All Product Support Topics

Product Support Topics2018-10-13T09:52:54+00:00

Product Support Topics

iBioGuide

Click on a Category to see the associated topics.

Advaita’s iPathwayGuide – Combining Sciex SWATH 2.0 Data and mRNA Data2018-08-26T10:36:43+00:00

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.

iPathwayGuide – Meta Analysis2018-08-26T10:38:05+00:00

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+00:00
Uploading Affymetrix CEL files in iPathwayGuide2018-08-26T10:40:23+00:00
iPathwayGuide Overview2018-08-26T10:41:21+00:00
iPathwayGuide’s Pathway Diagram2018-08-26T10:42:14+00:00
iPathwayGuide – Uploading a Custom File2018-08-26T10:43:10+00:00
iPathwayGuide – GEO2R2018-08-26T10:44:09+00:00
iPathwayGuide – CuffDiff and DESeq2018-08-26T10:45:07+00:00
iPathwayGuide – Data Uploading Overview2018-08-26T10:46:07+00:00
Pathway Analysis of Time Course Expression Data2018-08-26T10:24:00+00:00
Comparison of Protein and mRNA expression profiles2018-08-26T10:25:05+00:00
Integrative Analysis of Breast Cancer Subtypes Using iPathwayGuide2018-08-26T10:26:09+00:00
Uploading Data Webinar2018-08-26T10:27:08+00:00
Leveraging Public Data: Comparing Therapeutic Response in Melanoma2018-08-26T10:28:10+00:00
Webinar: iPathwayGuide Overview2018-08-26T10:29:09+00:00
Publications by Advaita Bioinformatics Team2018-08-25T07:45:38+00:00

Paper

# of Citations

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

901

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

787

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

640

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

633

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

502

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

480

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

398

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

581

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

405

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

106

Statistics and data analysis for microarrays using R and bioconductor.
(Book) CRC Press

60

Analysis and correction of crosstalk effects in pathway analysis.
Genome Research, 2013, Vol. 23 (9).

56

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 iPathwayGuide2018-08-24T09:30:14+00:00

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.

Worthington, R., Ball, E., Wolf, B. and Takacs, G., 2017. Method to Identify Silent Codon Mutations That May Alter Peptide Elongation Kinetics and Co-translational Protein Folding. In Proteomics for Drug Discovery (pp. 237-243). Humana Press, New York, NY.

Kumar, A., Bicer, E.M., Pfeffer, P., Monopoli, M.P., Dawson, K.A., Eriksson, J., Edwards, K., Lynham, S., Arno, M., Behndig, A.F. and Blomberg, A., 2017. Differences in the coronal proteome acquired by particles depositing in the lungs of asthmatic versus healthy humans. Nanomedicine: Nanotechnology, Biology and Medicine.

Lin, C.K.E., Kaptein, J.S. and Sheikh, J., 2017. Differential expression of microRNAs and their possible roles in patients with chronic idiopathic urticaria and active hives. Allergy & Rhinology, 8(2), pp.e67-e80.

Kumar, A., Terakosolphan, W., Hassoun, M., Vandera, K.K., Novicky, A., Harvey, R., Royall, P.G., Bicer, E.M., Eriksson, J., Edwards, K. and Valkenborg, D., 2017. A Biocompatible Synthetic Lung Fluid Based on Human Respiratory Tract Lining Fluid Composition. Pharmaceutical Research, pp.1-12.

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.

Schatton, D., Pla-Martin, D., Marx, M.C., Hansen, H., Mourier, A., Nemazanyy, I., Pessia, A., Zentis, P., Corona, T., Kondylis, V. and Barth, E., 2017. CLUH regulates mitochondrial metabolism by controlling translation and decay of target mRNAs. J Cell Biol, pp.jcb-201607019.

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.

