data Report specifications. Notes: Georgia reported data for individuals who continue to be eligible following a change in circumstances and were granted a new 12-month eligibility period during the reporting period, along with data on individuals due for renewal in the month. North Carolina reports renewal outcomes for only initiated renewals scheduled for completion in the report month, and as such, the data do not reflect renewals that should have been completed in the reporting period that the state was unable to initiate by the end of the report month.

The number of experiments in which gene was up/down regulated in RNA-seq data and the average of ChIP-seq MACS2 values of HIF1A and EPAS1(HIF2A) in ChIP-Atlas database.Both were calculated from public NGS database (SRA).For up/donw regulated gene selection, 2 fold threshold was adopted.

This collection portal provides finding aids for the CHIPS Metrology Exchange to Innovate for Semiconductors (METIS) digital assets including data, code, and a variety of resources. It spans multiple discipline areas: Materials Research, Semiconductors, Simulations, Microelectronics, and will evolve in alignment with the CHIPS Metrology program. In addition to NIST physical reference materials, these digital assets support commercial industry and partner laboratories validation of their analytical methods. The collection data is curated to promote provenance and traceability through use of standards and best practices in data driven systems.

As of November 2019, application-specific integrated circuits (ASIC) are forecast to have a growing share of the training phase artificial intelligence (AI) applications in data centers, making up for a projected 50 percent of it by 2025. Comparatively, graphics processing units (GPUs) will lose their presence by that time, dropping from 97 percent down to 40 percent.

AI chips

In order to provide greater security and efficiency, many data centers are overseeing the widespread implementation of artificial intelligence (AI) in their processes and systems. AI technologies and tasks require specialized AI chips that are more powerful and optimized for advanced machine learning (ML) algorithms, owning to an overall growth in data center chip revenues.

The edge

An interesting development for the data center industry is the rise of the edge computing. IT infrastructure is moved into edge data centers, specialized facilities that are located nearer to end-users. The global edge data center market size is expected to reach 13.5 billion U.S. dollars in 2024, twice the size of the market in 2020, with experts suggesting that the growth of emerging technologies like 5G and IoT will contribute to this growth.

All ChIP-Seq data analyzed on ChIP-Atlas. BigWig, Bed, BigBed format files are provided for each individual Experiment. Bed files are provided for data assembled by antigens and cell types. Analysis data from target genes analysis and colocalization analysis are provided in tab separated values (tsv). See details here: https://github.com/inutano/chip-atlas/wiki#peak_browser_doc * The dataset of past version can not be downloaded.

Transcriptomic profiling is an immensely powerful hypothesis generating tool. However, accurately predicting the transcription factors (TFs) and cofactors that drive transcriptomic differences between samples is challenging. A number of algorithms draw on ChIP-seq tracks to define TFs and cofactors behind gene changes. These approaches assign TFs and cofactors to genes via a binary designation of ‘target’, or ‘non-target’ followed by Fisher Exact Tests to assess enrichment of TFs and cofactors. ENCODE archives 2314 ChIP-seq tracks of 684 TFs and cofactors assayed across a 117 human cell lines under a multitude of growth and maintenance conditions. The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings: 1) A cell line expressing or lacking single TF, 2) Breast tumors divided along PAM50 designations 3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype 4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels. In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis was performed to evaluate whether CgCrzA plays a role in regulating CWI-related genes. Compared to the control, the ChIP-seq samples exhibited enrichment of CgCrzA-bound DNA fragments under CFW conditions

This repository contains datasets necessary for using the Virtual ChIP-seq software.

Virtual ChIP-seq requires the following datasets to predict transcription factor binding:

chipExpDir_AtoH_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters A-H.

chipExpDir_ItoZ_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters I-Z.

refTables_V1.1.0.tar.gz: PhastCons genomic conservation, FIMO PWM scores for JASPAR motifs, and ChIP-seq data of ENCODE and Cistrome database.

hg38_chrsize.tsv: Length of chromosomes in hg38

trainedModels_V1.0.0.tar.gz: Virtual ChIP-seq scikit-learn trained models saved in joblib format

.tar.gz: Pre-calculated matrices suitable for training with other algorithms or re-training with Virtual ChIP-seq.

Some predictive features of TF binding are the same in each cell type and are stored together for simplicity in refTables_V1.0.0.tar.gz. You can use datasets from other cell types (named here as .tar.gz) for the purpose of re-training the model. The .tar.gz files contain pre-calculated predictive features of transcription factor binding in 4 chromosomes (5, 10, 15, 20).

These features include:

PhastCons genomic conservation

FIMO score for sequence motifs of TF in the JASPAR database

Chromatin accessibility

TF binding in ENCODE + Cistrome DB datasets

Virtual ChIP-seq expression score

Top artificial intelligence firms are racing to build the biggest and most powerful Nvidia server chip clusters to win in AI. Below, we mapped the biggest completed and planned server clusters. Check back often, as we'll update the list when we confirm more data.

A curated database that strives to annotate gene promoters identified from ChIP-Seq experiment results. The long term goal of the database is to provide an integrated resource for mammalian gene transcriptional regulation and epigenetics. Users can search based on Enterz gene id/symbol, or by tissue/cell specific activity and filter results based on any combination of tissue/cell specificity, known/novel, CpG/NonCpG, and protein-coding/non-coding gene promoters. It is also integrated with GBrowse genome browser for visualiztion of ChIP-seq profiles and display the annotations.

