Introduction to Bioinformatics

Transcription

1 Introduction to Bioinformatics Dr. rer. nat. Jing Gong Cancer Research center Medicine School of Shandong University

2 Chapter 2 Databases 2

3 Why Do WE Need Databases? What s that? gcattac ttgatctaatca ataggatctaatctt tactagaacgcc ttgatctaatca ttgcaa 3

4 Why Do We Need Databases? This is the entire HIV1 genome containing total 9752 nucleotides with only 9 genes. gcattac ttgatctaatca ataggatctaatctt tactagaacgcc ttgatctaatca ttgcaa 4

5 Why Do We Need Databases? Human Genome : 3 Gbp = 3,000,000,000 bp 5000bp/page 600pages/book 1000 x 3cm/book 600,000 pages 1000 books = 30m bookshelf Over 1000 species : 26.6m 1000 x 30m-bookshelves 200 x 5 layers/bookshelf = 2 x 医学院图书馆 450,000 册 5

6 Why Do We Need Databases? 10cm All sequenced genomes: collect access x m update 14.6cm 1TB = 1000GB = 1,000,000MB = 1,000,000,000KB = 1.000,000,000,000B manage 6

7 History of Biological Databases Biological Databases - A biological database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. Biological databases make it possible to answer today s biological questions by enabling us to analyze sequences that may have been determined as many as 30 years ago, when the whole technology emerged. The first biological database was created within a short period after the Insulin protein sequence was made available in insulin = MALWMRLLPLLALLALWGPDPAAAFVNQHL CGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG GPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN Frederick Sanger (1918-) nobel prize

8 History of Biological Databases Around mid 1960 s, the first nucleic acid sequence of Yeast trna with 77 bases was found out by Holley. During this period, three dimensional structure of proteins were studied and the well known Protein Data Bank was developed as the first protein structure database with only 10 entries in This has now grown into a large database with over 75,000 entries. Robert W. Holley ( ) nobel prize 1968 Max Ferdinand Perutz ( ) nobel prize 1962 John Cowdery Kendrew ( ) nobel prize

9 History of Biological Databases At beginning, the initial databases of protein sequences were maintained at the individual laboratories. The development of a consolidated formal database known as Swiss-Prot protein sequence database was initiated in Now it has about 530,000 protein sequences from more than 12,000 organisms. 9

10 History of Biological Databases The Los Alamos National Laboratory (USA) established the Los Alamos Sequence Database in 1979, which culminated in 1982 with the creation of the public GenBank. Later, the nucleotide sequence database of European Molecular Biology Laboratory (EMBL) and the DNA Data Bank of Japan (DDBJ) were created. Today these three databases represent the largest and fundamental biological databases and they together called International Nucleotide Sequence Database Collaboration (INSDC). 10

11 Classification of Biological Databases Nucleotide Database Protein Database >2000 Primary Nucleotide Database Primary Protein Database Protein Sequence DB Protein Structure DB INSDC UniProt Secondary Nucleotide Database Secondary Protein Database Specific Database 11

12 Classification of Biological Databases Nucleotide Database Protein Database >2000 Primary Nucleotide Database Primary Protein Database Protein Sequence DB Protein Structure DB INSDC UniProt Secondary Nucleotide Database Secondary Protein Database Specific Database 12

13 Nucleotide Databases Primary Nucleotide Database is produced and maintained by the National Center for Biotechnology Information ( NCBI ). The NCBI is a part of the National Institutes of Health ( ) in the United States ( ). GenBank receive sequences produced in laboratories throughout the world. In about 30 years since its establishment, Its data were accessed and cited by millions of researchers around the world. GenBank continues to grow at an exponential rate, doubling every 18 months. Release produced in 2008, contained over 99 billion nucleotide bases in more than 98 million sequences. 13

14 Nucleotide Databases Primary Nucleotide Database The European Molecular Biology Laboratory (EMBL) is supported by 20 European ( ) member states and one associate member state, Australia ( ). It consists of five facilities: the main Laboratory in Heidelberg ( ), outstations in Hinxton (, the European Bioinformatics Institute (EBI)), Hamburg ( ), Grenoble ( ), and Monterotondo ( ). The EMBL Nucleotide Sequence Database constitutes Europe's primary nucleotide sequence resource. Main sources for DNA and RNA sequences are direct submissions from individual researchers, genome sequencing projects and patent applications. The current release contains 212 millions sequence entries comprising 326 billions nucleotides. 14

15 Nucleotide Databases Primary Nucleotide Database The DNA Data Bank of Japan (DDBJ) is a biological database that collects DNA sequences. It is located at the National Institute of Genetics ( ) in the Shizuoka ( 静冈 ) of Japan. DDBJ began data bank activities in 1986 at NIG and remains the only nucleotide sequence data bank in Asia. Although DDBJ mainly receives its data from Japanese researchers, it can accept data from contributors from any other country. The current release contains 138 millions sequence entries and 128 billions bases. 15

