[{"id":246,"title":"Bionode - Modular and universal bioinformatics","sha":"8199658b25e657cae1363df4da92f200","user_id":null,"state":"published","body":"\u003e Highly reusable tools and code for bioinformatics, focused on\r\n* [Node.js](http://nodejs.org)/JavaScript to be *\"write once, run anywhere\"*\r\n* Node.js [Streams](https://nodejs.org/api/stream.html) and [Events](https://nodejs.org/api/events.html) for scalable and reactive pipelines\r\n* CLI for easy integration with other tools and languages\r\n\r\nThe [huge decrease in sequencing cost](https://upload.wikimedia.org/wikipedia/commons/b/b7/DNA_Sequencing_Cost_per_Genome_Over_Time.jpg) and consequent [explosion of data](https://www.nlm.nih.gov/about/image/2014CJ_fig_5.png) is reinforcing the need for well tested and scalable tools in biology. It is also causing a [boom in biological web services and databases](https://en.wikipedia.org/wiki/List_of_biological_databases) that often require some data to be processed in the browser. [Bionode](http://bionode.io) provides bioinformatics tools written in Node.js. Bionode is an improvement on other [bio* libraries](http://www.open-bio.org/wiki/Projects#Main_projects) because it can be run anywhere (browsers or servers), it is streamable, and it can be integrated  with other projects and programming languages (each tools is a Node.js module but also comes with a [CLI](https://github.com/bionode/bionode-ncbi#usage)). Bionode tools follow the UNIX philosophy \"do one thing and do it well\". \r\n\r\nAll the development is public and transparent. The code is [MIT](https://opensource.org/licenses/MIT) licensed on [GitHub](http://github.com/bionode), and discussions happen on [Gitter](http://gitter.im/bionode/bionode) and IRC ([freenode #bionode](http://irccloud.com/#!/ircs://irc.freenode.net:6697/%23bionode)). We welcome all kinds of contributions (code, ideas, etc) and we collaborate and integrate with existing projects, such as [Dat](http://dat-data.com) (e.g., [Mozfest 2014 workshop](http://try-dat.com/guide/08-extra-credit.html\r\n)), [BioJS](http://biojs.net) (e.g., [applied together for GSoC 2015](http://biojs.net/gsoc/2015/projects/bionode_biojs.html\r\n)), and [CWL](http://common-workflow-language.github.io\r\n) (e.g., [brainstormed and hacked together at #biohack15 Japan](https://github.com/dbcls/bh15/wiki/Software#reproducible-and-distributable-software-and-data\r\n)). We apply the best practices from web development startups (e.g., [testing, coverage, CI, etc](https://github.com/bionode/bionode-template#principles)) into developing scientific software. Our best practices have [already inspired other projects](https://github.com/hydronode). We have a [small community](https://www.dropbox.com/s/njgeis4e1urolud/Screenshot%202015-09-30%2015.06.19.png?dl=0) but need more contributors.\r\n\r\n* [Bionode website](http://bionode.io)\r\n* [Wurmlab website](http://wurmlab.github.io)\r\n* [Bionode GitHub repository](http://github.com/bionode)\r\n* [Bionode Gitter chat room](http://gitter.im/bionode/bionode)\r\n* [Online shell to try Bionode](http://try.bionode.io)\r\n* [Dat Mozfest14 workshop with Bionode chapter (8)](http://try-dat.com)\r\n* [BioJS GSoC15 page with Bionode project ideas](http://biojs.net/gsoc/2015/)\r\n* [Recent talk at biocoders meetup in Cambridge](http://bmpvieira.com/biocoders15a)\r\n","created_at":"2015-09-30T21:04:51.709Z","updated_at":"2015-10-01T06:00:27.295Z","zenodo_id":63050,"doi":"http://dx.doi.org/10.5281/zenodo.31637","subject":null,"tags":["open research accelerator","bionode","bioinformatics","biology","node.js","javascript"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":"bionode-logo.min.png","attachment_content_type":"image/png","attachment_file_size":18044,"attachment_updated_at":"2015-09-30T21:04:51.382Z"},{"id":1044,"title":"Docker containers with embedded R packages and domain-specific tools","sha":"09ce69715f6bbf24b137859378015779","user_id":null,"state":"published","body":"R packages are collections of functions, documentations, tests, sample data, and dependency declarations, which come together to promote modularity, reproducibility, and adherence to good coding practices. Containerization using technologies such as Docker allow for building a specific computing environment with all required dependencies, configuration files, and system libraries needed to run any computing process reproducibly and at scale. Further, Docker allows computing environments to be _specified in code_, such that the instructions for building the infrastructure itself can be specified in code. Many data science and bioinformatics tasks involve running some domain-specific tools for initial analysis, followed by postprocessing using R. The `rpdd` package provides a demonstration on how to create a Docker image with an embedded R package and domain-specific tools. The `rpdd` package comes with a build script, which first builds the R package, then builds a Docker image containing that package, along with domain-specific tools and dependencies. When the container is instantiated, it runs a script that uses domain-specific tools to preprocess input data, and runs functions from the R package to postprocess the output from the first step. Code and further documentation is available at \u003chttps://github.com/stephenturner/rpdd\u003e.","created_at":"2023-02-02T14:29:21.946Z","updated_at":"2023-03-30T09:35:14.831Z","zenodo_id":7662885,"doi":"https://doi.org/10.5281/zenodo.7662885","subject":null,"tags":["docker","r packages","bioinformatics","data science","reproducible research"],"vote_count":1,"view_count":1,"score":1.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":616,"title":"The Growing Need for Domain-Driven Languages","sha":"d216d707e6077f0c53aabf8cb1751bd9","user_id":null,"state":"published","body":"With computer science permeating every research disciple such that even rudimentary computational skills are now required for most research careers, it should not be the case that one needs advanced knowledge of computer science to be effective in their field.\r\n\r\nIf we take Bioinformatics as an example, the non-computational biologist practitioner should be able to express theories in a truncated form of how they already speak of biology -- a Domain-Driven Language (DDL) which can then be computationally processed. Tools such as Nextflow and Snakemake, as workflow managers, make progress in this direction, but as they do not abstract away the data flow model they rely upon, do not go far enough.\r\n\r\nI posit that unified domain-driven grammars are possible consisting of: verbs which define the action to be done (e.g., align); adjectives which define how the verb is to be done (e.g., local); nouns which define what the verb acts upon (e.g., sequence 1 and sequence 2); intents which define manner of action (e.g., longest match); and helpers which are meant to clarify intent (e.g., sequence 1 is reference, sequence 2 is query). An executed DDL should be able to use the same grammar to describe what was run in terms the executor understands.","created_at":"2019-09-05T00:26:51.488Z","updated_at":"2020-06-16T16:30:37.128Z","zenodo_id":3445545,"doi":"https://doi.org/10.5281/zenodo.3445545","subject":null,"tags":["dsl","bioinformatics","abstraction"],"vote_count":5,"view_count":1,"score":5.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":1200,"title":"Gender Commonality in the Upregulated Transcriptome of Patients with Ischemic Stroke","sha":"8745af354485d0428e23f7d1751274f0","user_id":null,"state":"published","body":"The present study systematically searched the Gene Expression Omnibus (GEO) DataSets for and retrieved the GSE22255 dataset that included transcriptomic data from Ischemic Stroke patients. This was an expression profiling by array using peripheral blood mononuclear cell samples. The mRNAs with adjusted P-value \u003c 0.05    and log2FC ≥ 2 were deemed as significant Differentially Expressed Genes (DEGs). DEGs were compared between and within gender subjects from patients in reference to control groups. \r\n\r\nWhen male stroke patients were compared with male controls, the top upregulated gene was the C-X-C motif chemokine Ligand 3 also known as CXCL3 (log2FC =6.77, P value=2.73 e-6). Likewise, when female stroke patients were compared over female controls, the top upregulated gene was again CXCL3 (log2FC =3.83, P value=0.035). However, the CXCL3 gene (log2FC =5.15, P value=8.98 e-4) was found to be significantly upregulated in male over female patients with stroke.\r\n\r\nCXCL3 plays a key role in neuroinflammation by acting as a chemoattractant for neutrophils. This analysis provides evidence of CXCL3 as a marker for ischemic stroke in both genders but men have more severe neuroinflammation in acute ischemic attack. Laboratory studies are needed to validate the gender commonality of CXCL3 expression in stroke.","created_at":"2024-06-02T00:55:03.579Z","updated_at":"2024-09-30T07:53:39.271Z","zenodo_id":12660228,"doi":"https://doi.org/10.5281/zenodo.12660228","subject":null,"tags":["ischemic stroke","no gender gap","gene expression","bioinformatics","neuroinformatics","cerebrovascular","angiology"],"vote_count":1,"view_count":1,"score":1.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":"CXCL3_in_Stroke_both_gender_by_Dr._Debprasad-Dutta.pdf","attachment_content_type":"application/pdf","attachment_file_size":168077,"attachment_updated_at":"2024-06-02T01:08:05.191Z"}]