# Hybrid Bioinformatics Workflows: Combining Web and Local Tools By the end of this guide you will be able to design a hybrid bioinformatics workflow that uses local command-line tools for the steps where you need control and speed, and web platforms for the steps where a browser interface saves you hours of setup. You will run a complete worked example on real sequencing-style data, including data retrieval, quality control, trimming, the local-versus-web decision point, alignment, and result handoff. You will also learn how to verify that files survived the trip between environments unchanged, and how to keep the whole thing reproducible. Here is what you need on hand before starting: - A computer with at least 8 GB of RAM (16 GB or more is comfortable for small alignment jobs) running macOS, Linux, or Windows with WSL2 - The Conda package manager, ideally the faster Mamba fork - Roughly 20 GB of free disk space for test data and indexes - A web browser and, for some platforms, a free account - For very large datasets, either a lab server, a cloud instance, or patience with long uploads ## Why Hybrid Workflows Exist Most bioinformatics analysis breaks into steps with very different computational profiles. Raw data processing, like adapter trimming and read alignment, is compute-heavy and benefits from running on hardware you control. Interpretation steps, like browsing results in a genome viewer or comparing candidate domains, are interaction-heavy and benefit from a polished web interface. Forcing every step into a single environment wastes time on one end or the other. The fragmentation problem is well documented. Proteomics pipelines, for example, have historically consisted of disconnected software packages glued together with in-house scripts and manual steps running on local hardware, which are costly to maintain and difficult to scale [1]. Workflow systems such as Taverna and Galaxy each built large user communities, yet workflows written for one system could not be run in the other without significant rework, a lack of interoperability that duplicated development effort across the community [2]. Modern platforms respond to this in different ways. Some consolidate the whole pipeline behind a web interface, an API, and a command-line client so that automated and interactive access use the same backend [1]. Others pair a server-side processing engine, such as Galaxy, with a locally installed web server for interactive result exploration, so researchers process and interpret their own data without advanced skills or direct access to compute clusters [3]. A hybrid workflow takes the same logic down to the level of a single project. You deliberately split your analysis: steps that need your CPU, your reference genome, and your version control run locally, while steps that need a curated database, a graphical interface, or a specialized algorithm maintained by someone else run in the browser. ## Web Versus Local: An Honest Bioinformatics Tools Comparison Before building anything, it helps to be clear about what each environment does well. The table below compares the two modes on the dimensions that actually decide project outcomes. | Dimension | Local tools (CLI, R, Python) | Web platforms | |--|--|--| | Setup cost | High at first (install once), near zero afterward | Near zero at first, but repeated per platform | | Compute ceiling | Limited by your hardware or cluster access | Often large server-side resources, sometimes free | | Data transfer | None, files stay in place | Uploads and downloads, limited by bandwidth | | Reproducibility | Full control over versions, parameters, and seeds | Depends on whether the platform documents tool versions | | Privacy | Data never leaves your machine | Depends on the platform's policy and hosting location | | Interactivity | Text-based, scriptable | Point-and-click, visual, often mobile-accessible [4] | | Maintenance | You update, debug, and pin versions | The operators maintain the stack for you | Neither column wins outright. Web platforms lower the barrier dramatically for experimental researchers. A fully automated ChIP-seq platform can take a BioProject ID and carry the analysis through quality control, trimming, alignment, peak calling, and annotation with minimal input, eliminating local installations, programming skills, and large file uploads [4]. A similar pattern appears in microbiome analysis, where an integrated web platform chains [preprocessing](/blog/data-analysis/what-is-data-preprocessing), taxonomic profiling, diversity analysis, differential abundance testing, and machine learning modules so that preprocessing choices propagate automatically to downstream analyses [5]. In [molecular docking](/knowledge/bioinformatics/docking-algorithms-autodock-glide-and-beyond), a web GUI removes the need to download and compile DOCK6 yourself and runs the calculations on remote high-performance hardware [6]. These are genuine productivity gains. Local tools win wherever volume, privacy, or version control dominate. A web platform's operators decide when the server is up, which software versions run, and how long your session lasts. Locally, you decide. A machine learning study on hospital data illustrates why this matters: the team developed a dual-platform application, web-based and desktop, so that deployment could match the sensitivity of the data and the needs of the users [7]. The same reasoning applies to genomic data, which is often identifiable and subject to institutional policy. The best recent platforms increasingly refuse to pick a side. Profiler, an open multi-omics platform, ships as a web application deployed on high-performance computing and as a desktop version for local execution, letting the same analysis move between infrastructures [8]. ARNy Plotter runs in the browser with no installation or registration, and can also be run offline [9]. TADMaster is served from a public website and is also distributed for local use, so the same TAD comparison analysis can run either way [10]. When you evaluate the best bioinformatics software for your project, this dual availability is one of the strongest signals to look for. ## Designing the Split: Which Steps Go Where ![Decision path asking about privacy rules, upload size and need for a specialized algorithm to choose web or local execution.](/article-images/illustrations/hybrid-bioinformatics-workflow-web-local-web-or-local.webp) Every hybrid project needs an explicit decision about each step. Use these rules of thumb, then record your decisions in the project README so collaborators can follow the logic. 1. **Raw data acquisition.** Download locally whenever the data is large or will be reused. Use `sra-tools`, `wget`, or `curl`. Web databases are excellent for discovery (searching, browsing records) and poor as bulk transfer endpoints. A centralized crop-genomics platform like TefDB, which integrates tens of thousands of gene models, protein sequences, and literature records behind a searchable, filterable, downloadable interface, is designed exactly for this pattern: find records in the browser, download them, analyze locally [11]. 2. **Quality control and trimming.** Run locally. These steps are cheap, fast, and every downstream result depends on their exact parameters, so you want them pinned in your own scripts. 3. **Alignment and feature-level compute.** Depends on data size. A few million reads run fine on a laptop. Whole-genome depth belongs on a server or a platform that absorbs the compute for you [6],[1]. 4. **Specialized algorithms you would never install yourself.** Web. Structural motif searches against nucleic acid 3D structures, for example, are served through interfaces that accept PDB and mmCIF uploads and return exportable alignments [12]. Installing and benchmarking such tools locally for one query is rarely worth it. 5. **[Statistical modeling](/blog/guides/understanding-model-assumptions-in-statistical-analysis) and machine learning.** Local or hybrid. You will iterate on these steps many times, and iteration is fastest on your own machine. 6. **Visualization and collaboration.** Web. Network editing in the browser avoids the classic desktop bottleneck where every small change to a Cytoscape network forces files to be passed back and forth between collaborators, and platforms like NDEx let teams share and modify the same network without redundant copies [13]. 7. **Version control and documentation.