Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

- This release includes GEO series up to Dec-31, 2020;

- Fixed xlrd missing optional dependency, which affected import of some xls files, previously we were using only openpyxl (thanks to anonymous reviewer);

- All files in supplementary _RAW.tar files were checked for p values, previously _RAW.tar files were completely omitted, alas (thanks to anonymous reviewer).

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series

- output/publications.csv, publication info of NCBI GEO series

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, NCBI SRA sequencing run metadata

- suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

- This release includes GEO series published up to Dec-31, 2020;

geo-htseq.tar.gz archive contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series.

- output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions.

- output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO.

- output/publications.csv, publication info of NCBI GEO series.

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/spots.csv, NCBI SRA sequencing run metadata.

- output/cancer.csv, cancer related experiment accessions.

- output/transcription_factor.csv, TF related experiment accessions.

- output/single-cell.csv, single cell experiment accessions.

- blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import.

Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.

geo-htseq-updates.tar.gz archive contains files:

- results/detools_from_pmc.csv, differential expression analysis programs inferred from published articles

- results/n_data.csv, manually curated sample size info for NCBI GEO HT-seq series

- results/simres_df_parsed.csv, pi0 values estimated from differential expression results obtained from simulated RNA-seq data

- results/data/parsed_suppfiles_rerun.csv, pi0 values estimated using smoother method from anti-conservative p-value sets

Table of Contents

1. Main Description
2. File Descriptions
3. Linked Files
4. Installation and Instructions

1. Main Description

---------------------------

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled `marengo_code_for_paper_jan_2023.R` was used to generate the figures from the single-cell RNA sequencing data.

The following libraries are required for script execution:

• Seurat
• scReportoire
• ggplot2
• stringr
• dplyr
• ggridges
• ggrepel
• ComplexHeatmap

File Descriptions

---------------------------

• The code can be downloaded and opened in RStudios.
• The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper
• The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113).
• The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots.
• The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

---------------------

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

• Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment.
• Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data.
• Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

• Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment.
• Description: This submission contains the **raw sequencing** or `.fastq.gz` files, which are tab delimited text files.
• Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

• Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.
• Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code.
• Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

--------------------------------------

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

> Ensure you have R version 4.1.2 or higher for compatibility.

> Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).

2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.

3. Set your working directory to where the following files are located:

• marengo_code_for_paper_jan_2023.R
• Install_Packages.R
• Marengo_newID_March242023.rds
• genes_for_heatmap_fig5F.xlsx
• all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

> setwd(directory)

4. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.

5. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.

6. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.

7. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

Secretory factors in animals play an important role in communication between different cells, tissues and organs. Especially, the secretory factors with specific expression in one tissue may reflect important functions and unique status of that tissue in an organism. In this study, we identified potential tissue-specific secretory factors in the fat, muscle, heart, lung, kidney and liver in the mouse by analyzing microarray data from NCBI’s Gene Expression Omnibus (GEO) public repository and searching and predicting their subcellular location in GeneCards and WoLF PSORT, and then confirmed tissue-specific expression of the genes using semi-quantitative PCR reactions. With this approach, we confirmed 11 lung, 7 liver, 2 heart, 1 heart and muscle, 7 kidney and 2 adipose and liver-specific secretory factors. Among these genes, 1 lung-specific gene - CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), 3 kidney-specific genes - SERPINA1F (serpin peptidase inhibitor, Clade A, member 1F), WFDC15B (WAP four-disulfide core domain 15B) and DEFB29 (defensin beta 29) and 1 liver-specific gene - MUP19 (major urinary protein 19) have not been reported as secretory factors. These genes were tagged with hemagglutinin at the 3’end and then transiently transfected to HEK293 cells. Through protein detection in cell lysate and media using Western blotting, we verified secretion of the 5 genes and predicted the potential pathways in which they may participate in the specific tissue through data analysis of GEO profiles. In addition, alternative splicing was detected in transcripts of CTLA2A and SERPINA1F and the corresponding proteins were found not to be secreted in cell culture media. Identification of novel secretory factors through the current study provides a new platform to explore novel secretory factors and a general direction for further study of these genes in the future.

The dataset contains panel sequencing data of 170 genes from 380 patients of the EORTC-26101 trial. The corresponding methylation data is available via the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) repository with the accession number GSE237103.

We analyzed the field of expression profiling by high throughput sequencing, or RNA-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 56% to field-wide reproducibility, based on the types of files submitted to GEO.

