The graph shows the number of articles published in the discipline of ^.

Table. 1. Recent Bioinformatics Tools for Discovery, Prediction, and Analysis of Natural Product Pathways. (2020–2024).

List of bioinformatics tools and databases students used.

Bioinformatic databases survey

The dataset surveys bioinformatic databases published in the NAR database issue from 1995 to 2022. It evaluates the current number of citations and availability of each ressources.

Data content

The dataset is composed of two tables :

A. Databases table : Contains the information of each database published in the NAR database issue.

• db_id : Database ID in the dataset
• resource_name : Name(s) of the database
• current_access : Latest known web address of the database
• is_a_pun : The database name is a play on word
• available_2022 : The database was accessible online during the 2022 survey
• last_accessible_year : If not accessible, latest point in time where the database was found online (using the Internet web archive snapshots)
• unavailable_message : If not accessible, the message/error when trying to access the ressource
• year_first_publication : Year of first publication of the database
• year_last_publication : Year of latest publication of the database (including database update publications)
• total_citations_2022 : Cumulative number of citation for all articles of the database
• nb_authors_max : Maximum number of authors associated to any articles published for that database
• nb_articles_2022 : Number of articles published for that database in 2022

B. Articles table : Contains the information collected for the NAR articles

• collector : Person who contributed to add this database in the dataset
• article_global_id : DOI of the article surveyed
• db_id : Database ID of the ressource described in the article
• article_id : Article unique ID
• article_year : Article publication year
• Authors : list of authors of the article. Separated by ";"
• Author.ID : list of ORCID of the authors of the article. Separated by ";"
• Title : Title of the atricle
• Source.title : Journal name
• Volume : Volume number
• Issue : Issue number
• Funding.Details : Funding information of the article
• Funding.Text : Funding text provided by the authors
• PubMed.ID : Pubmed ID of the article
• citations_2016 : Number of citations of the article in 2016 (if published)
• citations_2022 : Number of citations of the article in 2022
• nb_authors : Number of authors in the article
• Index.Keywords : Keywords associated to the publication

Data sources

Note that the presented dataset leverage and expand on the dataset gathered and published in Imker, H.J., 2020. Who Bears the Burden of Long-Lived Molecular Biology Databases?. Data Science Journal, 19(1), p.8. The original dataset collected by Dr. Imker is available at : https://doi.org/10.13012/B2IDB-4311325_V1

The dataset was collected and is maintained by undergraduate students of a CURE class (Course-based Undergraduate Research Experience) held at the University of Arizona. All students of the class have participated to the collection, update and curation the dataset that is available as a database and a web-portal at https://hurwitzlab.shinyapps.io/DS_Heroes/. Students could elect to be added or not as author to this Zenodo repository.

The CURE class BAT102 "Data Science Heroes: An undergraduate research experience in Open Data Science Practices" gives the students an opportunity to learn about open science and investigate open data practices in bioinformatics through a survey of the databases published in the NAR database issue.

List of Top Authors of Genomics, Proteomics and Bioinformatics sorted by articles.

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.

Bioinformatics analyses of ITPR1 missense variants.

List of Top Authors of Current Protocols in Bioinformatics sorted by articles.

List of Top Authors of Briefings in Bioinformatics sorted by articles.

Secondary data sources.

The BAltic Gene Set gene catalogue v1.1 encompasses 66,530,673 genes.The 66 million genes are based on metagenomic data from Alneberg at al. (2020) from 124 seawater samples, that span the salinity and oxygen gradients of the Baltic Sea and capture seasonal dynamics at two locations. To obtain the gene catalogue, we used a mix-assembly approach described in Delgado et al. (2022).The gene catalogue has been functionally and taxonomically annotated, using the Mix-assembly Gene Catalog pipeline (https://github.com/EnvGen/mix_assembly_pipeline). The taxonomy annotation was performed using Mmseqs21 and CAT3.Here you find representative mix-assembly gene and protein sequences, and different types of annotations for the proteins. Also, contigs for the co-assembly are included (see Delgado et al. 2022), gene and protein sequences from each individual assembly and the co-assembly, and a table containing the genes in each of the clusters. See README for details.When using the BAGSv1.1 gene catalogue, please cite:1. Delgado LF, Andersson AF. Evaluating metagenomic assembly approaches for biome-specific gene catalogues. Microbiome 10, 72 (2022)2. Alneberg J, Bennke C, Beier S, Bunse C, Quince C, Ininbergs K, Riemann L, Ekman M, Jürgens K, Labrenz M, Pinhassi J, Andersson AF (2020) Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun Biol 3, 119 (2020)

This record contains the data (references, reads, assemblies) used in the analyses for the Trycycler paper.

List of Top Authors of Advances in Bioinformatics sorted by articles.

List of Top Authors of Bioinformatics and Biology Insights sorted by articles.

A summary of the bacterial genomes used in the prophage analysis. This file contains the columns GENOMEID, Number of Contigs, Total Length (bp), Shortest Contig (bp),Longest Contig (bp) separated by tabs

The table contains the name of the repository, the type of example (issue tracking, branch structure, unit tests), and the URL of the example. All URLs are prefixed with https://github.com/.

These are the reference genomes against which we assessed reads and consensus sequences.

Data about Phakopsora pachyrhizi, causative agent of soybean rust. Genomics and Bioinformatics Research Unit, Stoneville, MS. Resources in this dataset:Resource Title: All supplemental files. File Name: AllSupplementalFiles.zipResource Description: Word, Excel, and JAVA files Resource Title: Data dictionary for BMC Genomics P. pachyrhizi Supplemental Data. File Name: Data Dictionary - BMC Genomics P. pachyrhizi Supplemental Data.csv

Hypertensive nephropathy (HN), mainly caused by chronic hypertension, is one of the major causes of end-stage renal disease. However, the pathogenesis of HN remains unclarified, and there is an urgent need for improved treatments. Gene expression profiles for HN and normal tissue were obtained from the Gene Expression Omnibus database. A total of 229 differentially co-expressed genes were identified by weighted gene co-expression network analysis and differential gene expression analysis. These genes were used to construct protein–protein interaction networks to search for hub genes. Following validation in an independent external dataset and in a clinical database, POLR2I, one of the hub genes, was identified as a key gene related to the pathogenesis of HN. The expression level of POLR2I is upregulated in HN, and the up-regulation of POLR2I is positively correlated with renal function in HN. Finally, we verified the protein levels of POLR2I in vivo to confirm the accuracy of our analysis. In conclusion, our study identified POLR2I as a key gene related to the pathogenesis of HN, providing new insights into the molecular mechanisms underlying HN.

This study investigated DNA methylation patterns in two cryptic species (B and Q) of the sweet potato whitefly, Bemisia tabaci (Gennadius), following the acquisition of the tomato yellow curl virus, a single-stranded DNA virus. The methylation levels in genomic features such as promoters, gene bodies, and transposable elements in both cryptic species were described in this study. While overall trends were found to be similar, specific differences in methylation levels were observed. Virus-induced differentially methylated regions (DMRs) were associated with different genes in each cryptic species and were negatively correlated with differential gene expression. These DMRs were analyzed for changes in gene expression and alternative splicing, revealing clusters of hyper- and hypomethylated genes related to virus-vector interactions, immune functions, and detoxification processes. These methylation differences may help explain the distinct biological and physiological traits observed between the B and Q cryptic species.