Look-up Table Dot—Matrix 1 2 3 4 5 6 7 8 9 10 11 4,5,9,10 T 1,8 G 6 C 2,3,7,11 A Location Q * * * * A * * G * C * * * * T * * * * A * * G * * G A T T G A C T T A A G
( a) Find runs of identical words
Identify regions shared by the two sequences that have the highest
density of single identities (ktup=1) or two consecutive
(b) Re-score using PAM matrix
Longest diagonals are scored again using the PAM-250 matrix (or other
matrix). The best scores are saved as “init1” scores.
( c) Join segments using gaps and eliminate other segments
Long diagonals that are neighbors are joined. The score for this
joined region is “initn”. This score may be lower due to a penalty
for a gap.
(d) Use DP to create the optimal alignment
construct an optimal alignment of the query sequence and the
library sequence (SW algorithm).This score is reported as the
FASTA Steps 1 Local regions of identity are found Different offset values Identical offset values in a contiguous sequence 2 Rescore the local regions using PAM or Blos. matrix Diagonals are extended 3 Eliminate short diagonals below a cutoff score 4 Create a gapped alignment in a narrow segment and then perform S-W alignment
OUTPUT: HIT LIST
ALIGNMENT OF QUERY TO A HIT
VERSIONS OF FASTA
FASTA-nucleotide or protein sequence searching
FAST x-compares a translated DNA query sequence
FAST y - a protein sequence database (forward
or backward translation of the query)
t FAST x-compares protein query sequence
t FAST y DNA sequence database that has been
translated into three forward and three
reverse reading frames
Quality control and pre processing of metagenomic datasets.
CENTROIDFOLD: a web server for RNA secondary structure prediction.
1) Schmieder R, Edwards R/ Epub /2011 Mar 15;27(6):863-4.
2) Sato K, Hamada M, Asai K, Mituyama T/ Epub/2009 Jul;1:W277-80