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add example to compare mecA vs mecA1
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‎cookbook/01-sequences.md‎

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@@ -228,8 +228,7 @@ meaning the majority of reads are high quality.
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More information about converting ASCII characters and PHRED scores to quality scores can be found [here](https://people.duke.edu/~ccc14/duke-hts-2018/bioinformatics/quality_scores.html).
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Now that we've learned how to read files in and manipulate them a bit,
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let's see if we can align the _mecA_ gene to the _Staphylococcus aureus_ genome.
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This will tell us if this particular _S. aureus_ is MRSA.
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Now that we've learned how to read files in and manipulate them a bit,
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let's see if we can align 2 _mecA_ genes to compare them.
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‎cookbook/02-alignments.md‎

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rss_descr = "Align a gene against a reference genome using BioAlignments.jl"
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As mentioned in the previous [tutorial]("../01-sequences.md"), in this chapter, we will learn about alignments.
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We will explore pair-wise alignment as a tool to compare two copies of the _mecA_ gene found on NCBI.
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# Pairwise Alignment
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On the most basic level, aligners take two sequences and use algorithms to try to "line them up"
@@ -87,20 +90,58 @@ and what is known about the two sequences.
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Now that we have a good understanding of how `pairalign` works, let's run an example!
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In this example, we'll compare two similar genes: mecA found in _S. aureus_ ([link to gene on NCBI here](https://www.ncbi.nlm.nih.gov/nuccore/NG_047945.1)), and a homologue, mecA1, found on a _S. sciuri_ ([link to gene on NCBI here](https://www.ncbi.nlm.nih.gov/gene/?term=PBP2a+family+beta-lactam-resistant+peptidoglycan+transpeptidase+MecA1)).
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The two Staphs are closely related species in the same _Staphylococcaceae_ family.
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Because we are comparing homologous genes from two closely related species, we wouldn't expect too many differences.
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Although mecA1 doesn't confer resistance to beta-lactams in _S. sciuri_ like _mecA_ does to _S. aureus_,
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the gene should be mostly conserved.
