1. Practice – file types (Cont.) Load the “Mysequence.doc” file to Webcutter using “Choose file” and then “Upload sequence file”. -Notice that the “sequence” in the sequence box are nonsense characters. Clear input; Browse and then load the.txt file. Run an analysis. Always keep you sequences in.txt file for downstream analysis.

2. Representation of sequence The need to represent associated info with sequence Structured data entry Specialized databases  3-d Structure  Mutation / Diseases  Protein family / Protein domain  Interaction  Pathway  ….

3. Representation of sequence The need to represent associated info with sequence Structured data entry Specialized databases Complex / customized data structure - Object-oriented data representation (Mount, p44-45)

4. Public Resources for Bioinformatics Databases Analysis Tools Observe: List of databases and service at NCBI, EBI, KEGG, and Ensembl.

5. What can we know about this gene?  Search for “curated” databases.  To prepare for future analysis, save annotated sequence files as genename.html (in a target folder).  For downstream sequence analysis, save pure sequence as FASTA format file. MDM2, or your favorite gene Pet Project:

6. Where and how much information are available for my gene? Observe: The information contents and presentation format for the same gene in SwissProt, NCBI protein, NCBI Genes, etc..

7. Public Resources (I) – Databases and data sources Over 1,000 in the sea of databases. Content-specific, such as DNA, Protein, Structure, etc. Species-specific, such as flybase, wormbase, OMIM, etc. System-specific, such as MetaCyc, AFCS, etc.

8. Database concept: Database - efficiently store, update, and retrieve information (data). Database management systems – Access, Sybase MySQL, Oracle, etc. Types of Databases – Relational DB, Object DB, native XML DB.

9. Database concept – tables in relational databases Accessi on Organ.Ref.NameKey words Features ….….. medline1 TNF…..…….….. …. medline2 P53….……..…… “TNF”=TNF[All Fields]TNF[Name] Protein table

10. Database concept – relationship between tables Accessi on Organ.Ref.NameKey words Features ….…..medline1P27…..…….….. …. medline2P53….……..…… Protein table IDtitleyearauthorabstract medline1…..1970….….. medline2….1980…. … Reference table

11. Representation of sequence The need to represent associated info with sequence Structured data entry Specialized databases Complex / customized data structure - Object-oriented data representation (Mount, p44-45)

12. Observe/Practice Search for MDM2 in the Gene database and the and Proteins databases. Search for MDM2 in “All Text” v.s “gene name” in the Gene database. Compare results. Download the human MDM2 protein sequences for all 8 isoforms.

13. Public Resources (II) – Analysis tools  Web-based analysis tools – easy to use, but often with less customization options.  Stand-alone analysis tools – requires installation and configuration, but provides more customizatio0n options.  Commercial analysis tools  Scripting for bioinformatics projects

14. Practice: navigating the related resources through links Using the “PubMed” link, search annotated references on MDM2. Using the “GEO Profiles” link, search gene expression information on MDM2. Using the “Map Viewer” link to observe the chromosome location and gene structure of the MDM2 locus – change the option of “Map Viewer” to include prediction of CpG island.

15. Public Resources for Bioinformatics Databases : how to find relevant information. Analysis Tools

16. Public Resources (II) – Analysis tools  Web-based analysis tools – easy to use, but often with less customization options.  Stand-alone analysis tools – requires installation and configuration, but provides more customizatio0n options.  Commercial analysis tools  Scripting for bioinformatics projects

17. web-based tools Identification of web-based bioinformatics resources. –Portals, lists, –Google search Organization –Book mark. –html page.

18. web-based tools Practice –retrieve genomic sequence from Ensemble and perform reverse complementation with SMS

19. Stand-alone tools 1. Rules of the thumb:  Make a folder for each program.  Make a sub-folder for input/output if necessary.  Link GUI-based.exe application to program menu

20. Stand-alone tools 2. 1.Download the zip file to the GMS6014 folder. 2.Unzip the files to a folder named “clustalx”. 3.Edit the 3TNF file with WordPad and save. 4.Activate the.exe file. 5.Load sequence file, select sequences, perform alignment. 6.Write the alignment to a ps file. Practice –the ClustalX application.

