▁sequences . ▁This ▁process ▁of ▁finding ▁similar ▁sequences ▁is ▁called ▁seed ing . ▁It ▁is ▁after ▁this ▁first ▁match ▁that ▁B LA ST ▁begins ▁to ▁make ▁local ▁align ments . ▁While ▁attempting ▁to ▁find ▁similarity ▁in ▁sequences , ▁sets ▁of ▁common ▁letters , ▁known ▁as ▁words , ▁are ▁very ▁important . ▁For ▁example , ▁suppose ▁that ▁the ▁sequence ▁contains ▁the ▁following ▁stretch ▁of ▁letters , ▁GL K FA . ▁If ▁a ▁B LA ST ▁was ▁being ▁conducted ▁under ▁normal ▁conditions , ▁the ▁word ▁size ▁would ▁be ▁ 3 ▁letters . ▁In ▁this ▁case , ▁using ▁the ▁given ▁stretch ▁of ▁letters , ▁the ▁searched ▁words ▁would ▁be ▁GL K , ▁L K F , ▁K FA . ▁The ▁he ur istic ▁algorithm ▁of ▁B LA ST ▁loc ates ▁all ▁common ▁three - letter ▁words ▁between ▁the ▁sequence ▁of ▁interest ▁and ▁the ▁hit ▁sequence ▁or ▁sequences ▁from ▁the ▁database . ▁This ▁result ▁will ▁then ▁be ▁used ▁to ▁build ▁an ▁alignment . ▁After ▁making ▁words ▁for ▁the ▁sequence ▁of ▁interest , ▁the ▁rest ▁of ▁the ▁words ▁are ▁also ▁assemble d . ▁These ▁words ▁must ▁satisfy ▁a ▁requirement ▁of ▁having ▁a ▁score ▁of ▁at ▁least ▁the ▁threshold ▁T , ▁when ▁compared ▁by ▁using ▁a ▁scoring ▁matrix . ▁▁ ▁One ▁commonly ▁used ▁scoring ▁matrix ▁for ▁B LA ST ▁searches ▁is ▁B L OS UM 6 2 , ▁although ▁the ▁optimal ▁scoring ▁matrix ▁depends ▁on ▁sequence ▁similarity . ▁Once ▁both ▁words ▁and ▁neighborhood ▁words ▁are ▁assemble d ▁and ▁compiled , ▁they ▁are ▁compared ▁to ▁the ▁sequences ▁in ▁the ▁database