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In fact, mecA1 is considered a pre-cursor to mecA.
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Research indicates that there is 80% nucleotide identity between the two genes.[1].
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Due to the similarity in the genes we are comparing, it makes the most sense to run a global alignment.
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In this first example, we'll align two strings that contain the genes.
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#### Running Alignment on BioSequences Object
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```julia
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using BioAlignments
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s1 =
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s2 =
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mecA =
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"ATGAAAAAGATAAAAATTGTTCCACTTATTTTAATAGTTGTAGTTGTCGGGTTTGGTATATATTTTTATGCTTCAAAAGATAAAGAAATTAATAATACTATTGATGCAATTGAAGATAAAAATTTCAAACAAGTTTATAAAGATAGCAGTTATATTTCTAAAAGCGATAATGGTGAAGTAGAAATGACTGAACGTCCGATAAAAATATATAATAGTTTAGGCGTTAAAGATATAAACATTCAGGATCGTAAAATAAAAAAAGTATCTAAAAATAAAAAACGAGTAGATGCTCAATATAAAATTAAAACAAACTACGGTAACATTGATCGCAACGTTCAATTTAATTTTGTTAAAGAAGATGGTATGTGGAAGTTAGATTGGGATCATAGCGTCATTATTCCAGGAATGCAGAAAGACCAAAGCATACATATTGAAAATTTAAAATCAGAACGTGGTAAAATTTTAGACCGAAACAATGTGGAATTGGCCAATACAGGAACAGCATATGAGATAGGCATCGTTCCAAAGAATGTATCTAAAAAAGATTATAAAGCAATCGCTAAAGAACTAAGTATTTCTGAAGACTATATCAAACAACAAATGGATCAAAATTGGGTACAAGATGATACCTTCGTTCCACTTAAAACCGTTAAAAAAATGGATGAATATTTAAGTGATTTCGCAAAAAAATTTCATCTTACAACTAATGAAACAAAAAGTCGTAACTATCCTCTAGAAAAAGCGACTTCACATCTATTAGGTTATGTTGGTCCCATTAACTCTGAAGAATTAAAACAAAAAGAATATAAAGGCTATAAAGATGATGCAGTTATTGGTAAAAAGGGACTCGAAAAACTTTACGATAAAAAGCTCCAACATGAAGATGGCTATCGTGTCACAATCGTTGACGATAATAGCAATACAATCGCACATACATTAATAGAGAAAAAGAAAAAAGATGGCAAAGATATTCAACTAACTATTGATGCTAAAGTTCAAAAGAGTATTTATAACAACATGAAAAATGATTATGGCTCAGGTACTGCTATCCACCCTCAAACAGGTGAATTATTAGCACTTGTAAGCACACCTTCATATGACGTCTATCCATTTATGTATGGCATGAGTAACGAAGAATATAATAAATTAACCGAAGATAAAAAAGAACCTCTGCTCAACAAGTTCCAGATTACAACTTCACCAGGTTCAACTCAAAAAATATTAACAGCAATGATTGGGTTAAATAACAAAACATTAGACGATAAAACAAGTTATAAAATCGATGGTAAAGGTTGGCAAAAAGATAAATCTTGGGGTGGTTACAACGTTACAAGATATGAAGTGGTAAATGGTAATATCGACTTAAAACAAGCAATAGAATCATCAGATAACATTTTCTTTGCTAGAGTAGCACTCGAATTAGGCAGTAAGAAATTTGAAAAAGGCATGAAAAAACTAGGTGTTGGTGAAGATATACCAAGTGATTATCCATTTTATAATGCTCAAATTTCAAACAAAAATTTAGATAATGAAATATTATTAGCTGATTCAGGTTACGGACAAGGTGAAATACTGATTAACCCAGTACAGATCCTTTCAATCTATAGCGCATTAGAAAATAATGGCAATATTAACGCACCTCACTTATTAAAAGACACGAAAAACAAAGTTTGGAAGAAAAATATTATTTCCAAAGAAAATATCAATCTATTAACTGATGGTATGCAACAAGTCGTAAATAAAACACATAAAGAAGATATTTATAGATCTTATGCAAACTTAATTGGCAAATCCGGTACTGCAGAACTCAAAATGAAACAAGGAGAAACTGGCAGACAAATTGGGTGGTTTATATCATATGATAAAGATAATCCAAACATGATGATGGCTATTAATGTTAAAGATGTACAAGATAAAGGAATGGCTAGCTACAATGCCAAAATCTCAGGTAAAGTGTATGATGAGCTATATGAGAACGGTAATAAAAAATACGATATAGATGAATAACAAAACAGTGAAGCAATCCGTAACGATGGTTGCTTCACTGTTTTATTATGAATTATTAATAAGTGCTGTTACTTCTCCCTTAAATACAATTTCTTCATTT"
