Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

Abstract Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website .

Program and web site of bioinformatics used in this study.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

Database of curated links to molecular resources, tools and databases selected on the basis of recommendations from bioinformatics experts in the field. This resource relies on input from its community of bioinformatics users for suggestions. Starting in 2003, it has also started listing all links contained in the NAR Webserver issue. The different types of information available in this portal: * Computer Related: This category contains links to resources relating to programming languages often used in bioinformatics. Other tools of the trade, such as web development and database resources, are also included here. * Sequence Comparison: Tools and resources for the comparison of sequences including sequence similarity searching, alignment tools, and general comparative genomics resources. * DNA: This category contains links to useful resources for DNA sequence analyses such as tools for comparative sequence analysis and sequence assembly. Links to programs for sequence manipulation, primer design, and sequence retrieval and submission are also listed here. * Education: Links to information about the techniques, materials, people, places, and events of the greater bioinformatics community. Included are current news headlines, literature sources, educational material and links to bioinformatics courses and workshops. * Expression: Links to tools for predicting the expression, alternative splicing, and regulation of a gene sequence are found here. This section also contains links to databases, methods, and analysis tools for protein expression, SAGE, EST, and microarray data. * Human Genome: This section contains links to draft annotations of the human genome in addition to resources for sequence polymorphisms and genomics. Also included are links related to ethical discussions surrounding the study of the human genome. * Literature: Links to resources related to published literature, including tools to search for articles and through literature abstracts. Additional text mining resources, open access resources, and literature goldmines are also listed. * Model Organisms: Included in this category are links to resources for various model organisms ranging from mammals to microbes. These include databases and tools for genome scale analyses. * Other Molecules: Bioinformatics tools related to molecules other than DNA, RNA, and protein. This category will include resources for the bioinformatics of small molecules as well as for other biopolymers including carbohydrates and metabolites. * Protein: This category contains links to useful resources for protein sequence and structure analyses. Resources for phylogenetic analyses, prediction of protein features, and analyses of interactions are also found here. * RNA: Resources include links to sequence retrieval programs, structure prediction and visualization tools, motif search programs, and information on various functional RNAs.

The Bioinformatics Cloud Platform market is booming, projected to reach $10 billion by 2033 with a 20% CAGR. Discover key trends, drivers, restraints, and leading companies shaping this rapidly evolving sector in genomics, drug discovery, and academic research. Learn more about SaaS, PaaS, and IaaS solutions.

Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

The dataset contains the training and test sets of protein binding sites with DNA, RNA, peptide, protein, ATP, HEM, Zn2+, Ca2+, Mg2+ and Mn2+. Each protein is associated with 3 lines indicating the protein name (PDB accession code and chain), sequence and residue labels (0 for non-binding and 1 for binding), respectively. The ESMFold-predicted structures are also provided.

The global Genetic Data Analysis Software market is experiencing robust growth, projected to reach a market size of $348.5 million in 2025. While the provided CAGR (Compound Annual Growth Rate) is missing, considering the rapid advancements in genomics and the increasing adoption of precision medicine, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 15%. This growth is fueled by several key drivers. The rising prevalence of genetic disorders necessitates sophisticated software for analysis and interpretation. Furthermore, the decreasing cost of genomic sequencing is making large-scale genetic studies more feasible, leading to a greater demand for robust and efficient analysis tools. The market is segmented by deployment (web-based and cloud-based) and application (hospitals and health systems, research organizations, and others). Cloud-based solutions are gaining traction due to their scalability and accessibility, while hospitals and health systems represent a significant portion of the market share due to their increasing focus on personalized medicine. Major players like Agilent Technologies, Illumina, and QIAGEN Digital Insights are driving innovation through continuous product development and strategic partnerships. Technological advancements such as artificial intelligence and machine learning are enhancing the capabilities of these software solutions, leading to improved accuracy and faster analysis times. The integration of these advanced analytics with electronic health records (EHRs) is another significant trend further propelling market expansion. The market's growth trajectory is influenced by several factors. The increasing availability of high-throughput sequencing technologies continues to generate massive amounts of genomic data, further stimulating demand for advanced analytics. However, the complexity of genomic data analysis and the need for skilled professionals can act as a restraint, alongside data privacy and security concerns. Despite these challenges, the long-term outlook for the Genetic Data Analysis Software market remains highly positive, driven by the continuous advancements in genomics research, the expanding applications of genomic information in healthcare, and the increasing investments in precision medicine initiatives globally. The market is expected to witness considerable expansion across all geographical regions, with North America and Europe maintaining a significant market share due to their well-established healthcare infrastructure and advanced research capabilities.

Comparative analysis of existing bioinformatics web portals catering for BLAST search.

bioinformatics.com.cn is ranked #4446 in CN with 119.87K Traffic. Categories: . Learn more about website traffic, market share, and more!

We used the human genome reference sequence in its GRCh38.p13 version in order to have a reliable source of data in which to carry out our experiments. We chose this version because it is the most recent one available in Ensemble at the moment. However, the DNA sequence by itself is not enough, the specific TSS position of each transcript is needed. In this section, we explain the steps followed to generate the final dataset. These steps are: raw data gathering, positive instances processing, negative instances generation and data splitting by chromosomes.

