# Making sense of a newly assembled genome

### Overview

Questions:
• I just assembled a genome. How does it compare with already sequenced genomes?

• How do I find rearranged, inserted, or deleted regions?

Objectives:
• Identification of the most closely related genome to my new assembly

• Perform sequence comparison to locate rearrangements

• Identify genes located in deletions

Requirements:
Time estimation: 4 hours
Supporting Materials:
Last modification: Mar 23, 2021

In this tutorial we begin with a new genome assembly just produced in the Unicycler tutorial. This is an assembly of E. coli C, which we will be comparing to assemblies of all other complete genes of this species.

# Finding closely related genomes

E. coli is one of the most studied organisms. There are thousands of complete genomes (in fact, the total number of E. coli assemblies in Genbank is over 10,500). Here we will shows how to uploaded all (!) complete E. coli genomes at once.

The first part of this tutorial can take a significant amount of time to find the most related genomes. If you want, you can upload this (outdated) copy of the NCBI E. Coli Genomes table to your history:

1. Import the following URL:

https://zenodo.org/record/3382053/files/genomes_proks.txt

• Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

• Select Paste/Fetch Data
• Paste the link into the text field

• Press Start

• Close the window
2. And skip ahead to comparing the most related genomes.

## Getting complete E. coli genomes into Galaxy

Our initial objective is to compare our assembly against all complete E. coli genomes to identify the most related ones and to find any interesting genome alterations. In order to do this we need to align our assembly against all other genomes. And in order to do that we need to first obtain all these other genomes.

NCBI is the resource that would store all complete E. coli genomes. This list contains over 500 genomes and so uploading them by hand will likely result in carpal tunnel syndrome, which we want to prevent. Galaxy has several features that are specifically designed for uploading and managing large sets of similar types of data. The following two Hands-on sections show how they can be used to import all completed E. coli genomes into Galaxy.

### hands_on Hands-on: Preparing a list of all complete E. coli genomes

1. Import the genome list from Zenodo:

https://zenodo.org/record/3382053/files/genomes_proks.txt


### details Getting the data directly from NCBI

For this tutorial we made this dataset available from Zenodo, but it is of course also possible to obtain the data directly from NCBI. Note that the format of the files on NCBI may change, which means some of the parameter settings of tools in this tutorial will need to be altered (e.g. column numbers) when using data directly from NCBI.

Below we describe how you could obtain this data from NCBI.

1. Open the NCBI list of of E. coli genomes in a new window

2. Click on “Filters” at the top right:

3. Select only the “Complete” genomes with the filter at the top

5. Upload this table to Galaxy

6. As this file is a CSV file, we need to convert it to TSV before Galaxy can use it.

### Tip: Converting the file format

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, click on the galaxy-gear Convert tab on the top
• Select Convert CSV to Tabular
• Click the Convert datatype button
7. Rename this file to genomes.tsv

### Tip: Converting the file format

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, click on the galaxy-gear Convert tab on the top
• Select Convert CSV to Tabular
• Click the Convert datatype button

### hands_on Hands-on: Preparing a list of all complete E. coli genomes

1. Cut Tool: Cut1 columns from a table:

• “Cut columns”: c8,c20
• “From”: genome_proks.txt

### solution Solution

It should look like this: 1 | 2 – | – GCA_000005845.2 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/005/845/GCA_000005845.2_ASM584v2 GCA_000008865.2 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/008/865/GCA_000008865.2_ASM886v2 GCA_003697165.2 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/003/697/165/GCA_003697165.2_ASM369716v2 GCA_003018455.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/003/018/455/GCA_003018455.1_ASM301845v1 GCA_001650295.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/001/650/295/GCA_001650295.1_ASM165029v1 GCA_003018035.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/003/018/035/GCA_003018035.1_ASM301803v1 GCA_003112225.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/003/112/225/GCA_003112225.1_ASM311222v1 GCA_001695515.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/001/695/515/GCA_001695515.1_ASM169551v1 GCA_001721125.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/001/721/125/GCA_001721125.1_ASM172112v1 GCA_000091005.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/091/005/GCA_000091005.1_ASM9100v1 GCA_005037725.2 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/005/037/725/GCA_005037725.2_ASM503772v2 GCA_005037815.2 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/005/037/815/GCA_005037815.2_ASM503781v2 GCA_004358405.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/004/358/405/GCA_004358405.1_ASM435840v1 GCA_003018575.1 | ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/003/018/575/GCA_003018575.1_ASM301857v1

Now that the list is formatted as a table in a spreadsheet, it is time to upload it into Galaxy. There is a problem though: the URLs (web addresses) in the list do not actually point to sequence files that we would need to perform alignments. Instead they point to directories. For example, this URL: GCA_000008865.1_ASM886v1 points to a directory (rather than a file) containing many files, most of which we do not need.

So to download sequence files we need to edit URLs by adding filenames to them. For example, in the case of the URL shown above we need to add /GCA_000008865.1_ASM886v1 and _genomic.fna.gz to the end to get this:

ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/008/865/GCA_000008865.1_ASM886v1/GCA_000008865.1_ASM886v1_genomic.fna.gz


This can be done as a two step process where we first copy the end part of the existing URL (/GCA_000008865.1_ASM886v1) and then add a fixed string _genomic.fna.gz to the end of it. Doing this by hand is crazy and trying to do it in a spreadsheet is complicated. Fortunately, Galaxy’s new rule-based uploader can help, as shown in the next Hands-on section:

2. Switch to the Rule-based tab on the right

### tip Tip: Using the Rule-based Uploader

There is a detailed tutorial on using the Rule based Uploader if you want to learn about the more advanced features available.

• “Upload data as”: Collection(s)
• “Load tabular data from”: History Dataset
• “Select dataset to load”: output of the cut tool

### tip Tip: dataset not there?

If the dataset doesn’t appear in the select list, refresh your page.