** Local, always, with remote backups. A self-hosted Git-based platform demonstrates the pattern: repositories, access control, and project documentation live in one versioned place while the actual analyses run wherever compute is available [14]. ## A Hybrid Bioinformatics Workflow With Web and Local Tools, Step by Step This walkthrough uses a small paired-end sequencing dataset to keep runtimes short, but every command scales to real project data. The workflow: retrieve data locally, QC and trim locally, then split at the alignment step, where we show both the local path and the web path, and finally verify and package results. ### Step 1: Create a Pinned Local Environment Open a terminal and create an isolated environment so that tool versions are locked: ```bash mamba create -n hybrid -c conda-forge -c bioconda \ fastqc fastp multiqc samtools bwa sra-tools mamba activate hybrid ``` Check what you installed and write it down: ```bash conda list -n hybrid > environment_snapshot.txt head -5 environment_snapshot.txt ``` Expected output looks like this: ``` # packages in environment at /home/you/mamba/envs/hybrid: # # Name Version Build Channel bwa 0.7.17 h5bf99c6_8 bioconda fastp 0.23.4 h5f740d0_2 bioconda ``` The snapshot file is your reproducibility anchor. At the end of the project you will export the full environment specification with `mamba env export -n hybrid > environment.yml` and commit it to version control [14]. ### Step 2: Retrieve Raw Data Locally Create a project skeleton and pull a small publicly available paired-end test dataset from the Sequence Read Archive using `fasterq-dump`: ```bash mkdir -p hybrid-demo/{raw,trimmed,reports,aligned,refs} cd hybrid-demo fasterq-dump SRR390728 --split-files -O raw/ pigz raw/*.fastq ``` `-split-files` writes forward and reverse reads into separate files. `pigz` is a parallel gzip compressor. Confirm the files: ```bash ls -lh raw/ ``` Expected output (yours will differ slightly): ``` -rw-r--r-- 1 you you 48M raw/SRR390728_1.fastq.gz -rw-r--r-- 1 you you 48M raw/SRR390728_2.fastq.gz ``` Record checksums now. This is the reference point for every later transfer: ```bash sha256sum raw/*.fastq.gz > checksums_raw.txt cat checksums_raw.txt ``` ### Step 3: Quality Control and Trimming Locally Run FastQC on the raw files, then trim with fastp, which also produces its own HTML report: ```bash fastqc raw/*.fastq.gz -o reports/ fastp \ -i raw/SRR390728_1.fastq.gz \ -I raw/SRR390728_2.fastq.gz \ -o trimmed/SRR390728_R1.trim.fastq.gz \ -O trimmed/SRR390728_R2.trim.fastq.gz \ --detect_adapter_for_pe \ --qualified_quality_phred 20 \ --length_required 50 \ --html reports/SRR390728.fastp.html \ --json reports/SRR390728.fastp.json ``` Parameters that matter here: - `-detect_adapter_for_pe` enables adapter detection for paired-end data. Without it, adapter reads pass through into your alignments. - `-qualified_quality_phred 20` keeps bases with Phred quality 20 or higher, meaning a 1 percent chance of error per base. This is a common default, not a law. Adjust based on your FastQC report. - `-length_required 50` discards reads shorter than 50 bases after trimming, which prevents fragments too short to align uniquely. Expected fastp terminal output ends with lines like this: ``` Read1 before filtering: total reads: 1281684 total bases: 192252600 Q20 bases: 188783286(98.1957%) Read1 after filtering: total reads: 1265012 total bases: 174502913 ``` The numbers you care about are the percentage of reads surviving filtering and the Q20 fraction. If survival drops below roughly 70 percent on a healthy library, stop and look at the FastQC per-base quality plots before proceeding, because something is wrong with the run itself, not with your trimming stringency. Aggregate everything with MultiQC: ```bash multiqc reports/ -o multiqc_out/ ``` MultiQC writes a single `multiqc_report.html` combining every sample. This file is also a natural handoff artifact: it is small enough to email or upload, and reviewers can see your QC evidence without your raw data. ### Step 4: The Decision Point, With Bandwidth Arithmetic ![Calculation of upload time for a 5 GB compressed FASTQ at 12.5 MB per second and at a slower lab upstream speed.](/article-images/illustrations/hybrid-bioinformatics-workflow-web-local-upload-bandwidth.webp) This is where the hybrid workflow gets its name. Alignment is the compute-heavy step, and you have two options. The choice comes down to arithmetic and policy. **The upload calculation.** A paired-end library with 25 million reads at 150 bases each produces roughly 15 GB of uncompressed FASTQ, which compresses to about 5 GB. At an upload speed of 100 megabits per second, which is about 12.5 megabytes per second, that file takes roughly seven minutes to upload: ``` 5,000 MB ÷ 12.5 MB/s = 400 seconds ≈ 7 minutes ``` At a lab's typical 10 megabits per second upstream, the same file takes about 70 minutes. Multiply by 40 samples and you are looking at two days of pure upload time. That is the moment to align locally or use a platform that pulls data from the archive itself. Automated ChIP-seq platforms illustrate the alternative: when the platform accepts a BioProject accession and retrieves the raw data on its own, the upload problem disappears entirely [4]. **The privacy calculation.