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mecA1 = "ATGAAAAAATTAATCATCGCCATCGTGATTGTAATCATCGCTGTTGGTTCAGGCGTATTCTTTTATGCATCTAAAGATAAGAAAATAAACGAAACAATTGATGCCATTGAAGATAAAAACGTTAAGCAAGTCTTTAAAAATAGTACTTACCAATCTAAAAACGATAATGGTGAAGTAGAAATGACAGACCGCCCTATTAAGATTTATGACAGTCTCGGCGTCAAAGATATCAACATTAAAGATCGTGATATCAAAAAGGTTTCGAAAAACAAAAAACAAGTCACAGCAAAGTATGAACTTCAAACGAATTACGGCAAAATTAATCGTGACGTTAAATTAAACTTTATTAAAGAAGATAAAGATTGGAAATTGGATTGGAATCAAAATGCCATTATTCCAGGCATGAAGAAAAATCAATCCATCAATATTGAACCATTGAAATCAGAACGAGGTAAGATTTTAGACAGGAACAATGTAGAGTTAGCCACTACAGGAACAACACATGAAGTTGGTATTGTTCCTAATAATGTTTCCACAAGTGATTACAAAGCAATCGCTGAAAAGTTAGACCTTTCAGAATCGTATATTAAACAGCAAACAGAACAGGATTGGGTTAAAGATGATACATTCGTCCCTCTCAAGACTGTTCAAGATATGAATCAAGATTTAAAGAATTTTGTTGAAAAGTATCATCTCACATCACAGGAAACAGAAAGTCGACAGTATCCGCTTGAAGAAGCAACAACGCACTTACTTGGATATGTTGGCCCTATTAATTCAGAAGAATTGAAGCAAAAAGCATTTAAAGGTTATAAAAAGGATGCCATCGTTGGTAAAAAAGGTATCGAAAAACTATACGATAAAGACCTTCAAAATAAAGACGGATACCGTGTCACAATAATTGATGATAATAATAAAGTTATTGATACATTAATAGAGAAAAAGAAAATAGACGGCAAAGATATTAAATTAACCATTGATGCTAGAGTCCAAAAAAGTATTTATAACAACATGAAAGATGACTACGGTTCGGGGACTGCTATTCATCCACAAACTGGTGAACTCTTAGCACTTGTCAGCACGCCATCTTATGATGTTTATCCATTTATGAATGGAATGAGCGATGAAGATTATAAGAAATTAACTGAAGATGATAAAGAGCCACTCCTTAATAAGTTCCAAATTACGACATCACCAGGTTCGACTCAAAAAATATTAACAGCCATGATTGGCTTAAACAATAAGACATTAGACGGCAAAACAAGTTATAAAATTAATGGAAAAGGTTGGCAAAAAGATAAATCTTGGGGTGACTACAACGTTACAAGATACGAAGTTGTGAATGCCGATATCGACTTAAAACAAGCTATTGAATCATCAGATAATATCTTCTTTGCGAGAGTTGCACTTGAATTAGGCAGCAAAAAATTCGAAGAAGGAATGAAACGCCTTGGTGTTGGTGAAGATATCCCGAGTGATTATCCATTCTACAATGCACAAATTTCAAATAAGAACTTAGATAATGAAATATTGTTAGCTGACTCAGGTTATGGCCAAGGTGAAATATTAATCAATCCTGTTCAAATTCTTTCAATATACAGCGCATTAGAGAACAAAGGTAATGTGAATGCACCACATGTACTCAAAGATACGAAAAATAAAGTCTGGAAGAAGAACATCATTTCCCAGGAAAATATTAAATTGTTAACAGACGGTATGCAACAAGTCGTGAACAAAACACATAGAGAAGATATTTATAGATCATATGCCAACTTAGTTGGTAAATCAGGTACAGCTGAACTCAAGATGAAACAAGGTGAGACAGGACAACAAATAGGTTGGTTCATTTCATATGATAAAGATAATCCAAATATAATGATGGCTATTAATGTGAAAGATGTACAAGATAAAGGCATGGCAAGTTACAATGCCAAAATATCTGGAAAAGTGTATGACGATTTATATGATAACGGTAAGAAAACGTATCGTATTGATAAATAA"
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scoremodel = AffineGapScoreModel(EDNAFULL, gap_open=-5, gap_extend=-1);
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res = pairalign(GlobalAlignment(), s1, s2, scoremodel) # run pairwise alignment
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res = pairalign(GlobalAlignment(), mecA, mecA1, scoremodel)
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# run pairwise alignment
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```
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res
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#### Running Alignment on FASTX files
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In this next example, we'll repeat the same alignment,
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but read in the files directly from the FASTA files containing the gene.
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Running the alignment on strings is straightforward with short sequences, but when comparing entire genes, simply reading in the file is easier.