3. From Column, select Using a Regular Expression
• “From Column”: B
• Select Create columns matching expression groups
• “Regular Expression: .*(\/GCA.*) • “Number of Groups”: 1 • Click Apply 4. From Column, select Concatenate Columns • “From Column”: B • “From Column”: C • Click Apply 5. From Column, select Fixed Value • “Value”: _genomic.fna.gz • Click Apply 6. From Column, select Concatenate Columns • “From Column”: D • “From Column”: E • Click Apply 7. From Rules menu, select Add / Modify Column Definitions • Add Definition, List Identifier(s), Select Column A • Add Definition, URL, Column F • Click Apply 8. Set the Type in the bottom left to fasta.gz 9. Give the upload a name like Complete genomes 10. Upload Now we have all complete E. coli genomes in Galaxy’s history. It is time to do a few things to our assembly. ## Preparing assembly Before starting any analyses we need to upload the assembly produced in Unicycler tutorial from Zenodo: ### hands_on Uploading E. coli assembly into Galaxy 1. Upload Tool: upload1 : • Click Paste/Fetch data button (Bottom of the interface box) • Paste https://zenodo.org/record/1306128/files/Ecoli_C_assembly.fna into the box. • “Type”: fasta • Click Start ### tip Tip: Finding tools mentioned in this tutorial Galaxy instances contain hundreds of tools. As a result, it can be hard to find tools mentioned in tutorials such as this one. To help with this challenge, Galaxy has a search box at the top of the left panel. Use this box to find the tools mentioned here. The assembly we just uploaded has two issues that need to be addressed before proceeding with our analysis: 1. It contains two sequences: the one of E. coli C genome (the one we really need) and another representing phage phiX174 (a by product of Illumina sequencing where it is used as a spike-in DNA). 2. Sequences have unwieldy names like >1 length=4576293 depth=1.00x circular=true. We need to rename it to something more meaningful. Let’s fix these two problems. Because phiX173 is around 5,000bp, we can remove those sequences by setting a minimum length of 10,000: ### hands_on Hands-on: Fixing assembly 1. Filter sequences by length Tool: toolshed.g2.bx.psu.edu/repos/devteam/fasta_filter_by_length/fasta_filter_by_length/1.2 with the following parameters: • “Fasta file”: the dataset you’ve just uploaded. (https://zenodo.org/record/1306128/files/Ecoli_C_assembly.fna). • “Minimal length”: 10000 2. Replace Text Tool: toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_line/1.1.2 in entire line: • “File to process”: the output of the Filter sequences by length tool • “1: Replacement” • “Find Pattern”: ^>1.* • “Replace with”: >Ecoli_C ### Tip: Renaming a dataset • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes • In the central panel, change the Name field to E. coli C • Click the Save button ### tip Regular Expressions The program we just entered is a so-called Regular Expression The expression ^>1.* contains several pieces that you need to understand. Let’s write it top-to-bottom and explain: • ^ - says start looking at the beginning of each line • > - is the first character we want to match. Remember that name of the sequence in FASTA files starts with > • 1 - is the number present is our old name (>1 length=4576293 depth=1.00x circular=true to >Ecoli_C) • . - dot has a special meaning. It signifies any character • * - is a quantifier. From Wikipedia: “The asterisk indicates zero or more occurrences of the preceding element. For example, ab*c matches ac, abc, abbc, abbbc, and so on.” So in short we are replacing >1 length=4576293 depth=1.00x circular=true with >Ecoli_C. The Regular expression ^>1.* is used here to represent >1 length=4576293 depth=1.00x circular=true. Detailed description of regular expressions is outside of the scope of this tutorial, but there are other great resources. Start with Software Carpentry Regular Expressions tutorial. ### question Questions 1. What is the meaning of ^ character is SED expression? ### solution Solution 1. It tells SED to start matching from the beginning of the string. ## Generating alignments Now everything is loaded and ready to go. We will now align our assembly against each of the E. coli genomes we have uploaded into the collection. To do this we will use LASTZ—an aligner designed for long sequences. ### hands_on Hands-on: Running LASTZ 1. LASTZ Tool: toolshed.g2.bx.psu.edu/repos/devteam/lastz/lastz_wrapper_2/1.3.2 with the following parameters: • “Select TARGET sequence(s) to align against”: from your history • param-collection “Select a reference dataset”: the “Complete genomes” collection we uploaded earlier • param-file “Select QUERY sequence(s)”: our E. coli assembly which was prepared in the previous step. • Chaining • “Perform chaining of HSPs with no penalties”: Yes • Output • “Specify the output format”: blastn Note that because we started LASTZ on a collection of E. coli genomes, it will output alignment information as a collection as well. A collection is simply a way to represent large sets of similar data in a compact way within Galaxy’s interface. ### warning It will take a while! Please understand that alignment is not an instantaneous process: allow several hours for these jobs to clear. ### Understanding LASTZ output LASTZ produced data in so-called blastn format (because we explicitly told LASTZ to output in this format, see previous step), which looks like this:  1 2 3 4 5 6 7 8 9 10 11 12 ------------------------------------------------------------------------- Ecoli_C BA000007.2 66.81 232 51 6 3668174 3668397 5936 6149 3.2e-40 162.7 Ecoli_C BA000007.2 57.77 206 38 8 643802 643962 5945 6146 1.6e-18 90.6 Ecoli_C BA000007.2 67.03 185 32 6 4849373 4849528 5965 6149 2.9e-28 122.9 Ecoli_C BA000007.2 63.06 157 33 3 1874604 1874735 5991 6147 5.8e-26 115.3  where columns are: 1. qseqid - query (e.g., gene) sequence id 2. sseqid - subject (e.g., reference genome) sequence id 3. pident - percentage of identical matches 4. length - alignment length 5. mismatch - number of mismatches 6. gapopen - number of gap openings 7. qstart - start of alignment in query 8. qend - end of alignment in query 9. sstart - start of alignment in subject 10. send - end of alignment in subject 11. evalue - expect value 12. bitscore - bit score The alignment information produced by LASTZ is a collection. In this collection each element contains alignment data between each of the E. coli genomes and our assembly: . ## Collapsing collection Collections are a wonderful way to organize large sets of data and parallelize data processing like we did here with LASTZ. However, at this point we need to combine all data into one dataset. Follow the steps below to accomplish this: ### hands_on Hands-on: Combining collection into a single dataset 1. Collapse Collection Tool: toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/4.2 with the following parameters: • “Collection of files to collapse”: the output of LASTZ (collecion input) This will produce one gigantic table (over 12 million lines) containing combined LASTZ output for all genomes. ## Getting taste of the alignment data To make further analyses we need to get an idea about alignment data generated with LASTZ. To do this let’s select a random subsample of the large dataset we’ve generated above. This is necessary because processing the entire dataset will take time and will not give us a better insight anyway. So first we will select 10,000 lines from the alignment data: ### hands_on Hands-on: Selecting random subset of data 1. Select random lines from a file Tool: random_lines1 with the following parameters: • “Randomly select”: 10000 • “from”: the output from Collapse Collection Now we can visualize this dataset to discover generalities: ### hands_on Hands-on: Graphing alignment data 1. Expand random subset of alignment data generated on the previous step by clicking on it. 2. You will see “chart” button galaxy-barchart. Click on it. 3. In the central panel you will see a list of visualizations. Select Scatter plot (NVD3) 4. Click Select data galaxy-chart-select-data 5. Set Values for x-axis to Column: 3 (alignment identity) 6. Set Values for y-axis to Column: 4 (alignment length) 7. You can also click on configuration button galaxy-gear and specify axis labels etc. The relationship between the alignment identity and alignment length looks like this (remember that this is only a subsample of the data): You can see that most alignments are short and have relatively low identity. Thus we can filter the original dataset by identity and length. Judging from this graph we can select alignment longer than 10,000 bp with identity above 90%. ### hands_on Hands-on: Filtering data 1. Filter Tool: Filter1 data on any column using simple expressions: • “Filter”: the full dataset, from the output of the Collapse Collection tool. • “With following condition”: c3 >= 90 and c4 >= 10000 (here c stands for column). NOTE: You need to select the full dataset; not the down-sampled one, but the one generated by the collection collapsing operation. ## Aggregating data Remember, our objective is to find the genomes that are most similar to ours. Given the alignment data in the table we just created we can define similarity as follows: Genomes that have the smallest number of alignment blocks but the highest overall alignment length are most similar to our assembly. This essentially means that they have longest uninterrupted region of high similarity to our assembly. However, to extract this information from our data we need to aggregate it. In other words, for each E. coli genome we need to calculate the total number of alignment blocks, their combined length, and average identity. The following section explains how to do this: ### hands_on Hands-on: Aggregating the data 1. Datamash (operations on tabular data) Tool: toolshed.g2.bx.psu.edu/repos/iuc/datamash_ops/datamash_ops/1.1.0 with the following parameters: • “Input tabular dataset”: output of the previous Filter step. • “Group by fields”: 2. (column 1 contains name of the E. coli genome we mapped against) • “Sort input”: Yes • “Operation to perform on each group”: • “Type”: Count • “On column”: Column: 2 • param-repeat “Insert operation to perform on each group” • “Operation to perform on each group”: • “Type”: Mean • “On column”: Column: 3. • param-repeat “Insert operation to perform on each group” • “Operation to perform on each group”: • “Type”: Sum • “On column”: Column: 4 ## Finding closest relatives The dataset generated above lists each E. coli genome accession only once and will have aggregate information for the number of alignment blocks, mean identity, and total length. Let’s graph these data: ### hands_on Hands-on: Graphing aggregated data 1. Expand the aggregated data generated on the previous step by clicking on it. 2. You will see “chart” button galaxy-barchart. Click on it. 3. In the central panel you will see a list of visualizations. Select Scatter plot (NVD3) 4. Click Select data galaxy-chart-select-data 5. Set Data point labels to Column: 1 (Accession number of each E. coli genome) 6. Set Values for x-axis to Column: 2 (# of alignment blocks) 7. Set Values for y-axis to Column: 4 (Total alignment length) 8. You can also click on configuration button galaxy-gear and specify axis labels etc. The relationship between the number of alignment blocks and total alignment length looks like this: A group of three dots in the upper left corner of this scatter plot represents genomes that are most similar to our assembly: they have a SMALL number of alignment blocks but HIGH total alignment length. Mousing over these three dots (if you set Data point labels correctly in the previous step) will reveal their accession numbers: LT906474.1, CP024090.1, and CP020543.1. ### warning Things change It is possible that when you repeat these steps the set of sequences in NCBI will have changed and you will obtain different accession numbers. Keep this in mind. Let’s find table entries corresponding to these: ### hands_on Hands-on: Extracting into about best hits 1. Select lines that match an expression Tool: Grep1 with the following parameters: • “Select lines from”: to the output from Datamash • “the pattern”: LT906474|CP024090|CP020543. (Here | means or). This will generate a short table like this:  CP020543.1 11 99.9264 4486976 CP024090.1 12 99.9117 4540487 LT906474.1 8 99.94 4575200 From this it appears that LT906474.1 is closest to our assembly because it has eight alignment blocks, the longest total alignment length (4,575,223) and highest mean identity (99.94%). # Comparing genome architectures Now that we know the three genomes most closely related to ours, let’s take a closer look at them. First we will re-download sequence and annotation data. ## Getting sequences and annotations ### hands_on Hands-on: Uploading sequences and annotations Using the three accession listed above we will fetch necessary data from NCBI. We will use the spreadsheet we uploaded at the start to accomplish this. 1. Upload Tool: upload1 the E. coli C genome if you have not done so already: • Click Paste/Fetch data button (Bottom of the interface box) • Paste https://zenodo.org/record/1306128/files/Ecoli_C_assembly.fna into the box. • “Type”: fasta • Click Start 2. Select lines that match an expression Tool: Grep1 with the following parameters: • “Select lines from”: the genomes.tsv you uploaded earlier • “the pattern”: LT906474|CP024090|CP020543 3. Cut Tool: Cut1 columns from a table: • “Cut columns”: c8,c20 • “From”: the output of the select lines tool It should look like: GCA_002079225.1 ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/002/079/225/GCA_002079225.1_ASM207922v1 GCA_002761835.1 ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/002/761/835/GCA_002761835.1_ASM276183v1 GCA_900186905.1 ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/900/186/905/GCA_900186905.1_49923_G01  4. Again Upload tool data 1. Switch to the Rule-based tab on the right • “Upload data as”: Collection(s) • “Load tabular data from”: History Dataset • “Select dataset to load”: output of the cut tool ### tip Tip: dataset not there? If the dataset doesn’t appear in the select list, refresh your page. ### tip Take a Shortcut This step is quite long and potentially error prone. If you want to skip those steps, you can copy and paste this bit of text: {"rules":[{"type":"add_column_regex","target_column":1,"expression":".*(\\/GCA.*)","group_count":1},{"type":"add_column_concatenate","target_column_0":1,"target_column_1":2},{"type":"remove_columns","target_columns":[1,2]},{"type":"add_column_value","value":"_feature_table.txt.gz"},{"type":"add_column_value","value":"_genomic.fna.gz"},{"type":"add_column_concatenate","target_column_0":1,"target_column_1":2},{"type":"add_column_concatenate","target_column_0":1,"target_column_1":3},{"type":"remove_columns","target_columns":[1,2,3]},{"type":"add_column_value","value":"Genes"},{"type":"add_column_value","value":"DNA"},{"type":"add_column_regex","target_column":1,"expression":".*\\/(.*)","group_count":1},{"type":"swap_columns","target_column_0":0,"target_column_1":5},{"type":"remove_columns","target_columns":[5]},{"type":"split_columns","target_columns_0":[1,3],"target_columns_1":[2,4]}],"mapping":[{"type":"list_identifiers","columns":[0],"editing":false},{"type":"url","columns":[1]},{"type":"collection_name","columns":[2]}]}


