Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub “stars,” “watchers,” and “forks” (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for biostatistics, bioinformatics, and systems biology in the U.S.

In 2022, the value of the bioinformatics market in Latin America was estimated at **** billion U.S. dollars. The figure was forecast to increase to **** billion U.S. dollars by 2025 and could reach **** billion U.S. dollars by 2027.

Markdown source, PDF, and HTML rendering of bioinformatics training resources from http://stephenturner.us/p/edu.

The dataset was collected through whole-transcriptome RNA-Sequencing technologies. The processing method was described in the manuscript.

(A) Bioinformatics Summary statistics and (B) Sequence identity matrix between strains. (XLSX)

Portuguese National Registry on low weight newborns between 2013 and 2018, made available for research purposes. Dataset is composed of 3823 unique entries registering birthweight, biological sex of the infant (1-Male; 2-Female), CRIB score (0-21) and survival (0-Survival; 1-Death).

Multidimensional scaling (MDS) is a dimensionality reduction technique for microbial ecology data analysis that represents the multivariate structure while preserving pairwise distances between samples. While its improvements have enhanced the ability to reveal data patterns by sample groups, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. In this study, we introduce a new MDS-based ordination, “F-informed MDS,†which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using simulated compositional datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and ..., , # Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

• Dataset DOI: 10.5061/dryad.vmcvdnd3x
• Software: https://github.com/soob-kim/FinfoMDS
  (also in prep for Bioconductor submission)
• File or folder names are italicized. Package or variable names are monospaced.Â

File: Data.zip

Description:Â Raw data used in this study. Includes 3 folders and 1 file (see below).

1. Folder Simulated contains pairwise distances and ordination results from three simulated datasets. Includes 7 subfolders and 6 files.
   • Six files are the original dataset and its associated labels set. The names are formatted as "sim_<*x*>-<*type*>.*csv*" where <*x*> is the replicate number and <*type*> indicates whether the file is the design matrix ("data") or response vector ("Y").
   • Seven subfolders are grouped by the ordination method. Likewise, the file ...,

The bioinformatics market, valued at USD 15,135.48 million in 2023, is expected to grow at a steady CAGR of 10.2%, reaching USD 32,663.77 million by 2031. Asia-Pacific is forecasted to grow at the fastest CAGR of 10.9%.

VCF files containing filtered mutated sites in SARS-CoV-2 genomes obtained from GISAID EpiCoV and submitted from the UK and the US, separated by individual mutations. The columns correspond to viral genome accession ID, nucleotide position in the genome, mutation ID (left blank in all rows), reference nucleotide, identified mutation, quality, filter, and information columns (all left blank), format (GT in all rows), column corresponding to reference genome (all 0, referring to reference nucleotide column), and columns corresponding to isolate genomes, with each row identifying the nucleotide in the POS column, and whether it is non-mutant (0), or the mutant indicated in the identified mutation column (1). The files is tab delimited, with the UK file having 12696 rows including the names, and 18135 columns, and the US file having 15588 rows including the names, and 16277 columns.

The file was generated to test the hypothesis whether the different SARS-CoV-2 genes or protein coding regions are positively or negatively selected differently between 14408C>T / 23403A>G double mutants and double wildtype isolates, using mutation rate models, and whether regional distributions affect the mutation rates. Our findings have shown that the RdRp coding region and the S gene show the highest amount of selection across viral generations, and that different countries can affect the synonymous and nonsynonymous mutation rates for individual genes.

The global bioinformatics services market size is projected to grow from USD 4.21 billion in 2025 to USD 18.41 billion by 2035, recording a CAGR of 15.9%. Companies leading innovation in the industry are Illumina, Thermo Fisher, QIAGEN, BGI, Eurofins Scientific, contributing to the sector’s development and expansion.

Financial overview and grant giving statistics of International Society of Big Data and Bioinformatics Inc.

Data utilised in Survival outcomes are associated with genomic instability in luminal breast cancers.

The global bioinformatics market size is expected to expand from USD 14.4 billion in 2025 to USD 52 billion by 2035, with CAGR growth exceeding 13.7%. Top companies operating in the industry include Illumina, Thermo Fisher Scientific, QIAGEN, PerkinElmer, BGI Genomics, shaping competitive strategies across the sector.

Financial overview and grant giving statistics of Phoenix Bioinformatics Corporation

Raw datafile of the survey data collected from the survey distributed to collect knowledge and attitudes among life scientists towards reproducibility within journal articles.

Excel document containing precision, recall and F1 scores for metagenomic classifiers used in the benchmarking of expam's performance. Classifiers were tested on 140 simulated metagenomic communities, at different taxonomic ranks.

The presence of prophages in bacterial genomes.

This file has these columns: 0. GENOMEID - Genbank genome assembly accession 1. Genome Name - Definition of the genome in the genbank file 2. Contigs > 5kb - Number of contigs longer than 5 kb (only these were used to predict prophages) 3. Genome Contigs - Total number of contigs in the genome 4. Number of Coding Sequences - Total number of coding sequences in the genome 5. Too short - Number of phage predictions that were too short (less than 5 genes in the prediction) 6. Not enough phage hits - Number of phage predictions that did not have a single HMM match to VOGdb version 99 7. Kept - Number of high quality prophage predictions 8. Note - Outcome of the computation. You should read this column, especially if the sum of prophage predictions is zero

This archive is a database generated using the novel Virus Pop pipeline, which simulates realistic protein sequences and adds new branches to a protein phylogenetic tree. An article describing the pipeline is currently under review.

The database contains simulations of 995 different proteins from 93 virus genera, providing a total of 24,138,277 sequences, both in amino acid and nucleotide.