21. Stand-alone tools 3. Command line applications:  Accounts for a large number of high-quality, sophisticated programs. Practice – (install and) run standalone blast in your own computer

22. Identifying the ortholog of MDM2 (Tumor necrosis factor) in an insect genome. Pet Projects:

23. Practice – Install the blast program (1) 1.Download the BLAST executable file, save the file in a folder, such as c:\GMS6014\blast\ 2.Run the installation program by double click. Inspect the folder following installation. 3.Add three more folders to your /blast directory, “/query”, “/dbs”, and “/out”.

24. Practice – Install the blast program (2) 5.Inspect the contents of the doc, data, and bin folder. Move the programs from blast\bin to the blast folder. 6.Bring a command (cmd) window by typing “cmd” in the Start  Run box. 7.Go to the blast folder by typing “cd C:\GMS6014\blast” 8.Try to run the program by typing “blastall”, read the output.

25. Practice -- BLAST search in your own computer 1.Download data file from the course web page, or Ensemble. Save in the blast\dbs folder. 2.Start a CMD window, navigate to the C:\GMS6014\blast folder. 3.At the prompt “C:\GMS6014\blast >” type the command “formatdb –i dbs\Dm.P –p T” -- format the dataset for the program. 4.Compose the query sequence save as “3TNF.txt” in the “blast\query\” folder. 5.Initiated the search by typing “blastall –p blastp –d dbs\Dm.P –i query\4_MMD2.fasta –o out\Mdm2_DmP.html –T T”

26. What’s in a command? formatdb –i dbs\Dm.P –p T Program – format database for search. Feed me the input file name Tell me is it a protein sequence file? For more info, refer to the “user manual” file in the blast\doc folder.

27. Advantages of Running BLAST at Your Own Machine  Do it at any time, no waiting on the line.  Search for multiple sequences at once.  Search a defined data set.  Automate Blast analysis.  Combine Blast with other analysis.  …..

28. BLAST is a program implemented in C/C++ void BlastTickProc(Int4 sequence_number, BlastThrInfoPtr thr_info) { if(thr_info->tick_callback && (sequence_number > (thr_info->last_db_seq + thr_info->db_incr))) { NlmMutexLockEx(&thr_info->callback_mutex); thr_info->last_db_seq += thr_info->db_incr; thr_info->tick_callback(sequence_number, thr_info->number_of_pos_hits); thr_info->last_tick = Nlm_GetSecs(); NlmMutexUnlock(thr_info->callback_mutex); } return; } /* Sends out a message every PERIOD (i.e., 60 secs.) for the index. THis function runs as a separate thread and only runs on a threaded platform. Should I care ?