First, we need an interface in order to download the raw data, which is composed by every transcript sequence in the human genome. We used Ensembl release 104 (Howe et al., 2020) and its utility BioMart (Smedley et al., 2009), which allows us to get large amounts of data easily. It also enables us to select a wide variety of interesting fields, including the transcription start and end sites. After filtering instances that present null values in any relevant field, this combination of the sequence and its flanks will form our raw dataset. Once the sequences are available, we find the TSS position (given by Ensembl) and the 2 following bases to treat it as a codon. After that, 700 bases before this codon and 300 bases after it are concatenated, getting the final sequence of 1003 nucleotides that is going to be used in our models. These specific window values have been used in (Bhandari et al., 2021) and we have kept them as we find it interesting for comparison purposes. One of the most sensitive parts of this dataset is the generation of negative instances. We cannot get this kind of data in a straightforward manner, so we need to generate it synthetically. In order to get examples of negative instances, i.e. sequences that do not represent a transcript start site, we select random DNA positions inside the transcripts that do not correspond to a TSS. Once we have selected the specific position, we get 700 bases ahead and 300 bases after it as we did with the positive instances.

Regarding the positive to negative ratio, in a similar problem, but studying TIS instead of TSS (Zhang135
et al., 2017), a ratio of 10 negative instances to each positive one was found optimal. Following this136
idea, we select 10 random positions from the transcript sequence of each positive codon and label them137
as negative instances. After this process, we end up with 1,122,113 instances: 102,488 positive and 1,019,625 negative sequences. In order to validate and test our models, we need to split this dataset into three parts: train, validation and test. We have decided to make this differentiation by chromosomes, as it is done in (Perez-Rodriguez et al., 2020). Thus, we use chromosome 16 as validation because it is a good example of a chromosome with average characteristics. Then we selected samples from chromosomes 1, 3, 13, 19 and 21 to be part of the test set and used the rest of them to train our models. Every step of this process can be replicated using the scripts available in https://github.com/JoseBarbero/EnsemblTSSPrediction.

The National Arboretum's National Species Knowledge Information System provides information on each item and detailed information on biological information resources, and an operation to search the list of biological-related sites is provided. The information includes biological classification items such as plants, insects, and mushrooms, as well as site names and site URLs.

Data resource catalog that collates metadata on bioinformatics Web-based data resources including databases, ontologies, taxonomies and catalogues. An entry includes information such as resource identifier(s), name, description and URL. ''''Query'''' lines are defined for each resource that describe what type(s) of data are available, in what format, how (by what identifier) the data can be retrieved and from where (URL). DRCAT was developed to provide more extensive data integration for EMBOSS, but it has many applications beyond EMBOSS. DRCAT entries (including ''''Query'''' lines) are annotated with terms from the EDAM ontology of common bioinformatics concepts.

A bioinformatics platform that is a joint project of several South of France laboratories with available services based on their expertise, issued from their research activities which involve phylogenetics, population genetics, molecular evolution, genome dynamics, comparative and functional genomics, and transcriptome analysis. Most of the software and databases on ATGC are (co)authored by researchers from South of France teams. Some are widely used and highly cited. South of France laboratories: * CRBM (transcriptomes and stem cells). * IBC (computational biology). * MiVEGEC (evolution and phylogeny). * LGDP (plant genomics). * LIRMM (computer science). * South Green (plant genomics).

bioinformatics.com.cn is ranked #3498 in CN with 173.32K Traffic. Categories: . Learn more about website traffic, market share, and more!

This FAIRsharing record describes: Poxvirus Bioinformatics Resource Center has been established to provide specialized web-based resources to the scientific community studying poxviruses. This resource is no longer being maintained. For tools and data supporting virus genomics, especially related to poxviruses and other large DNA viruses, please visit the Viral Bioinformatics site maintained by our collaborator, Chris Upton: http://virology.ca For information on virus taxonomy, please visit the ICTV web site at http://www.ictvonline.org/ For updated sequence data and analytical tools, please visit http://www.viprbrc.org

Drosophila Melanogaster

Drosophila Melanogaster, the common fruit fly, is a model organism which has been extensively used in entymological research. It is one of the most studied organisms in biological research, particularly in genetics and developmental biology.

When its not being used for scientific research, D. melanogaster is a common pest in homes, restaurants, and anywhere else that serves food. They are not to be confused with Tephritidae flys (also known as fruit flys).

https://en.wikipedia.org/wiki/Drosophila_melanogaster

About the Genome

This genome was first sequenced in 2000. It contains four pairs of chromosomes (2,3,4 and X/Y). More than 60% of the genome appears to be functional non-protein-coding DNA.

![D. melanogaster chromosomes][1]

The genome is maintained and frequently updated at [FlyBase][2]. This dataset is sourced from the UCSC Genome Bioinformatics download page. It uses the August 2014 version of the D. melanogaster genome (dm6, BDGP Release 6 + ISO1 MT). http://hgdownload.soe.ucsc.edu/downloads.html#fruitfly

Files were modified by Kaggle to be a better fit for analysis on Scripts. This primarily involved turning files into CSV format, with a header row, as well as converting the genome itself from 2bit format into a FASTA sequence file.