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of GEO series

- output/publications.csv, publication info of GEO series

- output/scopus_citedbycount.csv, Scopus citation info of GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, sequencing run metadata: number of spots and bases

- suppfilenames.txt, list of all supplementary file names of GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

This repository contains a collection of three datasets we use to introduce the Gene Mover Distance in [1] and described below. The three datasets are exported with a basic text-based format (.csv file) like other public datasets largely used in the Machine Learning community. The three datasets are extracted from the Gene Expression Omnibus (GEO) database [2], where they appear, respectively, with access number GSE116256 (blood leukemia, [3]), GSE84133 (human pancreas, [4]), and GSE67835 (human brain, [5]). In GEO, the datasets are decomposed into several files, which contain much more details than those reported in this version. However, the proposed format should facilitate other researchers in using this data. The Gene Mover's Distance is a measure of similarity between a pair of cells based on their gene expression profiles obtained via single-cell RNA sequencing. The underlying idea of GMD is to interpret the gene expression array of a single cell as a discrete probability measure. The distance between two cells is hence computed by solving an Optimal Transport problem between the two corresponding discrete measures. The Gene Mover's Distance can be used, for instance, to solve two classification problems: the classification of cells according to their condition and according to their type. The repository contains a python script to check the basic statistics of the data. [1] Bellazzi, R., Codegoni, A., Gualandi, S., Nicora, G., Vercesi, E. The Gene Mover's Distance: Single-cell similarity via Optimal Transport. https://arxiv.org/abs/2102.01218 [2] Gene Expression Omnibus (GEO) database, http://www.ncbi.nlm.nih.gov/geo [3] van Galen, P., Hovestadt, V., Wadsworth II, M.H., Hughes, T.K., Griffin, G.K., Battaglia, S., Verga, J.A., Stephansky, J., Pastika, T.J., Story, J.L. and Pinkus, G.S., 2019. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. Cell, 176(6), pp.1265-1281. [4] Baron, M., Veres, A., Wolock, S.L., Faust, A.L., Gaujoux, R., Vetere, A., Ryu, J.H., Wagner, B.K., Shen-Orr, S.S., Klein, A.M. and Melton, D.A., 2016. A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure. Cell systems, 3(4), pp.346-360. [5] Darmanis, S., Sloan, S.A., Zhang, Y., Enge, M., Caneda, C., Shuer, L.M., Gephart, M.G.H., Barres, B.A. and Quake, S.R., 2015. A survey of human brain transcriptome diversity at the single cell level. Proceedings of the National Academy of Sciences, 112(23), pp.7285-7290.

Hematopoietic stem cells and progenitors from controls and Med12Flox; mxCre mice treated with pI:pC 4 days afters injection were sorted and Micrroarray Affymetrix mouse 430.2 platform.Results provide insight into the gene signatures regulated by Med12 that are essential for the homeostasis of the hematopoietic system. Microarrays we used to characterize the gene expression programs regulated by Med12 and identified down-regulated signatures

Affymetrix Bovine GeneChip® Gene 1.0 ST Array RNA expression analysis was performed on four somatic ovarian cell types: the granulosa cells (GCs) and theca cells (TCs) of the dominant follicle and the large luteal cells (LLCs) and small luteal cells (SLCs) of the corpus luteum. The normalized linear microarray data was deposited to the NCBI GEO repository (GSE83524). Subsequent ANOVA determined genes that were enriched (≥2 fold more) or decreased (≤−2 fold less) in one cell type compared to all three other cell types, and these analyzed and filtered datasets are presented as tables. Genes that were shared in enriched expression in both follicular cell types (GCs and TCs) or in both luteal cells types (LLCs and SLCs) are also reported in tables. The standard deviation of the analyzed array data in relation to the log of the expression values is shown as a figure. These data have been further analyzed and interpreted in the companion article "Gene expression profiling of ovarian follicular and luteal cells provides insight into cellular identities and functions", Romereim et al., (2017) Mol. Cell. Endocrinol. 439:379-394. https://doi.org/10.1016/j.mce.2016.09.029 Resources in this dataset:Resource Title: RNA Expression Data from Four Isolated Bovine Ovarian Somatic Cell Types. File Name: Web Page, url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83524 NCBI Gene Expression Omnibus (GEO) Accession Display. Analysis of the RNA present in each bovine cell type using Affymetrix microarrays yielded new cell-specific genetic markers, functional insight into the behavior of each cell type via Gene Ontology Annotations and Ingenuity Pathway Analysis, and evidence of small and large luteal cell lineages using Principle Component Analysis. Enriched expression of select genes for each cell type was validated by qPCR. This expression analysis offers insight into the lineage and differentiation process that transforms somatic follicular cells into luteal cells. The orignal Affymetrix .CEL files and the normalized linear expression data are included in this submission.