29. Programming language comparison /* TRANSLATION: 3 or 6 frame translate cDNA sequences */ //--------------------------------------------------------------------------- #include "translation.hpp" int main(int argc, char **argv) { int num_seq=0; char string[MAXLINE]; DSEQ * dseq; infile.getline (string,MAXLINE); if (string[0]=='>') strncpy (dbname,string,MAXLINE); while (!infile.eof()) { dseq=Get_Lib_Seq (); if (dseq->reverse==0) Translation (&dseq->name[1], dseq->seq); else Translation (&dseq->name[1], dseq->r_seq); num_seq++; if (num_seq%1000==0) { cout<<num_seq<<endl; cout name<<endl; } delete dseq; } infile.close(); outfile.close(); cout<<num_seq<<" translated"<<endl; getch(); return 0; } DSEQ* Get_Lib_Seq() { int i,n; char str[MAXLINE]; DSEQ* dseq; n = 0; dseq=new DSEQ; strcpy (dseq->name, dbname); while(infile.getline(str,MAXLINE)) {if (str[0] == '>') { strcpy( dbname, str); break; } for(i=0;i<strlen(str);i++) {if(n==MAXSEQ) break; dseq->seq[n++] = str[i]; } dseq->seq[n]='\0'; if(n==MAXSEQ) cout<<"WARNING: sequence"<<dbname<<"too long!"<<endl; dseq->len=n; if (dseq->name[9]=='3') Reverse (dseq); else dseq->reverse=0; return dseq; } void Reverse (DSEQ* dseq) //Reverse dseq {int i,j; j=0; for (i=(dseq->len-1);i>0;i--) { if (dseq->seq[i]=='A'||dseq->seq[i]=='a') dseq->r_seq[j++]='T'; if (dseq->seq[i]=='C'||dseq->seq[i]=='c') dseq->r_seq[j++]='G'; if (dseq->seq[i]=='G'||dseq->seq[i]=='g') dseq->r_seq[j++]='C'; if (dseq->seq[i]=='T'||dseq->seq[i]=='t') dseq->r_seq[j++]='A'; if (dseq->seq[i]=='N'||dseq->seq[i]=='n') dseq->r_seq[j++]='N'; } dseq->r_seq[j++]='\0'; dseq->reverse=1; } void Translation (char name[], char seq[]) { char ppseq[MAXSEQ/3]; for (int f=0; f<3; f++) { outfile "<<"F_"<<f<<name<<endl; int j=0; int len=strlen(seq); for( int i=f; i<len; i=i+3) ppseq[j++]=Translate(&seq[i]); ppseq[j++]='\0'; int m=strlen(ppseq)/50; // output 50 aa per line for (int n=0; n<=m; n++) {for (int i=n*50; i<50*(n+1); i++) {outfile<<ppseq[i]; if (ppseq[i]=='\0') break; } outfile<<endl; } char Translate(char s[]) { int c1,c2,c3; char P, code[3]; //***standard translation table, A(0),C(1), G(2), T(3)***** char table [4][4][4]= {{{'K','N','K','N'},{'T','T','T','T'},{'R','S','R','S'},{'I','I','M','I'}}, {{'Q','H','Q','H'},{'P','P','P','P'},{'R','R','R','R'},{'L','L','L','L'}}, {{'E','D','E','D'},{'A','A','A','A'},{'G','G','G','G'},{'V','V','V','V'}}, {{'*','Y','*','Y'},{'S','S','S','S'},{'*','C','W','C'},{'L','F','L','F'}}}; //*********** table2 for n at 3rd position******************** char table2 [4][4]={{'X','T','X','X'},{'X','P','R','L'}, {'X','A','G','V'},{'X','S','X','X'}}; strncpy (code, s, 3); c1=Convert(code[0]); c2=Convert(code[1]); c3=Convert(code[2]); if (c1>=4 || c2>=4) P='X'; //can be Optimized further here by considering.... else { if (c3>=4) P=table2[c1][c2]; else P=table[c1][c2][c3]; //P=table[Convert(code[0])][Convert(code[1])][Convert(code[2 ])]; } return (P); } int Convert (char c) { char s=c; if (s=='A'||s=='a') return (0); if (s=='C'||s=='c') return (1); if (s=='G'||s=='g') return (2); if (s=='T'||s=='t'||s=='U'||s=='u') return (3); if (s=='N'||s=='n') return (4); else return (5); } f#Translation -- read from fasta DNA file and translate into three frames # import string from Bio import Fasta from Bio.Tools import Translate from Bio.Alphabet import IUPAC from Bio.Seq import Seq ifile = "S:\\Seq\\test.fasta" parser = Fasta.RecordParser() file =open (ifile) iterator = Fasta.Iterator (file, parser) cur_rec = iterator.next() cur_seq = Seq (cur_rec.sequence,IUPACUnambiguousDNA()) translator = Translate.unambiguous_dna_by_id[1] translator.translate (cur_seq) Translation : C Translation : Python

30. Observe: scripting is not that difficult Example: Python and bioPython. 1.Simple python scripts. 2.Batch Blast with a Python script.

32. Representation of sequence The need to include annotations and functional information with each sequence. Structured data entry GeneBank EMBL / SwissProt Observe: The difference of data structure between SwissProt, NCBI protein, and NCBI Genes.