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```julia
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using BioSequences
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using FASTX
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# Write a function to get sequence out of a fasta file with 1 record
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function fasta_sequence(fasta_path)
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record = open(FASTA.Reader, fasta_path) do reader
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first(reader)
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end
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seq = LongDNA{4}(String(FASTX.sequence(record)))
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return (seq)
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end
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mecA_fasta = fasta_sequence("assets/mecA.fasta")
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mecA1_fasta = fasta_sequence("assets/mecA1.fasta")
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res_fasta = pairalign(GlobalAlignment(), mecA_fasta, mecA1_fasta, scoremodel) # run pairwise alignment
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```
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### Understanding how alignments are represented
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### Understanding how Alignments are Represented
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The output of an alignment is a series of `AlignmentAnchor` objects.
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This data structure gives information on the position of the start of the alignment,
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sections where nucleotides match, as well as where there may be deletions or insertions.
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The next nucleotides are a match in the query and reference sequences.
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The last 8 nucleotides in the alignment are deleted in the query sequence.
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To learn more about the output of the alignment created using BioAlignments.jl, see [here](https://biojulia.dev/BioAlignments.jl/stable/alignments/).
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To learn more about the output of the alignment created using BioAlignments.jl, see [here](https://biojulia.dev/BioAlignments.jl/stable/alignments/).
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#### Interpreting the Example Output
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```
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# run pairwise alignment
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res
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PairwiseAlignmentResult{Int64, String, String}:
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score: 6375
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seq: 1 ATGAAAAAGATAAAA-ATTGTTCCA-CTT-ATTTTAAT-A-----GTTGTAGTTGTCGGG 51
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|||||||| || || || | ||| | | ||| |||| | |||| ||| | ||
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ref: 1 ATGAAAAA-ATTAATCATCG--CCATCGTGATTGTAATCATCGCTGTTG--GTT--CAGG 53
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seq: 52 TTTGGTATATAT-TTTTATGCTTCAAAAGATAAAGAAATTAAT--AATACTATTGATGCA 108
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|||| | |||||||| || |||||||| |||| ||| || || ||||||||
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ref: 54 C---GTAT---TCTTTTATGCATCTAAAGATAA-GAAAATAAACGAA-ACAATTGATGCC 105
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seq: 109 ATTGAAGATAAAAA--TTTCAAACAAGT-TTATAAAGATAGCAGTTAT--ATTTCTAAAA 163
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|||||||||||||| || || ||||| || |||| |||| | ||| || ||||||
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ref: 106 ATTGAAGATAAAAACGTT--AAGCAAGTCTT-TAAAAATAGTACTTACCAAT--CTAAAA 160
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seq: 164 GCGATAATGGTGAAGTAGAAATGACTGAACGTCCGATAAAAATATATAATAGTTTAGGCG 223
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|||||||||||||||||||||||| || || || || || || ||| | ||| | ||||
