BxGenomics enables biologists to easily analyze RNA-Seq data, identify changed genes and enriched pathways, and further visualize the results and compare across projects using interactive data mining tools.

BxGenomics Logo

Top Reasons to Utilize BxGenomics and Related Service


Biologically Meaningful Answers

Immediately find changed genes and functional pathways in instant reports.

Full Potential of Data

Explore your results in an interactive online database to delve into details.

Expert Bioinformatics Support

Rest assured that your data are in good hands and help is always available.

RNA-Seq Analysis

Upload your raw RNA-Seq data, and use the automatic pipeline in BxGenomics to perform RNA-Seq data analysis and receive easy-to-understand reports.

Fast and Easy Service

RNA-Seq Analysis service is easy to use. Just provide raw data and sample description, and a professional report and personal online database will be delivered to you in 1-2 weeks.

An expert-designed analysis pipeline will work for you behind the scenes, including comprehensive QC of raw data and gene counts, robust statistical analysis for differentially expressed genes, advanced functional pathway analysis, and more. All results are reviewed by experts before final delivery.

Meaningful and Easy-to-Understand Report

The reports are designed to be easily understandable by biologists. It is based on experience working with hundreds of researchers on RNA-Seq projects. The easy-to-read reports place the most relevant biological information on the front page, with technical details in secondary links.

  • Secure web portal for easy access to all results
  • Differentially expressed genes
  • Enriched pathways and functions
  • Comprehensive QC and unbiased sample clustering to identify potential issues

RNA-Seq Analysis Report

Differentially Expressed Genes (DEGs)

Robust methods are used to identify DEGs, to identify what has changed between biological conditions. Results can be viewed as a summary table, individual gene lists, or a heatmap. Results can also be exported to Excel.

Enriched Pathways from Changed Genes

BxRNASeq provides functional enrichment results from two methods, hypergeometric test and gene set enrichment analysis (GESA).

The hypergeometric test compares DEGs with all genes in the genome to find enriched or over-represented functions.

GESA examines the fold change of all genes and focuses on changes of expression of groups of genes within the same biological pathway. These two methods often provide similar and complimentary reports.

Comprehensive Quality Assessment

It is important to avoid garbage in, garbage out when processing genomic data. BioInfoRx's RNA-Seq Analysis Service incorporates several QC steps to ensure reliable and accurate results.

Raw data QC ensures the sequencing run generates high quality data, gene level QC detects issues like PCR duplications or contaminated samples, finally sample grouping is critical to verify biological differences are above technical variations in the experiment.

Access Results Easily Online

Access your results on a secure website, including all the raw data, alignments, QC, DEG and functional enrichment results. Scientists can directly load the read alignments from our server to genome browsers. Full description of methods is included which can be used in manuscripts.

Data-Mining Platform

Access all your RNA-Seq data from anywhere, and make continuous discoveries with a variety of data mining tools. The results from BxGenomics RNA-Seq analysis pipeline are automatically loaded into the data mining platform. In addition, scientists can upload their previous RNA-Seq results, or results from published papers. All the results uploaded to the platform are integrated and ready to be used for visualization of gene expression, sample relationship, or regulated pathways.

Gain Biological Insights Through Data Visualization

A picture is worth a thousand words, and BxGenomics data mining platform helps you create meaningful graphs from your data easily.

For example:

  • The gene expression plot enables one to view how the expression level for a specific gene change in disease samples
  • The PCA plot helps one to decide how various samples are grouped based on genotypes and treatments
  • The pathway visualization tool allows scientist to focus on genes changes in the important pathways in their projects
  • The Venn diagram tools helps one to visualize similarities and differences between related result
All these graphs can be created within a few clicks using the built-in tools in BxGenomics.

Achieve Data Integration and Archive in One Step

Once the RNA-Seq data is upload to the BxGenomics data mining platform, authorized users can access the data with a browser anytime from anywhere with internet connection.

Different kinds of gene IDs are automatically recognized and converted so gene expression data are easily integrated between different projects analyzed with different types of gene IDs.

The BxGenomics platform serves dual purposes, as a data mining system of all RNA-Seq results in the laboratory to enable continuous discovery, and as a data archive system to enable data longevity and long-term access.

Data Mining Tool Examples

Gene Expression Graphs

View one or a few genes using gene expression tools, or use heatmap to look at the profiles of many genes.