** Human genomic data may be restricted by consent terms, IRB agreements, or institutional policy. If your data cannot leave your institution, the web branch of your workflow is limited to tools you can run behind your own firewall, such as a locally installed web server backed by your own compute [3] or a self-hosted platform [14]. ### Step 5a: The Local Alignment Path Assuming a small genome or a subset of chromosomes, index a reference and align: ```bash cp /path/to/reference/genome.fa refs/ bwa index refs/genome.fa bwa mem -t 8 refs/genome.fa \ trimmed/SRR390728_R1.trim.fastq.gz \ trimmed/SRR390728_R2.trim.fastq.gz \ | samtools sort -@ 8 -o aligned/SRR390728.bam samtools index aligned/SRR390728.bam ``` `-t 8` tells BWA-MEM to use eight threads. Match this to your actual core count, since oversubscribing threads on a laptop slows everything down. The pipe into `samtools sort` converts the text alignment stream into a coordinate-sorted BAM file, which is the format downstream tools expect. Verify the alignment: ```bash samtools flagstat aligned/SRR390728.bam ``` Expected output, with example numbers: ``` 2533364 + 0 in total (QC-passed reads + QC-failed reads) 0 + 0 secondary 5632 + 0 supplementary 0 + 0 duplicates 2384120 + 0 mapped (94.12% : N/A) 2525416 + 0 paired in sequencing 1262708 + 0 read1 1262708 + 0 read2 2284044 + 0 properly paired (90.44% : N/A) ``` The two numbers that matter are the mapped percentage and the properly paired percentage. For a clean bacterial or small eukaryotic library, mapped fractions above 95 percent and properly paired above 90 percent are typical. A mapped fraction below 80 percent usually means the wrong reference genome, heavy contamination, or badly trimmed reads. ### Step 5b: The Web Path If your compute is thin or you want the platform's curated pipeline, hand the trimmed files, or better the original accession, to a web platform. The practical tips: 1. **Check what the platform accepts before you prepare files.** Some accept FASTQ, some accept BAM, some accept only accessions [4]. Preparing the wrong format wastes an upload cycle. 2. **Upload the smallest sufficient file.** A trimmed FASTQ is smaller than the raw one. Never upload an uncompressed FASTQ. 3. **Verify on arrival.** Download the platform's output, recompute checksums, and compare against what you submitted when checksums are provided. 4. **Screenshot or export the platform's parameter summary immediately.** Sessions expire. If the platform does not state its tool versions, record that fact in your [lab notebook](/knowledge/molecular-biology/lab-notebook), because it becomes a reproducibility limitation you must disclose. The same pattern holds across domains. For virtual screening, DOCKweb handles the setup and submission that previously required command-line fluency and executes the job on TACC hardware, so the local machine never compiles anything [6]. For proteomics, an API-driven platform lets the same pipeline be triggered from a web interface, a CLI client, or raw API calls, which means your local scripts can drive a web backend programmatically rather than through the browser [1]. That last pattern, script driving web service, is the most powerful form of the hybrid workflow and works with any platform that exposes an API. ### Step 6: Specialized Web Analysis on Local Results Once you have a local or platform-generated intermediate result, many specialized questions are best answered by a dedicated web tool. Two concrete examples: - **Consistency checking.** If two different peak callers, TAD callers, or domain predictors produced conflicting results, a comparison webserver can quantify concordance, shared boundaries, and overlap, and return consensus plus unique features so you can justify which call set to carry forward [10]. Running this comparison in the browser costs minutes. Rebuilding the comparison logic locally costs an afternoon. - **Interactive visualization.