You can click the tool next to the header Rules tool, and paste the contents there, before clicking Apply, checking “Add nametag for name” and then Upload.

2. From Column, select Using a Regular Expression
• “From Column”: B
• Select Create columns matching expression groups
• “Regular Expression: .*(\/GCA.*\$)
• “Number of Groups”: 1
• Click Apply
3. From Column, select Concatenate Columns
• “From Column”: B
• “From Column”: C
• Click Apply
4. From Rules, select Remove Columns(s)
• “From Column”: B, C
• Click Apply
5. From Column, select Fixed Value
• “Value”: _feature_table.txt.gz
• Click Apply
6. From Column, select Fixed Value
• “Value”: _genomic.fna.gz
• Click Apply
7. From Column, select Concatenate Columns
• “From Column”: B
• “From Column”: C
• Click Apply
8. From Column, select Concatenate Columns
• “From Column”: B
• “From Column”: D
• Click Apply
9. From Rules, select Remove Columns(s)
• “From Column”: B, C, D
• Click Apply
10. From Column, select Fixed Value
• “Value”: Genes
• Click Apply
11. From Column, select Fixed Value
• “Value”: DNA
• Click Apply
12. From Column, select Using a Regular Expression
• “From Column”: B
• Select Create columns matching expression groups
• “Regular Expression: .*\/(.*)
• “Number of Groups”: 1
• Click Apply
13. From Rules menu, select Swap Column(s)
• “Swap Column”: A
• “With Column”: F
• Click Apply
14. From Rules, select Remove Columns(s)
• “From Column”: F
• Click Apply
15. From Rules menu, select Split Column(s)
• “Odd Row Column(s)”: B, D
• “Even Row Column(s)”: C, E
• Click Apply
16. From Rules menu, select Add / Modify Column Definitions
• Add Definition, List Identifier(s), Select Column A
• Add Definition, URL, Column B
• Add Definition, Collection Name, Column C
• Click Apply
17. Check Add nametag for name

At the end of this you should have two collections: one containing genomic sequences and another containing annotations.

