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Product Support Topics2020-08-26T12:03:20-04:00

Product Support Topics

pathway analysis software for RNASeq analysis Advaitabio

Click on a Category to see the associated topics.

Upstream Regulators Analysis2019-04-03T09:52:24-04:00
iPathwayGuide Release Notes – Winter 20202020-02-11T11:27:55-05:00

On January 31, 2020, Advaita released a major update to its platform, with two brand new capabilities and three major improvements, now available in iPathwayGuide. As with all iPathwayGuide releases, many of the improvements are available in older analyses. Other features, including the two new capabilities, are only available in analyses generated after the update was released. To access these new capabilities for older analyses, please update the analysis using the button found in report information, “Re-run as new analysis.”

NEW CAPABILITIES: Available in newly created or updated analyses

New Module: Upstream Chemicals, Drugs, and Toxicants
Predicted Upstream Chemical Analysis allows you to predict chemicals, drugs, and toxicants that might be present (overly-abundant) or absent (insufficient) in your experiment. This analysis compares chemical-to-gene regulatory interactions with patterns of downstream gene expression to find chemicals with large numbers of consistent downstream DE genes. To support this analysis, the Advaita knowledgebase was expanded (to v1910) to include 170,997 chemicals and 774,553 chemical-to-gene regulatory interactions. This new analysis capability is available from the  Printable Report, Meta-Analysis, and Network Analysis, and navigation bar, where it can be found under the heading Upstream Regulators, along with Upstream Genes and miRNAs.

New Visualization: Dendrograms
iPathwayGuide now offers dendrograms, as an additional way to visualize relationships across results. The dendrogram visualization groups together significant results (annotations) that have DE genes in common. This visualization is available for all GO terms, pathways, upstream regulatory genes, and chemicals. It is also available in Step 1 of the updated Network Analysis module, where it can be used to select the set of genes of interest based on annotations that have DE genes in common.

MAJOR IMPROVEMENTS: Available in existing analyses

New Analysis Workflow: Select genes of interest in Network Analysis
The Network Analysis page has been redesigned to allow users select genes of interest from the results of all analyses modules (biological processes, pathways, diseases, miRNAs, upstream regulators and more). Other improvements to the Network Analysis module include:

  • the ability to add Chemicals to networks to visualize interactions with downstream genes
  • the ability to save & reload networks
  • the ability to export network images as .png and .svg
  • and two new network layouts: Gatekeepers and Regulators
  • new onboarding tutorial and videos

Note: these capabilities are available in analyses generated or updated after October 2018.

New Analysis Workflow: The Intake Page now displays file parsing warnings. This allows you to preview which lines from your input file were ignored, and for what reason.

New Feature: Results from any module may be searched by associated genes, e.g. find significant pathways containing BRCA1

Upstream Regulators Details2019-04-03T09:50:46-04:00
Release Notes – Summer 20192020-04-30T14:22:50-04:00

On June 21, 2019, Advaita released a major update to its platform, with improvements to iPathwayGuide, iVariantGuide, and iBioGuide.

IMPROVEMENTS

The Advaita Knowledgebase was updated to version 1906 and now includes:

  • 3 organisms: homo sapiens, mus musculus, rattus norvegicus
  • 216,544 Genes
  • 2,307 Diseases
  • 45,049 GO terms
  • 5,694 Drugs
  • 985 Pathways
  • 5,396 miRNAs
  • 92,745 Proteins
  • 29,761,157 References
  • 3,042,479 Interactions
  • 477,289 Experiments

For a complete list of databases and versions, please see report information within each application.

 

NEW FEATURES

  • iBioGuide: Users can now log in to iBioGuide using their Advaita credentials, the same account they use for iPathwayGuide and iVariantGuide.
  • iPathwayGuide: Experiment and references for Network Analysis are now shown in a paged view, improving load time.

BUG FIXES

  • iPathwayGuide: Meta-Analysis icon was updated to stay consistent with menu selections.
iPathwayGuide Release Notes – Spring 20192019-04-02T19:42:23-04:00

On March 16, 2019, Advaita released a major update to its platform, with several major improvements to iPathwayGuide. As with all major releases, new features are available in all analyses generated after the update was released. To see new analysis modules with older analyses, please update the analysis using the button found in report information, “Re-run as new analysis.”

NEW FEATURES
– NEW MODULE: Predicted Upstream Regulator Analysis allows you to find genes that have regulatory interactions consistent with expression patterns in DE genes. Several parts of the application were updated to include the new analysis module, including: Printable Report, Meta-Analysis, API, toolbar, and more.

MAJOR IMPROVEMENTS
– DE down-regulated genes are now shown as blue in the volcano plot, corresponding with the coloring used in gene bar plots, on pathways, and in networks.
– Genes data export now includes option to export all genes as TSV file— convenient for uploading to iPathwayGuide as a new analysis.
– Pathway data export now includes p-values for pORA and pAcc in addition to the combined p-value, pComb.
– Updated text of the printer-friendly report that is auto-generated with every analysis.
– Improvements to registration flow and application selection page
– Updated layout for the table of annotation sources inside the report info section. The new layout accommodates sources for Network Analysis and Predicted Upstream Regulator Analysis.

BUG FIXES
– Registration page layout is fixed for the newest version of Chrome browsers
– Tooltips are now more responsive and more visible in iPathwayGuide
– Fixed a bug in network analysis causing long GO terms to spill out of their boxes
– Fixed a bug affecting the navigation bar display on smaller screens

How do I find a “Deprecated” Analysis2020-08-18T10:51:09-04:00
Question: I went back to analyses that I had previously paid for, opened them and a warning came up for indicated that the analyses were “deprecated” so I hit the re-analyze button only to find that the files are now locked and require payment to open. What is going on and how do I access the analysis I already paid for?
We periodically update our knowledge base with the latest annotations and references. This  is necessary in order to offer our users an analysis that uses the most recent knowledge available. This in fact, is one of the main value points that we provide to our users. Just to give you an idea, the sheer data available now is approximatively 4 times larger than what we had 18 months ago. Furthermore, brand new analysis capabilities are added all the time. For instance, during the past year alone we added an extremely powerful network analysis which is able to discover new mechanisms, as well as a very sophisticated upstream regulator analysis. If you have not seen these yet, it would be worth your time to schedule a 30 min phone call for us to show these to you.
When new knowledge becomes available, our platform lets you know by marking your existing analyses as “deprecated”. Thus, you are aware that they are not based on the latest knowledge available in the field. It is better to find this out from us, rather than from the reviewers of your grant or paper pointing out that your analysis does not mention such and such recent discovery. You original analysis is still fully available to you. You can simply close the warning messages at the top by clicking the X symbol at the top left, immediately to the left of the analysis title. Every single piece of information will still be there, as you saw it when the analysis was first performed.
When new knowledge becomes available, you may or may not choose to analyze the data again, using the latest knowledge available. If you choose do resubmit the data for a new analysis and you have a valid subscription, this additional analysis will incur no additional cost. However, if your subscription has expired, you will need to either renew it or pay for the individual analyses that you wish to have done. Please keep in mind that updating an existing analysis involves a resubmission of the original data and a complete re-analysis of that data from scratch. For each analysis, we ourselves are incurring all the costs associated to the computation and the storage on the AWS platform.
Here is a blog comparing the single analysis vs. subscription usage: https://advaitabio.com/ipathwayguide/subscription-vs-purchasing-individual-analyses-which-one-do-i-want/
iPathwayGuide – Meta Analysis2018-08-26T10:38:05-04:00

iPathwayGuide’s powerful meta-analysis tool allows you to compare and contrast upto 5 differential experiments at the same time. With meta-analysis, you can rapidly identify several characteristics of your phenotype comparisons and drill down to pinpoint plausible biomarkers and signatures. Watch the video below to learn the nuts and bolts of iPathwayGuide’s meta-analysis. Be sure to watch some of our webinars on the topic as well.

iPathwayGuide – Purchasing iPathwayGuide Reports2018-08-26T10:39:16-04:00
iPathwayGuide – Uploading a Custom File2018-08-26T10:43:10-04:00
iPathwayGuide – GEO2R2018-08-26T10:44:09-04:00
iPathwayGuide – CuffDiff and DESeq2018-08-26T10:45:07-04:00
Pathway Analysis of Time Course Expression Data2018-08-26T10:24:00-04:00
Comparison of Protein and mRNA expression profiles2018-08-26T10:25:05-04:00
Integrative Analysis of Breast Cancer Subtypes Using iPathwayGuide2018-08-26T10:26:09-04:00
Uploading Data Webinar2018-08-26T10:27:08-04:00
Leveraging Public Data: Comparing Therapeutic Response in Melanoma2018-08-26T10:28:10-04:00
Webinar: iPathwayGuide Overview2018-08-26T10:29:09-04:00
Publications by Advaita Bioinformatics Team2019-10-11T13:47:14-04:00

Paper

# of Citations

Ontological analysis of gene expression data: current tools, limitations, and open problems.
Bioinformatics 21 (18), 3587-3595

939

A systems biology approach for pathway level analysis.
Genome Research, 2007, Vol. 17 (10), pages 1537-1545.

860

A novel signaling pathway impact analysis (SPIA).
Bioinformatics (2009), Vol. 25 (1), pages 75-82.

691

Global functional profiling of gene expression.
Genomics 81 (2), 98-104

662

Reliability and reproducibility issues in DNA microarray measurements.
TRENDS in Genetics 22 (2), 101-109

663

Data analysis tools for DNA microarrays.
(Book) CRC Press

527

Profiling gene expression using onto-express.
Genomics 79 (2), 266-270

507

Use and misuse of the gene ontology annotations.
Nature Reviews Genetics 9 (7), 509-515

447

Onto-tools, the toolkit of the modern biologist: onto-express, onto-compare, onto-design and onto-translate.
Nucleic acids research 31 (13), 3775-3781

408

Onto-Tools: New Additions and Improvements in 2006.
Nucleic Acids Research, Vol. 35, pages W206-W211, July 2007.

111

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(Book) CRC Press

76
69

A system biology approach for the steady-state analysis of gene signaling networks.
​In Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications (CIARP’07).

25
Select Publications Citing iPathwayGuide2021-07-22T16:15:51-04:00

Comba, A., Motsch, S., Dunn, P.J., Hollon, T.C., Argento, A.E, Zamler, D.B., Kish, P.E., Kahana, A., Kleer, C.G., Castro, M.G., & Lowenstein, P.R. (2021). Spatiotemporal analysis of glioma heterogeneity reveals Col1A1 as an actionable 1 target to disrupt tumor mesenchymal differentiation, invasion and malignancy. bioRxiv

Duan, S., Nordmeier, S., Byrnes, A.E., & Buxton, I.L.O. (2021). Extracellular vesicle-mediated purinergic signaling contributes to host microenvironment plasticity and metastasis in triple negative breast cancer. International Journal of Molecular Sciences, 22(2), 597.

Helmer, R.A., Martinez-Zaguilan, R., Kaur, G., Smith, L.A., Dufour, J.M., & Chilton, B.S. (2021). Helicase-like transcription factor-deletion from the tumor microenvironment in a cell line-derived xenograft model of colorectal cancer reprogrammed the the human transcriptome-S-nitroso-proteome to promote inflammation and redirect metastasis. PLOS ONE 16(5): e0251132.

Jiagge, E.M., Ulintz, P.J., Wong, S., McDermott, S.P., Fossi, S.I., Suhan, T.K., Hoenerhoff, M.J., Bensenhaver, B.M., Salem, B., Dziubinski, M., Oppong, J.K., Aitpillah, F., Ishmael, K., Osei-Bonsu, E., Adjei, E., Baffour, A., Aldrich, J., Kurdoglu, A., Fernando, K., Draig, D.W., Trent, J.M., Li, J., Chitale, D., Newman, L.A., Carpten, J.D., Wicha, M.S., & Merajver, S.D. (2021). Multiethnic PDX models predict a possible immune signature associated with TNBC of African ancestry. Breast Cancer Research and Treatment, 186, 391-401.

