add rough draft alignments

Author: danielle-pinto

cookbook/02-alignments.md

@@ -0,0 +1,75 @@
++++
+title = "Pairwise alignment"
+rss_descr = "Align a gene against a reference genome using BioAlignments.jl"
++++
+
+# Pairwise Alignment
+
+On the most basic level, aligners take two sequences and use algorithms to try and "line them up" 
+and look for regions of similarity.  
+
+Pairwise alignment differs from multiple sequence alignment (MSA) because. 
+it only aligns two sequences, while MSA's align three or more.  
+In a pairwise alignment, there is one reference sequence, and one query sequence,   
+though this may not always be specified by the user.  
+
+
+### Running the Alignment
+There are two main parameters for determining how we wish to perform our alignment:     
+the alignment type and score/cost model.  
+
+The alignment type specifies the alignment range (is the alignment local or global?)  
+and the score/cost model explains how to score insertions and deletions.   
+
+#### Alignment Types
+Currently, four types of alignments are supported:
+- GlobalAlignment: global-to-global alignment
+    - Aligns sequences end-to-end 
+    - Best for sequences that are already very similar
+- SemiGlobalAlignment: local-to-global alignment
+    - a modification of global alignment that allows the user to specify that gaps will be penalty-free at the beginning of one of the sequences and/or at the end of one of the sequences (more information can be found [here](https://www.cs.cmu.edu/~durand/03-711/2023/Lectures/20231001_semi-global.pdf)).
+- LocalAlignment: local-to-local alignment
+    - Identifies high-similarity, conserved sub-regions within divergent sequences
+    - Can occur anywhere in the alignment matrix
+- OverlapAlignment: end-free alignment
+    - a modification of global alignment where gaps at the beginning or end of sequences are permitted
+
+Alignment type can also be a distance of two sequences:
+- EditDistance
+- LevenshteinDistance
+- HammingDistance
+
+The alignment type should be selected based on what is already known about the sequences the user is comparing  
+(Are they very similar and we're looking for a couple of small differences?   
+Are we expecting the query to be a nearly exact match within the reference?).   
+and what you may be optimizing for  
+(Speed for a quick and dirty analysis?   
+Or do we want to use more resources to do a fine-grained comparison?).  
+
+Now that we have a good understanding of how `pairalign` works, 
+
+```julia
+res = pairalign(GlobalAlignment(), s1, s2, scoremodel)  # run pairwise alignment
+
+```
+
+
+### Understanding how alignments are represented
+The output of an alignment is a series of `AlignmentAnchor` objects.  
+This data structure gives information on the position of the start of the alignment,   
+sections where nucleotides match, as well as where there may be deletions or insertions.  
+
+Below is an example Alignment:
+```julia
+julia> Alignment([
+           AlignmentAnchor(0,  4, 0, OP_START),
+           AlignmentAnchor(4,  8, 4, OP_MATCH),
+           AlignmentAnchor(4, 12, 8, OP_DELETE)
+       ])
+```
+In this example, the alignment starts at the 0 position for the query sequence and 4th position for the reference sequence.  
+The next nucleotides are a match in the query and reference sequence.     
+The last 8 nucleotides in the alignment are missing/deleted in the query sequence.  
+
+To understand more about the output of the alignment created using BioAlignments.jl,   
+more information can be found [here](https://biojulia.dev/BioAlignments.jl/stable/alignments/).