Penelusuran database bioinformatika. Lutfan Lazuardi

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Penelusuran database

bioinformatika

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Spektrum dan hirarki dari

informatika kesehatan

Bioinformatics Molecular Cellular Imaging Informatics Tissues, Organs Clinical Informatics Individual Patients Public Health Informatics Populations

Adapted from Shortliffe

Diadaptasi dari Shortliffe

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Apa itu bioinformatika?

• Disiplin ilmu yang meliputi bagaimana mendapatkan, memproses, menyimpan,

mendistribusikan dan menganalisis informasi biologi dan kedokteran

• Dalam arti luas:

– Setiap penelitian yang berhubungan dengan

proses biologi dengan mempergunakan komputer

• Dalam arti sempit:

– Analisis berbasis komputer terhadap data sekuens dari struktur makromolekul

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S C I T S I T A T S S C I M O N E G S C I M O T P I R C S N A R T S C I M O L O B A T E M S C I T A M E H T A M G N I L E D O M R A L U C E L O M S C I M O E T O R P Y G O L O T N O H C E T O F N I N O I T U L O V E H C E T O I B S C I M O L L E C S C I M O I S Y H P Y G O L O I B

Bioinformatika menggabungkan ilmu Biologi, Kedokteran, Kimia, Matematika, Statistik dan

ilmu Komputer untuk memahami proses biologis dari kehidupan

Bioinformatika menjembatani banyak disiplin ilmu

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DNA sequence

Gene & Genome Molecular evolution

Protein Structure, Folding, Function & Interaction

Metabolic Pathways Regulation

Signaling

Networks

Physiology & Cell Biology Interspecies

Interaction

Ecology & Environment

Genome sequencing Genomic data analysis

Statistical genetics

Proteomics _{Protein structure prediction,} dynamics, folding & design

Functional genomics (microarrays, 2D-PAGE, etc)

Data standards, representations, & analytical tools for Complex biological data

Dynamical Systems modeling High-tech Field ecology Computational ecology Experimental

Hardware & instrumentation

Information technology

Computation

Mathematical & physical models

Se quenc e  P h ysiol o gy (and be yo nd)

Methodology & expertise

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Penelitian biologi abad 21

“ The new paradigm, now emerging is that all the 'genes' will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical.”

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The Pyramid of Life

30,000 Genes 10,000 Proteins 1400 Chemicals Metabolomics Proteomics Genomics B I O I N F O R M A T I C S Wishart (2004)

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Entrez: Neighbors and Hard Links

Genomes Taxonomy PubMed abstracts Nucleotide sequences Protein sequences 3-D Structure 3 -D Structure Word weight VAST BLAST BLAST Phylogeny Source NCBI

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Data sekuens

Pencarian Obat Baru Diagnosis Pengembangan Vaksin Hypothesis-Driven Research

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penelitian tradisional

• Satu gen setiap eksperimen • butuh waktu panjang

• Melelahkan • hasil terbatas

• In vitro, in vivo, ex vivo

NGS, Microarray

• fast tracking

• ribuan gen setiap kali eksperimen

• fungsi dari gen, baik sendiri atau interaksi dengan yang lainnya

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Contoh basisdata

• Nucleotide Database (GenBank)

– BLAST (Basic Local Alignment Search Tool)

• Protein Sequence Database

• Protein Structure Database (PDB) • Genome Database

• Microarray Database

• Metabolic Pathway and Protein Function Database

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Contoh tipe data

Nucleotide/protein sequence

Gene expression level

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GenBank

• Basis data sekuens

• Koleksi anotasi sekuens DNA

• 171.744.486 sekuens (April 2014)

• Data sekuens didapatkan dari submisi langsung dari para ilmuwan/author

• Basis data genbank didesain untuk

menyediakan informasi sekuens yang

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Sumber data GenBank

• Submisi langsung dari individu peneliti melalui form (BankIt, Sequin)

• Submisi melalui Batch email (EST, GSS, STS) • Melalui akun FTP (File Transfer Protocol)

• Data dari tiga kolaborasi basis data:

– GenBank

– DNA Database of Japan (DDBJ).

