Blast2GO Teaching Exercises

Transcription

1 Blast2GO Teaching Exercises Ana Conesa and Stefan Götz 2012 BioBam Bioinformatics S.L. Valencia, Spain

2 Contents 1 Annotate 10 sequences with Blast2GO 2 2 Perform a complete annotation process with Blast2GO 4 3 Creating GO-DAGs and Pies 6 4 Enrichment Analysis with Blast2GO 7 5 Functional Analysis/Data Mining 9 1

3 1 Annotate 10 sequences with Blast2GO The aim of this exercise is to annotate 10 sequences following the Blast2GO scheme and to modulate the annotation of these sequences. Open Blast2GO from the application website http: // At the Blast2GO file menu, select the option to Load 10 Example Sequences. Perform the following steps and answer the questions: 1.1 Annotate 10 sequences with Blast2GO Perform the following steps and answer the questions: BLAST against NCBI NR database (do not forget to provide your address). Check the application messages tab to see how the BLAST progresses. How long does it take to complete? Are all sequences successfully blasted? Launch mapping. This is a time-consuming step. While mapping is proceeding, make the following checks: Browse BLAST results for any of the sequences (single sequence menu, right mouse click). Localize different hits and check the local alignment values. As the first mapping result is received, draw the GO graph of mapping results (single sequence menu, right mouse click) and localize annotation scores. Try to understand which GO terms will be annotated. Export top-blast data (at File Menu - Export). Open the file with a SpreadSheet. Compare this information with the information displayed at the Blast results tab. Once mapping is finished: How many GO terms have you fetched for each sequence? Annotate the sequences with the default parameters. How many GO terms do you obtain for each sequence? 1.2 Let s check some annotations more in detail Generate the annotation graph (DAG) of Sequence 7 (single sequence menu (right mouse click) - Draw graph of mapping results with highlighted annotations). Interpret and save the molecular function graph. Select sequences 1 and 8. Reset annotation of these sequences and re-annotate them at an annotation threshold of 80? How does it change? There are a number of sequences with mapping but without annotation. What happened? Try to annotate them manually. Tip: go to the Blast results of these sequences to learn about them, decide on the functions you would give to these sequences. Go to the Gene Ontology resource and look for appropriate GO terms. Add these manually to the sequences and mark them as annotated manually. 1.3 Let s augment/modify the annotations Get InterPro annotation for these sequences. How long does it take? Merge InterPro results with Blast annotations. How much does your annotation improve? Run Annex on these sequences. How does you annotation improve? Get KEGG maps for these sequences. results? For how many sequences do you obtain KEGG Get the GOSlim of these sequences. How many GO terms do you have now? Export annotation results in different formats (.annot, GeneSpring, Sequence Table). Open these files with SpreadSheet. Which format do you like the most? 2

4 1.4 Extra exercise: Merge two.dat file Save your project as.dat file. Create and save 2 subsets of disjoint sequence sets (Tip: use select check-boxes and Delete Sequence Selection function). Close the project. Merge the 2.dat files again using B2G merge function. 3

5 2 Perform a complete annotation process with Blast2GO The aim of this exercise is to perform all steps of analysis for a set of 1100 Citrus clementina sequences. In this way we can learn more about the features Blast2GO offers to get a better understanding of your dataset. Perform the following steps and answer the questions: 2.1 Annotation of 1100 sequences with Blast2GO 1. Download a.dat file of 1100 sequences already blasted against the non-redundant database from the NCBI and mapped against the B2G database: Download: mapping example nr.dat (Alternatively you can download the SwissProt dataset and compare your results with the results of one of your colleagues: mapping example swissprot.dat) 2. Please start Blast2GO with 1024MB of memory and load the.dat file 3. First of all please go to Tools - Database Configuration and check that everything is ok. 4. Perform the Annotation step with default parameters. (in case of a slow Internet connection please download and use the.dat file which is already annotated with default parameters: annotation example nr.dat) 5. Generate now for each Blast2GO step (blast,mapping,annotation) the corresponding statistic charts like e.g.: Blast: e-value distribution, species distribution, similarity distribution Mapping: Evidence code distribution, DB Sources of mapping Annotation: GO annotation level distribution etc. 6. Export a chart which represents the result of the annotation process as.png and.pdf files 2.2 Augment annotation via InterPro and Annex 1. Import InterProScan XML results to the already annotated sequences and merge the annotations. 2. Download the zipped XMLs (.zip, tar.gz) and unzip them. (Unzip files under Linux with a right mouse click on the file extract here ) 3. Perform the Annex augmentation 4. Now save you project as a.dat file and export the annotations as.annot file. 5. Perform a GO-Slim reduction and generate again a âgo annotation level distributionâ chart. 2.3 Try different annotation strategies 1. Change Annotation strategy: (reset the existing annotation) Lets be very restrictive: Set all experimental evidence codes (plus TAS) to 1 (first six ones in Blast2GO) and the rest to 0. Set the annotation rule to 70. Set the e-value filter cut-off to 1E-10. To compensate the restrictive setting we increase the GO-weight to 10. Now redo the annotation step and analyse the results generate a data-distribution chart. 4

