Implement k-mer distance metrics and constrained generation

Adds support for Hamming distance, constrained k-mer generation, and optimized Longest Common Subsequence (LCS) calculations using scalar, batch, and SIMD methods.

This includes new methods for k-mer distance calculation, structures for k-mer constraints, and SIMD-based batch processing for LCS to improve performance and correctness.

pkg/obikmer/debruijn.go

@@ -5,7 +5,6 @@ import (
 	"container/heap"
 	"fmt"
 	"math"
-	"math/bits"
 	"os"
 	"slices"
 
@@ -610,14 +609,6 @@ func (graph *DeBruijnGraph) WriteGml(filename string) error {
 	return err
 }
 
-// Calculating the hamming distance between two k-mers.
-func (g *DeBruijnGraph) HammingDistance(kmer1, kmer2 uint64) int {
-	ident := ^((kmer1 & kmer2) | (^kmer1 & ^kmer2))
-	ident |= (ident >> 1)
-	ident &= 0x5555555555555555 & g.kmermask
-	return bits.OnesCount64(ident)
-}
-
 type UInt64Heap []uint64
 
 func (h UInt64Heap) Len() int           { return len(h) }