Genome Browser. Shruti Bhide Abhiram Das Khanjan Gandhi Viswateja Nelakuditi

Transcription

1 Genome Browser Shruti Bhide Abhiram Das Khanjan Gandhi Viswateja Nelakuditi

2 Present Scenario Need of Databases and Genome Browser

3 Present Scenario Need of Databases and Genome Browser Put all the ingredients together to form a Dish

4 Presentation Layout * About GBrowse Need and Architecture * Database Strategy Assembly and Prediction * Database Strategy Comparative Genomics & Functional Annotation * Current Position

5 Generic Genome Browser and its Architecture Viswateja Nelakuditi

6 What is a Genome Browser? Genome browsers facilitate genomic analysis by presenting alignment, experimental and annotation data in the context of genomic DNA sequences. Melissa S Cline & James W Kent, 2009

7 Why do we need a Genome Browser? Can be best explained by an example! contig002 CONSENSUS gene BLAST_OR= contig002 CONSENSUS gene BLAST_OR=1 contig002 CONSENSUS gene BLAST_OR=1 contig002 CONSENSUS gene BLAST_OR=1 contig002 CONSENSUS gene BLAST_OR=

8 Genes

9 Why we need a Genome browser? Viewing genomic data in the form of plain text is not so useful. Genome browsers provide rapid visualization of genomic data along with annotations. User can view any portion of a genome at any level of detail, view annotations, add his/her own private annotations and compare them against public annotations.(explained in detail in further slides) Genome browser itself doesn t draw any conclusions; rather, it collates all useful information about a genome in one location, leaving the exploration and interpretation to the user.

10 List of some Genome Browsers Ensembl ( FlyBase ( WormBase ( NCBI Map Viewer ( =4577) UCSC Browser ( Neisseria Base ( )

11 Model organism databases(mods) MODs are built around the information needs of scientists working on single model organism or group of closely related organisms. Four well established model organism databases are Fly-Base, SGD, MGD, WormBase, which are associated, respectively, with Drosophila melanogaster, Sacchromyces cerevisiae, Mus musculus and Caenorhadbditis elegans. These are four of the five model organism systems(fifth was Ecoli) targeted by NIH component of Human Genome Project in As cost of genomic sequencing has come down, an increasing number of organisms are being sequenced or already has been sequenced,which necessitates the need for new MODs to manage these data sets.

12 GMOD Major component of the cost of creating a new MOD is the development of database schemata, middle ware and visualization software. Recognizing this the four MODs (Fly-base, SGD, MGD, Worm base) started working on creating reusable components that could be available to scientific community free of charge under an open source license (year 2000). The goal of this project, christened Generic Model Organism database (GMOD), is to generate a model organism database that would allow a new MOD to be assembled by mixing and matching various components.

13 Two outcomes of this project are 1) Apollo genome annotation editor 2) Generic Genome Browser(GBrowse) Although GBrowse is targeted at maintainers of model organism databases, it is suitable for any research group that must manage a set of sequence annotations, ranging from those needing to display raw features such as similarity hits through those maintaining high level genome features such as fully curated gene models.

14 What does GBrowse do? GBrowse takes in bulk data an makes it pretty ( As shown in previous slides) and easy to view and analyze. The data should be in the form of GFF (General Feature Format). GFF file is a tab delimited text file with 9 fields which are sufficient to represent any genomic feature. Example: contig002 CONSENSUS gene BLAST_OR=

15 The version of GBrowse that we are using for our class (1.69) works well with GFF3 version. The main difference in GFF3 and its previous versions is GFF3 allows representation of feature and its associated sub features. Example: 2 ctg123. gene ID=gene00001;Name=EDEN 3 ctg123. mrna ID=mRNA00001;Parent=gene00001;Name=EDEN.1 4 ctg123. mrna ID=mRNA00002;Parent=gene00001;Name=EDEN.2

16 A feature in a GFF3 file is displayed as a track. Eg: contigs, cds, IS, rrna, trna

17 We can zoom into any portion of genome and at any level of detail. Semantic zooming!!

18 It can show us the details of every feature.

19 We can add private annotations and compare them with public annotations.

20 How can we extend GBrowse? We can add our own tracks. (like trna,rrna) We can add additional tracks like 6 frame translation, GC content and more!

21 We can change the order of appearance of tracks. We can add third party plugins. (Note: A plugin is just a module which when incorporated enhances the functionality of a software.) We can add our own annotations to the features. We can use different glyphs for different features. We can change the appearance of GBrowse itself! We can connect GBrowse to variety of dbms like Oracle, Mysql etc. We can do a lot more!

22 What GBrowse cant do?? Sorry, But GBrowse cannot analyze data for you!( Otherwise it must have been named GBA - Genome Browser and Analyzer?) It is user friendly but not so developer friendly (No sufficient documentation). Limited search functionality( Although we can add our own search modules).

