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

Bioinformatics Services Market will grow from USD 4,399.58 Million to USD 16,297.10 Million by 2034, showing an impressive CAGR of 15.7%.

Ameloblastoma is a highly aggressive odontogenic tumor, and its pathogenesis is associated with multiple participating genes. Objective: Our aim was to identify and validate new critical genes of conventional ameloblastoma using microarray and bioinformatics analysis. Methods: Gene expression microarray and bioinformatic analysis were performed to use CHIP H10KA and DAVID software for enrichment. Protein-protein interactions (PPI) were visualized using STRING-Cytoscape with MCODE plugin, followed by Kaplan-Meier and GEPIA analysis that were employed for the candidate's postulation. RT-qPCR and IHC assays were performed to validate the bioinformatic approach. Results: 376 upregulated genes were identified. PPI analysis revealed 14 genes that were validated by Kaplan-Meier and GEPIA resulting in PDGFA and IL2RA as candidate genes. The RT-qPCR analysis confirmed their intense expression. Immunohistochemistry analysis showed that PDGFA expression is parenchyma located. Conclusion: With bioinformatics methods, we can identify upregulated genes in conventional ameloblastoma, and with RT-qPCR and immunoexpression analysis validate that PDGFA could be a more specific and localized therapeutic target.

Data used to compare gene vs domain based methods found in figure 5

gedepir is an R package that simplifies the use of deconvolution tools within a complete transcriptomics analysis pipeline. It simplify the definition of a end-to-end analysis pipeline with a set of base functions that are connected through the pipes syntax used in magrittr, tidyr or dplyrR packages

This project evaluated the binding of antibody fragments to membrane proteins fused to a short epitope sequence (“MPER”). This dataset includes atomic coordinates (.pdb files) for bioinformatic models of antibody fragment binding to an MPER epitope – membrane protein fusion.

Files contains results for in-silico PCR.

The common limpet, Patella vulgata, has been shown to produce the strongest known biological material. The teeth of the radula, a tongue-like organ used to scrape algae off rocky surfaces, have a tensile strength greater than spider silk and comparable to man-made carbon fibres. Here, we generate a complete transcriptome resource for the common limpet from three main tissues; the main muscle of the foot of the limpet, the radula Formation Zone, and the radula itself (subdivided into 4 segments supporting distinct stages of tooth development). We generated 871,497,501 paired-end reads and assembled into a transcriptome of 464,975 transcripts with an N50 score of 994 bp and an Ex90N50 score of 1,553 bp. Analysis of transcriptome completeness identified presence of 97.6 % of metazoan universal single copy orthologs. The filtered Patella vulgata transcriptome consists of 36,806 high-confidence transcripts representing 16,100 genes. This resource represents a profile of the transcriptome of the radula, and in particular the unique transcripts involved with the unique developmental stages of the limpet tooth formation.

QIIME2 .qzv file containing taxonomic barplots per sample.

QIIME2 .qzv file of taxonomic barplots collapsed per sample type.

Supplementary Material 4: Supplementary Table 3. Rankings of DEGs.

Overview This item contains references and test datasets for the Cactus pipeline. Cactus (Chromatin ACcessibility and Transcriptomics Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study.

Test datasets The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (.fastq.gz), parameter files (.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAS (Differential Analysis Subsets) for enrichment analysis. Datasets downloaded: - Worm and Humans: GSE98758 - Fly: GSE149339 - Mouse: GSE193393

References One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references. Genomic sequences and annotations were downloaded from Ensembl (Cunningham et al., 2022). The ENCODE API (Luo et al., 2020) was used to download the CHIP-Seq profiles of 2,714 Transcription Factors (TFs) (Landt et al., 2012; Boyle et al., 2014) and chromatin states in the form of 899 ChromHMM profiles (Boix et al., 2021; van der Velde et al., 2021) and 6 HiHMM profiles (Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations, allowing users to analyze only CHIP-Seq profiles relevant to their study. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021), respectively.

Documentation More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.

All the data and analytical codes utilized in the study are stored on the HKU CPOS server. Accessing paths can be found in the README file.

a. Not Assigned.

Paper Data For "Identification of ACHE as the Hub Gene targeting Solasonine Associated with NSCLC Using Integrated Bioinformatics Analysis"

Gene expression dataset from Ebenig et al., 2022

Object 4-S8. COGs – curated lists for analysis, figure generation.

DAVID analysis and DEG from the meta-analysis

The zip file contains the following files to perform TAD intra-density analysis.run_TADintra.sh: bash script to perform TAD loop analysisTADintra_cm_10k_top5.tsv: output of HiCMapToolsplot_TADintra.R: Rscript for the box plot of TAD loop intensityintraIntensity4epiTAD.pdf: the box plot of TAD loop intensity

fly epiTADs is generated by hicpipe* based on top5 scaling factor with 10k window (10k_top5). It is formed in bed format where number represent epiTAD class:1:red:active2:gray:null3:PcG:blue4:HP1:green*Yaffe, E. & Tanay, A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat. Genet. 43, 1059–65 (2011).