Wang, S., Campos, J., Gallotta, M., Gong, M., Crain, C., Naik, E., Coffman, R.L. and Guiducci, C., 2016. Intratumoral injection of a CpG oligonucleotide reverts resistance to PD-1 blockade by expanding multifunctional CD8+ T cells. Proceedings of the National Academy of Sciences, p.201608555.

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 one11(10), p.e0164804.

Wadhwa, R., Nigam, N., Bhargava, P., Dhanjal, J.K., Goyal, S., Grover, A., Sundar, D., Ishida, Y., Terao, K. and Kaul, S.C., 2016. Molecular Characterization and Enhancement of Anticancer Activity of Caffeic Acid Phenethyl Ester by γ Cyclodextrin.Journal of Cancer7(13), pp.1755-1771.

Zhou, H., Manthey, J., Lioutikova, E., Yang, W., Yoshigoe, K., Yang, M.Q. and Wang, H., 2016. The up-regulation of Myb may help mediate EGCG inhibition effect on mouse lung adenocarcinoma. Human Genomics10(2), p.103.

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.

Mitt, M., Altraja, A. and Altraja, S., 2016. Altered Gene Expression Profiles In Human Bronchial Epithelial Cells Exposed To E-Cigarette Liquid: Results From A Genome-Wide Monitoring. In B58. BIG AND BIGGER (DATA): OMICS AND BIOMARKERS OF COPD AND OTHER CHRONIC LUNG DISEASES (pp. A4053-A4053). American Thoracic Society.

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.

Westphalen, C.B., Takemoto, Y., Tanaka, T., Macchini, M., Jiang, Z., Renz, B.W., Chen, X., Ormanns, S., Nagar, K., Tailor, Y. and May, R., 2016. Dclk1 Defines Quiescent Pancreatic Progenitors that Promote Injury-Induced Regeneration and Tumorigenesis. Cell Stem Cell18(4), pp.441-455.

Eddens, T., Campfield, B.T., Serody, K., Manni, M.L., Horne, W., Elsegeiny, W., McHugh, K.J., Pociask, D., Chen, K., Zheng, M. and Alcorn, J.F., 2016. A Novel CD4+ T-cell Dependent Murine Model of Pneumocystis Driven Asthma-like Pathology. American Journal of Respiratory And Critical Care Medicine, (ja).

Takeda, K., Sriram, S., Chan, X.H.D., Ong, W.K., Yeo, C.R., Tan, B., Lee, S.A., Kong, K.V., Hoon, S., Jiang, H. and Yuen, J.J., 2016. Retinoic Acid Mediates Visceral-specific Adipogenic Defects of Human Adipose-derived Stem Cells. Diabetes, p.db151315.

Williams, K.E., Lemieux, G.A., Hassis, M.E., Olshen, A.B., Fisher, S.J. and Werb, Z., 2016. Quantitative proteomic analyses of mammary organoids reveals distinct signatures after exposure to environmental chemicals.Proceedings of the National Academy of Sciences, p.201600645.

Ortea, I., Rodríguez-Ariza, A., Chicano-Gálvez, E., Vacas, M.A. and Gámez, B.J., 2016. Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction. Journal of Proteomics. 2016 Feb 18.

Lamontagne, J., Mell, J.C. and Bouchard, M.J., 2016. Transcriptome-Wide Analysis of Hepatitis B Virus-Mediated Changes to Normal Hepatocyte Gene Expression. PLoS Pathog12(2), p.e1005438.

Zhou, H., Manthey, J., Lioutikova, E., Yang, M.Q., Yang, W., Yoshigoe, K. and Wang, H., 2015, November. The upregulation of Myb and Peg3 may mediate EGCG inhibition effect on mouse lung adenocarcinoma. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on (pp. 1532-1535). IEEE.

Andres-Terre, M., McGuire, H.M., Pouliot, Y., Bongen, E., Sweeney, T.E., Tato, C.M. and Khatri, P., 2015. Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses.Immunity43(6), pp.1199-1211.