Market Analysis for Data Center PCIe Chip The global Data Center PCIe Chip market is projected to grow at a CAGR of 10.2% during the forecast period of 2025-2033, reaching a value of 210.8 million units by 2033. Key drivers of this growth include the increasing adoption of cloud computing, big data analytics, and artificial intelligence (AI), which require high-performance computing capabilities. Additionally, the rising demand for data storage and processing in various industries, such as finance, telecommunications, and government, is further driving market expansion. Trends shaping the market include the adoption of advanced PCIe generations (Gen 4.0 and Gen 5.0) for faster data transfer speeds and enhanced performance. Additionally, the growing preference for NVMe (Non-Volatile Memory Express) protocols is driving the adoption of NVMe-compatible PCIe chips. Furthermore, the increasing need for data security and reliability is leading to the integration of security features into PCIe chips. Key players in the market include Broadcom Inc., Microchip Technology, ASMedia Technology Inc., Diodes Incorporated, and Texas Instruments, among others. The market is characterized by intense competition and technological innovation, with companies focusing on developing chips that offer high speed, low latency, and improved data security.

Forecasts show that application-specific integrated circuits (ASIC) will have a growing share of the inference phase artificial intelligence (AI) applications in data centers, making up a projected 40 percent by 2025. On the other hand, central processing units (CPUs) will lose their presence by that time.

AI chips

In order to provide greater security and efficiency, many data centers are overseeing the widespread implementation of artificial intelligence (AI) in their processes and systems. AI technologies and tasks require specialized AI chips that are more powerful and optimized for advanced machine learning (ML) algorithms, owning to an overall growth in data center chip revenues.

The edge

An interesting development for the data center industry is the rise of the edge computing. IT infrastructure is moved into edge data centers, specialized facilities that are located nearer to end-users. The global edge data center market size is expected to reach 13.5 billion U.S. dollars in 2024, twice the size of the market in 2020, with experts suggesting that the growth of emerging technologies like 5G and IoT will contribute to this growth.

According to data from McKinsey, in 2025, it is forecast that data centers will make up the largest revenue of artificial intelligence (AI) chips, generating ** billion U.S. dollars, an increase of *** percent from 2017. AI chip revenues in edge computing, however, will undergo much more rapid growth during the same time span, reaching around *** billion U.S. dollars by 2025.

Data Processing Unit (DPU) Chip Market Outlook

The global Data Processing Unit (DPU) chip market size is projected to grow from USD 1.8 billion in 2023 to USD 7.5 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of 17.2% during the forecast period. The rapid advancement in data-intensive applications and the escalating demand for efficient data management are significant factors propelling this market's growth. The increasing adoption of technologies such as artificial intelligence (AI), machine learning (ML), and big data analytics in various industries is also driving the need for advanced processing units capable of handling complex data processing tasks.

One of the primary growth factors for the DPU chip market is the exponential increase in data generation across various sectors. With the proliferation of IoT devices, social media, and digital transformation initiatives, organizations are generating massive volumes of data that need to be processed, stored, and analyzed efficiently. DPUs, with their specialized architecture designed for handling data-centric workloads, are becoming essential in scaling and optimizing data processing capabilities, thereby driving their demand in the market.

Another significant driver for the DPU chip market is the rising demand for enhanced network security and data privacy. As cyber threats become more sophisticated, enterprises are increasingly looking for solutions that offer robust security mechanisms without compromising performance. DPUs offer integrated security features such as encryption, secure boot, and isolated processing environments, making them ideal for securing data in transit and at rest. This growing emphasis on data security is contributing to the market's expansion.

The shift towards edge computing is also playing a pivotal role in the growth of the DPU chip market. Edge computing requires efficient data processing at the edge of the network to reduce latency and improve real-time decision-making. DPUs, with their ability to offload and accelerate data-intensive tasks, are becoming crucial components in edge data centers and devices. This trend is expected to further fuel the adoption of DPUs across various applications and industries.

From a regional perspective, North America is poised to dominate the DPU chip market due to the presence of major technology companies and a robust IT infrastructure. The region's focus on innovation and early adoption of advanced technologies are key factors driving the market. Meanwhile, Asia Pacific is anticipated to witness the highest growth rate, attributed to the rapid digitization, growing investments in data centers, and the expansion of cloud services in countries like China and India.

Component Analysis

The DPU chip market can be segmented into three main components: hardware, software, and services. The hardware segment includes the physical DPU chips and related peripherals that facilitate data processing. This segment is expected to hold the largest market share due to the continuous advancements in semiconductor technologies and the increasing demand for high-performance computing solutions. The development of more powerful and energy-efficient DPUs is driving the growth of this segment.

The software segment encompasses the operating systems, firmware, and application software that enable the functionality of DPU chips. As DPUs become more integrated into data centers and enterprise networks, there is a growing need for specialized software solutions that can optimize their performance and manage workloads effectively. This segment is expected to witness substantial growth as companies invest in software development to harness the full potential of DPU hardware.

The services segment includes consulting, integration, maintenance, and support services related to DPU chip deployment. With the increasing complexity of data processing tasks and the need for seamless integration of DPUs into existing IT infrastructures, the demand for professional services is on the rise. Service providers are focusing on offering customized solutions to meet the specific needs of different industries, driving the growth of this segment.

Overall, the hardware segment is anticipated to maintain its dominance throughout the forecast period, while the software and services segments are expected to exhibit robust growth. The synergy between these components is crucial for the successful implementation and utilization of DPU chips in various applications.

Summary of the agreements between the inferred transcriptional regulations and the regulations obtained from ChIP-seq data in the ENCODE database for several sampled TFs.