Bioinformatics Theory and Practice by 唐继军

1. Bioinformatics生物信息学理论和实践唐继军jtang@cse.sc.edu北京林业大学计算生物学中心www.bjfuccb.comBioinformatics生物信息学理论和实践唐继军jtang@cse.sc.edu北京林业大学计算生物学中心www.bjfuccb.com

2. Download and install programs • Unzip or untar • unzip • If file.tar.gz, tar xvfz file.tar.gz • Go to the directory and “./configure” • Then “make”

3. System subroutine system ("ls –ltr");

4. sub ReadFasta { my ($fname) = @_; open(FILE, $fname) or die "Cannot open $fname\n"; my $data = ""; my @dnas = (); while(my $line = <FILE>) { if ($line =~ /^>/) { if ($data ne "") { push(@dnas, $data); } $data = ""; } $data .= $line; } if ($data ne "") { push(@dnas, $data); } close FILE; return @dnas; }

5. print "Please input file name:\n"; my $fname = <STDIN>; my @dnas = ReadFasta($fname); my $len = $#dnas + 1; for (my $i = 0; $i < $len; $i++) { for (my $j = $i+1; $j < $len; $j++) { for (my $k = $j+1; $k < $len; $k++) { $fname = "$i\_$j\_$k"; print $fname; open(OUT, ">$fname"); print OUT $dnas[$i]; print OUT $dnas[$j]; print OUT $dnas[$k]; close OUT; system ("./clustalw2 $i\_$j\_$k"); } } }

6. Debug • Notice there are problems in a program is hard • Find the source of the problem is even harder • Good debug tool: print • Better tool: debugger

7. Perl debugger • perl –d program arguments • n: next line • s: step in • r: run until the end of the current sub • <RETURN>, repeat • c: continue to the next breakpoint

8. Check source • l • List next several lines • l 8-10 • List line 8-10 • l 100 • List line 100 • l subname • List subroutine subname • f restrcit.pl • Switch to view restrict.pl

9. Breakpoint • b 100 • Add a breakpoint at line 100 of the current file • b subname • Add a breakpoint at this subroutine • B • Remove a break point • B 100 will remove a breakpoint at line 100 • B * will remove all breakpoints

10. See variable • p $var • Print the value of the variable • y var • Display my variable • V display variables • V var • w $var • Watch this var, stop when the value is changed

11. Working with Single DNA Sequences

12. Learning Objectives • Discover how to manipulate your DNA sequence on a computer, analyze its composition, predict its restriction map, and amplify it with PCR • Find out about gene-prediction methods, their potential, and their limitations • Understand how genomes and sequences and assembled

13. Outline • Cleaning your DNA of contaminants • Digesting your DNA in the computer • Finding protein-coding genes in your DNA sequence • Assembling a genome

14. Cleaning DNA Sequences • In order to sequence genomes, DNA sequences are often cloned in a vector (plasmid, YAC, or cosmide) • Sequences of the vector can be mixed with your DNA sequence • Before working with your DNA sequence, you should always clean it with VecScreen

15. VecScreen • http://www.ncbi.nlm.nih.gov/VecScreen/VecScreen.html • Runs a special version of Blast • A system for quickly identifying segments of a nucleic acid sequence that may be of vector origin

20. What to do if hits found • If hits are in the extremity, can just remove them • If in the middle, or vectors are not what you are using, the safest thing is to throw the sequence away

21. Computing a Restriction Map • It is possible to cut DNA sequences using restriction enzymes • Each type of restriction enzyme recognizes and cuts a different sequence: • EcoR1: GAATTC • BamH1: GGATCC • There are more than 900 different restriction enzymes, each with a different specificity • The restriction map is the list of all potential cleavage sites in a DNA molecule • You can compile a restriction map with www.firstmarket.com/cutter

22. Cannot get it work!

23. http://biotools.umassmed.edu/tacg4

25. Making PCR with a Computer • Polymerase Chain Reaction (PCR) is a method for amplifying DNA • PCR is used for many applications, including • Gene cloning • Forensic analysis • Paternity tests • PCR amplifies the DNA between two anchors • These anchors are called the PCR primer

26. Designing PCR Primers • PCR primes are typically 20 nucleotides long • The primers must hybridize well with the DNA • On biotools.umassmed.edu, find the best location for the primers: • Most stable • Longest extension

30. Analyzing DNA Composition • DNA composition varies a lot • Stability of a DNA sequence depends on its G+C content (total guanine and cytosine) • High G+C makes very stable DNA molecules • Online resources are available to measure the GC content of your DNA sequence • Also for counting words and internal repeats

31. http://helixweb.nih.gov/emboss/html/

32. Counting words • ATGGCTGACT • A, T, G, G, C, T, G, A, C, T • AT, TG, GG, GC, CT, TG, GA, AC, CT • ATG, TGG, GGC, GCT, CTG, TGA, GAC, ACT

33. www.genomatix.de/cgi-bin/tools/tools.pl

34. EMBOSS servers • European Molecular Biology Open Software Suite • http://pro.genomics.purdue.edu/emboss/

38. ORF • EMBOSS • NCBI

41. ncbi.nlm.nih.gov/gorf/gorf.html

43. Internal repeats • A word repeated in the sequence, long enough to not occur by chance • Can be imperfect (regular expression) • Dot plot is the best way to spot it

44. arbl.cvmbs.colostate.edu/molkit

46. Predicting Genes • The most important analysis carried out on DNA sequences is gene prediction • Gene prediction requires different methods for eukaryotes and prokaryotes • Most gene-prediction methods use hidden Markov Models

47. Predicting Genes in Prokaryotic Genome • In prokaryotes, protein-coding genes are uninterrupted • No introns • Predicting protein-coding genes in prokaryotes is considered a solved problem • You can expect 99% accuracy

48. Finding Prokaryotic Genes with GeneMark • GeneMark is the state of the art for microbial genomes • GeneMark can • Find short proteins • Resolve overlapping genes • Identify the best start codon • Use exon.gatech.edu/GeneMark • Click the “heutistic models”

49. Predicting Eukaryotic Genes • Eukaryotic genes (human, for example) are very hard to predict • Precise and accurate eukaryotic gene prediction is still an open problem • ENSEMBL contains 21,662 genes for the human genome • There may well be more genes than that in the genome, as yet unpredicted • You can expect 70% accuracy on the human genome with automatic methods • Experimental information is still needed to predict eukaryotic genes

50. Finding Eukaryotic Genes with GenomeScan • GenomeScan is the state of the art for eukaryotic genes • GenomeScan works best with • Long exons • Genes with a low GC content • It can incorporate experimental information • Use genes.mit.edu/genomescan