This repository contains original and gated single-cell data used to generate figures in the manuscript entitled "Proliferative tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells".

Included here: FCS files for mass cytometry data and a CSV file for processed single-cell RNA-sequencing data. Raw single-cell RNA-sequencing data and metadata are available on Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo; GEO accession number: GSE119139).

With questions, please reach out to Zinaida Good (zinaida@stanford.edu) or Sean Bendall (bendall@stanford.edu).

An extended version of the Java-based Vortex software and documentation can be accessed at: https://github.com/nolanlab/vortex.

The data are maintained at the National Center for Biotechnology Information (NCBI) GEO depository https://www.ncbi.nlm.nih.gov/geo/ . There are three accession numbers (which can be entered at this site): • GSE160535 - Development of omic biomarkers for Fathead minnow larva (Pimephales promelas) exposed to ethinyl estradiol A superseries which links to the two separate data sets from the same experiment (below) • GSE158857 - Development of omic biomarkers for Fathead minnow larva (Pimephales promelas) exposed to ethinyl estradiol [non-coding small RNA] The non-coding small RNA dataset (includes microRNA and PIWI-RNA data and metadata • GSE160520 - Development of omic biomarkers for Fathead minnow larva (Pimephales promelas) exposed to ethinyl estradiol [mRNA] The mRNA data set including metadata. This dataset is associated with the following publication: Toth, G., J. Martinson, D. Bencic, D. Lattier, M. Kostich, and A. Biales. Development of omcis biomarkers for estrogen exposure using mRNA, miRNA and piRNAs. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 235: 105807, (2021).

Summary

This metadata record provides details of the data supporting the claims of the related manuscript “A tumor microenvironment specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients”.

The related study aimed to determine whether used tumor microenvironment (TME) specific gene signature to identify colorectal cancer (CRC) subtypes with distinctive clinical relevance was possible.

Data access

The data analysed during the related study were downloaded from public databases including Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA; TCGA CRC datasets available from the Synapse repository at: https://www.synapse.org/#!Synapse:syn2623706/files/). For a list of accession IDs for the analysed data, see Supplementary Table S1 of the manuscript, also included as part of this metadata record. The Renji RNA-seq data is available from GEO: https://identifiers.org/geo:GSE158559.

The output data of the related study are included with this data record, and are as follows:- Table S1 to S10 - supplementary tables 1 to 10 for the related manuscript- Cetuximab_GSE5851.PRJEB34338.combined.Rdata - two combined CRC Cetuximab treated gene expression matrix- combined_five_GEObatch_GSE14333_GSE17536_GSE17537_GSE33113_GSE37892.Rdata - five combined CRC gene expression matrix- FOLFOX_GSE19860_GSE28702_GSE69675.Rdata - three combined CRC FOLFOX treated gene expression matrix- FOLFOX_GSE104645_GSE72970.Rdata - two combined CRC FOLFOX or FOLFIRI treated gene expression matrix- GSE39395.expMatrix.Rdata - GSE39395 gene expression matrix- GSE39396.expMatrix.Rdata - GSE39396 gene expression matrix- GSE39582_after_ComBat.Rdata - GSE39582 gene expression matrix- GSE62080_exp_pdata.Rdata - GSE62080 gene expression matrix- GSE72056.melanoma.sfm.signature.rds - scRNA melanoma processed data- GSE75688.BRCA.sfm.signature.rds - scRNA breast cancer processed data- GSE81861.sfm.signature.rds - scRNA CRC processed data- GSE103322.head-neck.sfm.signature.rds - scRNA head and neck processed data- TCGA.CRC.expMatrix.Rdata - TCGA CRC gene expression matrix- TCGA.CRC.microbiome.abundance.Rdata - TCGA CRC gut microbiome abundance

Manuscript link: pending

This repository contains, or provides links to, the code and associated data used to generate figures 3 and 4 of the manuscript. Due to limited storage space here, the experimental documentation, raw FASTQs, Space Ranger Inputs, and Space Ranger (v3.1.3) Outputs for the H&E dataset are available through Gene Expression Omnibus series accession number GSE296623 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296623. The Loupe files, .csv annotation exports from Loupe, and code are provided here. Please note the cell type annotations are incomplete; PrintPattern "Random_TME_ALL" was annotated for the manuscript.

Space Ranger was run again using an IF image, and the data is deposited here. *NOTE: The IF image is a live cell image (cells are transduced) taken right before fixation. The cells have migrated slightly between the image and when they were fixed (long scan/image acquisition time) so please proceed with extreme caution if using that version of the data since the image is slightly different than what was actually transferred to the capture area. This data was used only for illustrative purposes. I did not analyze any of the IF outs.

