# GenomeBeans > Extended context file summarizing the main public GenomeBeans pages linked from llms.txt. This file is intended for broad site understanding. It summarizes public-facing marketing pages, pipeline overviews, sample-result pages, and selected educational articles. It does not include account-only content behind login. ## Homepage Source: GenomeBeans presents itself as an NGS analysis platform and bioinformatics service provider where scientists can upload raw sequencing data and access interpreted results. The page emphasizes a simple five-step workflow: create an experiment, upload samples, select parameters, check out, and wait for results. The homepage also highlights platform selling points such as no bioinformatics expertise required, automated processing, rapid interpretation, privacy/security messaging, data ownership, and 90-day archival. It surfaces the major pipeline families and recent blog content. ## About Us Source: [About Us](https://www.genomebeans.com/about-us) The About page describes GenomeBeans as a biological sequencing-data solution provider based in India and the USA. It positions the company as a one-stop shop for analyzing, visualizing, and interpreting multi-omic data without requiring deep computational expertise. It adds mission/vision language around accelerating scientific discovery, enabling affordable research and precision medicine, and supporting healthcare, agriculture, and related genomics use cases. ## Bioinformatics Services Source: [Bioinformatics Services](https://www.genomebeans.com/bioinformatics-services) This page expands the service model beyond the self-serve platform. It says GenomeBeans supports custom pipeline development, automation, data integration, structural biology analysis, publication-ready figures, and interpretation support. The page also frames GenomeBeans as a consulting partner for institutions and research groups, with training services, post-service assistance, quality assurance, and secure data management. ## Bulk Transcriptomics Source: [Bulk Transcriptomics](https://www.genomebeans.com/bulk-transcriptomics) The bulk transcriptomics page describes an RNA-seq workflow that starts from raw FASTQ files and proceeds through quality control, trimming, indexing, alignment, sorting, gene counting, and report generation. It also emphasizes post-processing steps such as differential expression analysis, enrichment analysis, and protein-network visualization. The page positions this pipeline as a broad RNA-seq analysis environment for coding/noncoding RNA, alternative transcripts, fusions, allele-specific expression, and discovery-oriented transcriptomics work. ## Variant Calling Source: [Variant Calling](https://www.genomebeans.com/variant-calling) The variant-calling page focuses on germline and somatic analysis. It describes preprocessing, alignment, sorting, variant calling, filtering, annotation, and visualization. Specific tools mentioned publicly include GATK HaplotypeCaller for SNP/indel calling, Funcotator (with GRCh38/hg19 packages) plus optional tools like SnpEff/gnomAD for annotation, and IGV for visualization. ## Single Cell Transcriptomics Source: [Single Cell Transcriptomics](https://www.genomebeans.com/single-cell-transcriptomics) This page introduces scRNA-seq as a way to study cellular heterogeneity, dynamic states, and disease mechanisms. It explains the basic flow from cell isolation and RNA-to-cDNA conversion through sequencing and computational analysis. Publicly described capabilities include QC/filtering, dimensionality reduction (PCA and UMAP), clustering, cluster annotation, differential expression, functional enrichment, and visualization for interpretation. ## Metagenomics Source: The metagenomics page presents a workflow for profiling microbial communities. It moves from de-noising and quality control into taxonomic classification, abundance profiling, binning, MAG recovery, and functional annotation. The page emphasizes outputs that help users infer organism composition, marker genes, functional proteins, pathways, and possible organism-environment interactions. ## Mtb Gene Mutation Annotation Chatbot Source: [Mtb Gene Mutation Annotation Chatbot](https://www.genomebeans.com/gene-ai) Gene AI is a focused MTB mutation-annotation chatbot. GenomeBeans describes it as an LLM-based system that combines public resources such as TBProfiler and WHO variant datasets with retrieval-augmented generation and fine-tuning. The public page frames it as a faster way to ask position-based or mutation-based questions, including mutation lookup, affected drug, confidence level, and gene lookup. ## Blog Index Source: [Blog Index](https://blogs.genomebeans.com/index.html) The blog index is the main rolling feed of educational content. Recent public topics include tuberculosis, Down syndrome, Bioinformatics Day, and multi-omics integration. For LLM use, this page is best treated as a topical discovery surface rather than a canonical documentation hub. ## Multi-Omics Integration: How Combining Data Unlocks Deeper Biological Insights Source: [Multi-Omics Integration](https://www.genomebeans.com/blog/multi-omics-integration-how-combining-data-unlocks-deeper-biological-insights) This article is a high-level explainer on combining genomics, transcriptomics, proteomics, and metabolomics to build a more complete picture of biological systems. It argues that integrated omics helps reveal disease mechanisms, biomarkers, and pathway relationships, while also noting practical challenges such as scale, data compatibility, and analysis complexity. ## Single-Cell vs Bulk Transcriptomics: Is Bioinformatics just about Coding Source: [Single-Cell vs Bulk Transcriptomics](https://www.genomebeans.com/blog/single-cell-vs-bulk-transcriptomics-is-bioinformatics-just-about-coding) This article compares single-cell and bulk transcriptomics. Single-cell methods are framed as better for uncovering heterogeneity and rare populations, while bulk RNA-seq is framed as cheaper and easier to interpret but more averaging in nature. A second theme is that bioinformatics is broader than coding alone, requiring experiment design, QC, statistics, and biological interpretation. ## Metagenomics in Practice: Simplifying Microbiome Data Analysis with No-Code Tools Source: [Metagenomics in Practice](https://www.genomebeans.com/blog/metagenomics-in-practice-simplifying-microbiome-data-analysis-with-no-code-tools) This article explains the appeal of metagenomics for studying whole microbial communities without culturing. It emphasizes that sequencing may be easy to generate, but analysis remains difficult because of QC, assembly/classification, and infrastructure requirements. The GenomeBeans framing is that no-code workflows make microbiome analysis more accessible, reproducible, and collaborative across medicine, agriculture, and environmental science. ## Bulk Transcriptomics Pre-processing Report Source: [Bulk Transcriptomics Pre-processing Report](https://www.genomebeans.com/pre-processing-result/bulk-transcriptomics) This sample page is the clearest public example of what a GenomeBeans deliverable looks like for preprocessing. It uses a MultiQC-style interface and exposes modules for general stats, featureCounts, STAR, Cutadapt, and FastQC. For LLMs, this page is useful for understanding the shape of GenomeBeans QC outputs, the terminology used in reports, and the kinds of metrics users can expect to review. ## Bulk Transcriptomics Post-processing Report Source: [Bulk Transcriptomics Post-processing Report](https://www.genomebeans.com/post-processing-result/bulk-transcriptomics) This sample page shows the downstream analysis layer for bulk RNA-seq. Public sections include metadata, mean-variance trend, differential expression analysis, gene-level QC, PCA, volcano plot, hierarchical clustering, protein network graph, and gene ontology analysis. For LLMs, this page is the best public reference for how GenomeBeans structures biological interpretation after preprocessing. ## Variant Calling Sample Result Source: [Variant Calling Sample Result](https://www.genomebeans.com/sample-result/variant-calling) This public sample page is lightweight but useful. It describes VCF as the standard output format and shows downloadable variant tables plus IGV-oriented visualization context. It helps clarify that GenomeBeans' public-facing variant outputs are meant to bridge raw variant calls and practical interpretation/inspection.