Appendix N: The website link of the bioinformatics tools and online resources used in this thesis were summarised

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.

List of online and data bioinformatics training tools.

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.

Columns—GitHub: name of GitHub repository where the tools and documentation are available (NA: not applicable, as case study)–the prefix of the GitHub folders implies a typical workflow (outlined in Fig 2); tool: tool name (or in case of studies, a codeword); year: year of original publication; PMID: PubMed identifier; citations: number of citations reported by Google Scholar on 28-Mar-2023; citations/yr: number of citations per year since original publication; short description: self-explanatory, for further details, please see original publications. Table is sorted on PMID (which reflects the time of publication).

A comprehensive index for locating and compiling bioinformatics and online science tools. Users can browse, rate, share and save various tools listed in the LabWorm repository. Resources contain a short description, a list of related sites, comments, a list of users who have shared and rated the resource, and the main site URL. Resources may be saved to a personal toolbox collection. User news feeds can be customized to include new scientific literature from journals of choice. Users who want to utilize LabWorm tools to improve their own website can access the alternate Developer site.

The Sol Genomics Network (SGN) is a clade-oriented database dedicated to the biology of the Solanaceae family which includes a large number of closely related and many agronomically important species such as tomato, potato, tobacco, eggplant, pepper, and the ornamental Petunia hybrida. SGN is part of the International Solanaceae Initiative (SOL), which has the long-term goal of creating a network of resources and information to address key questions in plant adaptation and diversification. A key problem of the post-genomic era is the linking of the phenome to the genome, and SGN allows to track and help discover new such linkages. Data:

Solanaceae and other Genomes SGN is a home for Solanaceae and closely related genomes, such as selected Rubiaceae genomes (e.g., Coffea). The tomato, potato, pepper, and eggplant genome are examples of genomes that are currently available. If you would like to include a Solanaceae genome that you sequenced in SGN, please contact us. ESTs SGN houses EST collections for tomato, potato, pepper, eggplant and petunia and corresponding unigene builds. EST sequence data and cDNA clone resources greatly facilitate cloning strategies based on sequence similarity, the study of syntenic relationships between species in comparative mapping projects, and are essential for microarray technology. Unigenes SGN assembles and publishes unigene builds from these EST sequences. For more information, see Unigene Methods. Maps and Markers SGN has genetic maps and a searchable catalog of markers for tomato, potato, pepper, and eggplant. Tools SGN makes available a wide range of web-based bioinformatics tools for use by anyone, listed here. Some of our most popular tools include BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an Alignment Analyzer and browser for phylogenetic trees. The VIGS tool can help predict the properties of VIGS (Viral Induced Gene Silencing) constructs.

The data in SGN have been submitted by many different research groups around the world. A web form is available to submit data for display on SGN. SGN community-driven gene and phenotype database: Simple web interfaces have been developed for the SGN user-community to submit, annotate, and curate the Solanaceae locus and phenotype databases. The goal is to share biological information, and have the experts in their field review existing data and submit information about their favorite genes and phenotypes. Resources in this dataset:Resource Title: Website Pointer to Sol Genomics Network. File Name: Web Page, url: https://solgenomics.net/ Specialized Search interfaces are provided for: Organisms/Taxon; Genes and Loci; Genomic sequences and annotations; QTLs, Mutants & Accessions, Traits; Transcripts: Unigenes, ESTs, & Libraries; Unigene families; Markers; Genomic clones; Images; Expression: Templates, Experiments, Platforms; Traits.

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

Details of GEO datasets included in bioinformatics study.

Advances in high-throughput techniques have resulted in a rising demand for scientists with basic bioinformatics skills as well as workshops and curricula that teach students bioinformatics concepts. DNA Detective is a workshop we designed to introduce students to big data and bioinformatics using CyVerse and the Dolan DNA Learning Center's online DNA Subway platform. DNA Subway is a user-friendly workspace for genome analysis and uses the metaphor of a network of subway lines to familiarize users with the steps involved in annotating and comparing DNA sequences. For DNA Detective, we use the DNA Subway Red Line to guide students through analyzing a "mystery" DNA sequence to distinguish its gene structure and name. During the workshop, students are assigned a unique Arabidopsis thaliana DNA sequence. Students "travel" the Red Line to computationally find and remove sequence repeats, use gene prediction software to identify structural elements of the sequence, search databases of known genes to determine the identity of their mystery sequence, and synthesize these results into a model of their gene. Next, students use The Arabidopsis Information Resource (TAIR) to identify their gene's function so they can hypothesize what a mutant plant lacking that gene might look like (its phenotype). Then, from a group of plants in the room, students select the plant they think is most likely defective for their gene. Through this workshop, students are acquainted to the flow of genetic information from genotype to phenotype and tackle complex genomics analyses in hopes of inspiring and empowering them towards continued science education.

The Computational Biology market is projected to be valued at $10 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $31.5 billion by 2034.