Srivastava, A., Ritesh, K.C., Tsan, Y.C., Liao, R., Su, F., Cao, X., Hannibal, M.C., Keegan, C.E., Chinnaiyan, A.M., Martin, D.M. and Bielas, S.L., 2015. De novo Dominant ASXL3 Mutations Alter H2A Deubiquitination and Transcription in Bainbridge-Ropers Syndrome. Human molecular genetics, p.ddv499.

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 Journal9(3), pp.239-246.

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 Urology193(4), p.e865.

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

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.

Why do I need to correct my p-values?2018-08-30T21:46:54+00:00

Hi there. Advaita is dedicated to bringing you the most advanced, easiest-to-use Bioinformatics tools out there. And that includes educational materials designed to help you take advantage of all the powerful features we offer. Our last post about p-value correction factors was a bit confusing. This blog post explains how each method works, so you can decide when to use each one.

We are lucky to have a few bioinformaticians around the office, including Dr. Sorin Draghici our CEO and founder. If you don’t have a bioinformatics expert in-house, you might want to pick up his book. It’s full of useful information, and I think the best part about it is how easy it is to read— he makes it fun! For now, if you want to know more about getting the most from your analyses, read on…

​A p-value represents the probability of observing an event by random chance. For example, if there are 5 differentially expressed (DE) genes on pathway X out of 100 DE genes in the dataset, the over-enrichment p-value for pathway X is the probability that from a randomly selected set 100 genes in the dataset, 5 or more fall on pathway X. Significance is determined by setting a threshold, in many cases 0.05. If the p-value is less than 0.05, pathway X is considered significant because the chance of randomly observing the same result is less than 5%.

This means that there is still a chance that the observation was in fact due to randomness and pathway X is not significant, what we would call a “false positive.” The chance of pathway X being a false positive is small, but when we perform this test multiple times as we would for multiple pathways, the chance of reporting at least one false positive increases quickly. That is because the probability of reporting a false positive in a group of independent tests is the sum of the individual p-values. When this is done for hundreds of pathways, we are virtually guaranteed to have some pathways that appear to be significant just by chance. This is known as the “multiple comparisons problem,” and we tell you how to correct for it in the first section.

Enrichment tests are used in a number of settings including enrichment pathway analysis [1] and gene ontology (GO) enrichment analysis. However, the GO has an additional structure that includes a hierarchical organization of its terms, as well as a “true path rule” that allows genes to be associated with entire paths through the ontology, rather than single terms [2]. Because of these additional properties, specific enrichment analysis methods (and associated multiple comparison strategies) have been developed for GO enrichment analysis. Two of these methods will be briefly discussed in the second section.

I. Methods of Correcting for Multiple Comparisons

General methods for multiple comparison corrections may be applied to any enrichment analysis. There are two strategies to limit the number of false positives across a large number of significance tests, and several methods have been developed for each strategy.

Strategy 1. Limit the probability of making a mistake (reporting a false positive) for each individual test
Strategy 2. Limit the rate of false positives, i.e. the proportion of false positive tests

In iPathwayGuide and iVariantGuide, we offer the most widely-cited method for each strategy. Furthermore, the methods we chose provide a range of stringency so that you can choose what is appropriate for your data. Try it out now!

Bonferroni

The Bonferroni correction is considered to be the most conservative method to correct for multiple comparisons, meaning that the fewest false positives are returned. The drawback is that some truly meaningful events may not be reported as significant. The Bonferroni method guarantees that the chance of any individual test yielding a false positive is less than the chosen significance threshold [3,4]. In other words, for a 5% significance threshold, the Bonferroni correction guarantees that the probability of generating at least one false positive is less than 5%. The more tests we run, the smaller the individual (raw) p-values must be for them to remain significant after the Bonferroni correction.

False Discovery Rate

In contrast to Bonferroni, FDR is one of the most lenient methods, allowing more true positives to be reported as significant with the drawback that some false positives may also be reported as such. Developed by Benjamini and Hochberg, FDR correction guarantees that the proportion of false positive tests will be smaller than the original significance threshold [5,6]. In other words, for a 5% significance threshold, FDR correction guarantees that the proportion of false positives is less than 5% of the total number of positive tests.