6 Lets be very permissive: Set all evidence codes to 1. Set the annotation rule to 50. Set the e-value filter cut-off to 1E-3. Set the GO-weight to 0 to inhibit the abstraction mode and compensate the very permissive settings. 2. Now reset and redo the annotation step and analyse the results generate a datadistribution chart. Open the file blast example swissprot.dat, annotate the set in the way you think it is most convenient and compare the performance with the different annotation styles you applied to the NR-dataset. 2.4 Questions about the used dataset and the annotation process 1. What do we observe about the obtained e-values for this dataset of sequences - and how behave the sequence similarities? 2. Have a look at the mapping charts and try to interpret them. 3. Having a look at the GO annotation level distribution : What is the mean level of all GO-annotations and how many annotations could be assigned. 4. For how many sequences could you obtain a InterPro results and how many of them contributed with GO-Terms. 5. Tell how many more sequences could be annotated by adding the InterProScan GO-terms? 6. How many more annotations could be assigned through the Annex step. 7. Finally, generate the level-distribution chart for the different settings and interpret the results. 8. Can you obtain more/less annotated sequences by modifying the annotation parameters? How? 5

7 3 Creating GO-DAGs and Pies The aim of this exercise is to learn how to create informative GO graphs (DAGs) and how to summarize your data in charts that can be included in a publication. Please use for this exercise the Blast2GO project file containing the 1500 sequences from the NFS Potato microarray 10K. Perform the following steps and answer the questions: 3.1 Create combined graphs and summarize them Create the complete graphs for all 3 GO branches. Can you extract any conclusion? Use the seq and score filters to reduce the number of GO terms. Try one type of filter at the time. How does the resulting graph look like? Which filtering value gives you a good view on the data? Can you see easily important terms? How? Which ones? Perform a GOSlim on these data (use plant specific). Create the DAG. How does it compare to the previous graphs? Generate pie charts with normal and multilevel pies and bar-charts. Try out different filter settings until you get a useful summary. Which functions are more abundant? Give a summary of the functions represented in this sub-array. 3.2 Extra exercise: Pie charts with Excel and custom-colored graphs Export the graph-data as plain text file (txt) and open it in Excel (SpreadSheet). Try to reproduce some of the charts you obtained with Blast2GO. Save your charts and DAGs and visualize them outside the application. Make a custom-colored graph with the top 100 GO terms ordered by the amount of annotated sequences. 6

8 4 Enrichment Analysis with Blast2GO 4.1 Blast2GO - FatiGO The aim of this exercise is to learn how to make use of the functional enrichment feature of Blast2GO. We provide you with the annotations of 4200 Citrus Contigs of a custom-made microarray. These Contigs were all annotated previously by the Blast2GO application. In continuation, this array was used in a set of experiments studying the response of citrus plant roots to salty soil. After a complete microarray data analysis (normalization, differential expression, clustering, etc.) over 650 genes could be identified as differential expressed (over and under). We want to know now in which way this sub-set of sequences is functionally different compared to the rest of the citrus microarray genechip. Tasks 1. Download the.annot file of the annotated sequences corresponding to the probes of the microarray: citruschip.annot. 2. Load the annotations into Blast2GO. 3. Download a file containing a list of differential expressed genes: salt gene set.txt. 4. Open the Enrichment Analysis menu and click Make Fisher s Exact Test. Select the salt gene set.txt as test-set Observe that now some 667 sequences are selected. Select one-tailed since we are only interested in over-represented functions. Leave the rest of the parameters unchanged. 5. Click run and switch to the Application Messages tab to observe the output. 6. After a while you will obtain a long list of red, overrepresented function. 7. To get a better idea of the biological meaning/context of these term generate a enriched graph. 8. Now perform again a enriched graph but change the parameter Thined-Out Graph Node Filter to Now browse the graphs and study the biological functional meaning in the context of the salty soil experiment: We observe terms like: thylakoid part (related to chloroplasts and pigmentation), lipoxygenase activity (related to oxidation processes), response to stress (water, virus, etc) or translation/ribosom (high activity of protein synthesis). 10. Try to export/save the one of the graphs in a proper resolution as image (.png) and as PDF file 11. Finally export the results as a text file, open it in a spread-sheet application (Excel) and search for all the sequences related to lipoxygenase activity. How many sequences can you find in the test set and how many in the rest/reference set? (obervations/results) 4.2 Fatiscan: Study of gene expression in breast tumour classes DataSource: Analysis of tumours of 49 breast cancer patients. Tumours classified into luminal and basal classes, and a novel molecular apocrine class. Apocrine tumors are estrogen receptor negative (ER-) and androgen receptor positive (AR+), while luminal tumors are ER+ and AR+, and basal tumors are ER- and AR-. (Farmer P, et al. (2005). Identification of molecular apocrine breast tumours by microarray analysis. Oncogene, Jul 7;24(29): ). 7

9 Gene-Set-Enrichment-Study: Download a file containing the genes differentially expressed between two tumour classes (rankedgenes.txt). To identify functionally enriched terms for blocks of genes we are going to perform a threshold-free FatiScan. Please go to babelomics.org and select: In FatiScan input form, please select the organism Homo sapiens, the GO categotry biological process and Two-tailed Fisher s Exact Test. Than press the RUN button. Questions: What is the most over-represented function among the over-expressed genes at GO level 9. What is the most over-represented function among the under-expressed genes at GO level 9. 8

10 5 Functional Analysis/Data Mining In this last exercise, rather than indicating analysis steps to perform, we suggest an analysis scenario you should address by your own: We have a Blast2GO annotation project corresponding to a 7K CitrusChip. This has been annotated with default B2G parameters. We also have an additional file with a manually annotated set of sequences of this chip which needs to be added to the automatic annotation. This annotation will be used to investigate enrichment analysis for a group of genes that respond to salinity stress. We want test how annotation policy influences the result of this enrichment analysis, having a graphical representation of similarities and differences. Thus the exercise is to merge automatic annotation with the curated data and then define 2 additional annotation styles, one strict and one permissive, do the enrichment and compare. 9