Kishimoto, K., Kanazawa., K, Nomura, M., Tanaka, T., Shigemoto-Kuroda, T., Fukui, K., Kurosawa, K., Kawai, M., Kato, H., Terasaki, K., Sakamoto, Y., Yamashita, Y., Sato, I., Tanuma, N., Tamai, K., Kitabayashi, I., Matsuura, K., Watanabe, T., Yasuda, J., Tsuji, H., & Shima, H. (2021). Ppp6c deficiency accelerates K‐rasG12D‐induced tongue carcinogenesis. Cancer Medicine, 10(13), 4451-4464.

Krieg, C., Carloni, S., Weber, L.M., Fosso, B., Hardiman, G., Mileti, E., El Aidy, S., Marzano, M., Pesole, G., Asnicar, F., Segata, N., Robinson, M.D., & Guglietta, S. (2021). Loss of C3aR induces immune infiltration and inflammatory microbiota in a novel spontaneous model of colon cancer. bioRxiv.

Lidberg, K.A., Muthusamy, S., Adil, M., Patel, R.S., Wang, L., Bammler, T.K., Reichel, J., Yeung, C.K., Himmelfarb, J., Kelly, E.J., & Shreeram, A., (2021). Multi-omic Characterization of Human Tubular Epithelial Cell Response to Serum. bioRxiv.

Liu, J., Qiu, J., Zhang, Z., Zhou, L., Li, Y., Ding, D., Zhang, Y., Zou, D., Wang, D., Zhou, Q., & Lang, T. (2021). SOX4 maintains the stemness of cancer cells via transcriptionally enhancing HDAC1 revealed by comparative proteomics study. Cell Biosci, 11, 23.

Merritt, N., Garcia, K,. Rajendran, D., Lin, Z.Y., Zhang, X., Mitchell, K.A., Borcherding, N., Fullenkamp, C., & Chimenti, M.S. (2021). TAZ-CAMTA1 and YAP-TFE3 alter the TAZ/YAP transcriptome by recruiting the ATAC histone acetyltransferase complex. eLife, 10:e62857.

Tsuji, Y., Nonoguchi, N., Okuzaki, D., Wada, Y., Motooka, D., Hirota, Y., Toho, T., Yoshikawa, N., Furuse, M., Kawabata, S., Miyatai, S.I., Nakamura, H., Yamamoto, R., Nakamura, S., Kuroiwa, T., & Wanibuchi, M. (2021). The Up-Regulation of CXCL12-CXCR4 Axis By Radiotherapy Could Accelerate Glioma Progression., Research Square.

Weber, H., Ruoff, R., & Garabedian, M.J. (2021). MED19 alters AR occupancy and gene expression in prostate cancer cells, driving MAOA expression and growth under low androgen. PLOS Genetics, 17(1): e1008540.

Wesley, Y.Y., Hill, S.T., Chan, E.R., Pink, J.J., Cooper, K., Leachman, S., Lund, A.W., Kulkarni, R., & Bordeaux, J.S. (2021). Computational Drug Repositioning Identifies Statins as Modifiers of Prognostic Genetic Expression Signatures and Metastatic Behavior in Melanoma. Journal of Investigative Dermatology, 141(7), 1802-1809.

Wills, C.A., Liu, X., Chen, L., Zhao, Y., Dower, C.M., Sundstrom, J., & Wong, H.G. (2021). Chemotherapy-induced upregulation of small extracellular vesicle-associated PTX3 accelerates breast cancer metastasis. Cancer Research, 81(2), 452-463.

Argento, A.E., Kadiyala, P., Ventosa, M., Patel, P., Zamler, D.B., Nunez, F.J., Zhao, L., Castro, M.G., & Lowenstein, P.R. (2020). Fyn tyrosine kinase, a downstream target of receptor tyrosine kinases, modulates antiglioma immune responses. Neuro-Oncology, 22(6), 806-818.

Balogh, A., Reiniger, L., Hetey, S., Kiraly, P., Toth, E., Karaszi, K., Juhasz, K., Gelencser, Z., Zvara, A., Szilagyi, A., Puskas, L.G., Matko, J., Papp, Z., Kovalszky, I., Juhasz, C., & Than, N.G. (2020). Decreased Expression of ZNF554 in Gliomas is Associated with the Activation of Tumor Pathways and Shorter Patient Survival. International Journal of Molecular Sciences, 21(16):5762.

Caro, D., Rivera, D., Ocampo, Y., Müller, K., & Franco, L.A., (2020). A promising naphthoquinone [8-hydroxy-2-(2-thienylcarbonyl) naphtho [2, 3-b] thiophene-4, 9-dione] exerts anti-colorectal cancer activity through ferroptosis and inhibition of MAPK signaling pathway based on RNA sequencing. Open Chemistry, 18(1):1242-1255.

Fernández, G., Jose, M. (2020). In vitro antitumor activity of arachidonic and docosahexaenoic acids as both monoacylglycerols and free fatty acids on colorectal cancer cells., Tesis Universidad de Almeria [90].

Herkenne, S., Ek, O., Zamberlan, M., Pellattiero, A., Chergova, M., Chivite, I., Novotna, E., Rigoni, G., Fonseca, T.B., Samardzic, D., Agnellini, A., Bean, C., Beneditti, G.D., Tiso, N., Argenton, F., Viola, A., Soriano, M.E., Giacomello, M., Ziviani, E., Sales, G., Claret, M., Graupera, M., & Scorrano, L. (2020). Developmental and tumor angiogenesis requires the mitochondria-shaping protein Opa1. Cell Metabolism, 31(5), 987-1003.e8.

Mendez, F., Kadiyala, P., Nunez, F.J., Carney, S., Nunez, F.M., Gauss, J.C., Ravindran, R., Pawar, S., Edwards, M, Garcia-Fabiani, M.B., Haase, S., Lowenstein, P.R., & Castro, M.G. (2020). Therapeutic efficacy of immune stimulatory thymidine kinase and fms-like tyrosine kinase 3 ligand (TK/Flt3L) gene therapy in a mouse model of high-grade brainstem glioma. Clinical Cancer Research, 26(15), 4080-4092.

Nagesh, P.K.B., Chowdhury, P., Hatami, E., Jain, S., Dan, N., Kashyap, V.K., Chauhan, S.C., Jaggi, M., & Yallapu, M.M. (2020). Tannic acid inhibits lipid metabolism and induce ROS in prostate cancer cells. Sci Rep, 10, 980.

Ocampo, Y., Caro, D., Rivera, D., Piermattey, J., Gaitan., R., & Franco, L.A. (2020). Transcriptome Changes in Colorectal Cancer Cells upon Treatment with Avicequinone B, Advanced Pharmaceutical Bulletin, 10(4), 638–647.

Sarmiento-Castro, A., Caamaño-Gutiérrez, E., Sims, A.H., Hull, N.J., James, M.I., Santiago-Gomez, A., Eyre, R., Clark, C., Brown, M.E., Brooks, M.D., Wicha, M.S., Howell, S.J., Clarke, R.B., Simoes, B.M. (2020). Increased expression of interleukin-1 receptor characterizes anti-estrogen-resistant ALDH+ breast cancer stem cells. Stem Cell Reports, 15(2), 307-316.

Ulm, M.A., Redfern, T.M., Wilson, B.R., Ponnusamy, S., Asemota, S., Blackburn, P.W., Wang, Y., ElNaggar, A.C., & Nararyanan, R. (2020). Integrin-Linked Kinase Is a Novel Therapeutic Target in Ovarian Cancer. Journal of Personalized Medicine, 10(4), 246.

Verma, S., Shankar, E., Chan, E.R., & Gupta, S. (2020). Metabolic Reprogramming and Predominance of Solute Carrier Genes during Acquired Enzalutamide Resistance in Prostate Cancer. Cells, 9(12), 2535.

Yong, K.M.A., Ulintz, P.J., Caceres, S., Cheng, X., Bao, L., Wu, Z., Jiagge, E.M., & Merajver, S.D., (2020). Heterogeneity at the invasion front of triple negative breast cancer cells. Sci Rep, 10, 5781.

Liu, Y., Lang, T., Jin, B., Chen, F., Zhang, Y., Beuerman, R.W., Zhou, L. and Zhang, Z., 2017. Luteolin inhibits colorectal cancer cell epithelial-to-mesenchymal transition by suppressing CREB1 expression revealed by comparative proteomics study. Journal of proteomics 161, pp.1-10.

Han, K., Lang, T., Zhang, Z., Zhang, Y., Sun, Y., Shen, Z., Beuerman, R.W., Zhou, L. and Min, D., 2018. Luteolin attenuates Wnt signaling via upregulation of FZD6 to suppress prostate cancer stemness revealed by comparative proteomics. Scientific reports, 8(1), p.8537.

Ortea, I., González-Fernández, M. J., Ramos-Bueno, R. P., & Guil-Guerrero, J. L. (2018). Proteomics study reveals that docosahexaenoic and arachidonic acids exert different in vitro anticancer activities in colorectal cancer cells. Journal of agricultural and food chemistry, 66(24), 6003-6012.

Wagner, S., Ball, G. R., Pockley, A. G., & Miles, A. K. (2018). Application of omic technologies in cancer research. Translational Medicine Reports, 2(1).

Mrowczynski, O.D., Madhankumar, A.B., Sundstrom, J.M., Zhao, Y., Kawasawa, Y.I., Slagle-Webb, B., Mau, C., Payne, R.A., Rizk, E.B., Zacharia, B.E. and Connor, J.R., 2018. Exosomes impact survival to radiation exposure in cell line models of nervous system cancer. Oncotarget, 9(90), p.36083.

Zeinali, M., Murlidhar, V., Fouladdel, S., Shao, S., Zhao, L., Cameron, H., Bankhead III, A., Shi, J., Cuneo, K.C., Sahai, V. and Azizi, E., 2018. Profiling Heterogeneous Circulating Tumor Cells (CTC) Populations in Pancreatic Cancer Using a Serial Microfluidic CTC Carpet Chip. Advanced Biosystems, 2(12), p.1800228.

Rücker, F. G., Dolnik, A., Blätte, T. J., Teleanu, V., Ernst, A., Thol, F., … & Bullinger, L. (2018). Chromothripsis is linked to TP53 alteration, cell cycle impairment, and dismal outcome in acute myeloid leukemia with complex karyotype. haematologica, 103(1), e17-e20.

Araújo, T., Khayat, A., Quintana, L., Calcagno, D., Mourão, R., Modesto, A., Paiva, J., Lima, A., Moreira, F., Oliveira, E. and Souza, M., 2018. Piwi like RNA-mediated gene silencing 1 gene as a possible major player in gastric cancer. World journal of gastroenterology, 24(47), 5338.

Ock, S., Ahn, J., Lee, S.H., Kim, H.M., Kang, H., Kim, Y.K., Kook, H., Park, W.J., Kim, S., Kimura, S. and Jung, C.K., 2018. Thyrocyte‐specific deletion of insulin and IGF‐1 receptors induces papillary thyroid carcinoma‐like lesions through EGFR pathway activation. International Journal of Cancer, 143(10), pp.2458-2469.

Renz, B.W., Tanaka, T., Sunagawa, M., Takahashi, R., Jiang, Z., Macchini, M., Dantes, Z., Valenti, G., White, R.A., Middelhoff, M.A. and Ilmer, M., 2018. Cholinergic Signaling via Muscarinic Receptors Directly and Indirectly Suppresses Pancreatic Tumorigenesis and Cancer Stemness. Cancer discovery, 8(11), pp.1458-1473.

