functional annotation preliminary results

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1 functional annotation preliminary results March 16, 216 Alicia Francis, Andrew Teng, Chen Guo, Devika Singh, Ellie Kim, Harshmi Shah, James Moore, Jose Jaimes, Nadav Topaz, Namrata Kalsi, Petar Penev, Tannishtha Som

2 overview recap background functional annotation final tools modified workflow

3 overview recap background functional annotation final tools modified workflow

4 Functional Annotation Consists of attaching biological information to genomic elements. Goal is to better understand the function of the genes, and their respective proteins, within the organism. Information generally includes: biochemical function biological function regulatory functions and interactions expression

5 overview recap background functional annotation final tools modified workflow

6 NT Haemophilus influenzae Gram negative bacteria Facultatively anaerobic, requires NAD and hemin to grow First free living organism to have its genome sequenced Virulence and diseases otitis media, sinusitis, conjunctivitis, and exacerbations of chronic obstructive pulmonary disease ear infections in children and bronchitis in adults, but may also cause invasive disease, such as bacteremia and pneumonia. Adaptability and transformation horizontal gene transfer Out of 182 genes, 1699 are CDS Source:

7 overview recap background functional annotation final tools modified workflow

8 Protein and NonCoding Functions Protein Function: Structural Regulatory Transmembrane Receptor Enzyme Virulence Factors Metabolic Processes NonCoding Functions: CRISPRs Operons

9 Functional Assignment Name Gene Symbol Protein Name Role Function of the protein in the cell Associated Information Supporting Evidence: Domains/Motifs Transmembrane Regions Orthologous domains Pathways

10 Levels of Annotation

11 Levels of Annotation 1

12 CRISPR Clustered regularly interspaced short palindromic repeats (CRISPRs) highly conserved short sequences (24 bps) separated by spacers of a similar length. Part of acquired immunity in prokaryotes Resistance to bacteriophage invasion Present in ~4% of bacterial genomes Used for subtyping through analysis of spacers with a high degree of polymorphism Salmonella (Fabre et al.) spacer content was strongly correlated with both serotype and multilocus sequence typing (MLST) type Mycobacterium tuberculosis (Gori et al.) Spoligotyping (PCR) amplification of a highly polymorphic direct repeat locus

13 CRISPR: Pilercr and CRT Consensus Results M5964 M27986 M28745 M29197 M294 M36564 M7572 M27987 M2877 M2922 M29658 M3658 M154 M28356 M2881 M29227 M29684 M36582 M1618 M2845 M28853 M2937 M29695 M M2626 M28687 M28888 M29323 M29697 M3666 M2632 M2872 M29179 M29331 M36557 M37982

14 CRISPR: Pilercr Command line: pilercr in <input fasta> out <text file> seq <consensus sequence>

15 CRISPR: CRT Command line: java cp CRT1.2CLI.jar crt <input fasta> <output file>

16 Domains & Motifs Structure and localization fundamental for function Domains selfcontained cooperative folding units Motifs short consensus regions, crucial for the function Using homology Same sequence same function however: Higher sequence similarity = higher probability of same function Using abinitio methods Source

17 InterProScan Scans through the InterPro databases and provides annotation based on homology and gene ontology terms. Databases: Prosite, Coils, PIRSF, Pfam, ProDom, Superfamily, Gene3d, SMART, TIGRFAM, PRINTS Command:./interproscan.sh appl (applications to include) i (input file) Output: TSV, XML, GFF3

18 InterProScan Results: Sample Total # Genes InterPro Unique Annotations % Annotated M % M % M % M % M % M % M %

19 InterProScan Results: Sample Coils Gene3D Pfam PIRSF PRINTS ProDom PSPatterns PSProfiles SMART SF TF M M M M M M M SF = Superfamilies, TF = TIGRFAM

20 Gramnegative bacteria Secretory Pathway Secretion : Transport of proteins, enzymes, toxins from interior of bacterial cell to its exterior. 6 types in gramnegative bacteria : Type II and V : proteins carry signal peptides Type I, III, IV and VI : proteins do not carry signal peptides Proteins in the bacterial membrane also use this pathway. Membrane proteins : Lipoproteins : Functions as virulence factors, nutrient uptake, adhesion Transmembrane proteins Signal Peptidase I : Cleavage of preproteins translocated across membranes. Signal Peptidase II : Cleavage of bacterial prolipoproteins.

21 Transmembrane Proteins alphahelical betabarrels Topology Orientation of Nterminus single/multipass Function Transmembrane helices have longer hydrophobic region and no cleavage site Signal Peptides Source

22 LipoP Based on Hidden Markov Model Classifies genes into four classes: SpI: signal peptide (signal peptidase I) TMH: nterminal transmembrane helix SpII: lipoprotein signal peptide (signal peptidase II) CYT: cytoplasmic. It really just means all the rest Command: LipoP short [Input.fasta] > [Output.gff] Example Output: (short summarizes the best prediction for each gene)

23 LipoP Results Sample No. SpI SpII TMH CYT Total M M M M M M M

24 SignalP Predicts signal peptide and cleavage site Based on neural network Command: signalp t <type of organism> f <format> [Input.faa] > <outputfile> Example Output: (short summarizes the best prediction for each gene)