Samples Relationship

The interactive PCA tools gives a quick and easy way to view sample grouping and decide on the most important differences to focus on.

The heatmap tool can also cluster samples to give further insights on sample grouping and how specific genes contribute to the grouping between focuses on changes of expression of groups of genes within the same biological pathway.

These two methods often provide similar and complimentary reports.

Comparison Plots

To view comparison data, or gene expression changes between samples, volcano plots gives a nice overview, and bubble plots can help users focus on specific gene groups.

Enriched Functions and Pathway

For any uploaded comparisons, BxGenomics system runs a comprehensive evaluation of enriched pathways and provides summary graphs as well as detailed reports. The pathway heatmap tool can display enriched pathways across several related comparisons.

Pathway Visualization

Map gene changes to your favorite pathways and delve into the details of gene regulation of specific biological functions. BxGenomics supports pathway overlap for KEGG pathways, Wikipathways, and Reactome Pathways.

Expert Support

Our team members have strong backgrounds in life science research, bioinformatics, programming and web technologies. We are experts in solving biological questions with computational approaches, and we have contributed to over 50 peer-reviewed publications. Our RNA-Seq experts are dedicated to ensure your data are processed correctly, support you on the various data mining tools, and have your questions answered in timeline manner.

Knowledgeable and Helpful Service Team

The BxGenomics RNA-Seq service team consists of highly educated scientists and customer support managers with years of experience in biological studies and genomics.

The team works collaboratively with customers to provide the utmost service for the success of every RNA-Seq project. Customers benefit from continuous high-quality technical support by a friendly and responsive team.

Accessible Demo Data and Detailed User Guides

New users can use demo data curated by our team to test the system, and get familiar with the RNA-Seq pipeline and various other features. The data mining system also comes with a lot of public data ready to be explored and visualized. Scientist can easily compare their own data with the pre-loaded public data. Our support team have also produced numerous help document and user guides to help you go through each step.

Publications using BxGenomics RNA-Seq System

Here is a list of selected peer-reviewed publications using BxGenomics and related data analysis and visualization technologies.