Luo, M., Shang, L., Brooks, M.D., Jiagge, E., Zhu, Y., Buschhaus, J.M., Conley, S., Fath, M.A., Davis, A., Gheordunescu, E. and Wang, Y., 2018. Targeting breast cancer stem cell state equilibrium through modulation of redox signaling. Cell metabolism, 28(1), pp.69-86.

Todorova, K., Metodiev, M.V., Metodieva, G., Mincheff, M., Fernández, N. and Hayrabedyan, S., 2016. Micro-RNA-204 Participates in TMPRSS2/ERG Regulation and Androgen Receptor Reprogramming in Prostate Cancer. Hormones and Cancer, pp.1-21.

Simonik, E.A., Cai, Y., Kimmelshue, K.N., Brantley-Sieders, D.M., Loomans, H.A., Andl, C.D., Westlake, G.M., Youngblood, V.M., Chen, J., Yarbrough, W.G. and Brown, B.T., 2016. LIM-Only Protein 4 (LMO4) and LIM Domain Binding Protein 1 (LDB1) Promote Growth and Metastasis of Human Head and Neck Cancer (LMO4 and LDB1 in Head and Neck Cancer). PloS one, 11(10), p.e0164804.

Klener, P., Fronkova, E., Berkova, A., Jaksa, R., Lhotska, H., Forsterova, K., Soukup, J., Kulvait, V., Vargova, J., Fiser, K. and Prukova, D., 2016. Mantle cell lymphoma‐variant Richter syndrome: Detailed molecular‐cytogenetic and backtracking analysis reveals slow evolution of a pre‐MCL clone in parallel with CLL over several years. International Journal of Cancer.

Colacino, J.A., McDermott, S.P., Sartor, M.A., Wicha, M.S. and Rozek, L.S., 2016. Transcriptomic profiling of curcumin-treated human breast stem cells identifies a role for stearoyl-coa desaturase in breast cancer prevention.Breast Cancer Research and Treatment, pp.1-13.

Kravchenko, D.S., Lezhnin, Y.N., Kravchenko, J.E., Chumakov, S.P. and Frolova, E.I., 2016. Study of Molecular Mechanisms of PDLIM4/RIL in Promotion of the Development of Breast Cancer. Biol Med (Aligarh), 8(2), p.2.

Na, Y., Kaul, S.C., Ryu, J., Lee, J.S., Ahn, H.M., Kaul, Z., Kalra, R.S., Li, L., Widodo, N., Yun, C.O. and Wadhwa, R., 2016. Stress chaperone mortalin contributes to epithelial-mesenchymal transition and cancer metastasis.Cancer research, pp.canres-2704.

Sanford, T., Welty, C., Meng, M. and Porten, S., 2015. MP68-18 MOLECULAR ANALYSIS OF UROTHELIAL TUMORS IN PATIENTS WITH AND WITHOUT METASTASIS STRATIFIED BY T STAGE. The Journal of Urology, 193(4), p.e865.

Hernandez, C., Huebener, P., Pradere, J. P., Antoine, D. J., Friedman, R. A., & Schwabe, R. F. (2018). HMGB1 links chronic liver injury to progenitor responses and hepatocarcinogenesis. The Journal of clinical investigation, 128(6).

Bacich, Dean, Wasim H. Chowdhury, Ronald Rodriguez, and Zhiping Wang. “Increased expression of TRIP13 drives the tumorigenesis of bladder cancer in association with the EGFR signaling pathway.”

Racioppi, L., Nelson, E.R., Huang, W., Mukherjee, D., Lawrence, S.A., Lento, W., Masci, A.M., Jiao, Y., Park, S., York, B. and Liu, Y., 2019. CaMKK2 in myeloid cells is a key regulator of the immune-suppressive microenvironment in breast cancer. Nature communications, 10(1), p.2450.

Cartwright, R., Franklin, L., Tikkinen, K.A.O., Kalliala, I., Miotla, P., Rechberger, T., Offiah, I., McMahon, S., O’Reilly, B., Lince, S., Kluivers, K., Post, W., Poelmans, G., Palmer, M.R., Wessels, H., Wong, A., Kuh, D., Kivimaki, M., Kumari, M., Mangino, M., Spector, T., Guggenheim, J.A., Lehne, B., De Silva, N.M.G., Evans, D.M., Lawlor, D., Karhunen, V., Mannikko, M., Marczak, M., Bennett, P.R., Khullar, V., Järvelin, M.R., &Walley, A. (2021). Genome wide association study identifies two novel loci associated with female stress and urgency urinary incontinence. Journal of Urology.

Cortes-Selva, D., Gibbs, L., Maschek, J.A., Nascimento, M., Van Ry, T., Cox, J.E., Amiel, E., & Fairfax, K.C. (2021). Metabolic reprogramming of the myeloid lineage by Schistosoma mansoni infection persists independently of antigen exposure. PLOS Pathogens, 17(1): e1009198.

Gomez-Lopez, N., Romero, R., Varrey, A., Leng, Y., Miller, D., Done, B., Xu. Y., Bhatti, G., Motomura, K., Gershater, M., Pique-Regi, R., & Tarca, A.L. (2021). RNA sequencing reveals diverse functions of amniotic fluid neutrophils and monocytes/macrophages in intra-amniotic infection. Journal of Innate Immunity, 13, 63-82.

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Creeth, H.D., McNamara, G.I., Tunster, S.J., Boque-Sastre, R., Allen, B., Sumption, L., Eddy, J.B., Isles, A.R. and John, R.M., 2018. Maternal care boosted by paternal imprinting in mammals. PLoS biology, 16(7), p.e2006599.

Foote, A.P., Keel, B.N., Zarek, C.M. and Lindholm-Perry, A.K., 2017. Beef steers with average dry matter intake and divergent average daily gain have altered gene expression in the jejunum. Journal of Animal Science.

Lee, S.E., Son, G.W., Park, H.R., Jin, Y.H., Park, C.S. and Park, Y.S., 2015. Integrative analysis of miRNA and mRNA profiles in response to myricetin in human endothelial cells. BioChip Journal, 9(3), pp.239-246.

Huang, Q., Sun, M. A., & Yan, P. (2018). Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes. In Transcriptome Data Analysis (pp. 35-55). Humana Press, New York, NY.

Valianou, M., Filippidou, N., Johnson, D.L., Vogel, P., Zhang, E.Y., Liu, X., Lu, Y., Jane, J.Y., Bissler, J.J. and Astrinidis, A., 2019. Rapalog resistance is associated with mesenchymal-type changes in Tsc2-null cells. Scientific reports, 9(1), p.3015.

Maxwell, A.J., Ding, J., You, Y., Dong, Z., Chehade, H., Alvero, A., Mor, Y., Draghici, S., & Mor, G. (2021). Identification of key signaling pathways induced by SARS‐CoV2 that underlie thrombosis and vascular injury in COVID‐19 patients. Journal of Leukocyte Biology, 109(1), 35-47.

Pushparaj, P.N., Damiati, L.A., Denetiu, L., Bakhashab, S., Asif, M., Hussain, A., Ahmed, S., Hamdard, M.H., & Rasool, M. (2021). Deciphering Novel SARS CoV-2 specific Disease Pathways from RNA Sequencing Data of COVID-19 Infected A549 Cells and Potential Therapeutics using Next Generation Knowledge Discovery Platforms. Research Square.

Bock, J.O. & Ortea, I. (2020). Re-analysis of SARS-CoV-2 infected host cell proteomics time-course data by impact pathway analysis and network analysis. A potential link with inflammatory response. Aging (Albany NY), 12(12), 11277-11286.

Ionescu, M.I. (2020). An overview of the crystallized structures of the SARS-CoV-2. The Protein Journal, 39, 600-618.

Reyes, F.R., & Pérez, G.G. (2020). Pandemia SARS-CoV-2/COVID-19, perspectivas y desafíos. Revista del Centro de Investigacion de la Universidad La Salle, 14(54).

The Science of Impact Analysis2018-10-13T11:16:39-04:00

Most existing pathway analysis methods focus on either the number of differentially expressed genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.

More recently, biological annotations have started to include descriptions of gene interactions in the form of gene signaling networks, such as KEGG (Ogata et al., 1999), BioCarta (www.biocarta.com) and Reactome (Joshi-Tope et al., 2005). This richer type of annotations have opened the possibility of an automatic analysis aimed to identify the gene signaling networks that are relevant in a given condition, and perhaps even the specific signals or signal perturbations involved. This approach is not well suited for a systems biology approach that aims to account for system-level dependencies and interactions, as well as identify perturbations and modifications at the pathway or organism level (Stelling, 2004).

Advaita’s products are based on Impact Analysis method that leverages the information about type, function, position and interaction between genes in a given pathway.  Impact Analysis combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition.  We illustrate the capabilities of the novel method on four real datasets.  The results obtained on these data show that Impact Analysis has better specificity and more sensitivity than several widely used pathway analysis methods.

Poster Download – Using Pathway Analysis to Predict Drug Response2018-10-13T11:17:57-04:00
AdvaitaBio pathway analysis research for drug response prediction
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Release Notes – Winter 20182020-07-06T16:00:54-04:00

On January 12, 2018, Advaita released a major update to its platform, with improvements to iPathwayGuide, iVariantGuide, and iBioguide.

IMPROVEMENTS

The Advaita Knowledgebase was updated to version 1711 and now includes:

  • 3 organisms: homo sapiens, mus musculus, rattus norvegicus
  • 213,390 Genes
  • 1,933 Diseases
  • 44,976 GO terms
  • 4,791 Drugs
  • 955 Pathways
  • 5,710 miRNAs
  • 3,161,730 References
  • For a complete list of databases and versions, please see report information within each application.

NEW FEATURES

  • iVariantGuide: API Client now accepts multi-sample analyses
  • Improvements to account registration page to ensure proper organization affiliation.

BUG FIXES

  • iPathwayGuide: Improvements to parsing of CuffDiff-formatted files to maintain association of phenotype labels. Fold changes and p-value parsing remains untouched.
Advaita Releases API client for iVariantGuide and iPathwayGuide2018-08-22T11:03:25-04:00

3/13/2017

With Advaita’s latest update to its applications and knowledge base, Advaita updated its API for iVariantGuide and iPathwayGuide.

An API or (Application Program Interface) is a set of routines, protocols, and tools for building software applications. An API specifies how software components should interact. Advaita’s API is designed.

Winter 2017 Release Notes2018-08-25T09:59:16-04:00

On February 27, 2017, Advaita released a major updates to its platform. These are the release notes.

IMPROVEMENTS TO: iPathwayGuide, iVariantGuide, iBioguide, and the Advaita Knowledge Base

  • Changes to AWS services in preparation for HIPAA compliance
  • Updated knowledge base to version Advaita KB v1702, which includes the following data sources and versions:
Database Version iPG Annotations iVG Annotations
KEGG Release 81.0+/01-20, Jan 17​ Pathways, Diseases, Drugs​ Pathways
Gene Ontology ​2016-Sep26 GO Terms GO Terms
Targetscan Targetscan v7.1 miRNA Target Genes miRNA Target Genes
MIRBASE MIRBASE v21,06/14 miRNA Sequences
dbSNP (incl 1k genomes) Build 149 Minor Allele freq.
RefSeq Release 71 July 2016 Impacted Transcripts
ClinVar Dec 1, 2016 Clinical Significance
​SNPEff ​v4.1L Predicted Impact

IMPROVEMENTS TO iPATHWAYGUIDE

  • NEW FEATURE! Onboarding carousel with top user benefits
  • NEW FEATURE! API (Premium feature)
  • Bug fix: genes selected in Genes Table on Pathways page are now highlighted on pathway map

IMPROVEMENTS TO iVARIANTGUIDE

  • Improved error messaging for sample upload & report creation
  • NEW FEATURE! Versioning: each report now shows which version of the Advaita Knowledgebase was used to annotate the sample. Outdated reports may be updated when viewing Report Info: either on the Reports page or from within the report itself. As is true for other Advaita applications, only the report owner may update it.