– European Molecular Biology Laboratory Database (EMBL)

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Basis data primer vs. sekunder

• Primary Databases

– Original submissions by experimentalists

– Database staff organize but don’t add additional

information

• Example: GenBank

• Derivative Databases (Secondary)

– Human curated

• compilation and correction of data

• Example: SWISS-PROT, NCBI RefSeq mRNA

– Computationally Derived

• Example: UniGene

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Format file

• Genbank Flatfile (GBFF) – Header – Features – Sequence • FASTA format

– Deskripsi dimulai dengan tanda> – Diikuti dengan data sekuens

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Contoh analisis

Kaohsiung J Med Sci. 2008 Feb;24(2):55-62. doi: 10.1016/S1607-551X(08)70098-6.

Phylogenetic study of dengue-3 virus in Taiwan with sequence analysis of the core gene.

Tung YC1_,_{Lin KH}_,_{Chang K}_,_{Ke LY}_,_{Ke GM}_,_{Lu PL}_,_{Lin CY}_,_{Chen YH}_,

Chiang HC.

URL:

http://www.sciencedirect.com/science/article/pii/S160 7551X08700986

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• Analisis kemiripan (BLAST) • Desain primer

• Komparasi sekuen • Multiple alignment • Phylogenetic analysis

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• high-density oligonucleotide human genome array GeneChips U133 Plus 2.0 (Affymetrix)

• This chip comprises more than 54.000 probe sets and analyzes the expression level of over 47.000 transcripts and variants including 38.500 well-characterized human genes

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Microarray assay life cycle

Biological question Sample preparation Microarray hybridization Microarray detection Data analysis

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Proses data Microarray

Prediction: Gene Value D26528_at 193 D26561_cds1_at -70 D26561_cds2_at 144 D26561_cds3_at 33 D26579_at 318 D26598_at 1764 D26599_at 1537 D26600_at 1204 D28114_at 707 Class Sno D26528 D63874 D63880 … ALL 2 193 4157 556 ALL 3 129 11557 476 ALL 4 44 12125 498 ALL 5 218 8484 1211 AML 51 109 3537 131 AML 52 106 4578 94 AML 53 211 2431 209 … Data Mining and analysis New sample

Microarray chips Images scanned by laser

Datasets

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Contoh skema data analisis microarray Microarray

Normalization

Statistical test; T-test/ANOVA (Analysis of Variance) Filtering steps

PTM

(Pavlidis template matching)

Cluster Analysis

Genes of interest validation

Biological Process/Function/Pathway

Gene expression profiles Raw data P repr oc essi ng H igh le vel anal ysi s

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Contoh analisis

Biogerontology. 2009 Apr;10(2):191-202. doi: 10.1007/s10522-008-9167-1.

Epub 2008 Aug 27.

Microarray analysis reveals similarity between CD8+CD28- T cells from young and elderly

persons, but not of CD8+CD28+ T cells.

Lazuardi L1_,_{Herndler-Brandstetter D}_,_{Brunner S}_,_{Laschober GT}_,_Lepperdinger

G, Grubeck-Loebenstein B.

URL:

http://link.springer.com/article/10.1007%2Fs10 522-008-9167-1

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contoh hierarchical cluster analysis Genes clusters (1-21) 2 1 2 0 1 9 1 2 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 3 Y1 _2 8P Y2 _2 8P 01 _2 8P 02 _2 8P Y1 _2 8N Y2 _2 8N 01 _2 8N 02 _2 8N 0 7 10 A Y1 _2 8 P Y2 _2 8 P 0 1 _2 8 P 0 2 _ 2 8 P Y1 _2 8 N Y2 _2 8 N 0 1 _2 8 N 0 2 _2 8 N B Linkage distance

Y1_28P & Y2_28P : CD8+_CD28+ _{T cells from young persons}

O1_28P & O2_28P : CD8+_CD28+_{T cells from elderly persons}

Y1_28N & Y2_28N : CD8+_CD28–_{T cells from young persons}

O1_28N & O2_28N : CD8+_CD28–_{T cells from elderly persons}

cluster 13 Exp re ss io n le ve l Cluster gen

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Links

• Genbank • https://www.ncbi.nlm.nih.gov/genbank/ • Protein database • http://www.wwpdb.org/ • http://www.rcsb.org/pdb/home/home.do • KEGG Pathway database

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Penelusuran database bioinformatika. Lutfan Lazuardi