  1. Liao Y, Duan B, Zhang Y, Zhang X, Xia B. Excessive ER-phagy mediated by the autophagy receptor FAM134B results in ER stress, the unfolded protein response, and cell death in HeLa cells. J Biol Chem. 2019 Dec 27;294(52):20009-20023. doi: 10.1074/jbc.RA119.008709. Epub 2019 Nov 20. PMID: 31748416, PMCID: PMC6937584
  2. Kong G, You X, Wen Z, Chang YI, Qian S, Ranheim EA, Letson C, Zhang X, Zhou Y, Liu Y, Rajagopalan A, Zhang J, Stieglitz E, Loh M, Hofmann I, Yang D, Zhong X, Padron E, Zhou L, Pear WS, Zhang J. Downregulating Notch counteracts Kras(G12D)-induced ERK activation and oxidative phosphorylation in myeloproliferative neoplasm. Leukemia. 2019 Mar;33(3):671-685. doi: 10.1038/s41375-018-0248-0. Epub 2018 Sep 11. PMID: 30206308, PMCID: PMC6405304
  3. Zhang J, Kong G, Rajagopalan A, Lu L, Song J, Hussaini M, Zhang X, Ranheim EA, Liu Y, Wang J, Gao X, Chang YI, Johnson KD, Zhou Y, Yang D, Bhatnagar B, Lucas DM, Bresnick EH, Zhong X, Padron E, Zhang J. p53-/- synergizes with enhanced NrasG12D signaling to transform megakaryocyte-erythroid progenitors in acute myeloid leukemia. Blood. 2017 Jan 19;129(3):358-370. doi: 10.1182/blood-2016-06-719237. Epub 2016 Nov 4. PMID: 27815262, PMCID: PMC5248933
  4. Suter B, Zhang X, Pesce CG, Mendelsohn AR, Dinesh-Kumar SP, Mao JH. Next-Generation Sequencing for Binary Protein-Protein Interactions. Front Genet. 2015 Dec 17;6:346. doi: 10.3389/fgene.2015.00346. eCollection 2015. PubMed [citation] PMID: 26734059, PMCID: PMC4681833
  5. Combined MEK and JAK inhibition abrogates murine myeloproliferative neoplasm. Kong G, Wunderlich M, Yang D, Ranheim EA, Young KH, Wang J, Chang YI, Du J, Liu Y, Tey SR, Zhang X, Juckett M, Mattison R, Damnernsawad A, Zhang J, Mulloy JC, Zhang J. The Journal of Clinical Investigation. 2014 May 8; 124(6): 2762-2773 PMC [article] PMCID: PMC4038579, PMID: 24812670, DOI: 10.1172/JCI74182
  6. Still AJ, Floyd BJ, Hebert AS, Bingman CA, Carson JJ, Gunderson DR, Dolan BK, Grimsrud PA, Dittenhafer-Reed KE, Stapleton DS, Keller MP, Westphall MS, Denu JM, Attie AD, Coon JJ, Pagliarini DJ. Quantification of mitochondrial acetylation dynamics highlights prominent sites of metabolic regulation. J Biol Chem. 2013 Sep 6;288(36):26209-19. doi: 10.1074/jbc.M113.483396. Epub 2013 Jul 17. PubMed [citation] PMID: 23864654, PMCID: PMC3764825
  7. Wang J, Kong G, Liu Y, Du J, Chang YI, Tey SR, Zhang X, Ranheim EA, Saba-El-Leil MK, Meloche S, Damnernsawad A, Zhang J, Zhang J. Nras(G12D/+) promotes leukemogenesis by aberrantly regulating hematopoietic stem cell functions. Blood. 2013 Jun 27;121(26):5203-7. doi: 10.1182/blood-2012-12-475863. Epub 2013 May 17. PMID: 23687087, PMCID: PMC3695364
  1. Grimsrud PA, Carson JJ, Hebert AS, Hubler SL, Niemi NM, Bailey DJ, Jochem A, Stapleton DS, Keller MP, Westphall MS, Yandell BS, Attie AD, Coon JJ, Pagliarini DJ. A quantitative map of the liver mitochondrial phosphoproteome reveals posttranslational control of ketogenesis. Cell Metab. 2012 Nov 7;16(5):672-83. doi: 10.1016/j.cmet.2012.10.004. PMID: 23140645, PMCID: PMC3506251
  2. Mougeot JL, Li Z, Price AE, Wright FA, Brooks BR. Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway. BMC Med Genomics. 2011 Oct 25;4:74. doi: 10.1186/1755-8794-4-74. PMID: 22027401, PMCID: PMC3219589
  3. Galliher-Beckley AJ, Williams JG, Cidlowski JA. Ligand-independent phosphorylation of the glucocorticoid receptor integrates cellular stress pathways with nuclear receptor signaling. Mol Cell Biol. 2011 Dec;31(23):4663-75. doi: 10.1128/MCB.05866-11. Epub 2011 Sep 19. PMID: 21930780, PMCID: PMC3232926
  4. Al-Dhaheri M, Wu J, Skliris GP, Li J, Higashimato K, Wang Y, White KP, Lambert P, Zhu Y, Murphy L, Xu W. CARM1 is an important determinant of ERα-dependent breast cancer cell differentiation and proliferation in breast cancer cells. Cancer Res. 2011 Mar 15;71(6):2118-28. doi: 10.1158/0008-5472.CAN-10-2426. Epub 2011 Jan 31. PMID: 21282336, PMCID: PMC3076802
  5. Grimsrud PA, den Os D, Wenger CD, Swaney DL, Schwartz D, Sussman MR, Ané JM, Coon JJ. Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol. 2010 Jan;152(1):19-28. doi: 10.1104/pp.109.149625. Epub 2009 Nov 18. PMID: 19923235, PMCID: PMC2799343
  6. Zhu Y, Davis S, Stephens R, Meltzer PS, Chen Y. GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus. Bioinformatics. 2008 Dec 1;24(23):2798-800. doi: 10.1093/bioinformatics/btn520. Epub 2008 Oct 7. PubMed [citation] PMID: 18842599, PMCID: PMC2639278
  7. Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS, Argmann C, Schueler KL, Edwards S, Steinberg HA, Chaibub Neto E, Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C, Attie AD. A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res. 2008 May;18(5):706-16. doi: 10.1101/gr.074914.107. Epub 2008 Mar 17. PMID: 18347327, PMCID: PMC2336811
  8. Zhu Y, Zhu Y, Xu W. EzArray: a web-based highly automated Affymetrix expression array data management and analysis system. BMC Bioinformatics. 2008 Jan 24;9:46. doi: 10.1186/1471-2105-9-46. PMID: 18218103, PMCID: PMC2265266