16 Nucleotide Databases Primary Nucleotide Database The International Nucleotide Sequence Database Collaboration (INSDC) consists of a joint effort to collect and disseminate databases containing DNA and RNA sequences New and updated data on nucleotide sequences contributed by research teams to each of the three databases are synchronized on a daily basis through continuous interaction between the staff at each the collaborating organizations. 16

17 Nucleotide Databases Reading into Genes and Genomes All living organisms can be sorted into one of two groups depending on the fundamental structure of their cells. These two groups are the prokaryotes (organisms lacking a true nucleus) and the eukaryotes (organisms having a true nucleus). archaea Nucleotide sequences are universal, but the structure of genes they encode is markedly different between prokaryotes and eukaryotes. prokaryotic cell eukaryotic cell 17

18 Nucleotide Databases Reading into Genes and Genomes Besides prokaryotes and eukaryotes, there is the third class of living organisms, archaea. They are bacteria-like organisms living in extreme conditions. In bioinformatic context, prokaryotes and archaea are very much the same. archaea prokaryotic cell eukaryotic cell 18

19 Nucleotide Databases Reading into Genes and Genomes Prokaryotes (archaea) have the following properties in common: They are microscopic organisms. Their genomes is single, circular DNA molecule. Their gene density is approximately one gene per 1,000 base pairs. Their genome contains few useless part (70% is coding for proteins). Their genes do not overlap. Their genes are transcribed to mrna right after a control region, called promoter. These mrna are collinear with the genome sequence. Protein sequences are derived by translating the longest open reading frame (ORF) spanning the gene-transcript sequence. 19

20 Nucleotide Databases Reading into Genes and Genomes Reading Frame - a reading frame is a way of breaking a DNA sequence into three letter codons which can be translated in amino acids. ORF x 3 x 3 = x 6 reading frames ORF (Open Reading Frame) - a DNA sequence that contains a start codon but does not contain a stop codon in a given reading frame. ATG Met (M) TAA TAG TGA 20

21 Nucleotide Databases Reading into Genes and Genomes Prokaryotes (archaea) have the following properties in common: They are microscopic organisms. Their genomes is single, circular DNA molecule. Their gene density is approximately one gene per 1,000 base pairs. Their genome contains few useless part (70% is coding for proteins). Their genes do not overlap. Their genes are transcribed to mrna right after a control region, called promoter. These mrna are collinear with the genome sequence.. Protein sequences are derived by translating the longest open reading frame spanning the gene-transcript sequence. 21

22 Nucleotide Databases Reading into Genes and Genomes According to these common properties, database entries describing a coding prokaryotic sequence should include three important features: The coordinates of some promoter elements The coordinates of the RBS The coordinates of the ORF boundaries. Not all genes encode proteins. For some of them, the function is directly carried out by the transcribed RNA molecule, including trna, rrna and a few others. 22

23 Nucleotide Databases Reading into Genes and Genomes Eukaryotes have the following properties in common: Their genome consists of multiple linear pieces of DNA called chromosomes. Their genome size is much bigger than in prokaryotes. Their gene density is much lower than that for prokaryotes. Their genome is not efficient, containing many useless parts. Genes on opposite DNA strands might overlap, although that s a relatively rare occurrence. Their genes are transcribed right after a control region called a promoter, but sequence elements located far away can have a strong influence on this process. Gene sequences are not collinear with the final messenger RNA (mrna) and protein sequences. Only small bits (the exons) are retained in the mature mrna that encodes the final product. 23

24 Nucleotide Databases Reading into Genes and Genomes A few points between prokaryotes and eukaryotes: genome size gene density cording region content Is gene collinear? Has mrna introns? Prokaryotes million bp one gene / 1,000 bp 70% yes no Eukaryotes ,000 million bp One gene / 100,000 bp (human) 5% no yes Eu. Pro. 24

25 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase X

26 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase

27 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase 27

28 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase LOCUS gives us the locus name, the size of the nucleotide sequence in base pairs, the nature of the molecule, its topology and the last updated date. 28

29 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase DEFINITION provides a short definition of the gene that corresponds to the entry sequence. Here, it s the E. coli dut gene. This gene can encode the enzyme dutpase. The full name of dutpase is deoxyuridine 5 - triphosphate nucleotidohydrolase ( 脱氧尿苷焦磷酸酶 ). 29

30 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase ACCESSION lists the accession number - a unique identifier within and across various databases. Here, the accession number is X

31 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase VERSION fills you in on synonymous or past ID numbers. 31

32 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase KEYWORDS introduces a list of terms that broadly characterize the entry. You can use these terms as keywords for certain database searches. 32

33 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase SOURCE reveals the common name of the relevant organism to which the sequence belongs. 33

34 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase ORGANISM gives a more complete identification of the organism, complete with its technical taxonomic classification. 34