IMPROVEMENTS TO iBIOGUIDE

  • Updated to use AKB v1702
Spring 2016 Release – June 19, 20162018-08-22T11:43:46-04:00

The following components were added or addressed in this release.

  • Extensive databases updates including:
    • KEGG pathways, drugs, and diseases
    • NCBI genes
    • TargetScan miRNAs
    • Gene Ontologies
    • PubMed references
  • New EdgeR import format support
  • Improvements to several of the exported images
  • Improvements to meta analysis to preserve order of comparisons
  • Several bug fixes
  • Changes to support additional AWS features
  • Enhancements to security
Summer 2015 Release – July 13, 20152018-08-22T11:45:49-04:00

The following components were added:

  • Support for Sciex SWATH 2.0 Proteomics Expression data
  • “Trash” bin on user dashboard
  • Pathway and ontology images are now locked for scrolling. They can be unlocked in the on-screen menu.
  • In the pathway images, individual genes can now be selected if you hover over the node in the image.
  • Coherent cascades now have arrow heads so you can see directionality of the cascade.
  • The gene table in the pathway detail page is more refined. Easier to filter.
  • Bar chart of DE genes for pathways is now presented below the pathway image.
  • Meta-analysis has a new view called “Rank layout” that lets you see how genes, GO terms, pathways, etc, rank compared to each other. (accessible from the
  • lower right corner of the Venn).
  • Several other improvements on the back-end and a few bug fixes.
Spring 2015 Release – April 13, 20152018-08-22T11:48:00-04:00

Knowledge Base Updates:

  • Genes – 195,222 (increase of 23,106)
  • Pathways – 871 (increase of 12)
  • micro RNAs – 8,837 (increase of 5,268)
  • GO Terms – 39,907 (increase of 1,880)
  • Drugs – 4,389 (increase of 229)
  • Diseases – 1,411 (increase of 12)
  • SNPs – 92,169,423 (increase of 32,120,131)
  • References – 3,010,588 (increase of 51,977)

New Features:

  • Support for nSolver data from NanoString Technologies
  • QC and Normalization metrics for Affymetrix CEL files
  • Stem-loop information for miRNAs
  • Printable report summary with detailed methods and references
  • “Line-up” comparative ranking chart in meta analysis
Getting Started with iPathwayGuide2019-12-02T10:39:47-05:00

1.    iPathwayGuide expects the following three items in your differential expression input file:

  • Gene Symbol
  • Log Fold Change
  • P-value (adjusted P-value recommended)

2.    iPathwayGuide accepts several file formats for RNA-Seq, microarrays, and proteomic profiling.  Refer to the full list of accepted data formats on the next page.
3.    Submit the entire list of genes, not just the significant genes.
This is important because we need to calculate the background to provide you with a comprehensive analysis of your data without false positives.

4.    You will have the opportunity to customize thresholds for the significant genes after you upload.

5.    Each dataset takes about 15 minutes to analyze.  You will get an automated email as soon as your analysis is complete.

6.    Don’t have your data ready?  We have sample datasets available for each data format.  Grab a sample file and try it… it’s easy!

7.    Uploading data is easy.  Here are two quick video tutorials on how to upload data.

Step-by-step guide on uploading data

How to customize thresholds and select D.E. genes

Disease Analysis2018-08-22T09:51:57-04:00

Disease Analysis

The differential expression data can yield insights on potential diseases enriched in the sample data. Such conclusions can be drawn by observing the number of differentially expressed genes or proteins in your data. One such computational approach is described below.

iPathwayGuide

iPathwayGuide provides a comprehensive analysis of differential gene/ protein expression data that includes disease analysis.

For each disease, the number of differentially expressed (DE) genes annotated to it is compared to the number of genes expected just by chance. iPathwayGuide uses an over-representation approach to compute statistical significance of observing more than the given number of DE genes. The p-value is computed using the hypergeometric distribution that can be corrected using False Discovery Rate or Bonferroni method.

Register for iPathwayGuide today and try this feature for free.

Understanding Gene Ontology Analysis2020-11-12T15:25:30-05:00

What is Gene Ontology (GO)?

The confusion about gene ontology and gene ontology analysis can start right from the term itself. There are actually two different entities that are commonly referred to as gene ontology or “GO”:

  1. the ontology itself, which is a set of terms with their precise definitions and defined relationships between them, and
  2. the associations between gene products and GO terms, which are used to capture the existing knowledge about what each gene is known to do.

But the term gene ontology, or GO, is commonly used to refer to both, which is sometimes a source of potential confusion. In order to avoid this, here we will use the term “GO ontology” to describe the set of terms and their hierarchical structure and “GO annotations” to describe the set of associations between genes and GO terms.

There are 3 types of terms, or domains if you wish, in the gene ontology:

  • Biological Processes (BP)
  • Molecular Functions (MF)
  • Cellular Components (CC)

GO structure and data representation

In general, an ontology such as the gene ontology consists of a number of explicitly defined terms that are names for biological objects or events. These terms are depicted as nodes (also called vertices) in a graph that describe the relationships between the nodes. For instance, “cytoplasm” is a node, which is linked by an edge to its parent “intracellular part“. The type of this edge is “is a” and this structure simply means that cytoplasm is an intracellular part.

But there is more to it. That graph formed with these nodes and edges is not just any kind of graph. It is a so called “directed acyclic graph” or DAG. There are several important features of DAGs. Firstly, the edges are directed i.e. there is a source and a destination for each edge. In the gene ontology, the source is referred to as the parent term and the destination is referred to as the child term. This tells us that the cytoplasm is an intracellular part rather than an intracellular part is a cytoplasm.

Secondly, unlike a general graph, a DAG does not have cycles, which is to say that one cannot complete a loop by following the directed edges. Among other things, this restriction means that two terms can not be both parents and children of each other (otherwise they would form a loop between themselves), and that there must be at least one node that has no children, ie. a root. A DAG is similar to a tree with the difference that in a tree each node can have only one parent, while in a DAG a node can have multiple parents. In the figure below, A shows a tree (each node has only one parent), B shows a DAG (node 3 has 2 parents), and C shows a general graphs (nodes 1, 2 and 3 form a loop). So let us remember that the structure of the GO ontology is a DAG like in panel B, below.

difference between a tree, directed acyclic graph and genera graph

The differences between a tree, directed acyclic graph and genera graph

Here is an example showing the biological process of “negative regulation of programmed cell death” and its various relationship with all its ancestors.

hierarchical structure and ancestry relationships in the gene ontology

The hierarchical structure and ancestry relationships in the gene ontology. Terms on the lower levels are more specific while terms higher up are more general.

What is a gene ontology analysis?

Fundamentally, the gene ontology analysis is meant to answer a very simple question:

“Given a list of genes found to be differentially expressed in my phenotype (e.g. disease) vs. control (e.g. healthy), what are the biological processes, cellular components and molecular functions that are implicated in this phenotype?”

In a nutshell, the premise here is that if many of the genes associated with a given biological process are differentially expressed in the given disease, that biological process is implicated in that disease. Essentially, the gene ontology analysis aims to identify those biological processes, cellular locations and molecular functions that are impacted in the condition studied.

But…the question now becomes, how do you decide whether or not a given gene ontology term is important or not? After all, any biological term can end up with some genes that are differentially expressed just but chance or just because those genes are also associated with other biological processes that could be more germane to the condition studied.

Therefore, I will briefly outline the main approaches used to address this problem. Some of these are better, some are worse. In fact, some are completely wrong. Nevertheless, I will review them here so you can understand how the thinking evolved about this problem and be in a position to choose wisely the approach you want to use.

Keep in mind that the following is just a brief outline with no mathematical details. If you really want a thorough discussion, you can lean more about these approaches in Chapter 16 of this book.

The simplest gene ontology analysis: Over-representation analysis (ORA) or enrichment analysis

If the processing of the list of differentially expressed (DE) genes were to be done manually, one would take each accession number corresponding to a DE gene, search various public databases and compile a list with, for instance, the biological processes that the gene is involved in. The same type of analysis could be carried out for other functional categories such as biochemical function, cellular role, etc. This task can be performed repeatedly, for each gene, in order to construct a master list of all biological processes in which at least one gene is involved. Further processing of this list can provide a list of those biological processes that are common between several of the DE genes. It is intuitive to expect that those biological processes that occur more frequently in this list would be more relevant to the condition studied. If 200 genes have been found to be differentially expressed and 160 of them are known to be involved in, let us say, mitosis, it is intuitive to conclude that mitosis is a biological process important in the given condition. Right?

Wrong!!

As we shall see in the following example, this intuitive reasoning is incorrect and a more careful analysis must be done in order to identify the truly relevant biological processes.

Let us consider that we are using a panel containing 2,000 genes to investigate the effect of ingesting a certain substance X. Let’s say that there are 200 differentially expressed genes. Let us focus on the biological processes for instance, and let us assume that the results for the 200 differentially regulated genes are as follows: 160 of the 200 genes are involved in mitosis, 80 in oncogenesis, 60 in the positive control of cell proliferation and 40 in glucose transport.

raw counts in gene ontology analysis

A common mistake in gene ontology analysis is to use raw counts. Here, mitosis seems to be the most important biological process in this experiment.

If we now look at the functional profile described above, we might conclude that substance X may be related to cancer since mitosis, oncogenesis and cell proliferation would all make sense in that context. However, a reasonable question is: what would happen if all the genes on the panel used were part of the mitotic pathway? Would mitosis continue to be significant? Clearly, the answer is no. Therefore, in order to draw correct conclusions, it is necessary to always compare the actual number of occurrences with the expected number of occurrences for each individual category.

This comparison is shown in the figure below by the line showing the ratio between what was observed vs what was expected (percentage shown on the vertical axis on the right). In this light, the same data tells a completely different story. There are indeed 160 mitotic genes but, in spite of this being the largest number, we actually expected to observe 160 such genes so this is not better than chance alone. The same is true for oncogenesis. The positive control of cell proliferation starts to be interesting because we expected 20 and observed 60. This is 3 times more than expected. However, the most interesting is the glucose transport. We expected to observe only 10 such genes and we observed 40, which is 4 times more than expected. Taking into consideration the expected numbers of genes radically changed the interpretation of the data. In light of these data, we may want to consider the correlation of X with diabetes instead of cancer.

gene ontology analysis: the need to compare raw counts with expected values

Gene ontology analysis: the need to compare raw counts with expected values. Even though mitosis had the highest number of differentially expressed genes, this was no more than what was expected by chance. In contrast, glucose transport, even though it had the lowest absolute count, had the most significant enrichment at 4x the number expected by chance.

This example illustrates that the simple frequency of occurrence of a particular functional category among the genes found to be regulated can be misleading. In order to draw correct conclusions, one must analyze the observed frequencies in the context of the expected frequencies.

The problem is that an event such as observing 40 genes when we expect 10 can still occur just by chance. This is unlikely, but it can happen. The bottom line is that one needs to assess the significance of these categories based on the probability of the observed values appearing just by chance. This can be done with various statistical models including hypergeometric, Fisher’s exact test, or chi-square. If you are interested in details, I explained the formulae elsewhere (Chapter 23 in this book) but here, the bottom line is that you should never try to draw conclusions from a count graph as above, but rather use software that calculate a p-value for each term and don’t forget to correct for multiple comparisons. Whatever you do, please do not publish graphs showing just raw counts of gene ontology terms. As shown in the example above, they are not only uninformative but they can also be completely misleading. And if I am one of the reviewers of your paper or grant, you will hear strong complaints from me.