II. Multiple Comparisons in GO enrichment analysis

Due to the True Path Rule, genes associated with a GO term are also associated with its parent terms (for more on this, see Chapter 22 of Dr. Draghici’s book [7]). This means that simply performing an enrichment analysis for each GO term will count each gene many times, which is a serious problem (see Draghici, Chapter 24). Furthermore, testing the enrichment of all GO terms is not necessary and due to the unavoidable multiple comparison curse will increase the number of false positives reported. Luckily, one can leverage the structure and additional properties of GO in order to limit the number of tests performed, and therefore the number of comparisons one must correct for. In 2006, Alexa [8] proposed two methods to accomplish this: “Elim” and “Weight.”

In iPathwayGuide and iVariantGuide we offer both methods, each of which follow the same outline.
1) Decouple GO terms from one another
2) Perform significance tests
3) Correct for multiple comparisons

​Elim

The Elim method assesses the significance of GO terms starting with the most specific terms first. The benefit of this approach is that it is easier to find specialized terms that are significant, e.g. “response to amphetamine” is more descriptive than “response to chemical.” This approach provides a very nice custom cut through the GO hierarchy that “magically” identifies the lowest level of abstraction that contains the significant GO terms in the given experiment.

Weight

Given a set of related GO terms, the Weight method is designed to identify the term that best represents the genes of interest, regardless of where the term falls in the hierarchy. This approach is less stringent than Elim, capturing more true positives with the drawback of including additional false positives.

iPathwayGuide and iVariantGuide are the only tools to provide these advanced correction factors to help you minimize false positives. Try them today for FREE and see what is truly significant in your data.

References

1. Khatri, P., Sirota, M., & Butte, A. J. (2012). Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol, 8(2), e1002375.
2. Rhee, S. Y., Wood, V., Dolinski, K., & Draghici, S. (2008). Use and misuse of the gene ontology annotations. Nature Reviews Genetics, 9(7), 509-515.
3. Dunn, O. J. (1959). Confidence intervals for the means of dependent, normally distributed variables. Journal of the American Statistical Association,54(287), 613-621.
4. Dunn 1961 Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56(293), 52-64.
5. Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 289-300.
6. Benjamini, Y. & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of statistics, 1165-1188.
7. Drăghici, S. (2011). Statistics and data analysis for microarrays using R and bioconductor. CRC Press. Available here.
8. Alexa, A., Rahnenführer, J., & Lengauer, T. (2006). Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22(13), 1600-1607.

When did you do your last background check?2018-08-24T08:59:46+00:00

​In the world of bioinformatics, we all need to be careful when analyzing our data. I receive countless questions about what background should be used when analyzing gene expression or protein expression data. In this blog post I will attempt to clarify this question.

We have all had the experience where you get a raffle ticket and the ticket says, “Must be present to win.” What they are doing is establishing the pool of candidates from which to draw a winning ticket (and also trying to keep you there, but that’s beyond the scope of this discussion). Performing pathway analysis and other enrichment analyses, is somewhat similar to that.
Picture

​​It is very intuitive that the size of the pool of candidates will dramatically affect the odds of winning. Using our raffle ticket example, let us say 1,000 tickets are given out and there will only be one winner. On the surface, we think our odds of winning are 1 in 1,000. But now let’s say the crowd of people that are actually present for the drawing is only 100 people. Because we “must be present to win,” our odds of winning are now actually 1 in 100. Furthermore, if the raffle organizers wanted to cheat, they could for instance have the raffle draw take place in a small room in which they invite only their friends and relatives. If this room hosts only 10 people at the time of the drawing, the odds of winning would now be 1 in 10. So the odds are really dependent on which background we choose. The same goes for pathway and other enrichment-based analyses.