Bioinformatics

Genomic analysis can be daunting to data scientists who haven't had much experience with bioinformatics before. We have tried to give basic explanations to each of the files in this dataset, as well as links to further reading on the biological basis for each. If you haven't had the chance to study much biology before, some light reading (ie wikipedia) on the following topics may be helpful to understand the nuances of the data provided here: [Genetics][3], [Genomics]4, [Chromosomes][7], [DNA][8], [RNA]9, [Genes][12], [Alleles][13], [Exons][14], [Introns][15], [Transcription][16], [Translation][17], [Peptides][18], [Proteins][19], [Gene Regulation][20], [Mutation][21], [Phylogenetics][22], and [SNPs][23].

Of course, if you've got some idea of the basics already - don't be afraid to jump right in!

Learning Bioinformatics

There are a lot of great resources for learning bioinformatics on the web. One cool site is [Rosalind][24] - a platform that gives you bioinformatic coding challenges to complete. You can use Kaggle Scripts on this dataset to easily complete the challenges on Rosalind (and see [Myles' solutions here][25] if you get stuck). We have set up [Biopython][26] on Kaggle's docker image which is a great library to help you with your analyses. Check out their [tutorial here][27] and we've also created [a python notebook with some of the tutorial applied to this dataset][28] as a reference.

Files in this Dataset

Drosophila Melanogaster Genome

• genome.fa

The assembled genome itself is presented here in [FASTA format][29]. Each chromosome is a different sequence of nucleotides. Repeats from RepeatMasker and Tandem Repeats Finder (with period of 12 or less) are show in lower case; non-repeating sequence is shown in upper case.

There are 3 additional files with meta information about the genome.

• meta-cpg-island-ext-unmasked.csv

This file contains descriptive information about CpG Islands in the genome.

https://en.wikipedia.org/wiki/CpG_site

• meta-cytoband.csv

This file describes the positions of cytogenic bands on each chromosome.

https://en.wikipedia.org/wiki/Cytogenetics

• meta-simple-repeat.csv

This file describes simple tandem repeats in the genome.

https://en.wikipedia.org/wiki/Repeated_sequence_(DNA) https://en.wikipedia.org/wiki/Tandem_repeat

Messenger RNA (mRNA) is an intermediate molecule created as part of the cellular process of converting genomic information into proteins. Some mRNA are never translated into proteins and have functional roles in the cell on their own. Collectively, organism mRNA information is known as a Transcriptome. mRNA files included in this dataset give insight into the activity of genes in the organism.

https://en.wikipedia.org/wiki/Messenger_RNA

• mrna-genbank.fa

This file includes all mRNA sequences from GenBank associated with Drosophila Melanogaster.

http://www.ncbi.nlm.nih.gov/genbank/

• mrna-refseq.fa

This file includes all mRNA sequences from RefSeq associated with Drosophila Melanogaster.

http://www.ncbi.nlm.nih.gov/refseq/

A gene is a segment of DNA on the genome which, through mRNA, is used to create proteins in the organism. Knowing which parts of DNA are coding (genes) or non-coding is difficult, and a number of different systems for prediction exist. This da...

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. An expanded version of the Alternative Splicing Annotation Project (ASAP) database with a new interface and integration of comparative features using UCSC BLASTZ multiple alignments. It supports 9 vertebrate species, 4 insects, and nematodes, and provides with extensive alternative splicing analysis and their splicing variants. As for human alternative splicing data, newly added EST libraries were classified and included into previous tissue and cancer classification, and lists of tissue and cancer (normal) specific alternatively spliced genes are re-calculated and updated. They have created a novel orthologous exon and intron databases and their splice variants based on multiple alignment among several species. These orthologous exon and intron database can give more comprehensive homologous gene information than protein similarity based method. Furthermore, splice junction and exon identity among species can be valuable resources to elucidate species-specific genes. ASAP II database can be easily integrated with pygr (unpublished, the Python Graph Database Framework for Bioinformatics) and its powerful features such as graph query, multi-genome alignment query and etc. ASAP II can be searched by several different criteria such as gene symbol, gene name and ID (UniGene, GenBank etc.). The web interface provides 7 different kinds of views: (I) user query, UniGene annotation, orthologous genes and genome browsers; (II) genome alignment; (III) exons and orthologous exons; (IV) introns and orthologous introns; (V) alternative splicing; (IV) isoform and protein sequences; (VII) tissue and cancer vs. normal specificity. ASAP II shows genome alignments of isoforms, exons, and introns in UCSC-like genome browser. All alternative splicing relationships with supporting evidence information, types of alternative splicing patterns, and inclusion rate for skipped exons are listed in separate tables. Users can also search human data for tissue- and cancer-specific splice forms at the bottom of the gene summary page. The p-values for tissue-specificity as log-odds (LOD) scores, and highlight the results for LOD >= 3 and at least 3 EST sequences are all also reported.