A step further in gene ontology analysis: Functional Class Scoring (FCS)

The simplest approach that would provide sound scientific results is the over-representation approach described above. However, more sophisticated methods have been developed over time. An important category of methods includes functional class scoring methods. The best known methods in this category is the Gene Set Enrichment Analysis or GSEA.

As a first step, GSEA ranks the genes based on the association of each gene with the phenotype. This association is established using an arbitrary test, for example a t-test. Once the ranked list of genes L is produced, an enrichment score (ES) is computed for each set in the gene set list. The list L is walked from the top to the bottom, and a statistic is increased every time a gene belonging to the set is encountered, and decreased otherwise. The value of the increment (or decrement) depends on the ranking of the gene. If you imagine a situation in which all genes at the top of the list are associated to a given biological process, the score for that process will increase with every gene. At the end of the list, the enrichment score is the maximum distance from zero encountered during the walk.

An example of the statistics calculated by the gene set enrichment analysis (GSEA). Image from Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, and Jill P. Mesirov PNAS October 25, 2005 102 (43) 15545-15550; first published September 30, 2005; https://doi.org/10.1073/pnas.0506580102

An example of the statistics calculated by the gene set enrichment analysis (GSEA).
Image from Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, and Jill P. Mesirov PNAS October 25, 2005 102 (43) 15545-15550; first published September 30, 2005; https://doi.org/10.1073/pnas.0506580102

In this figure, the upper graph shows the enrichment values during the walk through the gene list. The vertical lines represents the genes belonging to the set S at the positions they appear in the ranked list. The lower graph shows the degree to which each gene is correlated with the phenotype.

In principle, higher enrichment scores are yielded when the graph departs considerably from zero. However, the enrichment score by itself cannot be used to assess significance much like the raw counts of genes cannot be used that way. The reason is the same: in principle, any score can appear with a given non-zero probability. We have to focus only on those that appear more often than expected by chance. This is done with a bootstrap approach. In essence, the bootstrap approach assesses the frequency with which something appears just by chance by randomly permuting the labels. There are two possible permutation criteria: permutation of the phenotype samples or permutation of the gene labels. In general, the label permutation method is preferred as it preserves gene-gene correlations. This step of the algorithm produces a null distribution which allows the computation of an empirical p-value. The empirical p-value is calculated as the number of random bootstrap runs which resulted in an enrichment score equal to or larger than the one observed for the correct labels.

Next and last step, the significance levels are adjusted for multiple hypotheses testing. Each Enrichment Score is normalized with the size of the set obtaining a Normalized Enrichment Score (NES), and then the false discovery rate (FDR) of each NES is computed.

More sophisticated gene ontology methods: elim and weight

The approaches described above focus on the problem of accurately interpreting the number of differentially expressed genes associated with a gene ontology term. However, these approaches ignore the structure of the gene ontology and the relationship between various terms. In order to understand more sophisticated GO analysis methods we need to learn a few more things about the gene ontology.

The GO is organized in a hierarchical structure that uses the types of relationship described above: “is a”, “part of” and “regulates”. For instance, “induction of apoptosis by extracellular signals” is a type of “induction of apoptosis” which in turn is a “positive regulation of apoptosis” which in turn is a kind of “regulation of apoptosis”, etc. Generally speaking, traversing the DAG following “is a” relationships as above can be seen as moving across levels of abstraction. The root, BP (or “All”), would correspond to the highest level of abstraction, or the lowest level of details. In contrast, leaf nodes such as “induction of apoptosis by hormones” would correspond to the lowest level of abstraction, with the most details. Similarly, the “cell outer membrane” is part of the “cell envelope”, etc. Traversing “part of” relationships could be interpreted as changing scales. In general, terms closer to the roots (BP, CC, MF) are more general, while the ones closer to the leaves are more specific. In GO Consortium’s terminology, the children are more specialized than the parents. When annotating genes with GO terms, efforts are made to annotate the genes with the highest level of details possible. In a general way, this corresponds to the lowest level of abstraction. For example, if a gene is known to induce apoptosis in response to hormones, it will be annotated with the term “induction of apoptosis by hormones” and not merely with one of the higher level terms such as “induction of apoptosis” or “apoptosis”.

An example of the statistics calculated by the gene set enrichment analysis (GSEA). Image from Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, and Jill P. Mesirov PNAS October 25, 2005 102 (43) 15545-15550; first published September 30, 2005; https://doi.org/10.1073/pnas.0506580102

When a gene is annotated with a term, all inferences that can be inferred from the structure of the GO must also hold true. In other words, if the child term describes a gene product, then all its parent terms must also apply to that gene product. Let’s revisit the ontology from above (figure is repeated here for convenience). For instance, if a gene is annotated as having a role in “regulation of programmed cell death” then it will necessarily be involved in “cellular process” because “regulation of programmed cell death” regulates “programmed cell death,” which is a type of “cell death,” which is a type of “cellular process.” This property is known as the “True Path Rule.” But, because of this, if the GO analysis is done independently, for each term, each differentially expressed gene will be counted multiple times, once for every term from the lowest term it is annotated with, all the way up to the root. This has two consequences. First, it injects a great deal of redundancy in the process and second, it tends to report as significant lots of general terms which are really not very informative with respect to the phenotype studies.

Two methods have been proposed to deal with this by Alexa et al (2006). Elim starts at the lowest level of GO, with the most specific terms and calculates their enrichment p-value. If that is not significant, the approach moves up in the hierarchy and calculates the p-value as usual. If “induction of apoptosis by extracellular signals” is not significant, all DE genes associated with it will also be counted for its parent, “induction of apoptosis”, as if the two terms were independent. However, if the more specific term is indeed significant, elim will eliminate all genes associated with it from all its ancestors, thus eliminating the redundancy and giving a chance to the more specific terms to be reported as significant.

The second method proposed by Alexa is called weight. The idea behind this approach is that if many very specific terms are significant, and there is a term slightly more general that would encompass all those terms, it may be useful to identify this more general term.

Pitfalls in gene ontology analysis

Gene ontology analysis is a powerful tool. Like any powerful tool, it is subject to misuse and misunderstanding.

The most common mistake in gene ontology analysis is choosing the incorrect background (or not choosing an explicit background). What is this about?

Well, let’s go back to the enrichment analysis. Let us assume that Mary measured 1,000 genes in a panel, and she found 200 genes to be differentially expressed. That’s a proportion of 20%. Let’s consider a biological process that is associated with 100 genes and let us assume that she has found that 30 of these 100 genes are differentially expressed. In this case, if this process is not related to the phenotype, she would expect to find about 20% of its genes being differentially expressed just by chance, or about 20. We found 27 instead of 20, we can calculate the probability of this happening just by chance as being about 0.076 or 7.6%. This is not meeting the usually accepted significance threshold of 1% or even 5% so there is not enough evidence to indicate that this process is involved in the phenotype and Mary should not spend too much time studying it.

But… most people do this analysis with some software, not manually as above. And sometimes, such software only requires the user to upload the list of differentially expressed genes. This would be the list of 200 genes. Now, Mary is using such software and she does not specify that she only used a panel of 1,000 genes, ie. the statistical background of this analysis, the software might think that she has selected those 200 genes from a genome-wide RNA-Seq experiment. In that case, the numbers would be rather different. Now, the proportion of DE genes appears to be 200/30,000 or 2/300 (or 0.0066), and a biological process with 100 genes is only expected to get 1 gene just by chance, at most (actually 0.666…). The probability of having 25 genes just by chance, or the reported p-value, is essentially zero. With such hugely significant p-values, Mary gets super excited and there is a real danger that she would spend weeks or month trying to understand or validate the involvement of this biological process in this experiment, where in fact, this extremely significant p-value was only due to the incorrect choice of the background set.

The moral of this story is that an enrichment analysis must always be done by specifying explicitly the set of genes measured. The safest bet is to use a software platform that allows you to upload the entire list of genes measured and specify which of those you want to consider as differentially expressed. If that is not an option, you need to be able to either upload separately your reference set, or specify it in another way. Otherwise, your p-value may lead you to perdition…

The second most common mistake in this area is failing to correct for multiple comparisons. The need to do this correction is explained here or in Chapter 16 of this book. Since ontologies have a hierarchical relationships, it is important to apply appropriate correction factors to minimize errors. For instance, Using a False Discovery Rate (FDR) or Family-wise Error Rate correction factor may not be appropriate for GO analysis.

The third most common mistake is failing to detect the cross-talk between various biological phenomena. Many genes are involved in several processes, in addition to the redundancy stemming from the gene ontology hierarchy itself. Sometime, the same group of differentially expressed genes make several processes appear as significant. Unless this overlap and cross-talk is detected and eliminated, a lot of time may be wasted. A detailed mathematical approach to detect and eliminated cross-talk has been proposed here. A more practical and user friendly way to identify cross-talk is also included in iPathwayGuide.

Other, more subtle mistakes related to gene ontology analysis include:

  • Misinterpreting annotations such as
    • ND = no biological data available. If an ND evidence code surfaces in your analysis, you do not need to waste time on additional literature searches.
    • NOT = it can be confusing to interpret the negative annotation NOT. NOT is not a complete list, just a list of where the NOT was a surprise.
  • Misinterpreting the direction of arrows in the output of GO – the directed acyclic graph (DAG). Clear labeling is a must here.

A more detailed discussion of these and other pitfalls can be found in this paper:

Rhee, S., Wood, V., Dolinski, K., Draghici, S. (2008). Use and misuse of the gene ontology annotations Nature Reviews Genetics 9(7), 509-515. https://dx.doi.org/10.1038/nrg2363.

And most importantly, keep in mind that the ultimate purpose of gene expression experiments is to produce biological knowledge not numbers.
Sorin Draghici (2012)

Using iPathwayGuide for gene ontology analysis

Register to Explore iPathwayGuide

Analyze Now

  1. Register to explore demo data
  2. Subscribe to analyze your ‘omics data
  3. Review and interact with pathways impacted in your experiment
  4. Share your results with collaborators for interpretation and analysis iteration
  5. Create publication-ready figures simply and easily

What You Can Expect

  • Better Insights
  • Higher Quality
  • Superior Convenience
  • Unmatched Usability
  • Unparalleled Reproducibility
Register to Explore Advaita’s Platform with Demo Data

Get Started!

Get in touch with Advaita to learn how our software will improve quality and efficiency for your Core Facility, Enterprise Bioinformatics team, or Research Lab.

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Advaita’s iPathwayGuide – Combining Sciex SWATH 2.0 Data and mRNA Data2020-07-07T15:48:57-04:00

Advaita has teamed up with SCIEX, the leader in Data Independent Acquisition (DIA) Mass Spectrometry for the collection and analysis of proteomics-based data. Through this collaboration, users can now bring their SWATH data from the SCIEX protein expression workflow and analyze it in the context of pathways, gene ontologies, microRNAs, and diseases. The power of iPathwayGuide allows you to combine protein expression experiments and contrast them with other platforms including RNA-Seq, microarrays, and targeted panels. Watch the video below to see how SCIEX SWATH proteomics data was juxtaposed to mRNA data from an RNA-Seq experiment.

Uploading Affymetrix CEL files in iPathwayGuide2020-07-07T15:31:26-04:00

iPathwayGuide – Affy CEL file uploading

Affymetrix microarrays are one of the most widely used gene expression platforms in the industry. iPathwayGuide supports the most common platforms. Look at the FAQ for the latest list of supported platforms.

The resulting file from an Affymetrix microarray is commonly known as a CEL file because of the CEL extension place on the file name. To upload your CEL files, simply drag and drop the corresponding files for the condition group and the control group. iPathwayGuide requires at least 3 – unique files for each group. We recommend at least 4 per group in case one of the samples is rejected during QC and normalization.

Once your files are identified, click upload to begin the process. iPathwayGuide will upload your files, QC check them, reject and highlight any that do not pass, normalize the files, and calculate differential expression. Depending on the number of files, this process can take 2 to 5 minutes or more.