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ref: 161 ACGATAATGGTGAAGTAGAAATGACAGACCGCCCTATTAAGATTTATGACAGTCTCGGCG 220
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seq: 224 TTAAAGATATAAACATTCAGGATCGTAAAATAAAAAAAGTATCTAAAAATAAAAAACGAG 283
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| |||||||| |||||| | |||||| | || ||||| || || ||||| ||||||| ||
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ref: 221 TCAAAGATATCAACATTAAAGATCGTGATATCAAAAAGGTTTCGAAAAACAAAAAACAAG 280
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seq: 284 T-AGATGCTCAA--TATAAAATTAAAACAAACTACGGTAACATTGATCGCAACGTTCAAT 340
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| | | || || ||| || || |||| || ||||| || ||| |||| ||||| |||
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ref: 281 TCACA-GC--AAAGTATGAACTTCAAACGAATTACGGCAAAATTAATCGTGACGTTAAAT 337
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seq: 341 TTAATTTTGTTAAAGAAGAT---GGTATGTGGAAGTTAGATTGGGATCATA--GCGTCAT 395
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| || ||| ||||||||||| | || ||||| || |||||| |||| | || |||
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ref: 338 TAAACTTTATTAAAGAAGATAAAG--AT-TGGAAATTGGATTGGAATCAAAATGC--CAT 392
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seq: 396 TATTCCAGGAATGCAGAAAGACCAAAGCATACA-TATTGAA--AATTTAAAATCAGAACG 452
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ref: 393 TATTCCAGGCATGAAGAAAAATCAATCCAT-CAATATTGAACCA--TTGAAATCAGAACG 449
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seq: 453 TGGTAAAATTTTAGACCGAAACAATGTGGAATTGGCCAATACAGGAACAGCATATGA-GA 511
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ref: 450 AGGTAAGATTTTAGACAGGAACAATGTAGAGTTAGCCACTACAGGAACAACACATGAAGT 509
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seq: 512 TAGGCATCGTTCCAAAGAATGTATCTAAAAAAGATTATAAAGCAATCGCTAAAGAACTAA 571
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| || || ||||| || ||||| || | || ||||| |||||||||||| ||| ||
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ref: 510 T-GGTATTGTTCCTAATAATGTTTCCACAAGTGATTACAAAGCAATCGCT---GAA--AA 563
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seq: 572 GT-A----TTTCTGAA--GACTATATCAAACAACAAATGGATCAAAATTGGGT-ACAAGA 623
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|| | |||| ||| | ||||| ||||| |||| || || ||||||| | ||||
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ref: 564 GTTAGACCTTTCAGAATCG--TATATTAAACAGCAAACAGAACAGGATTGGGTTA-AAGA 620
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seq: 624 TGATACCTTCGTTCCACTTAAAACCGTTAAAAAAATGGATGAATATTTAAGTGA-TTTCG 682
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ref: 621 TGATACATTCGTCCCTCTCAAGACTGTTCAAGATATGAATCAAGATTTAAA-GAATTTTG 679
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seq: 683 CAAAAAAATTTCATCTTACA--ACTAATGAAACAAAAAGTCGTAAC--TATCCTCTAGAA 738
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ref: 680 TTGAAAAGTATCATCTCACATCAC--AGGAAACAGAAAGTCG--ACAGTATCCGCTTGAA 735
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seq: 739 AAAGCGACTTCACATCT-A-TTAGGTTATGTTGGTCCC-ATTAACTCTGAAGAATTAAAA 795
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ref: 736 GAAGCAACAACGCA-CTTACTT-GGATATGTTGG-CCCTATTAATTCAGAAGAATTGAAG 792
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seq: 796 CAAAAAGAATATAAAGGCTATAAAGATGATGC-AGTTATTGGTAAAAAGGG-ACTCGAAA 853
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ref: 793 CAAAAAGCATTTAAAGGTTATAAAAAGGATGCCA-TCGTTGGTAAAAAAGGTA-TCGAAA 850
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seq: 854 AACTTTACGATAAAAAGCTCCAACATGAAGATGGCTATCGTGTCACAATCGTTGACGATA 913
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ref: 851 AACTATACGATAAAGACCTTCAAAATAAAGACGGATACCGTGTCACAATAATTGATGATA 910
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seq: 914 ATAGCAATACA---ATCGCACATACATTAATAGAGAAAAAGAAAAAAGATGGCAAAGATA 970
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||| ||| | || | |||||||||||||||||||||||| ||| ||||||||||
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ref: 911 ATA---ATAAAGTTATTG---ATACATTAATAGAGAAAAAGAAAATAGACGGCAAAGATA 964
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seq: 971 TTCAACTAACTATTGATGCTAAAGTTCAAAAGAGTATTTATAACAACATGAAAAATGATT 1030
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|| || |||| |||||||||| ||| ||||| ||||||||||||||||||||| |||| |