## Visualizing rearrangements

Now we will perform alignments between our assembly and the three most closely related genomes to get a detailed look at any possible genome architecture changes. We will again use LASTZ:

### hands_on Hands-on: Aligning again

1. LASTZ Tool: toolshed.g2.bx.psu.edu/repos/devteam/lastz/lastz_wrapper_2/1.3.2 with the following parameters:
• “Select TARGET sequence(s) to align against”: from your history
• param-collection “Select a reference dataset”: DNA, the E. coli genomes we uploaded earlier
• param-file “Select QUERY sequence(s)”: E. coli C fasta file
• Chaining
• “Perform chaining of HSPs with no penalties”: Yes

### tip What does chaining do?

• Output
• “Specify the output format”: Customized general
• “Select which fields to include”: select the following
• score alignment score
• name1 name of the target sequence
• strand1 strand for the target sequence
• zstart1 0-based start of alignment in target
• end1 end of alignment in target
• length1 length of alignment in target
• name2 name of query sequence
• strand2 strand for the query sequence
• zstart2 0-based start of alignment in query
• end2 end of alignment in query
• identity alignment identity
• number alignment number
• “Create a dotplot representation of alignments?”: Yes
1. Rename the LASTZ on collection... mapped reads something more memorable like LASTZ Alignments

### Tip: Renaming a collection

1. Click on the collection
2. Click on the name of the collection at the top
3. Change the name to LASTZ Alignments
4. Press Enter

Because we chose to produce Dot Plots as well, LASTZ will generate two collections: one containing alignment data and the other containing DotPlots in PNG format:

A quick conclusion that can be drawn here is that there is a large inversion in CP020543 and deletion in our assembly.

### details Interpreting Dot Plots

If you are not sure how to interpret Dot Plots here is a great explanation by Michael Schatz:

For a moment let’s leave LASTZ result and create a browser that would allows us to display our results.

## Producing a Genome Browser for this experiment

The dot plots we’ve produced above are great, but they are static. It would be wonderful to load these data into a genome browser where one can zoom in and out as well as add tracks such as those containing genes. To create a browser we need a genome and a set of tracks. Tracks are features such as genes or SNPs with start and end positions corresponding to a coordinate system provided by the genome. Thus the first thing to do is to create a genome that would represent our experiment. We can create such a genome by simply combining the three genomes of closely related strains with our assembly in a single dataset—a hybrid genome.

### Collecting the genomes

The first step will be collapsing the collection containing the three genomes into a single file:

### hands_on Hands-on: Creating a single FASTA dataset with all genomes

1. Collapse Collection Tool: toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/4.0

• param-collection “Collection of files to collapse” the three genomes (collection) named DNA
2. Convert the datatype of this output to uncompress it

### Tip: Converting the file format

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, click on the galaxy-gear Convert tab on the top
• Select Convert compressed to uncompressed
• Click the Convert datatype button
3. Concatenate datasets Tool: toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cat/0.1.0 tail-to-head (cat):
• “Datasets to concatenate”: Collapse collection ... uncompressed, the output from the uncompression step.
• Click Insert Dataset button
• “Select”: the E. coli C file from the start of the history
4. Rename the output to DNA (E. coli C + Relatives)

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to DNA (E. coli C + Relatives)
• Click the Save button

The resulting dataset contains four sequences: three genomes plus our assembly.

### Preparing the alignments

Above we computed alignments using LASTZ. Because we ran LASTZ on a collection containing genomic sequences, LASTZ produced a collection as well (actually two collections: one containing alignments an the other with dot plots). To display alignments in the browser we need to do several things:

1. Fix unwanted % signs in LASTZ output
2. Create names for alignment blocks
3. Convert LASTZ output into BED format
4. Create a single BED track containing alignments against all four genomes.

To begin, let’s look at the LASTZ output:

1 2 3 4 5 6 7 8 9 10 11 12 13
10141727 CP020543.1 + 48 106157 106109 Ecoli_C + 0 106109 106107/106109 100.0% 1
5465 CP020543.1 + 121267 121367 100 Ecoli_C + 109317 109418 76/100 76.0% 2
4870 CP020543.1 + 159368 159512 144 Ecoli_C + 128706 128828 95/115 82.6% 3

One immediate problem is % character in column 12 (alignment identity). We need to remove it as we will use this for the score column of the BED file, and that must be a normal number and not a percentage.

Column 13 of the fields chosen by us for LASTZ run is number. This is an incrementing number given by LASTZ to every alignment block so it can be uniquely identified. The problem is that by running LASTZ on a collection of three genomes it generated a number for each output independently starting with 1 each time. So these alignments identified are unique within each individual run but are redundant for multiple runs. We can fix that by pre-pending each alignment identified (column 12) with the name of the target sequence (column 2). This would create alignments that are truly unique. For example, in the case of the LASTZ output shown above alignment identifier 1 will become CP020543.11, 2 will become CP020543.12 and so on.

### commentBED format

Our goal is to convert this into a format that will be acceptable to the genome browser. One of such formats is BED. In one of its simplest forms (there is one even simpler - 3 column BED) it has six columns:

1. Chromosome ID
2. Start
3. End
4. Name of the feature
5. Score (must be between 0 and 1000)
6. Strand (+, -, or . for no strand data).

### hands_on Hands-on: Convert LASTZ output to BED

1. Replace Text Tool: toolshed.g2.bx.psu.edu/repos/iuc/datamash_ops/datamash_ops/1.1.0 in a specific column:
• param-collection “File to process”: output of LASTZ (LASTZ Alignments)
• “in column”: Column 12
• “Find pattern”: %
• “Replace with”: leave empty
2. Merge Columns together Tool: toolshed.g2.bx.psu.edu/repos/devteam/merge_cols/mergeCols1/1.0.1 with the following parameters:
• param-collection “Select data”: the output of the previous step, Replace Text on collection ...
• “Merge column”: Column: 2 (this is the Target sequence name)
• “with column”: Column: 13 (this is the alignment block created by LASTZ)

### details Output information

The tool added a new column (Column 14) containing a merge between the target name and alignment id. Now we can differentiate between alignment blocks that exist between, for example, CP020543.1 and LT906474.1 because they will have accessions embedded within alignment block IDs. For example, the first alignment between CP020543.1 and our assembly Ecoli_C will have alignment block id CP020543.11, while the 225th alignment between LT906474.1 and Ecoli_C will have ID LT906474.1225. Because of this we can collapse the entire collection of alignments into a single dataset:

3. Collapse Collection Tool: toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/4.0 with the following parameters:
• param-collection “Collection of files to collapse”: the output of the previous step, Merge Columns on collection...

