1. Finding promoters containing TAF1 binding sites identified from a CHiP-seq experiment.

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Suppose you performed a CHiP-seq experiment and identified a series of genomic regions that bind TAF1-protein. You now need to identify a list of genes that contain such sites. This can be easily done with Galaxy in just a few steps. To run this example yourself you will need a file containing genomic coordinates for TAF1- binding sites from the CHiP-seq experiment (an example file can be downloaded from here.).

2. Finding Exons with the highest number of nucleotide polymorphisms.

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In this protocol we will work with two sets of genomic features: exon coordinates and single nucleotide polymorphism (SNP) coordinates. The objective will be to identify exons that contain the highest number of SNPs. Even if you are not particularly interested in exons and SNPs, we encourage you to follow along as the real goal of this protocol is to introduce you to joining, grouping, sorting, and filtering in Galaxy.

3. Saving your results and sharing data with others.

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How can you ensure that the analyses you have just conducted are safely stored and that you are able to go back to them at anytime? You will need to create a free account within Galaxy. This is the only requirement to save your analyses. The protocol below explains this and also introduces sharing analyses with colleagues.

4. Generating a workflow from a history.

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Screencasts 1 and 2 within this series demonstrate interactive analysis in Galaxy, the result is a history which documents each step of an analysis. Galaxy also allows you to construct reusable multi-step analysis workflows. In this protocol, we will demonstrate the creation of a workflow from an existing analysis history.

5. Generating workflows from scratch.

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In addition to creating workflows from existing histories, Galaxy allows us to create a workflow from scratch. In this protocol we will construct a simple workflow that finds the 50 longest intervals from a dataset in a 6 column BED file.

6. Extracting Sequences and alignments: A SNPs in exons example.

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Here we demonstrate how Galaxy is used to extract genomic sequences and multiple species alignments corresponding to regions of interest. In this protocol, we start with the data that we generated in Screencast 2, where we found human coding exons with high SNP counts. We will extract genomic sequences for our regions in FASTA format as well as obtain corresponding alignment blocks from multiple species whole genome alignments in their native format and also create a pseudo-global alignment in FASTA format, which is suitable for use in existing analysis software.

1. Processing and analysis of 454 data in metagenomic studies.

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Here we show the utility of Galaxy in metagenomic applications. We start with a series of 454 reads, assess their quality, select high quality segments, conduct blast searches, and process taxonomic data. All in a single screencast.

2. Building a complete metagenomic workflow.

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This example converts the analysis we covered in Screencast 1 into a workflow that can be applied to any 454 dataset or modified to other types of next-gen platforms.