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ref: 965 TTAAATTAACCATTGATGCTAGAGTCCAAAAAAGTATTTATAACAACATGAAAGATGACT 1024
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seq: 1031 ATGGCTCAGGTACTGCTATCCACCCTCAAACAGGTGAATTATTAGCACTTGTAAGCACAC 1090
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| || || || |||||||| || || ||||| |||||| | ||||||||||| ||||| |
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ref: 1025 ACGGTTCGGGGACTGCTATTCATCCACAAACTGGTGAACTCTTAGCACTTGTCAGCACGC 1084
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seq: 1091 CTTCATATGACGTCTATCCATTTATGTATGGCATGAGTAACGAAGAATATAATAAATTAA 1150
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| || ||||| || |||||||||||| |||| ||||| | ||||| ||||| |||||||
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ref: 1085 CATCTTATGATGTTTATCCATTTATGAATGGAATGAGCGATGAAGATTATAAGAAATTAA 1144
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seq: 1151 CCGAAGATAAAAAAGAACCTCTGCTCAACAAGTTCCAGATTACAACTTCACCAGGTTCAA 1210
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| |||||| | ||||| || || || || |||||||| ||||| || ||||||||||| |
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ref: 1145 CTGAAGATGATAAAGAGCCACTCCTTAATAAGTTCCAAATTACGACATCACCAGGTTCGA 1204
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seq: 1211 CTCAAAAAATATTAACAGCAATGATTGGGTTAAATAACAAAACATTAGACGATAAAACAA 1270
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ref: 1205 CTCAAAAAATATTAACAGCCATGATTGGCTTAAACAATAAGACATTAGACGGCAAAACAA 1264
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seq: 1271 GTTATAAAATCGATGGTAAAGGTTGGCAAAAAGATAAATCTTGGGGTGGTTACAACGTTA 1330
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ref: 1265 GTTATAAAATTAATGGAAAAGGTTGGCAAAAAGATAAATCTTGGGGTGACTACAACGTTA 1324
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seq: 1331 CAAGATATGAAGTGGTAAATG--GTAATATCGACTTAAAACAAGCAATAGAATCATCAGA 1388
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||||||| ||||| || |||| | ||||||||||||||||||| || |||||||||||
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ref: 1325 CAAGATACGAAGTTGTGAATGCCG--ATATCGACTTAAAACAAGCTATTGAATCATCAGA 1382
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seq: 1389 TAACATTTTCTTTGCTAGAGTAGCACTCGAATTAGGCAGTAAGAAATTTGAAAAAGGCAT 1448
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||| || |||||||| ||||| ||||| ||||||||||| || ||||| ||| |||| ||
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ref: 1383 TAATATCTTCTTTGCGAGAGTTGCACTTGAATTAGGCAGCAAAAAATTCGAAGAAGGAAT 1442
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seq: 1449 GAAAAAACTAGGTGTTGGTGAAGATATACCAAGTGATTATCCATTTTATAATGCTCAAAT 1508
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|||| || ||||||||||||||||| || |||||||||||||| || ||||| |||||
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ref: 1443 GAAACGCCTTGGTGTTGGTGAAGATATCCCGAGTGATTATCCATTCTACAATGCACAAAT 1502
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seq: 1509 TTCAAACAAAAATTTAGATAATGAAATATTATTAGCTGATTCAGGTTACGGACAAGGTGA 1568
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|||||| || || ||||||||||||||||| |||||||| |||||||| || ||||||||
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ref: 1503 TTCAAATAAGAACTTAGATAATGAAATATTGTTAGCTGACTCAGGTTATGGCCAAGGTGA 1562
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seq: 1569 AATACTGATTAACCCAGTACAGATCCTTTCAATCTATAGCGCATTAGAAAATAATGGCAA 1628
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|||| | || || || || || || |||||||| || ||||||||||| || || || ||
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ref: 1563 AATATTAATCAATCCTGTTCAAATTCTTTCAATATACAGCGCATTAGAGAACAAAGGTAA 1622
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seq: 1629 TATTAACGCACCTCACT-TATTAAAAGACACGAAAAACAAAGTTTGGAAGAAAAATATTA 1687
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| | || ||||| || | || | ||||| |||||||| ||||| |||||||| || || |
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ref: 1623 TGTGAATGCACCACA-TGTACTCAAAGATACGAAAAATAAAGTCTGGAAGAAGAACATCA 1681
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seq: 1688 TTTCCAAAGAAAATATCAA-TCTATTAACTGATGGTATGCAACAAGTCGTAAATAAAACA 1746
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||||| | |||||||| || | | ||||| || ||||||||||||||||| || ||||||