This will produce a single dataset combining all alignment info. We can tell which alignments are between which genomes because we have set identifiers such as CP020543.13.

4. We will reuse this file later so let’s rename it Unprocessed Alignments

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Unprocessed Alignments
• Click the Save button
5. Cut Tool: Cut1 columns from a table:

• “Cut columns”: c2,c4,c5,c14,c12,c8
• param-file “From”: the output of the previous step (Unprocessed alignments)

### details Converting to BED

Let’s look again at the data we generated in the last step:

1 2 4 4 5 6 7 8 9 10 11 12 13 14
10141727 CP020543.1 + 48 106157 106109 Ecoli_C + 0 106109 106107/106109 100.0 1 CP020543.11
5465 CP020543.1 + 121267 121367 100 Ecoli_C + 109317 109418 76/100 76.0 2 CP020543.12
4870 CP020543.1 + 159368 159512 144 Ecoli_C + 128706 128828 95/115 82.6 3 CP020543.13

Alignments are regions of high similarity between two sequences. Therefore each alignment block has two sets of coordinates associated with it: start/end in the first sequences (target) and start/end in the second sequence (query). But BED only has one set of coordinates. Thus we can create two BEDs: one using coordinates from the target and the other one from query. The first file will depict alignment data from the standpoint of target sequences CP020543.1, CP024090.1, LT906474.1 and the second from the standpoint of query - our own assembly we called Ecoli_C. In the first BED, column 1 will contain names of targets (CP020543.1, CP024090.1, and LT906474.1). In the second BED, column 1 will contain name of our assembly: Ecoli_C.

To create the first BED we will cut six columns from the dataset produced at the last step. Specifically, to produce the target BED we will cut columns 2, 4, 5, 14, 12, and 8. To produce the query BED columns 7, 9, 10, 14, 12, 8 will be cut.

### warning There are multiple CUT tools!

The Hands-On box below uses Cut tool. Beware that some Galaxy instances contain multiple Cut tools. The one that is used below is called Cut columns from a table while the other one, which we will NOT use is called Cut columns from a table (cut). It is a small difference, but the tools are different.

This will produce a dataset looking like this:

1 2 3 4 5 6
CP020543.1 48 106157 CP020543.11 100.0 +
CP020543.1 121267 121367 CP020543.12 76.0 +
CP020543.1 159368 159512 CP020543.13 82.6 +

### tip Not exactly the same?

Depending on the steps and other choices, the genomes may be in a different order here. This is unimportant, as all of the same alignments are contained in the file, just the ordering is different. As long as these columns look correct (start/end in column 2/3 are reasonable, a number between 0-100 in column 5, and a + or - in column 6) then it is OK.

6. Rename this “Target Alignments”

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Target Alignments
• Click the Save button
7. Cut columns from a table Tool: Cut1 with the following parameters
• “Cut columns”: c7,c9,c10,c14,c12,c8 (look at the data shown above and the definition of BED to see why we make these choices.)
• “From”: Unprocessed alignments, the output of collection collapse
8. Rename this “Query Alignments”

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Query Alignments
• Click the Save button
9. Concatenate datasets tail-to-head Tool: cat1
• “Concatenate Dataset”: Query Alignments
• Click “Insert Dataset” button
• “1: Dataset”: Target Alignments
10. Change the datatype of the output to BED and rename the output “Target & Query Alignments”

### Tip: Changing the datatype

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, click on the galaxy-chart-select-data Datatypes tab on the top
• Select bed
• Click the Save button

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Target & Query Alignments
• Click the Save button

This will produce a dataset looking like this:

1 2 3 4 5 6
Ecoli_C 0 106109 CP020543.11 100.0 +
Ecoli_C 109317 109418 CP020543.12 76.0 +
Ecoli_C 128706 128828 CP020543.13 82.6 +

### Extracting Genes

Earlier we downloaded gene annotations for the three genomes most closely related to our assembly. The data was downloaded as a collection containing annotations for CP020543.1, CP024090.1, and LT906474.1. The annotation data contains multiple columns described by NCBI as follows (you can look at the actual data by finding the annotation collection from above (called Genes)):

Tab-delimited text file reporting locations and attributes for a subset of annotated features. Included feature types are: gene, CDS, RNA (all types), operon, C/V/N/S_region, and V/D/J_segment.

The file is tab delimited (including a #header) with the following columns:

Column Definition
1 feature: INSDC feature type
2 class: Gene features are subdivided into classes according to the gene biotype computed based on the set of child features for that gene. See the description of the gene_biotype attribute in the GFF3 documentation for more details: ftp://ftp.ncbi.nlm.nih.gov/genomes/README_GFF3.txt ncRNA features are subdivided according to the ncRNA_class. CDS features are subdivided into with_protein and without_protein, depending on whether the CDS feature has a protein accession assigned or not. CDS features marked as without_protein include CDS features for C regions and V/D/J segments of immunoglobulin and similar genes that undergo genomic rearrangement, and pseudogenes.
3 assembly: assembly accession.version
4 assembly_unit: name of the assembly unit, such as “Primary Assembly”, “ALT_REF_LOCI_1”, or “non-nuclear”
5 seq_type: sequence type, computed from the “Sequence-Role” and “Assigned-Molecule-Location/Type” in the *_assembly_report.txt file. The value is computed as: if an assembled-molecule, then reports the location/type value. e.g. chromosome, mitochondrion, or plasmid if an unlocalized-scaffold, then report “unlocalized scaffold on ". e.g. unlocalized scaffold on chromosome else the role, e.g. alternate scaffold, fix patch, or novel patch
6 chromosome
7 genomic_accession
8 start: feature start coordinate (base-1). start is always less than end
9 end: feature end coordinate (base-1)
10 strand
11 product_accession: accession.version of the product referenced by this feature, if exists
12 non-redundant_refseq: for bacteria and archaea assemblies, the non-redundant WP_ protein accession corresponding to the CDS feature. May be the same as column 11, for RefSeq genomes annotated directly with WP_ RefSeq proteins, or may be different, for genomes annotated with genome-specific protein accessions (e.g. NP_ or YP_ RefSeq proteins) that reference a WP_ RefSeq accession.
13 related_accession: for eukaryotic RefSeq annotations, the RefSeq protein accession corresponding to the transcript feature, or the RefSeq transcript accession corresponding to the protein feature.
14 name: For genes, this is the gene description or full name. For RNA, CDS, and some other features, this is the product name.
15 symbol: gene symbol
16 GeneID: NCBI GeneID, for those RefSeq genomes included in NCBI’s Gene resource
17 locus_tag
18 feature_interval_length: sum of the lengths of all intervals for the feature (i.e. the length without introns for a joined feature)
19 product_length: length of the product corresponding to the accession.version in column 11. Protein product lengths are in amino acid units, and do not include the stop codon which is included in column 18. Additionally, product_length may differ from feature_interval_length if the product contains sequence differences vs. the genome, as found for some RefSeq transcript and protein products based on mRNA sequences and also for INSDC proteins that are submitted to correct genome discrepancies.
20 attributes: semi-colon delimited list of a controlled set of qualifiers. The list currently includes: partial, pseudo, pseudogene, ribosomal_slippage, trans_splicing, anticodon=NNN (for tRNAs), old_locus_tag=XXX

Our objective is to convert these data into BED. In this analysis we want to initially concentrate on protein coding regions. To do this let’s select all lines from the annotation datasets that contain the term CDS, then we will produce a collection with three datasets just like the original Genes collection but containing only CDS data. Next we need to cut out only those columns that need to be included in the BED format. There is one problem with this. We are trying to convert these data into 6 column BED. In this format the fifth column (score) must have a value between 0 and 1000. To satisfy this requirement we will create a dummy column that will always have a value of 0. Finally we can cut necessary columns from these datasets. These columns are 8 (start), 9 (end), 15 (gene symbol), 21 (dummy column we just created), and c10 (strand), and then we can add the genome name.