25 SignalP Results Sample No. Number of signal peptides Total % Signal Peptide M M M M M M M

26 LipoP vs. SignalP Sample No. LipoP Unique SignalP Unique Common NonSignal Total M M M M M M M

27 LipoP vs. SignalP LipoP clearly provides more unique information than SignalP for signal peptides.

28 TMHMM Based on Hidden Markov Model approach Prediction of transmembrane helices in proteins Command: cat <input faa file> /path/tmhmm short > <output file> Sample output:

29 TMHMM Results Sample No. Number of TM Proteins Total % TMH M M M M M M M

30 LipoP vs. TMHMM Sample No. LipoP Unique TMHMM Unique Common Total M M M M M M M

31 LipoP vs. TMHMM TMHMM predicts more transmembrane protein than LipoP Could be because the hydrophobic region of a signal peptide is mistaken as that of a TM protein. Next Step : Run Phobius which claims to have lesser false positive rates than the two.

32 VFDB Virulence Factors Virulence Factors: molecules produced by bacterial pathogens contributing to: Pathogenicity of the host Enabling them to achieve colonization Immunoevasion Immunosuppression Entrance to the cell Obtaining host nutrients Useful for understanding virulence mechanisms and interactions with host cell

33 VFDB Output Identification of protein sequences Command line: blastp db <DatabaseName> query <InputFile> outfmt "6 stitle qseqid sseqid sgi qcovs evalue" out <OutputFile> Tabular Output:

34 Levels of Annotation 2

35 Operons and Polycistronic mrna: Operons: Cluster of genes which are under the control of a single promoter Transcribed together into a single mrna strand Polycistronic mrna: A single mrna strand coding for many proteins

36 Operons OperonDB Input :.faa and.ptt files Blastp createoperondb.pl.ptt format is no longer used by NCBI. Alternative approach: Download operons from OperonDB and DOORS2 for our species of interest Blast it against our query sequence Compare results

37 Levels of Annotation 3

38 Pathways What is a biological pathway? Biological pathway diagrams are used to describe the biological reactions and interaction in a cell in a graphical way. There are many types of biological pathways but most wellknown are pathways involved in Metabolic pathways, generegulation pathways and signal transduction pathway. metabolic pathway: chemical reactions that occur in our bodies. generegulation pathway: turn genes on and off signal transduction pathways: move a signal from a cell s exterior to its interior. Why is it important? The computational approach for incorporating pathway knowledge to interpret highthroughput datasets plays a key role in understanding diseases mechanism from genetic studies. It helps many scientists to generate biologically meaningful hypotheses and it allows more comprehensive inferences made based on the pathway analysis.

39 Work in Progress Large (very) database Overlapping methods with other selected tools mysql issues Complicated user manual Large database (3gb+) Will install separately on home folder and run it there

40 Updated Pipeline

41 Exercise

42 Questions?

43 References Philip Jones, David Binns, HsinYu Chang, Matthew Fraser, Weizhong Li, Craig McAnulla, Hamish McWilliam, John Maslen, Alex Mitchell, Gift Nuka, Sebastien Pesseat, Antony F. Quinn, Amaia SangradorVegas, Maxim Scheremetjew, SiewYit Yong, Rodrigo Lopez, and Sarah Hunter (214). InterProScan 5: genomescale protein function classification. Bioinformatics, Jan 214; doi: 1.193/bioinformatics/btu31 Krogh, A., et al. (21). "Predicting transmembrane protein topology with a hidden markov model: application to complete genomes." Journal of Molecular Biology 35(3): G.E. Tusnady and I. Simon (1998), Principles Governing Amino Acid Composition of Integral Membrane Proteins: Applications to topology prediction, J. Mol. Biol. 283, Panwar, B., et al. (214). Prediction and classification of ncrnas using structural information. BMC Genomics 15:127. DOI: / Thomas Nordahl Petersen, Soren Brunak, Gunnar von Heijne & Henrik Nielsen. SignalP4.: discriminating signal peptides from transmembrane regions. Nature Methods, 8:785786, 211 Chen L, Xionq Z, Sun L, Yang J, Jin Q. VFDB 212 update: toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res. 212 Jan;4(Database issue):d6415. doi: 1.193/nar/gkr989. Epub 211 Nov 8. Koskinen, Patrik, et al. "PANNZER: highthroughput functional annotation of uncharacterized proteins in an errorprone environment." Bioinformatics 31.1 (215): PANNZER: Edgar, Robert C. "PILERCR: fast and accurate identification of CRISPR repeats." Bmc Bioinformatics 8.1 (27): 1. PILERCR: Mi H, Lazarevaulitsky B, Loo R, et al. The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 25;33(Database issue):d2848. Thomas PD, Campbell MJ, Kejariwal A, et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 23;13(9): Mi H, Poudel S, Muruganujan A, Casagrande JT, Thomas PD. PANTHER version 1: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 216;44(D1):D33642.

44 References Van Eldere, Johan et al. Nontypeable Haemophilus influenzae, an underrecognised pathogen. The Lancet Infectious Diseases, Volume 14, Issue 12, Marraffini, Luciano A. "CrisprCas Immunity in Prokaryotes." Nature (215): Fabre, Laëtitia et al. CRISPR Typing and Subtyping for Improved Laboratory Surveillance of Salmonella Infections. Ed. Igor Mokrousov. PLoS ONE 7.5 (212): e PMC. Web. 16 Mar. 216.