Let us say you have 1,000 significant genes or proteins that were selected as differentially expressed (DE) in your condition. As I prefaced in my opening paragraph, the question becomes what background should be used when trying to understand what pathways or GO terms are significant. The p-values calculated during the analysis are just another way to tell you about the odds of a given pathway being significant just by chance. And, as we saw in the raffle experiment the choice of the background can have a dramatic effect on the results (odds). Should we use all protein coding genes? How about all genes in NCBI or Ensemble?

The answer is we should always use the set of genes that were measured. This is akin to saying, “you must be present to win.” If the gene or protein was not measured, it should not be in the mix. So if you use an arbitrary set of genes for the background (e.g. all NCBI genes, or all Ensemble genes) your statistic will be heavily skewed. All enrichment programs that have you submit only DE genes or proteins do this. Similarly, if you only use the set of DE genes as the as the background, and further select from there, you can also skew your results (this is like doing the drawing among your 10 friends and increasing your odds of success).

To exemplify this, I took the set of 1,172 significant genes (p<0.05 and Log2FC>|0.6|) from a public dataset (GSE47363) and ran it through a simple enrichment analysis. In the first experiment, I used the set of genes that were measured as the background, about 20,000 genes. Then I ran the exact same set of DE genes, but this time I used “NCBI genes” as provided by another popular web-based pathway analysis application as the background (about 30,000 genes). See Figure 1 below.

Figure 1: Comparing the same set of DEGs, but with different backgrounds. On the left, we use the set of genes that were measured (20k). On the right we use 30k genes from NCBI as the background. Notice the dramatic difference in the number of significant pathways and the p-Values.

While the top pathway is the same in both instances, you will notice little else is the same. In the first set of results, obtained with the appropriate background, we see a total of 64 significant pathways (FDR pV<0.05). The second set of results, obtained with all NCBI genes as background, there are more than 150 significant pathways! Also, you will notice the p-values are much more significant when using NCBI as the background.

You could say: “Well, but the first pathway is the same. So if a pathway is truly relevant, it will be on top no matter what the background is.” First, this is not true. The fact that the two sets of results have the same pathway at the very top is just a coincidence. Secondly, this is an incorrect way of thinking. The very purpose of the p-values is to provide us with the means to distinguish between pathways that may have some differentially expressed genes just by chance, and the pathways that maybe truly be involved with the phenotype.

All pathways with a p-value less than the significance threshold (e.g. 5%) should be carefully studied, not just the very top result, or the top three for that matter. If you have too many significant pathways and you cherry pick from them only the ones that “look familiar”, your results will be severely biased.

A better way is to go back to the criteria you used to select your differentially expressed genes, use more stringent thresholds for p-values and/or fold changes and re-do your analysis. In most cases, using reasonable thresholds for your genes, will give you a set of significant pathways that will actually offer you a good understanding of the underlying biological phenomenon. Assuming of course, that you used a good pathway analysis method. But let us leave this for another posting.

To summarize, using the proper background set of genes or proteins can have a dramatic effect on the number of significant results and the number of false positives. You have to use the entire set of genes that were measured as the appropriate background when analyzing your data. Nothing more, nothing less! This is not a recommendation, nor an advice. This is a must in order to ensure the scientific validity of your findings. This is why in iPathwayGuide, we ask you to submit your entire list of genes. If you ever use an application that only requires you to submit the significant genes, ask yourself, “What is the background being considered?”

For more on this topic, you can read:

Chapter 24 in Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition (Chapman & Hall/CRC Mathematical and Computational Biology)

Use and misuse of the gene ontology annotations, Nature Reviews Genetics, 2008 July 9(7):509-515, PMID:18475267, DOI:10.1038/nrg2363

Poster Download – Using Pathway Analysis to Predict Drug Response2018-10-13T11:17:57+00:00
Download PDF
Release Notes – Winter 20182018-08-22T11:02:01+00:00

On January 12, 2018, Advaita released a major updates 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+00:00

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+00:00

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+00:00

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+00:00

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+00:00

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 iPathwayGuide2018-08-22T09:45:41+00:00

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+00:00

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 Ontology2018-08-22T10:00:07+00:00

Gene Ontology

Gene Ontology (GO) is a dynamic, structured, precisely defined, controlled vocabulary used to describe the roles of genes and gene products along with their hierarchical structure in any organism.