### hands_on Hands-on: Extract CDSs from annotation datasets

1. Select lines that match an expression Tool: Grep1 with the following parameters:
• param-collection “Select lines from”: the collection containing annotations, Genes
• “the pattern”: ^CDS

This is because we want to retain all lines that begin (^) with CDS.

• “Add this value”: 0
• param-collection “to Dataset”: the collection produced by the previous step (Select on collection...)

This will be used for the “score” field of the BED file since we do not have a proper “score”

3. Cut columns from a table Tool: Cut1 with the following parameters:

We will produce two BED files, one using the product name (e.g. “chromosomal replication initiator protein DnaA”) and one using the symbol (e.g. “thrA”). The product name is much more interesting to see in visualisations, but the symbol is more often used in other analyses and we will use that file later. We will start with the product name:

• “Cut columns”: c8,c9,c14,c21,c10
• param-collection “From” the collection produced at the previous step (Add column on collection...)

This will produce a collection with each element containing data like this:

1 2 3 4 5
49 1452 chromosomal replication initiator protein DnaA 0 +
1457 2557 DNA polymerase III subunit beta 0 +
2557 3630 DNA replication and repair protein RecF 0 +

As we mentioned above these datasets lack genome IDs such as CP020543.1. However, the individual elements in the collection we’ve created already have genome IDs. We will leverage this when collapsing this collection into a single dataset:

4. Collapse Collection Tool: toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/4.2 with the following parameters:
• “Collection of files to collapse”: the output of the previous step (Cut on collection...)
• “Prepend File name”: Yes
• “Where to add dataset name”: Same line and each line in dataset
5. Replace Text Tool: toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_column/1.1.3 in a specific column

Many bed parsers do not like whitespace in the Name column, so we will replace that

• param-collection “File to process”: output of the previous Collapse Collection tool step
• “in column”: Column 4
• “Find pattern”: [^A-Za-z0-9_-] (any character that isn’t a number or letter or underscore or minus)
• “Replace with”: _
6. Change the datatype of the collection to bed and rename it to Genes (E. coli Relatives)

### Tip: Changing the datatype

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, click on the galaxy-chart-select-data Datatypes tab on the top
• Select bed
• Click the Save button

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Genes (E. coli Relatives)
• Click the Save button

### solution Solution

The resulting dataset should look like this:

1 2 3 4 5 6
CP020543.1 49 1452 chromosomal_replication_initiator_protein_DnaA 0 +
CP020543.1 1457 2557 DNA_polymerase_III_subunit_beta 0 +
CP020543.1 2557 3630 DNA_replication_and_repair_protein_RecF 0 +

You can see that the genome ID is now appended at the beginning and this dataset looks like a legitimate BED that can be visualized.

For the BED file with the symbol:

1. Cut columns from a table Tool: Cut1 with the following parameters:

We will produce two BED files, one using the product name (e.g. “chromosomal replication initiator protein DnaA”) and one using the symbol (e.g. “thrA”). The product name is much more interesting to see in visualisations, but the symbol is more often used in other analyses and we will use that file later. We will start with the product name:

• “Cut columns”: c8,c9,c15,c21,c10
• param-collection “From” the collection produced at the previous step (Add column on collection...)

This will produce a collection with each element containing data like this:

1 2 3 4 5
49 1452 dnaA 0 +
1457 2557   0 +
2557 3630   0 +
2. Collapse Collection Tool: toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/4.0 with the following parameters:
• “Collection of files to collapse”: the output of the previous step (Cut on collection...)
• “Append File name”: Yes
• “Where to add dataset name”: Same line and each line in dataset
3. Change the datatype of the collection to bed and rename it to Genes (E. coli Relatives) with Symbol Name

### Tip: Changing the datatype

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, click on the galaxy-chart-select-data Datatypes tab on the top
• Select bed
• Click the Save button

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Genes (E. coli Relatives) with Symbol Name
• Click the Save button

### Extracting Gap Regions

It can be useful to have the complement of the aligned regions, to know which regions are unique.

### hands_on Hands-on: Creating a genome file

1. Compute sequence length Tool: toolshed.g2.bx.psu.edu/repos/devteam/fasta_compute_length/fasta_compute_length/1.0.1 :
• param-file “Compute length for these sequences”: DNA (E. coli + Relatives), the FASTA dataset we generated from Collapse Collection tool
• “Strip fasta description from header”: Yes
2. Sort Tool: toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sort_header_tool/1.1.1 data in ascending or descending order:
• param-file “Sort Dataset”: the output of the previous step (Compute sequence length on ...)
• “on column”: Column: 1
• “with flavor”: Alphabetical sort
• “everything in”: Ascending order

### question Question

How does the output look?

### solution Solution

This will generate a dataset that looks like this:

1 2
CP020543.1 4617024
CP024090.1 4592887
Ecoli_C 4576293
LT906474.1 4625968
3. SortBED order the intervals Tool: toolshed.g2.bx.psu.edu/repos/iuc/bedtools/bedtools_sortbed/2.27.0.0 with the following parameters
• param-file “Sort the following BED file”: Target & Query Alignments
• “Sort by” on its default setting (chromosome, then by start position (asc))
4. ComplementBed Extract intervals not represented by an interval file Tool: toolshed.g2.bx.psu.edu/repos/iuc/bedtools/bedtools_complementbed/2.27.0.0 with the following parameters:
• BED/VCF/GFF file”: output of the SortBED tool in the previous step
• “Genome file”: Genome file from your history
• “Genome file”: sorted genome file we’ve generated two steps age, Sort on ...
5. Filter Tool: Filter1 data on any column using simple expressions
• “Filter”: dataset from the last step (Complement of SortBed on ...)
• “With following condition”: c3-c2>=10000

Note: Here we are computing the length (difference between end (column 3) and start (column 2) and making sure it is above 10,000).

### solution Solution

The resulting dataset should look like this:

1 2 3
CP020543.1 1668702 1697834
CP020543.1 1700832 1742068
CP020543.1 3253711 3288956
CP020543.1 3289091 3304937
CP024090.1 3233375 3283074
LT906474.1 3252785 3288031
LT906474.1 3288166 3304009

You will notice that all three genomes have a region starting past 3,200,000 and only CP020543.1 has another region starting at 1,668,702. However, this region reflects some unique feature of CP020543.1 rather than that of our assembly. This is why we will concentrate on the common region which is deleted in our genome, but is present in the three closely related E. coli strains:

### hands_on Hands-on: Restricting list of deleted regions to the common deletion

1. Filter data on any column using simple expressions Tool: Filter1 with the following parameters:
• “Filter”: dataset from the last step (Filter on data...)
• “With following condition”: c2 > 2000000.