Once the QC metrics are available, iPathwayGuide will present the QC stats, QC Density Box Plot, and QC Density Plot. Any samples identified for removal will be highlighted in red.

Once the Normalization is complete, iPathwayGuide will present the Normalized Box Plots and the Normalized Density Plot. If you wish to include any of these graphs in a paper or report, you can download the graphs using the download button.

If you are satisfied, you may proceed to the Contrasts Intake page to set the number of significant differentially expressed genes along with title and description of the report.

iPathwayGuide – Data Uploading Overview2020-07-06T16:11:35-04:00
iPathwayGuide Overview2020-07-07T15:37:20-04:00
iPathwayGuide’s Pathway Diagram2020-07-07T15:50:46-04:00
Do you have a User Guide for iPathwayGuide?2020-07-07T15:56:20-04:00

Yes. Please download it here.

Open (PDF)
What kind of files can I upload?2018-10-13T11:06:51-04:00

iPathwayGuide supports analysis of Human, mouse, and rat. It supports the following files formats:
CuffDiff
DESeq
​EdgeR
SAS/JMP Genomics
nSolver (NanoString Technologies)
Generic tab delimited .txt file (must contain gene symbol or uniprot ID, log2FC, p-value)
SCIEX SWATH 2.0 proteomics data files
Select Affymetrix CEL files*

*Supported Affy CEL Files may take several minutes to upload

Human
Human Genome U133
Human Genome U133A 2.0
Human Genome U133 Plus 2.0
Human Genome U95
Human Genome U35K

Mouse
Mouse Expression Set 430
Mouse Expression Set 430 2.0
Mouse Genome 430A 2.0

Rat
Rat Expression Set 230
Rat Genome 230 2.0
Rat Genome U34

Download File (sample_generic_3_column_tab_delimited_file.txt)
I loaded a CuffDiff file but I cannot see it.2019-01-10T19:41:19-05:00

Please make sure your are using the “…gene_exp.diff” file that comes from CuffLinks. There are some applications that claim to emulate CuffDiff output (e.g. Galaxy). If you are using one of these applications, please make sure the output file has all columns populated. See below for specific columns that must be present. Also, use this link to view the Cuffdiff manual.

Column number Column name Example Description
1 Tested id XLOC_000001 A unique identifier describing the transcipt, gene, primary transcript, or CDS being tested
2 gene Lypla1 The gene_name(s) or gene_id(s) being tested
3 locus chr1:4797771-4835363 Genomic coordinates for easy browsing to the genes or transcripts being tested.
4 sample 1 Liver Label (or number if no labels provided) of the first sample being tested
5 sample 2 Brain Label (or number if no labels provided) of the second sample being tested
6 Test status NOTEST Can be one of OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing.
7 FPKMx 8.01089 FPKM of the gene in sample x
8 FPKMy 8.551545 FPKM of the gene in sample y
9 log2(FPKMy/FPKMx) 0.06531 The (base 2) log of the fold change y/x
10 test stat 0.860902 The value of the test statistic used to compute significance of the observed change in FPKM
11 p value 0.389292 The uncorrected p-value of the test statistic
12 q value 0.985216 The FDR-adjusted p-value of the test statistic
13 significant no Can be either “yes” or “no”, depending on whether p is greater then the FDR after Benjamini-Hochberg correction for multiple-testing
How long does an analysis take?2020-07-07T15:42:29-04:00

Generally, each analysis takes about 15 minutes to complete. If there are other analyses queued ahead of yours, it may take a bit longer. You will receive an email as soon as the analysis is complete.

Do you offer a free trial?2019-06-13T11:26:55-04:00

It is a great idea to use the software and explore the capabilities before making a purchase. The way to do this is to create a free account on our web site www.advaitabio.com. Once you create an account, you will have full access to several analysis results. These datasets cover a range of experiment types, conditions, and analysis outcomes. You will be able to fully use the software to explore these data sets in whichever way you choose. You will have full access to interact with all of the demo analyses and experience all of the features of iPathwayGuide. We also provide sample data files, which will show you some of the formats that can be used to upload data for analysis.  An alternative would be to setup a time and let one of us to show you around the software. You will learn more in a much shorter time, and you will also be able to get immediate answers to any questions you may have.

Once you get oriented with the software, if  you want to see an analysis of your own data, we offer the possibility of purchasing a single analysis. We need to charge for this because we use a backend cloud platform that costs us money for every analysis done. The price of a single analysis is meant to cover these costs. The price of the single analysis is USD 287, as of June 15th, 2019. Please see more details at https://advaitabio.com/blog/. A single analysis can be purchased with PayPal by clicking the Purchase button next to it.
Any analysis provides a full interactive report that you can use to further explore your results in many ways, including discovering mechanisms. The interactive report can be shared with others at no additional cost. The price also includes a PDF report that many of our users use as the Methods or Supplementary Materials in their published peer-reviewed articles. This automatically-generated PDF contains a full description of your input data, experiment, methods, results with figures, and references.
Once you see the capabilities of iPathwayGuide in action, we strongly suggest that you get a subscription rather than continue to purchase one analysis at a time. We will gladly refund the price of any single analyses done in the 30 days prior to ordering a subscription.  See the blog discussing single analysis vs. subscriptions at:
https://advaitabio.com/ipathwayguide/subscription-vs-purchasing-individual-analyses-which-one-do-i-want/.
Do you have a legend to help me read the various diagrams?2019-06-18T17:10:41-04:00

Here are the legends describing the symbols used on the pathway diagrams, gene networks and GO directed acyclic graph, respectively. Click an image for a larger view.

AdvaitaBio Bioinformatics pathway analysis product supportAdvaitaBio pathway analysis gene network product templateRelationship types in GO
Can I share a report?2018-08-22T08:49:40-04:00

Yes! From the dashboard, just click share on any completed report. Then enter the email address for the person you wish to share it with. If they do not have an account, they will be prompted to create on. Once registered, they will be able to view the report.

Why is the ‘Creation Time’ different from my clock?2018-08-22T08:50:16-04:00

We report the ‘Creation Time’ based on Coordinated Universal Time (UTC).

What browsers do you support?2018-08-22T08:51:10-04:00

iPathwayGuide is designed to work with all the latest major browser platforms:

  • Google Chrome
  • Mozilla Firefox
  • Apple Safari (Mac only, iOS not supported yet)
  • Microsoft Internet Explorer 11 – Some image download capabilities may not function​​
Do you have sample files I can try?2019-11-19T16:19:44-05:00

iPathwayGuide works with the most popular differential expression files. Some 3rd party emulators (e.g. Galaxy) may structure their data slightly differently. Click on the sample files below to see the structure of these files.

Download (human_deseq.res.csv)
Download (human_edger_sample_tabdelimited.txt)
Download (human_jmp_genomics_sample.txt)
Download (human_nanostring_nsolver_sample_dataset.txt)
Download (human_geo2r_limma_sample.txt)
Download (human_custom_file_sample.txt)
Download (human_sciex_swath_sample.txt)
Can I change my password?2018-08-22T08:52:32-04:00

Yes. From the login menu, click reset password. You will receive an email with the new password.

What databases are used in iPathwayGuide?2018-08-22T08:53:04-04:00

A list of databases and versions is available from within each report. See our Release Notes to see the latest data.

How should I cite iPathwayGuide?2021-07-29T17:57:59-04:00

Citing iPathwayGuide

Using Advaita Bio’s products or content for any form of publication (e.g. print, electronically) requires researchers to cite them. Please use one of the options below for citations:

• “The Data (significantly impacted pathways, biological processes, molecular interactions, miRNAs, SNPs, etc.) were analyzed using Advaita Bio’s iPathwayGuide (a> href=”/citations?view_op=view_citation&hl=en&user=fbkIhRYAAAAJ&citation_for_view=fbkIhRYAAAAJ:2osOgNQ5qMEC”>A systems biology approach for pathway level analysis
S Draghici, P Khatri, AL Tarca, K Amin, A Done, C Voichita, C Georgescu, …
Genome research 17 (10), 1537-1545
<href=”/citations?view_op=view_citation&hl=en&user=InEMxGMAAAAJ&citation_for_view=InEMxGMAAAAJ:zYLM7Y9cAGgC”>Analysis and correction of crosstalk effects in pathway analysis
M Donato, Z Xu, A Tomoiaga, JG Granneman, RG MacKenzie, R Bao, …
Genome research 23 (11), 1885-1893

A novel signaling pathway impact analysis

AL Tarca, S Draghici, P Khatri, SS Hassan, P Mittal, J Kim, CJ Kim, …
Bioinformatics 25 (1), 75-8

Identifying significantly impacted pathways and putative mechanisms with iPathwayGuide

S Ahsan, S Drăghici
Current protocols in bioinformatics 57 (1), 7.15. 1-7.15. 30
If you need more explanations on how the method works or if you ever get any questions from the reviewers about the analysis, we will be happy to help preparing the response to reviewers.
Why do I get different results with GEO2R vs. your CEL file uploader?2018-08-22T08:57:29-04:00

GEO2R does not perform normalization for Affymetrix CEL files. The Advaita iPathwayGuide CEL file uploader currently utilizes the Gene-chip Robust Multi-array Average (GCRMA) normalization method. As such, there can be discrepancies between CEL files processed with GEO2R vs. iPathwayGuide.

variant analysis software AdvaitaBio

Click on a Category to see the associated topics.

How do I find a “Deprecated” Analysis2020-08-18T10:51:09-04:00
Question: I went back to analyses that I had previously paid for, opened them and a warning came up for indicated that the analyses were “deprecated” so I hit the re-analyze button only to find that the files are now locked and require payment to open. What is going on and how do I access the analysis I already paid for?
We periodically update our knowledge base with the latest annotations and references. This  is necessary in order to offer our users an analysis that uses the most recent knowledge available. This in fact, is one of the main value points that we provide to our users. Just to give you an idea, the sheer data available now is approximatively 4 times larger than what we had 18 months ago. Furthermore, brand new analysis capabilities are added all the time. For instance, during the past year alone we added an extremely powerful network analysis which is able to discover new mechanisms, as well as a very sophisticated upstream regulator analysis. If you have not seen these yet, it would be worth your time to schedule a 30 min phone call for us to show these to you.
When new knowledge becomes available, our platform lets you know by marking your existing analyses as “deprecated”. Thus, you are aware that they are not based on the latest knowledge available in the field. It is better to find this out from us, rather than from the reviewers of your grant or paper pointing out that your analysis does not mention such and such recent discovery. You original analysis is still fully available to you. You can simply close the warning messages at the top by clicking the X symbol at the top left, immediately to the left of the analysis title. Every single piece of information will still be there, as you saw it when the analysis was first performed.
When new knowledge becomes available, you may or may not choose to analyze the data again, using the latest knowledge available. If you choose do resubmit the data for a new analysis and you have a valid subscription, this additional analysis will incur no additional cost. However, if your subscription has expired, you will need to either renew it or pay for the individual analyses that you wish to have done. Please keep in mind that updating an existing analysis involves a resubmission of the original data and a complete re-analysis of that data from scratch. For each analysis, we ourselves are incurring all the costs associated to the computation and the storage on the AWS platform.
Here is a blog comparing the single analysis vs. subscription usage: https://advaitabio.com/ipathwayguide/subscription-vs-purchasing-individual-analyses-which-one-do-i-want/
Graphical Filtering in iVariantGuide 2.02020-07-06T15:50:37-04:00

Explore all of the options for customizable graphical filtering in the brand new iVariantGuide. (2:03)

iVariantGuide Application Overview Webinar2018-08-25T09:11:29-04:00
Finding Significance in your Variant Data2018-08-25T09:10:03-04:00
Webinar: iVariantGuide for Service Providers2018-08-25T09:06:20-04:00

HOW TO SAVE MONEY & INCREASE CUSTOMER LOYALTY

In a 45-minute presentation, Dr. Cordelia Ziraldo recently covered all how service providers and core facilities are taking advantage of the all-new iVariantGuide: from interactive reporting to automatically-generated PDFs; from graphical filters to pathway and GO analysis, and so much more. Follow the link to watch the webinar and see what iVariantGuide can do for you.