There are two major components to GO terms:

  1. the ontologies themselves, used to defined the terms and describe the structure between them, and
  2. the associations between gene products and the GO terms, which serve to annotate genes based on existing knowledge

The GO ontology includes three independent branches:

  • Biological Processes
  • Molecular Functions
  • Cellular ComponentsGene Ontology (GO) is a dynamic, structured, precisely defined, controlled vocabulary used to describe the roles of genes and gene products along with their hierarchical structure in any organism.There are two major components to GO terms:
  • the ontologies themselves, used to defined the terms and describe the structure between them, and
  • the associations between gene products and the GO terms, which serve to annotate genes based on existing knowledge
  • The GO ontology includes three independent branches:
  • Biological Processes
  • Molecular Functions
  • Cellular Components

Application

Although the key objective of GO project is to unify the representation of genes and gene product attributes across the species gene ontologies are particularly useful to identify enriched GO terms based on the DEGs using enrichment analysis.

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. Advanced pruning methods like Elim and Weight as proposed by Alexa et al (2006) are more suited for such analysis.  You can lean more about different correction factors in Chapter 16 of this book.

Advaita’s iPathwayGuide – Combining Sciex SWATH 2.0 Data and mRNA Data2018-08-22T10:02:11+00:00

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 iPathwayGuide2018-08-22T10:04:47+00:00

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 Overview2018-08-22T10:08:55+00:00
iPathwayGuide Overview2018-08-22T10:06:00+00:00
iPathwayGuide’s Pathway Diagram2018-08-22T10:07:06+00:00
Do you have a User Guide?2018-10-13T11:04:54+00:00

Yes. Please download it here.

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

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.2018-08-22T08:39:59+00:00

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.

How long does an analysis take?2018-08-22T08:40:42+00:00

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.

How much will it cost?2018-08-22T08:41:15+00:00

iPathwayGuide is 100% FREE to use and view for your first 3 datasets. Your first 3 reports have a 72 hour FREE preview period. To unlock download and meta-analysis capabilities, you must purchase each dataset. After the first 3 datasets, all subsequent analyses must be purchased to view, download, or be used in any meta analysis.

Creation of multiple accounts to gain a new preview period is strictly forbidden under the terms set forth by the End User License Agreement (EULA). So please don’t do that.

Reports may be purchased individually via credit card or PayPal. 30-day and Annual subscriptions are also available for purchase. Call one of our representatives today to get a customized quotation. 734-922-0110

Do you have a legend to help me read the pathway maps?2018-08-22T08:46:15+00:00

Click image for a larger view or visit KEGG.

Can I share a report?2018-08-22T08:49:40+00:00

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+00:00

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

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

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 a sample files I can try?2018-10-13T11:09:13+00:00

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+00:00

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+00:00

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?2018-10-19T12:02:47+00:00

Please refer to the following guide or the user manual.

Citing iPathway Guide

Why do I get different results with GEO2R vs. your CEL file uploader?2018-08-22T08:57:29+00:00

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.

Click on a Category to see the associated topics.

Graphical Filtering in iVariantGuide 2.02018-08-25T08:43:25+00:00

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

Creating New Analyses in iVariantGuide 2.02018-08-25T08:47:34+00:00

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

iVariantGuide Application Overview Webinar2018-08-25T09:11:29+00:00
Finding Significance in your Variant Data2018-08-25T09:10:03+00:00
Case v. Control in iVariantGuide Webinar2018-08-25T09:08:55+00:00

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.