### solution Solution

The new set of regions will look like this:

1 2 3
CP020543.1 3253711 3288956
CP020543.1 3289091 3304937
CP024090.1 3233375 3283074
LT906474.1 3252785 3288031
LT906474.1 3288166 3304009
2. Rename this dataset Gaps

### Tip: Renaming a dataset

• Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
• In the central panel, change the Name field to Gaps
• Click the Save button

## Visualising the Genome

### JBrowse

JBrowse is an interactive genome browser, which has been integrated into Galaxy as a workflow-compatible tool that you can use to summarise all of the datasets we’ve created thusfar:

### hands_on Hands-on: View genomes

1. JBrowse Tool: toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.8+galaxy1 genome browser:
• “Reference genome to display”: Use a genome from history
• “Select the reference genome”: DNA (E. coli C + Relatives)
• “Genetic code”: 11. The Bacterial, Archael and Plant Plastid Code
• param-repeat Insert Track Group
• param-repeat Insert Annotation Track
• “Track Type”: GFF/GFF3/BED Features
• param-file “GFF/GFF3/BED Track Data”: Genes (E. coli Relatives) from Collapse Collection tool
• “JBrowse Track Type”: Canvas Features
• param-repeat Insert Annotation Track
• “Track Type”: GFF/GFF3/BED Features
• param-file “GFF/GFF3/BED Track Data”: Target & Query Alignments
• “JBrowse Track Type”: Canvas Features
• “JBrowse Feature Score Scaling & Colouring Options”
• “Color Score Algorithm”: Based on score
• “How should minimum and maximum values be determined for the scores of the features”: Manually Specify
• “Minimum expected score”: 0
• “Maximum expected score”: 100
• param-repeat Insert Annotation Track
• “Track Type”: GFF/GFF3/BED Features
• param-file “GFF/GFF3/BED Track Data”: Gaps

We have embedded a copy of the resulting JBrowse here, if something went wrong during one of the steps you can always just check this output:

Open JBrowse in a new tab

Let’s start by looking at the gaps in our alignments. The deletion from our assembly is easy to see. It looks like a gap in alignments because target genomes are longer than our assembly by the amount equal to the length of the deletion. Clicking on the following links to jump to the right locations in the genome browser above:

Close ups of deleted region (this region is deleted from our assembly and looks like a gap when our assembly is aligned to genomic sequences shown here). In CP0205543 and LT906474 the continuity of the region is interrupted by a small aligned region that has relatively low identity (~72%). This is a spurious alignment and can be ignored.

### Circos

Alternatively to JBrowse, we can use Circos to create a nice image of the alignments:

### hands_on Hands-on: Circos

1. LASTZ Tool: toolshed.g2.bx.psu.edu/repos/devteam/lastz/lastz_wrapper_2/1.3.2 with the following parameters:
• “Select TARGET sequence(s) to align against”: from your history
• param-collection “Select a reference dataset”: DNA, the E. coli genomes we uploaded earlier
• param-file “Select QUERY sequence(s)”: E. coli C fasta file
• Chaining
• “Perform chaining of HSPs with no penalties”: Yes
• Output
• “Specify the output format”: MAF
2. Collapse Collection Tool: toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/4.2 with the following parameters:
• “Collection of files to collapse”: the MAF output of LASTZ (collecion input)
3. Circos: Alignemnts to Links Tool: toolshed.g2.bx.psu.edu/repos/iuc/circos/circos_aln_to_links/0.69.8+galaxy7 reformats alignment files to prepare for Circos:
• “Alignment file”: the output of the Collapse Collection tool step
4. Circos: Interval to Tiles Tool: toolshed.g2.bx.psu.edu/repos/iuc/circos/circos_interval_to_tiles/0.69.8+galaxy7 reformats interval files for Circos’ use:
• BED File”: Genes (E. coli Relatives)
5. Circos Tool: toolshed.g2.bx.psu.edu/repos/iuc/circos/circos/0.69.8+galaxy7 genome browser:

• In the section “Karyoytype”
• “Reference genome source”: FASTA File from History
• “Source FASTA sequence”: DNA (E. coli + Relatives)
• In the section “Ideogram”
• “Limit/Filter Chromosomes”: Ecoli_C;LT906474.1;CP020543.1;CP024090.1 (This specifies the precise ordering in which we wish to see our genomes)
• “Reverse these Chromosomes”: Ecoli_C (It is not readily apparent from the tables or the Genome browser, but the sequence of the E. coli C genome we have is backwards relative to the others)
• In the section “Labels”
• “Radius”: 0.125
• “Font size”: 48
• “Spacing Between Ideograms (in chromosome units)”: 0.1
• In the section “2D Data Tracks”
• param-repeat Insert 2D Data Plot
• “Outside Radius”: 0.99
• “Inside Radius”: 0.94
• “Plot Type”: Tiles
• “Tile Data Source”: the output of the Circos: Interval to Tiles tool above
• In the section “Plot Format Specific Options”
• “Fill Colour”: select a nice colour like a middle blue
• “Stroke Thickness”: 0
• “Orient Inwards”: Yes
• In the section “Link Tracks”
• “Inside Radius”: 0.93
• “Link Data Source”: the output of the Circos: Alignments to links tool above
• “Link Type”: Ribbon
• “Link Colour”: pick another nice colour you like, it could be a green
• “Link Color Transparency”: 0.3
• In the section “Ticks”
• “Show Ticks”: Yes
• param-repeat Insert Tick Group
• “Tick Spacing”: 0.05
• “Tick Size”: 5.0
• “Color”: grey
• param-repeat Insert Tick Group
• “Tick Spacing”: 0.5
• “Tick Size”: 10.0
• “Color”: black
• “Show Tick Labels”: Yes
• “Label Format”: Float (one decmial)

This should produce a lovely Circos plot of your data:

## Extracting genes programmatically

Above we’ve been able to look at genes that appear to be deleted in our assembly. But what we really need is to create a list that can be interrogated further. For example, which of these genes are essential? We can easily create such a list by overlapping coordinates of genes with coordinates of our deletion. But to do this we first need to create a set of coordinates corresponding to the deletion. We could do this by inspecting the genome browser, or we can do it automatically by intersecting the gap regions with the list of genes:

### hands_on Hands-on: Finding genes deleted in our assembly

1. Intersect intervals find overlapping intervals in various ways Tool: toolshed.g2.bx.psu.edu/repos/iuc/bedtools/bedtools_intersectbed/2.27.0.2 with the following parameters:
• “File A to intersect with B”: Gaps
• “File(s) B to intersect with A”: Genes (E. coli Relatives) with Symbol Name
• “What should be written to the output file?”: Write the original A and B entries plus the number of base pairs of overlap between the two features. Only A features with overlap are reported. Restricted by the fraction- and reciprocal option (-wo)

