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

This is data required to reproduce the analysis in the cnidarian genomes paper. By adding this file to the code in the source repository at https://doi.org/10.5281/zenodo.8402538 it is possible to run the code.

The horizontal transfer of mobile gene elements between bacteria plays a crucial role in their evolutionary dynamics. Additionally, it enables the accumulation and dissemination of genes conferring antibiotic and heavy-metal resistance, and thus contributes to the worldwide emergence and spread of drug-resistance pathogens. This process is instrumental in maintaining genetic diversity within bacterial populations and facilitates their adaptation to novel environments. It allows bacteria to acquire genes responsible for the synthesis of enzymes that utilize alternative energy sources and substrates. Furthermore, bacteria can acquire genes associated with toxin production and increased virulence. Horizontal gene transfer serves as a pivotal mechanism in bacterial evolution, enabling the acquisition of novel genetic information and enhancing their capabilities. However, the proper detection and identification of horizontally transferred genes at the microbiome scale is challenging, whether using wet-lab experiments or bioinformatics approaches. In the paper, we summarize current bioinformatics tools for detecting Horizontal Gene Transfer and present the results of our bioinformatic analysis on a collection of genomes originating from chicken gut microbiota.

• Resources funded wholly or in part by NIDDK.List of resources currently available as Community Resources and abbreviations used in text.

Data about Phakopsora pachyrhizi, causative agent of soybean rust. Genomics and Bioinformatics Research Unit, Stoneville, MS.

The purpose of this project is to sequence and assemble the genome of Diabrotica undecimpunctata, a major pest of field crops such as corn and specialty crops such as cucumbers and other cucurbits.

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes. You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the versione above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Missing heritability and mixed models review ; Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Genetic architecture review ; Article about "omnigenic" model ; Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

Long-reads were used to generate a whole genome assembly of Anastrepha ludens, the Mexican fruit fly.

Timelime of bioinformatics in Mexicoa.

Genome sequencing and assembly of Amyelois transitella (navel orangeworm) was conducted by the USDA-ARS Ag100Pest Initiative.

Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to facilitate their exploration and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure etc. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.

The 20 journals most related to the journal “Bioinformatics” are displayed according to MeSH odds ratio, author odds ratio, and article pair odds ratio. For comparison, the 20 most related journals are displayed using the metric of Lu et al. [5] which is based on user clicking behavior during weblogs of PubMed retrieval sessions. Note that the first three metrics measures the similarity of “Bioinformatics” to itself, though Lu et al. do not. Note, also, that over half of the top journals listed by the weblog metric are biological and general journals (shown in italics) which are not included in any of the metrics reported in the present paper.The journals most related to the journal Bioinformatics according to various metrics.

DATA OMICS

Research foci and projects of bioinformatics research groups in Nigeria.

nCFD dataset consisting of 2108 variants in non-conserved regions of epigenetic factors.

Poster presented at ASM 2015 General Meeting

Bioinformatics skills are increasingly relevant to research in most areas of the life sciences. The availability of genome sequences and large data sets provide unique opportunities to incorporate bioinformatics exercises into undergraduate microbiology courses. The goal of this project was to develop a teaching module to investigate the abundance and phylogenetic relationships amongst bacteriophages using a set of freely available bioinformatics tools. Computational identification and examination of bacteriophage genomes, followed by phylogenetic analyses, provides opportunities to incorporate core bioinformatics competencies in microbiology courses and enhance students’ bioinformatics skills. The first activity consisted of using PHASTER (PHAge Search Tool Enhanced Release), a bioinformatics tool that identifies bacteriophage sequences within bacterial chromosomes. Further computational analyses were conducted to align bacteriophage proteins, genomes, and determine phylogenetic relationships amongst these viruses. This part of the project was carried out using the Clustal omega, MAFFT (Multiple Alignment using Fast Fourier Transform), and Interactive Tree of Life (iTOL) programs for sequence alignments and phylogenetic analyses. The laboratory activities were field tested in undergraduate directed research, and microbiology classes. The learning objectives were assessed by comparing the scores of pre and post-tests and grading final presentations. Post-tests were higher than pre-test scores at or below p = 0.002. The data suggest in silico phage hunting improves students’ ability to search databases, interpret phylogenetic trees, and use bioinformatics tools to examine genome structure. This activity allows instructors to integrate key bioinformatic concepts in their curriculums and gives students the opportunity to participate in a research-directed learning environment in the classroom.

Competition payouts and duration.

Background: Fusarium Head Blight (FHB) is a worldwide devastating disease of bread wheat (Triticum aestivum L.). Genetic resistance is the most effective way to control FHB and many QTL related to this trait have been mapped on the wheat genetic map. This information, however, must be refined to be more efficiently used in breeding programs and for the advance of the basic research. The objective of the present study was to in-depth analyze the QTLome of FHB resistance in bread wheat, further integrating genetic, genomic, and transcriptomic data, aiming to find candidate genes.Methods: An exhaustive bibliographic review on 76 scientific papers was carried out collecting information about QTL related to FHB resistance mapped on bread wheat. A dense genetic consensus map with 572,862 loci was generated for QTL projection. Meta-analysis could be performed on 323 QTL. Candidate gene mining was carried out within the most refined loci, containing genes that were cross-validated with publicly available transcriptional expression data of wheat under Fusarium infection. Most highlighted genes were investigated for protein evidence.Results: A total of 556 QTL were found in the literature, distributed on all sub-genomes and chromosomes of wheat. Meta-analysis generated 65 meta-QTL, and this refinement allows one to find markers more tightly linked to these regions. Candidate gene mining within the most refined meta-QTL, meta-QTL 1/chr. 3B, harvested 324 genes and transcriptional data cross-validated 10 of these genes, as responsive to FHB. One is of these genes encodes a Glycosiltransferase and the other encodes for a Cytochrome P450, and these such proteins have already been verified as being responsible for FHB resistance, but the remaining eight genes still have to be further studied, as promising loci for breeding.Conclusions: The QTLome of FHB resistance in wheat was successfully assembled and a refinement in terms of number and length of loci was obtained. The integration of the QTLome with genomic and transcriptomic data has allowed for the discovery of promising candidate genes for use in breeding programs.

The distribution of bioinformatics courses within different departments in Jordanian universities.