Webinar: iVariantGuide for Service Providers2018-08-25T09:06:20+00:00

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.

Pathway Analysis of High-Priority Variants2018-08-25T08:51:31+00:00

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 Webinar2018-08-25T08:53:58+00:00

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 iVariantGuide2018-08-25T09:00:08+00:00

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 iVariantGuide2018-08-25T08:57:02+00:00

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 Data2018-08-25T08:58:15+00:00
Getting started with iVariantGuide (3:15)2018-08-25T08:59:23+00:00
Getting Started in iVariantGuide2018-08-23T15:10:22+00:00

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.

Graphical Filtering in iVariantGuide 2.02018-08-23T15:12:31+00:00

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

Creating New Analyses in iVariantGuide 2.02018-08-23T16:52:15+00:00

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 Variants2018-08-23T16:54:00+00:00

​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 Webinar2018-08-23T16:55:29+00:00

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 iVariantGuide2018-08-23T16:58:28+00:00

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 iVariantGuide2018-08-23T16:59:45+00:00

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 Data2018-08-23T17:00:49+00:00
Getting Started with iVariantGuide2018-08-23T17:02:26+00:00

Uploading data:

iVariantGuide accepts variant call files meeting the following criteria:

Type: .vcf OR .vcf.gz
Size: .vcf less than 100MB, vcf.gz under 20MB
Version: .vcf 4.1 or later
Assembly: reference genomes hg19 (GRCh37) or GRCh38

Creating analyses:

Start in the top right corner of your reports page. You may create a report from one of your available samples or upload a new sample.
Click upload to add a new sample. Browse to select your VCF and choose the reference genome assembly. Once your data is uploaded and pre-processing is complete, summary statistics appear on your screen. If you are satisfied, enter a title and description and click create.

Explore:

You will automatically receive an email as soon as your analysis is complete. When the status bar is green, your report is ready to start exploring!

​Share:

You may enable a public link to share with anyone or enter an individual email address to send a private link.

I just registered and I can’t log in. What should I do now?2018-08-23T12:02:46+00:00

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+00:00

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?2018-10-13T11:11:39+00:00

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 Trial?2018-08-23T12:04:25+00:00

Yes, every new account comes with a demo report 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 create new filter Presets and explore Pathway and GO Analysis results with these demo reports.

Uploading and analyzing your own .vcf files is free with iVariantGuide. All .vcf files are annotated and analyzed for free. Without a subscription, your report will be classified as Trial status. All dynamic visual filters and variant details are 100% available, however, you cannot save the filters as a preset or view Pathway and GO Analyses.

With a subscription, you have access to additional features including Presets (saved filter combinations), Pathway and GO Terms Analysis, Results Export, and Printable Reports. You can access these features on any purchased report, including the demo reports shared with every account.

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

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+00:00

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+00:00

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+00:00

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

How long does an analysis take?2018-08-23T12:17:13+00:00

Generally, each analysis takes about 15 minutes to complete. You will receive an email as soon as the analysis is complete.

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

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+00:00

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+00:00

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+00:00

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 20182018-08-25T09:33:07+00:00

On January 12, 2018, Advaita released a major updates 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 iVariantGuide2018-08-25T09:51:55+00:00

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+00:00

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+00:00

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+00:00

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+00:00

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+00:00
  • 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+00:00

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.
iBioGuide

Click on a Category to see the associated topics.

iBioGuide Overview2018-08-26T19:04:53+00:00
Using iBioGuide2018-08-26T18:49:43+00:00

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 20182018-08-25T09:33:07+00:00

On January 12, 2018, Advaita released a major updates 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+00:00

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+00:00

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+00:00

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. ​

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

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

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

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+00:00

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+00:00

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+00:00

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+00:00

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+00:00

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+00:00

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

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

​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.

This website intends to use cookies to improve the site and your experience. By continuing to browse the site you are agreeing to accept our use of cookies. For further information, visit our Privacy Policy Page. OK