As a result we will get a list of all genes that overlap with the positions of the deletion. Because of the parameters we have selected, the tool joins rows from the two datasets if their coordinates overlap:

1 2 3 4 5 6 7 8 9 10
CP020543.1 3253711 3288956 CP020543.1 3253690 3253887   0 - 176
CP020543.1 3253711 3288956 CP020543.1 3254070 3256175   0 - 2105
CP020543.1 3253711 3288956 CP020543.1 3256356 3256769   0 - 413
CP020543.1 3253711 3288956 CP020543.1 3256772 3257518   0 - 746
CP020543.1 3253711 3288956 CP020543.1 3257518 3258375   0 - 857
CP020543.1 3253711 3288956 CP020543.1 3258389 3259999 entE 0 - 1610

## Are any of these genes essential?

Goodall et al. have recently published a list of essential genes for E. coli K-12 (Goodall et al. 2018). We can use their data to answer this question. This paper contains a supplementary file in Excel format listing genes and whether they are essential or not. We have converted this to a tab delimited file for you, but you could do this in any spreadsheet application:

### hands_on Hands-on: Import data

1. Import the table:

https://zenodo.org/record/3382053/files/inline-supplementary-material-7.tsv

• Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

• Select Paste/Fetch Data
• Paste the link into the text field

• Press Start

• Close the window

This dataset will look like this:

Gene Insertion Index Score Log Likelihood Ratio Essential Non-essential Unclear
thrL 0.242424242 54.62640955 0 1 0
thrA 0.149817296 32.64262069 0 1 0
thrB 0.177920686 39.389847 0 1 0

The two truly important columns here are 1 (gene name) and 4 (is gene essential?). Let’s join the results of the intersection with this list:

### hands_on Hands-on: Are there essential genes?

1. Join two Datasets Tool: join1 side by side on a specified field:
• “Join”: the results of the intersect operation(Intersect intervals on data...)
• “using column”: Column: 7 (because it contains gene names. If it is not a drop down enter 7.)
• “with”: the newly uploaded dataset with essential gene data
• “and column”: Column: 1 (as in this dataset the first column contains gene names)

Once the tool is finished we will find that every gene found in the gap regions is non-essential so our version of E. coli C is safe!

 Chrom Gap Start Gap End Chrom Start End Name Score Strand Overlap Gene Insertion Index Score Log Likelihood Ratio Essential Non-essential Unclear CP020543.1 3253711 3288956 CP020543.1 3258389 3259999 entE 0 - 1610 entE 0.105524519 21.7755231717594 0 1 0 CP020543.1 3253711 3288956 CP020543.1 3268031 3271912 entF 0 - 3881 entF 0.121329212 25.6956722811455 0 1 0 CP020543.1 3289091 3304937 CP020543.1 3303016 3305253 nfrB 0 + 1921 nfrB 0.124218052 26.4064112702847 0 1 0 CP024090.1 3233375 3283074 CP024090.1 3238053 3239663 entE 0 - 1610 entE 0.105524519 21.7755231717594 0 1 0 CP024090.1 3233375 3283074 CP024090.1 3247695 3251576 entF 0 - 3881 entF 0.121329212 25.6956722811455 0 1 0 CP024090.1 3233375 3283074 CP024090.1 3282681 3284918 nfrB 0 + 393 nfrB 0.124218052 26.4064112702847 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3252764 3252961 ybdD 0 - 176 ybdD 0.045454545 5.96222628062725 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3253144 3255249 cstA 0 - 2105 cstA 0.126305793 26.9190653824143 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3255430 3255843 entH 0 - 413 entH 0.120772947 25.5586264884819 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3255846 3256592 entA 0 - 746 entA 0.104417671 21.4987263249306 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3256592 3257449 entB 0 - 857 entB 0.088578089 17.4977059908147 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3257463 3259073 entE 0 - 1610 entE 0.105524519 21.7755231717594 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3259083 3260258 entC 0 - 1175 entC 0.102891156 21.1164388080323 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3260633 3261589 fepB 0 + 956 fepB 0.056426332 9.03336948257186 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3261593 3262843 entS 0 - 1250 entS 0.10871303 22.5711161654709 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3262954 3263958 fepD 0 + 1004 fepD 0.058706468 9.65601430679132 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3263955 3264947 fepG 0 + 992 fepG 0.057401813 9.30031146997753 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3264944 3265759 fepC 0 + 815 fepC 0.053921569 8.34384946192657 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3265756 3266889 fepE 0 - 1133 fepE 0.207231041 46.3482820150569 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3267105 3270986 entF 0 - 3881 entF 0.121329212 25.6956722811455 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3271204 3272406 fes 0 - 1202 fes 0.073150457 13.5095101001907 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3272649 3274889 fepA 0 + 2240 fepA 0.115573405 24.274540471134 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3274941 3275684 entD 0 + 743 entD 0.111111111 23.1678124325764 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3277760 3278878 ybdK 0 + 1118 ybdK 0.124218052 26.4064112702847 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3280480 3281727 mscM 0 + 1247 mscM 0.189530686 42.1543534360729 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3281795 3283171 pheP 0 - 1376 pheP 0.12345679 26.2192754708456 0 1 0 LT906474.1 3252785 3288031 LT906474.1 3286428 3287651 cusB 0 - 1223 cusB 0.14624183 31.7775137251818 0 1 0 LT906474.1 3288166 3304009 LT906474.1 3288157 3289530 cusC 0 - 1364 cusC 0.237991266 53.5875038409488 0 1 0

### Key points

• We learned how to download large sets of completed genomes from NCBI

• We learned how to use Galaxy’s rule-based collection builder

• We learned how to use a combination of Galaxy tools to create complex views of genome comparisons

• We learned about idiosyncrasies of data formats and how to deal with them using Galaxy tools

Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Assembly topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum

# References

1. Goodall, E. C. A., A. Robinson, I. G. Johnston, S. Jabbari, K. A. Turner et al., 2018 The Essential Genome of Escherichia coli K-12 (S. L. Chen & K. A. Kline, Eds.). mBio 9: 10.1128/mbio.02096-17

# Feedback

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# Citing this Tutorial

1. Anton Nekrutenko, Delphine Lariviere, Helena Rasche, 2021 Making sense of a newly assembled genome (Galaxy Training Materials). https://training.galaxyproject.org/archive/2022-05-01/topics/assembly/tutorials/ecoli_comparison/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

### details BibTeX

@misc{assembly-ecoli_comparison,
author = "Anton Nekrutenko and Delphine Lariviere and Helena Rasche",
title = "Making sense of a newly assembled genome (Galaxy Training Materials)",
year = "2021",
month = "03",
day = "23"
url = "\url{https://training.galaxyproject.org/archive/2022-05-01/topics/assembly/tutorials/ecoli_comparison/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
doi = {10.1016/j.cels.2018.05.012},
url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
year = 2018,
month = {jun},
publisher = {Elsevier {BV}},
volume = {6},
number = {6},
pages = {752--758.e1},
author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
title = {Community-Driven Data Analysis Training for Biology},
journal = {Cell Systems}
}
`