** Upload a network, structure, or ensemble to a visualization server, adjust the representation interactively, and share a link instead of a multi-gigabyte file [13],[12],[9]. The general rule: when a step is primarily about human judgment rather than raw compute, the browser usually wins, because the interface is the product. ### Step 7: Interactive Downstream Analysis, Either Way For statistics, differential analysis, and modeling, platforms built in R Shiny have made the browser competitive with local R for interactive analysis. SimpleMicrobiome chains preprocessing, diversity analysis, ANCOM-BC2 and MaAsLin2 differential testing, [Random Forest](/blog/data-analysis/what-is-random-forest) modeling with SHAP interpretation, and network inference in one interface where preprocessing choices flow downstream automatically [5]. SMIntegration provides an analogous chain for spatial metabolomics plus [transcriptomics](/knowledge/bioinformatics/modern-transcriptomics-bulk-single-cell-spatial), from spatial registration through gene-metabolite network construction, deployed via Docker [15]. When you use such a platform, treat it as one more tool in your pipeline, not a separate universe. Download its output tables, bring them into your local project directory, checksum them, and continue in R or Python. If the platform offers a Docker image, prefer running that image locally over the public instance for sensitive data, a deployment pattern also used for automated database platforms that run identically in local, cloud, or remote environments [16]. ### Step 8: Version Control and Packaging Close the loop with Git: ```bash git init git add environment.yml checksums_raw.txt checksums_results.txt README.md git add scripts/ git commit -m "Pin versions, record checksums, document hybrid split" git remote add origin https://your-git-server/your-lab/hybrid-demo.git git push -u origin main ``` A dedicated version-control server, even a self-hosted one in an academic lab, gives you repository management, access control, and complete project documentation across the life of the tool or analysis [14]. The README should answer three questions for a newcomer: which steps ran where, which tool versions ran each step, and where the checksums are. ## Checking That Your Hybrid Workflow Actually Worked Hybrid workflows fail quietly, so build verification into the pipeline itself: 1. **Checksum every transfer.** After downloading results from a web platform, run `sha256sum` on the files and compare against the values recorded at submission. A mismatch means truncation or corruption. 2. **Check row and read counts at each boundary.** If fastp reports 1,265,012 surviving reads, the alignment summary should account for the same reads, split between mapped and unmapped. Numbers that shrink without explanation indicate a lost file or a silent failure. 3. **Compare the two branches if you ran both.** Running the local path and the web path on the same small input, then comparing outputs, is the cheapest validation of a new platform you can do. The same concordance logic used to compare TAD call sets applies to comparing pipeline branches: quantify shared and unique features rather than eyeballing plots [10]. 4. **Use visual QC where a human can catch what metrics miss.** Platforms that support expert visual validation of automated results, where a user accepts, corrects, or excludes flagged items, embody this principle [17]. Build the same habit into your own review steps. ## Common Mistakes and Limitations These are the failures that actually cost people days, drawn from the recurring weak points of web-plus-local projects. **Mixing reference genome builds.** You align locally against GRCh38 and then upload a BED file to a web tool that assumes GRCh37. Coordinates are silently off by tens of kilobases or whole chromosomes, and downstream annotation looks plausible while being wrong. Record the genome build, release, and annotation version for every file, in the filename if necessary. **Uploading uncompressed or bloated files.** An uncompressed FASTQ can be three to four times the size of its gzip equivalent. Compress locally before any upload, and prefer CRAM over BAM for reference-aligned data when the receiving tool supports it. **Assuming the web tool's parameters match the local tool's.** Two tools with the same name can default to different thresholds, and web interfaces often hide defaults you would set explicitly on the command line. When results differ between the local and web branches, export or screenshot the full parameter set before assuming either result is wrong. **Trusting browser sessions to persist.