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ref: 1682 TTTCCCAGGAAAATATTAAAT-TGTTAACAGACGGTATGCAACAAGTCGTGAACAAAACA 1740
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seq: 1747 CATAAAGAAGATATTTATAGATCTTATGCAAACTTAATTGGCAAATCCGGTACTGCAGAA 1806
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|||| |||||||||||||||||| ||||| |||||| |||| ||||| ||||| || |||
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ref: 1741 CATAGAGAAGATATTTATAGATCATATGCCAACTTAGTTGGTAAATCAGGTACAGCTGAA 1800
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seq: 1807 CTCAAAATGAAACAAGGAGAAACTGG-CAGACAAATTGGGTGGTTTATATCATATGATAA 1865
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||||| ||||||||||| || || || || |||||| || ||||| || |||||||||||
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ref: 1801 CTCAAGATGAAACAAGGTGAGACAGGACA-ACAAATAGGTTGGTTCATTTCATATGATAA 1859
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seq: 1866 AGATAATCCAAACATGATGATGGCTATTAATGTTAAAGATGTACAAGATAAAGGAATGGC 1925
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|||||||||||| || ||||||||||||||||| |||||||||||||||||||| |||||
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ref: 1860 AGATAATCCAAATATAATGATGGCTATTAATGTGAAAGATGTACAAGATAAAGGCATGGC 1919
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seq: 1926 TAGCTACAATGCCAAAATCTCAGGTAAAGTGTATGATGAGCTATATGAGAACGGTAATAA 1985
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|| |||||||||||||| || || ||||||||||| || ||||||| |||||||| ||
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ref: 1920 AAGTTACAATGCCAAAATATCTGGAAAAGTGTATGACGATTTATATGATAACGGTAAGAA 1979
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seq: 1986 AAAATACGATATAGATGAATAACAAAACAGTGAAGCAATCCGTAACGATGGTTGCTTCAC 2045
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|| |||| ||
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ref: 1980 AA--------------------------------------CGTATCG------------- 1988
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seq: 2046 TGTTTTATTATGAATTATTAATAAGTGCTGTTACTTCTCCCTTAAATACAATTTCTTCAT 2105
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|||| || || |||||
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ref: 1988 -----TATT--GA--TA--AATAA------------------------------------ 2001
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seq: 2106 TT 2107
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ref: 2001 -- 2001
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```
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The score returned is entirely dependent on the scoring scheme
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(how we penalized gaps, gap extensions and rewarded matches).
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It is not an absolute number that we can compare from alignment to alignment.
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In our example, our score was influenced by -5 for the start of a gap, and -1 for a gap extension.
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If these values were to change, we would get a different score.
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However, generally, longer alignments produce larger scores (as there are more bases being compared).
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Overall, the two sequences are homologous over most of their length.
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There are many matches, but there are frequent small indels and substitutions.
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The biggest mismatch is in a section toward the end,
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where there are large stretches that are missing in the reference sequence (mecA1).
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### Citations
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[1]: Rolo J, Worning P, Boye Nielsen J, Sobral R, Bowden R, Bouchami O, Damborg P, Guardabassi L, Perreten V, Westh H, Tomasz A, de Lencastre H, Miragaia M. Evidence for the evolutionary steps leading to mecA-mediated β-lactam resistance in staphylococci. PLoS Genet. 2017 Apr 10;13(4):e1006674. doi: 10.1371/journal.pgen.1006674. PMID: 28394942; PMCID: PMC5402963. [link to the source](10.1371/journal.pgen.1006674=).
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