A web-based central resource that integrates vaccine literature data mining, vaccine research data curation and storage, and curated vaccine data analysis for vaccines and vaccine candidates developed against various pathogens of high priority in public health and biological safety. The vaccine data includes research data from vaccine studies using humans, natural and laboratory animals.VIOLIN extracts and stores vaccine-related, peer-reviewed papers from PubMed. Several powerful literature searching and data mining programs have been developed. These include an advanced keywords search program, a natural languagae processing (NLP) based literature retrieval program, a MeSH-based literature browser, and a literature alert program. Registered users can subscribe to our email alert service and will be notified of any newly published vaccine papers in the areas of interest. These literature mining programs are designed to help the user and VIOLIN database curators to find efficiently needed vaccine articles and sentences within full-text articles that contain searched keywords or categories.A web-based literature mining and curation system (Limix) is available for registered users/curators to search, curate, and submit structured vaccine data into the VIOLIN database. The curated vaccine-related information contains many categories such as general pathogenesis, protective immunity, vaccine preparation and characteristics, host responses including vaccination protocol and efficacy against virulent pathogen infections. All data within the database is edited manually and is derived primarily from peer-reviewed publications. The curated data is stored in a relational database and can be queried using various VIOLIN search programs. Vaccine-related pathogen and host genes are annotated and available for searchs based on a customized BLAST program. All VIOLIN data are available for download into an XML-based data exchange format.VIOLIN is designed to be a vital source of vaccine information and will provide researchers in basic and clinical sciences with curated data and bioinformatics tools to facilitate understanding and development of vaccines to fight infectious diseases. Category: Other Molecular Biology Databases Subcategory: Drugs and drug design

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free to-use data processing and analysis tools exist for various untargeted MS approaches, but choosing the ‘correct’ pipeline isn’t straight-forward. This data set can be used in conjunction with a user-friendly online guide which presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. The workflow provides practical advice concerning experimental design, organisation of data and downstream analysis, and offers details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, all..., The dataset was collected from soil analysis by MALDI-TOF-MS. Three types of soil were collected from the field – arable, orchard and forest soil. The samples were extracted into chloroform, methanol and water and the aqueous fraction (methanol:water) was mixed 1:1 with 5mg/ml CHCA and 1ul was spotted onto a MALDI target plate. Each sample was analysed using MALDI-TOF-MS over a scan range of 50-800m/z with a 1-minute scan time and data was collected for 1min. Each sample was run 3 times for technical replication and 3 biological replicates per soil type were run., The raw data files are available to download but need to be converted using Proteowizard to the universal mzML format. Proteowizard is an open-source tool for Windows users. https://proteowizard.sourceforge.io/download.html The mzML files are also available to download which can be used directly with the processing workflow. The workflow for processing the untargeted metabolomics data can be accessed from the following link. The workflow is based on open-source tools. https://untargeted-metabolomics-workflow.netlify.app/

jats:titleAbstract/jats:title jats:pEnsembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of interfaces to genomic data across the tree of life, including reference genome sequence, gene models, transcriptional data, genetic variation and comparative analysis. Data may be accessed via our website, online tools platform and programmatic interfaces, with updates made four times per year (in synchrony with Ensembl). Here, we provide an overview of Ensembl Genomes, with a focus on recent developments. These include the continued growth, more robust and reproducible sets of orthologues and paralogues, and enriched views of gene expression and gene function in plants. Finally, we report on our continued deeper integration with the Ensembl project, which forms a key part of our future strategy for dealing with the increasing quantity of available genome-scale data across the tree of life./jats:p

The digital biomanufacturing market is projected to be valued at $9.6 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12.5%, reaching approximately $30 billion by 2034.

In order to introduce students to the concept of molecular diversity, we developed a short, engaging online lesson using basic bioinformatics techniques. Students were introduced to basic bioinformatics while learning about local on-campus species diversity by 1) identifying species based on a given sequence (performing Basic Local Alignment Search Tool [BLAST] analysis) and 2) researching and documenting the natural history of each species identified in a concise write-up. To assess the student’s perception of this lesson, we surveyed students using a Likert scale and asking them to elaborate in written reflection on this activity. When combined, student responses indicated that 94% of students agreed this lesson helped them understand DNA barcoding and how it is used to identify species. The majority of students, 89.5%, reported they enjoyed the lesson and mainly provided positive feedback, including “It really opened my eyes to different species on campus by looking at DNA sequences”, “I loved searching information and discovering all this new information from a DNA sequence”, and finally, “the database was fun to navigate and identifying species felt like a cool puzzle.” Our results indicate this lesson both engaged and informed students on the use of DNA barcoding as a tool to identify local species biodiversity.

Primary Image: DNA Barcoded Specimens. Crane fly, dragonfly, ant, and spider identified using DNA barcoding.

The U.S. Molecular Modeling market is projected to be valued at $450 million in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 10%, reaching approximately $1 billion by 2034.

Genomic, Genetic and Breeding Resources for Pulse Crop Improvement. Crops supported include Adzuki bean, Bambara bean, Chickpea, Common bean, Cowpea, Faba bean, Lentil, Lupin, Pea, Pigeon pea, Vetch, and others. The Pulse Crop Database (PCD), formerly the Cool Season Food Legume Database (CSFL), is being developed by the Main Bioinformatics Laboratory at Washington State University in collaboration with the USDA-ARS Grain Legume Genetics and Physiology Research Unit, the USDA-ARS Plant Germplasm Introduction and Testing Unit, the USA Dry Pea and Lentil Council, Northern Pulse Growers and allied scientists in the US and across the world, to serve as a resource for Genomics-Assisted Breeding (GAB). GAB offers tools to identify genes related to traits of interest among other methods to optimize plant breeding efficiency and research, by providing relevant genomic, genetic and breeding information and analysis. Therefore, tools such as JBrowse and MapViewer can be found in this database, as well as key resources to provide the access to the annotation of available transcriptome data, helping pulse breeders and researchers to succeed in their programs. Resources in this dataset:Resource Title: Pulse Crop Database Resources. File Name: Web Page, url: https://www.pulsedb.org/ Resources include data submission and download, and search by gene and transcript, germplasm, map, marker, publication, QTL, sequence, megasearch, and trait/descriptor. A User Manual describes how to access data and use the tools on the Pulse Crop Database. Tools supported: BLAST, JBrowse, PathwayCyc, MapViewer, and Synteny Viewer

The U.S. Drug Discovery Informatics is projected to be valued at $3.2 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12.5%, reaching approximately $9.2 billion by 2034.