Getting started with iVariantGuide (3:15)2018-08-25T08:59:23-04:00
Creating New Analyses in iVariantGuide 2.02020-07-07T15:34:28-04:00

We’ve released a brand new version of iVariantGuide. Here are step by step instructions for uploading and analyzing your VCF files. (2:05)

Pathway Analysis of High-Priority Variants2020-07-06T16:14:47-04:00

​The Pathway Analysis module in iVariantGuide allows you to explore the pathways that are impacted by high-priority variants. See how you can use this powerful module to identify biological links between variants or make new functional hypotheses and design experiments to test them.

Case v. Control in iVariantGuide Webinar2020-07-07T15:46:18-04:00

In this video, Dr. Cordelia Ziraldo walks you through the steps needed to do a Case v Control analysis in iVariantGuide using RNAseq-based variant data in breast cancer subtypes. Dr. Ziraldo shows you how to identify which systems (pathways, biological processes, molecular functions, and cellular components) and the mechanisms that may be implicated in these breast cancer subtypes.

Gene Ontology Analysis for variants in iVariantGuide2020-07-07T15:52:26-04:00

Gene Ontology (or GO) Analysis identifies the biological processes, molecular functions, and cellular components that are likely affected by your high-priority variants. See how iVariantGuide leverages state-of-the-art algorithms to drill down to the specific biological phenomena relevant to your data.

Filtering Variants in iVariantGuide2020-07-07T15:39:09-04:00

iVariantGuide’s dynamic, graphical filters help you take your variant analysis to the next level. Find hidden correlations when visualizations of every annotation source update with every new selection you make.

How to Input Variant Data2020-07-07T15:44:03-04:00
I just registered and I can’t log in. What should I do now?2018-08-23T12:02:46-04:00

Check your email inbox and spam folder. Look for an automatic activation email sent from noreply@apps.advaitabio.com. If it did land in your spam folder, you should add noreply@apps.advaitabio.com to your address book so that future emails are routed correctly (including notifications when your analyses are complete).

Where can I find my API credentials?2018-08-23T12:03:34-04:00

You can generate API credentials on your Advaita Profile page. Once generated, your API ID will always be displayed, but your API secret will only be shown once, so write it down in a safe place! If you lose your API secret, you can reset it by revoking and generating new API credentials. Keep in mind that this will generate a new API ID in addition to the new API secret.

Do you have a User Guide for iVariantGuide?2020-07-07T15:57:48-04:00

Yes. Please use the button to open the Users Guide.

Open (PDF)
Is there a free trial? What does it mean that my report status is Locked?2018-11-09T17:11:19-05:00

Yes, every new account comes with several demo analyses shared by the Advaita Team. Once you complete your free registration and log in, click ‘Accept Share’ on the demo card, and the demo report will appear in your Reports Table along with any reports you generate. You can use all functionality of iVariantGuide within these demo reports— including creating new filter Presets, exploring Pathway and GO Analysis, and generating the Printable Report. You may upload and analyze your VCF at any time, but you will not be able to access the results until you unlock them via a subscription.

What variant file formats do you support? Do you have any example files I can try?2018-08-27T17:27:57-04:00

Your VCF needs to adhere to the standard format for VCF v4.1 or above. Here is the Specification File, produced by SAMtools. There is no limit to the number of variants or samples in your file, but very large files (> 1M variants) could have a slower browsing experience.

EXAMPLE

This VCF file meets the minimum specifications for iVariantGuide.

TROUBLESHOOTING

If your VCF is rejected by iVariantGuide, here are a few things to check:

  • The file should be tab-delimited. If your columns are separated with spaces, do a find/ replace to make sure you have one tab separating each column.
  • All header lines should begin with ##, except the last line of the file header, which contains column headers and should begin with #.
  • The last line of the file header must contain every column shown in the example (line 15), including at least one Sample Name column header.
  • The columns CHROM, POS, ID, REF, ALT give identifying information about the variant. CHROM and POS are mandatory. The others will accept . (period) in place of missing data.
  • iVariantGuide uses the values from the QUAL column as the quality score for each variant call.
  • The FILTER and INFO columns are required to preserve the integrity of the VCF format. FILTER information is displayed in the variant table, and data from the INFO column is ignored, with one exception: if excluded from FORMAT, read depth (DP) will be read from the INFO column. Regardless of how it is used in iVariantGuide, every field in the INFO column needs its own definition line in the header. In the example above, each field that appears in the INFO column is defined in lines 3-11 (shown with ##INFO).
  • The FORMAT column contains the key to parsing the data in the sample columns. iVariantGuide will prioritize genotype data in the order of: PL, GL, then GT.

The Sample Name column headers will be used as the sample names in iVariantGuide. In the example above, there is one sample and it will be called Sample_1 in iVariantGuide.

What if my variant files are in another format like .maf or .tsv?2018-08-23T12:15:26-04:00

There are several free downloadable tools that can convert between those formats and .vcf. They each come with documentation that makes them simple to use.

Is my data secure? Is it HIPAA compliant?2018-08-23T12:16:19-04:00

Yes! Advaita Cloud Systems adheres to the highest industry standards for data security. All data is encrypted during transfer and only you have access to your data, unless you share it. For those customers requiring HIPAA compliance, Advaita offers a HIPAA compliant environment. Please contact sales@advaitabio.com for additional information.

What reference genomes are supported?2018-08-23T12:16:45-04:00

​iVariantGuide currently supports human hg19 and hg38 (GrCh37 and GrCh38).

What sources are used for generating a report?2018-08-23T12:22:52-04:00

We provide annotations from dbSNP, ClinVar, and 1000 Genomes in addition to all sources contained in our KnowledgeBase, iBioGuide. We also provide links to additional information in iBioGuide as well as external sites such as OMIM, MedGen, and more. At this time iVariantGuide does not support user-defined annotation sources. If there is a specific database you would like us to support, please let us know!

FILTER
Variant Class
Clinical Significance
Functional Class
Impact
Region
Zygosity
Allele Frequency
Depth Distribution
Quality
Length of Indel
Substitution Types
Chromosomal Location
Pathways
GO Terms
SOURCE
SnpEff
ClinVar
SnpEff
SnpEff
SnpEff
iVariantGuide
dbSNP/1000 genomes
input vcf file
input vcf file
iVariantGuide
iVariantGuide
input vcf file
KEGG
Gene Ontology
Can I share my results? What will the recipient be able to see?2018-08-23T12:24:01-04:00

Yes! You may share a report with anyone you wish. They must register a free account to view it, but will have the level of access that you have. If you are sharing a purchased analysis, your sharee will also be able to see premium features such as pathways and GO analyses. You may also associate a filter preset to any purchased analysis and share that preset with the analysis. When sharing, you may also control which of your recipients may re-share the report or whether you want to maintain control over its dissemination. We also provide a stable public link direct to your report in case you wish to share it publicly (e.g. publication).

Can I export data and/or images from my report?2018-08-23T12:24:32-04:00

Yes! Everywhere you see a download arrow within a report, there are data and/or images that may be exported. iVariantGuide also provides a comprehensive summary report that can be printed or downloaded as a pdf. (Paid accounts only.)

How should I cite iVariantGuide?2018-08-23T12:25:29-04:00

Using Advaita Cloud Services’ products or content for any form of publication (e.g. print, electronically) requires researchers to cite them. Please use one of the options below for citations:

  • “The Data (SNPs, insertions, deletions, etc.) were analyzed using Advaita Bio’s iVariantGuide (http://ivariantguide.advaitabio.com)”.
  • LaTeX users may use the following code in a bibtex file: ~\cite{advaita2016}

@ONLINE{advaita2016,
author = {Advaita, Corporation},
title = {Variant Analysis with iVariantGuide},
month = Apr, year = {2016},
url = { http://ivariantguide.advaitabio.com }
}

Release Notes – Winter 20182020-07-06T16:00:54-04:00

On January 12, 2018, Advaita released a major update to its platform, with improvements to iPathwayGuide, iVariantGuide, and iBioguide.

IMPROVEMENTS

The Advaita Knowledgebase was updated to version 1711 and now includes:

  • 3 organisms: homo sapiens, mus musculus, rattus norvegicus
  • 213,390 Genes
  • 1,933 Diseases
  • 44,976 GO terms
  • 4,791 Drugs
  • 955 Pathways
  • 5,710 miRNAs
  • 3,161,730 References
  • For a complete list of databases and versions, please see report information within each application.

NEW FEATURES

  • iVariantGuide: API Client now accepts multi-sample analyses
  • Improvements to account registration page to ensure proper organization affiliation.

BUG FIXES

  • iPathwayGuide: Improvements to parsing of CuffDiff-formatted files to maintain association of phenotype labels. Fold changes and p-value parsing remains untouched.
Getting Started in iVariantGuide2020-09-14T20:07:04-04:00

Uploading and analyzing data is easy.  Here is a quick video tutorial explaining how.

1. SELECT/ UPLOAD FILE
The first step is to upload your VCF file containing all of the variants and samples you want to analyze. Here are a few tips:

  • Make sure the file meets the specs for VCF v4.1 or higher. Especially:
    • It contains each of the following columns: CHROM, POS, REF, ALT, FILTER, QUAL, INFO, FORMAT, and at least one Sample
    • All INFO and FORMAT tags are defined with their own line in the header
  • There is no limit to the number of variants or samples in your file, but very large files (> 1M variants) could have a slower browsing experience.
  • Don’t have your data ready? We have sample datasets available. Grab a sample file and try it… it’s easy!

Once uploaded, select the checkbox next to the file you’d like to analyze. You will then be prompted to verify the reference assembly and select the type of analysis you wish to perform, including:

  • Case v Control (Group vs Group)
  • Tumor/ Normal (Paired Samples)
  • Pedigrees (Trio, Quad, and larger families)
  • Individual Samples

Lastly, iVariantGuide allows you to pre-filter your variants by quality, read depth, and FILTER flags. If there are certain quality control measures you know you’ll apply anyway, this step will help to focus the variants in your analysis to only those you are confident of, while ensuring a more favorable browsing experience.

2. ADD SAMPLES TO GROUPS

You may assign information to each sample in the file (sex, group, parents) in the page or by uploading a file containing the necessary information. You may also re-name samples (in case the VCF sample names are not easy to read). iVariantGuide accepts two formats for sample information: ped for pedigree analysis and txt for group vs group and tumor/ normal analyses. For a description and example of each file format, see below.

File Formats for Specifying Sample Info

  • PED: a space or tab-delimited file with at least 6 columns, and one row per sample. Read more here and here. Download an example file.
  • TXT: a tab-delimited file with one header row and one row per sample.
    • To use this format, download the example file and open it in Excel or another spreadsheet program. Then replace the example values with the following sample information from your own data. The columns are as follows:
      • sample: the sample names from the VCF file
      • name: the sample names to display in iVariantGuide (if blank, will default to values in sample column)
      • sex: male or female. case-sensitive, if blank will be unknown.
      • paternal: sample name of father (if known)
      • maternal: sample name of mother (if known)
      • group: name of group (for group vs group and tumor/ normal analyses, this column must contain exactly two different group names)

IMPORTANT NOTE: Check the order of your samples! The first sample in the PED file is always the proband, and the first phenotype found is Affected. The second phenotype found (the first row with a phenotype different from that of the proband) is Unaffected, and the third is Unknown. For TXT files, the first group found is Tumor/ Case and the second is Normal/ Control.