23 Architecture of GBrowse

24 Database Strategy Assembly and Prediction Shruti

25 fattribute_to_feature fid fattribute_id fattribute_value fattribute fattribute_id fattribute_name fdna fref foffset fdna Database Schema fdata fid gid fref fstart fstop fbin ftypeid fstrand fscore fphase ftarget_start ftarget_stop fgroup gid gclass gname ftype ftypeid fmethod fsource

26 fdata table contig002 CONSENSUS gene BLAST_OR= fdata fid gid fref fstart fstop fbin ftypeid fstrand fscore fphase ftarget_start ftarget_stop contig

27 fdna table fdna fref foffset fdna >contig001 GGCGAGGCAACGCCGTACCGGTTTTTGT TAATCCACTATAAATGACGATATAAGTATT TTTATTTTAATCCGCCATATTAACGCACCC GGCCAAACAGCATAAAGGCACGGGCAGC CCGA fgroup table fgroup gid gclass gname gclass = class of feature(cds,is) gname = contig name

28 ftype table contig001 assembly2.1 contig Name=contig001 ftype ftypeid fmethod fsource contig assembly2.1

29 fattribute table fattribute fattribute_id fattribute_name ID's for attributes like BLAST_OR, name, family, origin etc. fattribute_to_feature table contig001 CONSENSUS gene BLAST_OR=1 fattribute_to_feature fid fattribute_id fattribute_value

30 Database Strategy Functional Annotation and Comparative Genomics Khanjan Gandhi

31 Basics of Database GTID Student STUDENT GPA GTID Name Major GPA DeptID Name Major Khanjan Bioinformatics 4.0 B23 Studies in DEPARTMENT Department ID Name Location B23 Biology, School of Cherry Emerson ID Location Name

32 Database Design Functional Annotation Tools Used by Functional Annotation * LipoP * SignalP * TMHMM * BLAST * PSORTB * PROTCOMPB * Interproscan

33 Database Design Functional Annotation LipoP protein has has has lipop_lipoprotein lipop_signalpeptidasei lipop_others lipop_lipoprotein Prot_ID Location Score FiveAA Post+2 lipop_signalpeptidasei Prot_ID Location Score FiveAA lipop_others Prot_ID Class Feature_type Score

34 Database Design Functional Annotation SignalP protein has has Signalp_scores signalp_scores Prot_ID Measure_type Position Value Cut off SignalP_NN results SignalPeptide signalp_cleavage _details SignalP_HMM results signalp_cleavage_details Prot_ID Prob_signalp Prob_cleavesite Cleavesite_Start Prediction

35 Database Design Functional Annotation TMHMM protein has has Tmhmm_results Tmhmm_stats TMHMM_results Prot_ID Location Start Stop TMHMM_stats Prot_ID Length Num_HMMS Num_AAs Num_60AAs Prob_Nterm

36 Database Design Functional Annotation BLAST protein Has BLAST results blast_hits protein_id hit_name e_value has blast hits blast_results blast_results blast_hits Query_ProteinID AccessionNo Per_identity Align_len Num_mis E-value Bit score

37 Database Design Functional Annotation PSORTB protein has has psortb_analysis psortb_scores psortb_analysis Prot_Id CMSVM CySVM HMMTOP Motif PPSVM Profile SCLblast Signal psortb_scores Prot_ID cyto_score cytomem periplasm outermem extra prediction

38 Database Design Functional Annotation PROTCOMPB protein has protcompb analysis results protcompb_results protcompb_results protein_id score prediction_neural_net score_neural_net prediction_integral score_integral Transmembrane_segments

39 Database Design Functional Annotation INTERPROSCAN protein has has has has database_specific_ details Interpro_evidence domainid_goid_corr espondence has has domain_information has domainid_pubmedid correspondence gene_ontology_info domainid_accessionid

40 Database Design Comparative Genomics Results of Comparative Genomics * Cluster of Orthologous Group S * Single Nucleotide Polymorphism * Horizontal Gene Transfer ( DarkHorse, Alien_Hunter, CodonO) * Genome Alignment (MAUVE, MUMMER) * Phylogeny

41 Database Design Comparative Genomics COGS & SNPs protein is described by COGS SNPs

42 Database Design Comparative Genomics OTHERS Horizontal Gene Transfer * Alien_hunter * CodonO * Dark horse * Output formats -.gff Phylogeny * Splitstree * Output formats -.jpg,.png,.bmp,.pdf Genome Alignment * MAUVE * MUMMER * Output formats -.jpg,.pdf

43 Completed work! Added cds,is,rrna,trna tracks. Changed GBrowse appearance. Added plugins for dumping GFF and FASTA files for a selected region of our genome. Added GC content and 6 frame translation tracks. Added tables for annotation and comparative genomics data.

44 Future work (Goals) Adding perl modules to display annotation and comparative genomics data in details page (currently working). Adding more tracks for comparative genomics (HGT etc). Adding custom search functionality (Time constraint). New pretty front page for GBrowse (currently working).

45 Thank You! House is Open to Discussion