This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

This dataset contains the raw data associated with and published in the following academic manuscript - McAuliffe et al. (2021), JITC: Heterologous prime-boost vaccination targeting MAGE-type antigens promotes tumor T-cell infiltration and improves checkpoint blockade therapy.

Details on the research hypothesis of this project, descriptions of data acquired and notable findings and interpretation of the data have been described in full within the text of the published manuscript.

This dataset contains raw data for all of the figures contained within the manuscript (organized and broken down as such within this dataset). This is with the exception of the high-throughput sequencing data comprising Fig. 4H, Fig. 5, Fig. S5 and Fig. S7 which has been uploaded to and made publicly available in the NCBI Gene Expression Omnibus (GEO) repository. GEO accession numbers to access these datasets have been included in the manuscript.

Full descriptions of the data acquired for each figure / figure sub-part are provided in the text and figure legends of the manuscript, and information on how it was gathered and how to interpret it are contained in the materials & methods and results sections of the manuscript.

Contains preprocessed single-cell data for sketching single-cell samples. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy. The HIV Vaccine Trials Network (HVTN) Flow Cytometry dataset (hvtn_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZZZV (http://flowrepository.org/id/FR-FCM-ZZZV). The preeclampsia CyTOF dataset (preeclampsia_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZYRQ (http://flowrepository.org/id/FR-FCM-ZYRQ). The NK-Cell CyTOF dataset (nk_cell_preprocessed.h5ad) from Ref. (https://www.nature.com/articles/ncomms14825) was originally downloaded from (https://github.com/eiriniar/CellCnn). The multiple sclerosis (MS) single-cell RNA sequencing dataset of peripheral blood samples (ms_preprocessed.h5ad) from Ref. (https://www.nature.com/articles/s41467-019-14118-w) was originally accessed from the Gene Expression Omnibus using the accession code GSE138266 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266).

We provided two datasets for testing Hi-C scaffolding tools. For the CHM13 test dataset, we randomly chunked the first 10Mb of chr1, chr2 and chr3 of the T2T-CHM13v1.1 human genome assembly (Nurk et al. 2022) into 57 contigs. The Hi-C data downloaded from the telomere-to-telomere consortium GitHub repository (https://github.com/marbl/CHM13) were mapped to the reference genome and the reads mapped to these regions were extracted to generate Hi-C alignment files. For the LYZE01 test dataset, the Saccharomyces cerevisiae strain W303 genome assembly (Matheson et al. 2017) was split at positions with gaps (‘N’) to get the original contigs. An independent Hi-C data library was downloaded from the NCBI repository (GEO Accession GSM2417297) and downsampled to approximately 20X. The downsampled Hi-C data were mapped to the contigs to generate Hi-C alignment files. We provided five files for each test dataset: the contig file in FASTA format, the FASTA index file generated with SAMtools faidx command, and the Hi-C alignment file in BAM format sorted by coordinate, in BAM format sorted by query names (with the identifier 'qn' in the file name), and in BED format. {"references": ["Matheson, Kinnari, et al. "Whole-genome sequence and variant analysis of W303, a widely-used strain of Saccharomyces cerevisiae." G3: Genes, Genomes, Genetics 7.7 (2017): 2219-2226.", "Nurk, Sergey, et al. "The complete sequence of a human genome." Science 376.6588 (2022): 44-53."]}

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

modENCODE_submission_3917This submission comes from a modENCODE project of Jason Lieb. For full list of modENCODE projects, see http://www.genome.gov/26524648Project Goal: The focus of our analysis will be elements that specify nucleosome positioning and occupancy, control domains of gene expression, induce repression of the X chromosome, guide mitotic segregation and genome duplication, govern homolog pairing and recombination during meiosis, and organize chromosome positioning within the nucleus. Our 126 strategically selected targets include RNA polymerase II isoforms, dosage-compensation proteins, centromere components, homolog-pairing facilitators, recombination markers, and nuclear-envelope constituents. We will integrate information generated with existing knowledge on the biology of the targets and perform ChIP-seq analysis on mutant and RNAi extracts lacking selected target proteins.For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf EXPERIMENT TYPE: CHIP-seq. BIOLOGICAL SOURCE: Strain: fem-2(b245); Developmental Stage: Germline containing young adult; Genotype: fem-2(b245)III; Sex: Hermaphrodite; EXPERIMENTAL FACTORS: Developmental Stage Germline containing young adult; temp (temperature) 20 degree celsius; Strain fem-2(b245); Antibody T09A5.8 SDQ4501 (target is T09A5.8)