3. CREATE REPORT

On the last page you can review the selections you made so far, and give your analysis a Title and Description. Once satisfied, click submit. Each dataset takes about 15 minutes to analyze. You will get an automated email as soon as your analysis is complete.

Advaita Releases API client for iVariantGuide and iPathwayGuide2018-10-09T14:41:41-04:00

With Advaita’s latest update to its applications and knowledge base, Advaita updated its API for iVariantGuide and iPathwayGuide.

An API or (Application Program Interface) is a set of routines, protocols, and tools for building software applications. An API specifies how software components should interact. Advaita’s API is designed for advanced users of iPathwayGuide and iVariantGuide who would like to streamline their data processing and bypass the UI for submitting data.

Advaita’s API is designed to take advantage of the AWS EC2 environment and allow users to submit one to several datasets in rapid succession. Results from the application are still viewed in the application and are just as informative.

The API documentation and links are viewable at: ​https://hub.docker.com/r/advaitabio/api-client/

API access and support is available to subscribers who have opted for API access to either iPathwayGuide or iVariantGuide customers. If you would like to add API access to your existing annual subscription, please contact us at sales@AdvaitaBio.com for additional information.

Winter 2017 Release Notes2018-08-25T09:59:16-04:00

On February 27, 2017, Advaita released a major updates to its platform. These are the release notes.

IMPROVEMENTS TO: iPathwayGuide, iVariantGuide, iBioguide, and the Advaita Knowledge Base

  • Changes to AWS services in preparation for HIPAA compliance
  • Updated knowledge base to version Advaita KB v1702, which includes the following data sources and versions:
Database Version iPG Annotations iVG Annotations
KEGG Release 81.0+/01-20, Jan 17​ Pathways, Diseases, Drugs​ Pathways
Gene Ontology ​2016-Sep26 GO Terms GO Terms
Targetscan Targetscan v7.1 miRNA Target Genes miRNA Target Genes
MIRBASE MIRBASE v21,06/14 miRNA Sequences
dbSNP (incl 1k genomes) Build 149 Minor Allele freq.
RefSeq Release 71 July 2016 Impacted Transcripts
ClinVar Dec 1, 2016 Clinical Significance
​SNPEff ​v4.1L Predicted Impact

IMPROVEMENTS TO iPATHWAYGUIDE

  • NEW FEATURE! Onboarding carousel with top user benefits
  • NEW FEATURE! API (Premium feature)
  • Bug fix: genes selected in Genes Table on Pathways page are now highlighted on pathway map

IMPROVEMENTS TO iVARIANTGUIDE

  • Improved error messaging for sample upload & report creation
  • NEW FEATURE! Versioning: each report now shows which version of the Advaita Knowledgebase was used to annotate the sample. Outdated reports may be updated when viewing Report Info: either on the Reports page or from within the report itself. As is true for other Advaita applications, only the report owner may update it.

IMPROVEMENTS TO iBIOGUIDE

  • Updated to use AKB v1702
iVariantGuide Commercial Release Notes2018-08-25T10:02:40-04:00

11/1/2016

The Commercial Release of iVairantGuide is here! With this commercial release we now have the following enhancements from the last Beta:

  • Redesigned uploading and intake navigation work flow with onboarding queues
  • Improved navigation and selection on visual filters/graphs
  • Improved sharing capabilities:
    • View share history
    • autofill prompts for often-used email addresses
    • Ability to associate filter presets to shared report
    • Ability to associate filter presets to public link (anyone can see what you see)
  • Improved tooltips and onboarding
  • Improved pathway and GO analysis with p-value ranking and advanced correction factors
  • UI improvement
  • User profile page
  • API credentials
  • Initial API (Premium Feature)
  • Printable Summary (Premium Feature)
  • Improved Pathway and GO Analysis (Premium Feature)
  • Harmonization of labeling and naming conventions
  • Numerous bug fixes and security enhancements
iVariantGuide Beta 2 Release Notes2018-08-25T10:04:45-04:00

9/6/2016

In the pre-commercial release of iVariantGuide the following issues have been addressed:

  • Redesigned uploading and intake navigation work flow with onboarding queues
  • Improved navigation and selection on visual filters/graphs
  • Improved sharing capabilities:
    • View share history
    • autofill prompts for often-used email addresses
    • Ability to associate filter presets to shared report
    • Ability to associate filter presets to public link (anyone can see what you see)
  • Improved tooltips
  • Improved pathway and GO analysis with p-value ranking and advanced correction factors
  • UI improvement
  • User profile page
  • Introduction of subscriptions (free during beta) to see premium features
  • Redesign of processing engine to paralellize analyses
  • Improved filtering speed
  • Harmonization of labeling and naming conventions
  • Numerous bug fixes and security enhancements
May 2016 Release Notes2018-08-25T10:05:56-04:00
  • Redesigned uploading and intake navigation work flow
  • Improved navigation and selection on visual filters/graphs
  • Improved tooltips
  • Redesigned notification of filter presets when navigating away from variants page
  • Harmonization of labeling and naming conventions
  • Numerous bug fixes and security enhancements
Beta Release Notes2018-10-11T11:29:30-04:00

4/11/2016

  • Analyses are currently limited to single sample analyses. Multi-sample analyses are in development.
  • Input file size is limited to ~100mb for now.
  • Input files must be .vcf or .vcf.gz version 4.1 or later; reference genomes hg19 (GRCh37) and GRCh38 are supported.
  • Supported filters include by quality, read depth, genomic region, variant class, predicted effect, clinical significance, and impact score.
  • Filter combinations may be saved as “Presets” for later use with new data sets.
  • Detailed variant view includes links to: iBioGuide, dbSNP, OMIM, MedGen, PubMed, and more.
  • View variants in context of impacted pathways. Pathway view highlights affected genes and provides ability to model miRNAs and drugs.
  • View variants in relation to GO terms. Navigate upstream and downstream to identify specific ontology terms.
  • Share reports with individuals or publicly.
How long does an analysis take?2020-07-07T15:42:29-04:00

Generally, each analysis takes about 15 minutes to complete. If there are other analyses queued ahead of yours, it may take a bit longer. You will receive an email as soon as the analysis is complete.

biological analysis software AdvaitaBio

Click on a Category to see the associated topics.

Release Notes – Summer 20192020-04-30T14:22:50-04:00

On June 21, 2019, Advaita released a major update to its platform, with improvements to iPathwayGuide, iVariantGuide, and iBioGuide.

IMPROVEMENTS

The Advaita Knowledgebase was updated to version 1906 and now includes:

  • 3 organisms: homo sapiens, mus musculus, rattus norvegicus
  • 216,544 Genes
  • 2,307 Diseases
  • 45,049 GO terms
  • 5,694 Drugs
  • 985 Pathways
  • 5,396 miRNAs
  • 92,745 Proteins
  • 29,761,157 References
  • 3,042,479 Interactions
  • 477,289 Experiments

For a complete list of databases and versions, please see report information within each application.

 

NEW FEATURES

  • iBioGuide: Users can now log in to iBioGuide using their Advaita credentials, the same account they use for iPathwayGuide and iVariantGuide.
  • iPathwayGuide: Experiment and references for Network Analysis are now shown in a paged view, improving load time.

BUG FIXES

  • iPathwayGuide: Meta-Analysis icon was updated to stay consistent with menu selections.
iBioGuide Overview2018-08-26T19:04:53-04:00
Using iBioGuide2018-08-26T18:49:43-04:00

iBioGuide is a free browser and search tool based on Advaita’s extensive knowledge base of over 100 million relationships. Search for any term and find all the related genes, microRNAs, pathways, biological processes, molecular functions, cellular components, drugs, diseases, and references.

Example 1: You are interested in identifying the pathways associated with the CDK4 gene. Enter the gene symbol and find a list of related pathways. Exploring any one of the pathways allows you to see the genes that interact with CDK4 and the miRNAs and drugs that target them along with relevant references.

Example 2: You are interested in learning about the regulation of cell cycle process. Enter this as your search term and discover the various entities related to this process. Exploring one of the GO terms, you quickly identify the genes annotated to the process and the miRNAs and drugs that target these genes and possibly this processes.

Release Notes – Winter 20182020-07-06T16:00:54-04:00

On January 12, 2018, Advaita released a major update to its platform, with improvements to iPathwayGuide, iVariantGuide, and iBioguide.

IMPROVEMENTS

The Advaita Knowledgebase was updated to version 1711 and now includes:

  • 3 organisms: homo sapiens, mus musculus, rattus norvegicus
  • 213,390 Genes
  • 1,933 Diseases
  • 44,976 GO terms
  • 4,791 Drugs
  • 955 Pathways
  • 5,710 miRNAs
  • 3,161,730 References
  • For a complete list of databases and versions, please see report information within each application.

NEW FEATURES

  • iVariantGuide: API Client now accepts multi-sample analyses
  • Improvements to account registration page to ensure proper organization affiliation.

BUG FIXES

  • iPathwayGuide: Improvements to parsing of CuffDiff-formatted files to maintain association of phenotype labels. Fold changes and p-value parsing remains untouched.
Spring 2016 Release – June 19, 20162018-08-23T10:37:53-04:00

The following components were added or addressed in this release.

  • Extensive databases updates including:
    • KEGG pathways, drugs, and diseases
    • NCBI genes
    • TargetScan miRNAs
    • Gene Ontologies
    • PubMed references
  • Improved search results
  • Several bug fixes
  • Changes to UI backend
2015 Release2018-08-23T10:39:41-04:00

The databases contained in iBioGuide were updated with the following releases:

  • Pathways, Drugs, Diseases – KEGG – Release 73.0, March 16, 2015
  • Gene Ontology Terms – Gene Ontology Consortium – September 19, 2014
  • MicroRNA
    • TargetScan – Release 6.2, March 2015
    • MIRBase – Version 21, June 2014
  • Genes – NCBI – March 2015
What terms can I search for?2018-08-23T10:21:34-04:00

iBioGuide connects several databases and their annotated contents. As such, iBioGuide will perform best if you use life-science terms such as genes, diseases, biological process, etc. ​

Can I filter my results?2018-08-23T10:16:55-04:00

Yes. Results are delivered in a global sense, but can quickly be narrowed to a specific domain by clicking one of the filters at the top of the page or selecting a specific organism.

Can I reach for sets of genes?2018-08-23T10:17:24-04:00

We’re working on it. In an upcoming release we will add gene set enrichment analysis capabilities.

Can I save my search results?2018-08-23T10:18:06-04:00

Yes. Just record or save the url. You can share the url as needed. Search history, unfortunately, is not savable at this time.

Do you support other organisms beyond Human, Mouse, or Rat?2018-08-23T10:18:39-04:00

Not specifically. Gene ontologies are not organism specific and are applicable to all organisms. If you have a specific organism you would like to see us support, please let us know.

What databases do you use and how often do you update them?2018-08-23T10:19:12-04:00

We use a variety of public and semi-public databases, like NCBI, KEGG, among others. These databases are updated at the same time as the databases in iPathwayGuide.

How is iBioGuide different from iPathwayGuide?2018-08-23T10:19:55-04:00

iBioGuide is meant to allow users to browse relationships between various biological entities and concepts. iPathwayGuide is designed to identify which entitles and systems are impacted in the context of experimental data.

Can I save the pathway images with my annotated miRNAs, Drugs, or genes?2018-08-23T10:20:28-04:00

iBioGuide is not have this capability. If you need to save an image, we encourage you to use a screenshot.

How much does it cost?2018-08-23T10:21:16-04:00

iBioGuide is 100% free. We don’t even ask you for your email address.

What is the difference between a ‘Parent” and a ‘Child’ in the GO structure?2018-08-23T10:20:54-04:00

​A “Parent’ term will be a more generalized term for that ontology domain. A “Child” term will be a more specific form of the currently selected term.

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