** A long upload interrupted by a laptop sleep or a server-side timeout is the most common hybrid workflow failure. Use `tmux` or `screen` for long local transfers, upload during off-peak hours, and check whether the platform supports resumable uploads before committing to a multi-gigabyte transfer. **Treating web results as versioned.** If a platform does not publish the exact versions of its underlying tools, your result cannot be exactly reproduced later, by you or by reviewers. Note this explicitly in your methods. Platforms that expose a CLI or API on a stable backend make this easier, since the backend version tends to be documented [1]. **Ignoring privacy terms until after upload.** Decide where data may go before the first upload, not after. For human data, prefer platforms that can run inside your institution, whether that is a locally installed web front end over your own compute [3], a Dockerized application on your own hardware [16],[15], or a self-hosted collaboration server [14]. **Skipping the local copy of web outputs.** Web platforms can retire, move, or change formats. Download every result you might ever need, with checksums, the day you get it. Treat the platform as a compute provider, not an archive. **Over-fragmenting the workflow.** Every extra platform is another account, another format quirk, another outage risk. A hybrid workflow with three or four well-chosen environments beats one with nine. The historical lesson from workflow-system fragmentation is that every additional incompatible environment multiplies glue work [2]. **Forgetting that local has limits too.** Your laptop cannot absorb a whole-genome alignment project, and a desktop version of an analysis tool will not always match its server deployment feature for feature [8]. Estimate the arithmetic honestly in Step 4 rather than discovering it at hour six of a stalled upload. ## Choosing Among Current Platforms A brief map of the current generation, organized by when each earns its place in a hybrid workflow: - **General pipeline platforms.** Galaxy remains the standard for browser-driven multi-step analysis, and its capacity to sit behind locally installed web front ends makes it a natural hybrid backbone [3]. Tavaxy demonstrated the deeper principle, that workflows from different systems can be merged into one environment with cloud execution, years before most tools followed [2]. - **Domain-specific end-to-end platforms.** H3NGST for ChIP-seq [4], SimpleMicrobiome for microbiome data [5], MSAID for proteomics [1], and DOCKweb for docking-based virtual screening [6] each remove an entire installation-and-scripting burden for their domain. If your project lives squarely inside one of these domains, start there and hybridize outward. - **Dual-deployment tools.** Profiler [8], ARNy Plotter [9], and TADMaster [10] run as public web services and can also be run locally or offline. These give you the same analysis in both environments, which is the ideal shape for the hybrid pattern. - **Data and visualization resources.** Curated web databases for discovery and download [11], browser-based network editing and sharing [13], and 3D structure search servers [12] cover the interpretive layer. - **Infrastructure.** Self-hosted Git [14] and containerized deployment [16],[15] are the [connective tissue](/knowledge/veterinary-medicine/veterinary-anatomy-physiology/connective-tissue-types-loose-dense-and-more) that make a hybrid workflow auditable. When you compare bioinformatics tools, weight three questions above feature lists: Can I run this same analysis locally if the website disappears? Does the platform document its software versions? Does it expose an API so my local scripts can drive it? Tools that answer yes to all three fit naturally into a hybrid workflow. ## Frequently Asked Questions ### What is a hybrid bioinformatics workflow? It is an analysis pipeline that deliberately splits steps between browser-based platforms and local command-line or scripting tools, using each environment for what it does best. Typical splits send heavy compute and sensitive data to local machines, and curated algorithms, databases, and visualization to web servers. ### When should I run an analysis on a web server instead of locally? Choose the web server when the step needs a specialized algorithm you would never maintain yourself, needs more compute than you have, or produces results meant for interactive exploration. Choose local when data privacy, upload volume, or exact version control dominates. ### How do I keep results from web tools