Sequora dna

Part 1-2 - FASTA Splitting

Parts 1 and 2 have been combined into one step. For this step you take one FASTA file containing several small genomes (such as prokaryotes or mitochondria). You then upload this file as the Query FASTA and then you upload it again as the Reference FASTA.

This part of the program will divide each genome into "chunks" that can then be analyzed later on in the pipeline. By default, the Query file is divided into 1,000 base pair chunks, and the Reference file is divided into 10,000 base pair chunks.

Reference chunks are then concatenated accordingly, with the default option to combine the first, second to last, and last chunk for circularity purposes. Reverse compliment chunks are handled in the same way so that the complimentary sequences are not overlooked.

When I first designed this tool, I used one FASTA file with 6 bacterial genomes as both the Query and Reference file. This meant that there were 6 FASTA headers in each file. I suggest using the same file for both the Query and Reference, however this is by no means mandatory.

Sequora dna

Part 3 - Genome Alignment

(December 01, 2024) Over the last few days, I've noticed some bugs in Part 3. 🐛🐛🐛 Please be patient as I work these out and continue testing...

Part 3 is the Advanced DNA Alignment Tool. This is where the majority of the actual work happens.

To use this part of the pipeline, upload the respective Query and Reference files that were generated in Parts 1-2.
Hit Run Alignment.
Then Download the resulting CSV file.

This section of the program uses k-mer matches from the Query sequence chunks to filter through the Reference k-mer chunks. Those that pass this filter are then aligned using a local alignment method. The final results are reported in the generated CSV grid.

This grid can then be used in Part 4.

Sequora dna

Part 4 - Query Highest Scores

In Part 4, you upload the file that was generated in Part 3, and it will find the highest "Score" for each Query chunk against all of the Reference chunks from each original genome.

So if I am comparing 6 bacterial genomes, Chunk 1 from Bacterium A will return the highest score from Bacterium A, Bacterium B, Bacterium C, Bacterium D, Bacterium E and Bacterium F, for a total of 6 columns. Where a Query chunk did not have a k-mer filtered match against a Reference genome, there will be an empty cell. (These unique sequences are noted later.)

Sequora dna

Part 5 - Genome Scoring Grid

In Part 5 the user uploads the Filtered CSV which was generated in Part 4. Part 5 will then give a small, convenient grid, with download options, summarizing the average scores between each Query and Reference genome.

Sequora dna

Part 6 - Phylogenomics Tree Generation

In Part 5 the user uploads the CSV file from Part 5 (if one of the two does not work, try the other). Two distance matrices, one for the distances with gaps and one without gaps, are generated. Two phylogenomics trees are then generated as well. The images for these trees can be copy/pasted into another document!