1. Comparison of Genomic DNA to
cDNA Alignment Methods
Miguel Galves and Zanoni Dias
Institute of Computing – Unicamp – Campinas – SP – Brazil
{miguel.galves,zanoni}@ic.unicamp.br
Scylla Bioinformatics – Campinas – SP – Brazil
{miguel,zanoni}@scylla.com.br

2. Agenda
 Introduction
 Problem
 Aligners
 Data set
 Subsets
 Evaluation Methods
 Results: Exact Alignments
 Results: EST Alignments
 Running Time Comparison
 Conclusions

3. Introduction
 Identifying genes in non-characterized DNA
sequences is one of the greatest challenges in
genomics
 EST-to-DNA alignment is one of the most common
methods
 EST are key to understanding the inner working of
an organism
– Human being has between 30000 and 35000 genes
– Alternative Splicing plays an important role in diversity

4. CCCGGGAAACGAAUAU CCUCUCACCCGGGA CUUGGCCCGGGAAACGAAUAU CCUCUCACCCGGG
A
CUUGG
Problem
Mature mRNA
mRNA
Intron
Exon

5. Problem: How to solve ?
 Classic algorithms
– Dynamic programming
 Heuristic based algorithms
– Multi-steps
– Based on other tools such as Blast and
local alignments.

6. Aligners
 Java version of global and semi-global
– Affine gap penalty function
– Linear space
– Global algorithm by Miller and Myers (1988)
– Semi-global based on global algorithm
 Heuristic based algorithms
– sim4, Spidey and est_genome

7. Data Set
 Human genome database
– Based on FASTA a GENBANK’s flat format file from
NCBI repository.
 Filtering criteria
– Genes, mRNAs and CDS with /pseudo tag
– mRNAs without any CDS
– Genes without any mRNA
– CDS matching wrong patterns
 23124 genes and 27448 mRNAs stored in database

8. Subsets
 Subset 1Subset 1:: 66 genes from chromossome Y whith
less than 100000 bases
 Subset 2: 50 complete genes from chromossome
Y whith less than 100000 bases
 Subset 3: 8056 complete genes from all
chromossomes whith less than 100000 bases
 Subset 4: 493 artificial EST based on complete
genes from chromossome 6 with less than
100000 bases

9. Evaluation methods
 Number of gaps introduced in the aligned
gene sequence
 Delta exons
 Bases similarity percentage
 Mismatch percentage

10. Experimental method
 Two score systems, from 15 previously
defined and an alignment strategy were
choosed, using subsets 1 and 2:
– Semi-global aligner
– (1,-2,-1,0) and (1,-2,-10,0) score systems
 The classic semi-global aligner was
compared to sim4, Spidey and est_genome,
both with subsets 3 and 4

11. Results: Exact Alignments
Extra Gap
Strategy Avg SD %Score 0
SG(1, -2, -1, 0) 0.00 0.00 100.00%
SG(1, -2, -10,
0)
0.00 0.00 100.00%
sim4 1.11 1.63 54.56%
est_genome 16.99 21.49 27.84%
Spidey 0.15 1.39 97.43%

12. Results: Exact Alignments
Delta Exons
Strategy Avg SD %Score 0
SG(1, -2, -1, 0) 0.00 0.00 100.00%
SG(1, -2, -10, 0) 0.01 0.07 99.91%
sim4 -0.01 0.20 97.46%
est_genome -0.14 0.30 76.79%
Spidey -4.04 3.10 0.00%

13. Results: Exact Alignments
Base Similarity
Strategy Avg SD %Scr. 100%
SG(1, -2, -1, 0) 99.89% 0.49% 53.56%
SG(1, -2, -10, 0) 99.89% 0.49% 53.49%
sim4 99.39% 1.34% 22.79%
est_genome 53.83% 35.00% 18.11%
Spidey 80.34% 36.49% 44.25%

14. Results: Exact Alignments
Mismatch Percentage
Strategy Avg SD %Scr. 100%
SG(1, -2, -1, 0) 0.00% 0.00% 100.00%
SG(1, -2, -10, 0) 0.01% 0.03% 99.47%
sim4 0.17% 0.21% 36.68%
est_genome 1.19% 1.26% 21.55%
Spidey 0.15% 0.98% 90.65%

15. Results: EST Alignments

16. Results: EST Alignments

17. Running Time Comparison
EST-to-DNA
(sec/alignment)
mRNA-toDNA
(sec/alignment)
sim4 0.013 0.170
Spidey 0.066 0.140
est_genome 0.640 3.400
Semi-global 0.670 5.170

18. Conclusions
 Classic semi-globl algorithm produces good
results
– Running time is a problem, although it can be
improved
 Sim4 produces the best results amont
external softwares tested

19. Thanks