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

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

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.

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.

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.

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.

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.

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.

Target genes of transcription factors from published ChIP-chip, ChIP-seq, and other transcription factor binding site profiling studies

A database of genome-wide chromatin immunoprecipitation (ChIP) data in human and mouse. Currently, the database contains >2000 samples from >500 ChIP-seq and ChIP-chip experiments, representing a total of >170 proteins and >10,000,000 protein-DNA interactions (March 2014). A web server provides an interface for database query. Protein-DNA binding intensities can be retrieved from individual samples for user-provided genomic regions. The retrieved intensities can be used to cluster samples and genomic regions to facilitate exploration of combinatorial patterns, cell type dependencies, and cross-sample variability of protein-DNA interactions.

This historic dataset with total enrollment in separate CHIP programs by month and state was created to fulfill reporting requirements under section 1902(tt)(1) of the Social Security Act, which was added by section 5131(b) of subtitle D of title V of division FF of the Consolidated Appropriations Act, 2023 (P.L. 117-328) (CAA, 2023). For each month from April 1, 2023, through June 30, 2024, states were required to submit to CMS (on a timely basis), and CMS was required to make public, certain monthly data, including the total number of beneficiaries who were enrolled in a separate CHIP program. Accordingly, this historic dataset contains separate CHIP enrollment by month and state between April 2023 and June 2024. CMS will continue to publicly report separate CHIP enrollment by month and state (beyond the historic CAA/Unwinding period) in a new dataset, which is available at [link]. Please note that the methods used to count separate CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing separate CHIP enrollment across the two datasets. Sources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state’s monthly total was a state-submitted value, rather than from T-MSIS.TAF data were pulled as follows:April 2023 enrollment - TAF as of August 2023May 2023 enrollment - TAF as of August 2023June 2023 enrollment - TAF as of September 2023July 2023 enrollment - TAF as of October 2023August 2023 enrollment - TAF as of November 2023September 2023 enrollment - TAF as of December 2023October 2023 enrollment - TAF as of January 2024November 2023 enrollment - TAF as of February 2024December 2023 enrollment - TAF as of March 2024January 2024 enrollment - TAF as of April 2024February 2024 enrollment - TAF as of May 2024March 2024 enrollment - TAF as of June 2024April 2024 enrollment – TAF as of July 2024May 2024 enrollment – TAF as of August 2024June 2024 enrollment – TAF as of September 2024 TAF are produced one month after the T-MSIS submission month. For example, TAF as of August 2023 is based on July T-MSIS submissions. Notes: The separate CHIP enrollment in this report is not inclusive of enrollees covered by Medicaid expansion CHIP. Enrollment includes individuals enrolled in separate CHIP at any point during the month but excludes those enrolled in both Medicaid and separate CHIP during the month. See the Data Sources and Metrics Definitions Overview document for a full description of the data sources, metric definitions, and general data limitations.Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have separate CHIP Programs. Maryland has a separate CHIP program that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage. This document includes separate CHIP data submitted to CMS by states via T-MSIS or a separate collection form. These data include reporting metrics consistent with section 1902(tt)(1) of the Social Security Act.CHIP: Children's Health Insurance Program Data notes: (a) State-submitted value; data not from T-MSIS(b1) May 2023 enrollment pulled from TAF as of September 2023(b2) Data was restated using TAF as of October 2023(b3) Data was restated using TAF as of April 2024(b4) Data was restated using TAF as of July 2024(b5) Data was restated using TAF as of August 2024(c) Enrollment counts include postpartum women with coverage funded via a Health Services Initiative

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 ** percent of it by 2025. Comparatively, graphics processing units (GPUs) will lose their presence by that time, dropping from ** percent down to ** 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 **** 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.

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.

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.

This dataset tracks the updates made on the dataset "Medicaid CHIP ESPC Database" as a repository for previous versions of the data and metadata.

Data Center Chip Market size was valued at USD 12.75 Billion in 2024 and is projected to reach USD 22.53 Billion by 2032, growing at a CAGR of 7.4% from 2026 to 2032.The Data Center Chip Market is driven by the rising adoption of cloud computing, AI, and big data analytics, which demand high-performance computing solutions. The rapid expansion of hyperscale data centers and the increasing need for energy-efficient chips further fuel market growth. Additionally, advancements in semiconductor technology, including specialized AI and GPU chips, are enhancing processing capabilities and efficiency.Growing investments in edge computing and 5G infrastructure are also key drivers, pushing demand for low-latency, high-speed data processing chips. Rising cybersecurity concerns are accelerating the need for secure and specialized processors. Furthermore, government initiatives and enterprise digital transformation efforts continue to boost market expansion.

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.

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 ** 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 **** 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.