reproducible? Record the platform's stated tool versions, export or screenshot all parameters, download all outputs with checksums immediately, and note any platform that does not publish version information as a limitation in your methods. Platforms with an API or CLI are generally easier to reproduce than browser-only tools. ### Can I upload human sequencing data to public web tools? Only if the consent terms, IRB protocol, and institutional policy allow it. For controlled or identifiable human data, keep the analysis inside your institution using locally installed web front ends, Dockerized applications, or self-hosted platforms. ### What hardware do I need for the local part of a hybrid workflow? For QC, trimming, and small-scale alignment, a laptop with 8 to 16 GB of RAM is enough. Whole-genome alignment at depth needs a lab server or cloud instance, and the bandwidth arithmetic in the walkthrough tells you when uploading to a remote platform beats buying compute. ### How do I move files between web and local tools without corruption? Compress before uploading, record a SHA-256 checksum on both ends of every transfer, and verify row or read counts at each handoff. Use `tmux` or `screen` for long transfers so a dropped connection does not restart the upload. ### Which tools work both on the web and locally? Several current platforms ship in both modes, including Profiler for [multi-omics analysis](/knowledge/bioinformatics/multi-omics-integration-a-practical-workflow-for-combining-proteomics-metabolomics-and-epigenomi), ARNy Plotter for RNA ensemble analysis, and TADMaster for TAD comparison. Galaxy-backed systems can also run their processing on a shared server while presenting results through a locally installed web interface. ### Do I need to know the command line to run a hybrid workflow? You need it for the local half, which is why the walkthrough starts with basic Conda, `fastp`, `bwa`, and `samtools` commands you can copy directly. The web half requires no programming, and platforms with APIs let you gradually automate the browser steps as your skills grow. ## Sources 1. [A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis.](https://pubmed.ncbi.nlm.nih.gov/39982847/) 2. [Tavaxy: integrating Taverna and Galaxy workflows with cloud computing support.](https://pubmed.ncbi.nlm.nih.gov/22559942/) 3. [PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data.](https://pubmed.ncbi.nlm.nih.gov/34709836/) 4. [H(3)NGST: a fully automated, web-based platform for end-to-end ChIP-seq analysis.](https://pubmed.ncbi.nlm.nih.gov/41068566/) 5. [SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.](https://pubmed.ncbi.nlm.nih.gov/42754330/) 6. [DOCKweb: a web-based GUI platform for molecular modeling with DOCK6.](https://pubmed.ncbi.nlm.nih.gov/42406155/) 7. [Development and implementation of explainable AI-based machine learning models for predicting hospital stay and treatment costs in cardiovascular patients.](https://pubmed.ncbi.nlm.nih.gov/41457074/) 8. [Profiler: an open web platform for multi-omics analysis.](https://pubmed.ncbi.nlm.nih.gov/41324558/) 9. [ARNy plotter: A comprehensive web server for RNA ensemble structural analysis and visualization.](https://pubmed.ncbi.nlm.nih.gov/41915679/) 10. [TADMaster: a comprehensive web-based tool for the analysis of topologically associated domains.](https://pubmed.ncbi.nlm.nih.gov/36333787/) 11. [Developing a web-based platform for Eragrostis tef resource management and analysis.](https://pubmed.ncbi.nlm.nih.gov/42639803/) 12. [ARTEM server: an online tool for nucleic acid 3D motif searches, 3D structure superposition and structure-based alignment.](https://pubmed.ncbi.nlm.nih.gov/42083865/) 13. [Data-dependent visualization of biological networks in the web-browser with NDExEdit.](https://pubmed.ncbi.nlm.nih.gov/35675360/) 14. [Implementing a Version Control Platform as an Interactive Educational Framework for Bioinformatics and Artificial Intelligence in Medicine.](https://pubmed.ncbi.nlm.nih.gov/42719450/) 15. [SMIntegration: A web tool for comprehensive spatial metabolomics and transcriptomics integrated analysis and visualization.](https://pubmed.ncbi.nlm.nih.gov/41874432/) 16. [Large Language Model-Based Critical Care Big Data Deployment and Extraction: Descriptive Analysis.](https://pubmed.ncbi.nlm.nih.gov/40079079/) 17. [A Web Platform for Automated Image-Based Quantification of Human and Rodent Isolated Pancreatic Islets.](https://pubmed.ncbi.nlm.nih.gov/42612131/)