35 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase REFERENCE introduces a section where the credits for the sequence determination are given (different parts of the sequences can be credited to different authors). The REFERENCE section contains multiple parts: AUTHORS, TITLE, JOURNAL, and PUBMED. 35

36 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase COMMENT contains free-formatted text, such as acknowledgments or information that doesn t fit in the previous sections. 36

37 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase FEATURES describes the gene regions and the associated biological properties that have been identified in the nucleotide sequence. This entire section is under the control of the FEATURES keyword, such as source, promoter, etc. 37

38 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase source indicates the origin of specific regions of the sequence. This is useful when you want to distinguish cloning vectors from host sequences. In X01714, the whole sequence comes from E. coli genomic DNA. 38

39 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase promoter shows the coordinates of a promoter element. In X01714, a -35 region is indicated from position 286 to 291 in the nucleotide sequence. 39

40 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase misc feature (miscellaneous feature) indicates the putative location of the transcription start (mrna synthesis). For X01714, this is from positions 322 to

41 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase RBS (Ribosome Binding Site) indicates the location of the last upstream element. For X01714, this is at position 330 to

42 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase CDS (CoDing Segment) introduces a complex section that describes the gene s open reading frame (ORF). 42

43 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase The first line indicates the coordinates of the ORF from its initial ATG to the last nucleotide of the first stop codon TAA. 43

44 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase Each of the following lines gives the name of a protein product, indicates the reading frame to use (here, 343 is the first base of the first codon), the genetic code to apply, and a number of IDs for the protein sequence. 44

45 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase /translation introduces the conceptual amino-acid sequence of the coding segment. This sequence is a computer translation that uses the coordinates, reading frame, and genetic code indicated in the preceding lines. 45

46 Nucleotide Databases X misc feature contains lines that point out putative stem-loop structures and repeats. These are potential regulatory elements of the dutpase gene. 46

47 Nucleotide Databases X This entry exhibits an extra putative gene, indicated by an additional RBS element and a second CDS section. GenBank entries containing more than one gene are frequent. 47

48 Nucleotide Databases X The last section is the nucleotide sequence section. It starts with the ORIGIN keyword and finishes with the end-of-entry line introduced by two slash marks (//). Each line of nucleotide sequence starts with the position number of the first nucleotide in that line. Each line contains 60 nucleotides. 48

49 Nucleotide Databases X Making sense of GenBank entry of a prokaryotic gene: E. coli dutpase 1. Way 2. Way 49

50 Nucleotide Databases U Making sense of GenBank entry of an eukaryotic mrna: human dutpase U

51 Nucleotide Databases U Making sense of GenBank entry of an eukaryotic mrna: human dutpase 51

52 Nucleotide Databases U Making sense of GenBank entry of an eukaryotic mrna: human dutpase A common problem in sequence databases: annotations may be incomplete. A word to the wise: You should never expect GenBank (or any sequence database) annotations to be up-to-date. 52

53 Nucleotide Databases U Making sense of GenBank entry of an eukaryotic mrna: human dutpase In the FEATURES section, the CDS indicates a coding region (63-821) sequence that corresponds to the mitochondrial form of human dutpase, following the conceptual amino-acid translation of the ORF. 53

54 Nucleotide Databases U Making sense of GenBank entry of an eukaryotic mrna: human dutpase The sig peptide keyword indicates the location of a mitochondrial targeting sequence, and the mat peptide keyword provides the exact boundaries of the mature peptide. 54

55 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry AF

56 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry This specifies that the entry encompasses exon 3 of the gene. 56

57 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry SEGMENT indicates that this current GenBank entry is the second segment of a super entry made of four. You need all four entries to reconstruct the complete mrna sequence used as a template for producing the protein. 57

58 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry The /map in the source section indicates that the sequence belongs to chromosome 15, and was more precisely mapped on the long arm (q) of this chromosome, within the q21.1 cytogenetic band. 58

59 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry The < at the beginning of the formula indicates that the gene might actually start before the indicated position, the > at the end of the formula indicates that the gene might actually continue beyond the indicated position. The gene keyword introduces complex-looking formulas. Their purpose is to describe precisely the reconstruction of the various mrnas spread over several separate entries. 59

60 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry 60

61 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry 61

62 Nucleotide Databases AF Making sense of GenBank entry of an eukaryotic genomic entry The exon keyword indicates the position of the sole exon present in this sequence. 62

63 Nucleotide Databases Using a Gene-Centric Database DUT [gene] human [organism] 2 DUT [gene] human [organism]

64 Nucleotide Databases Using a Gene-Centric Database DUT [gene] human [organism] The top of the entry provides a general description of what this gene is all about and what function its products are known to perform, as well as a large variety of links to other databases or NCBI files. 64

65 Nucleotide Databases Using a Gene-Centric Database DUT [gene] human [organism] A schematic view of the Human DUT gene structure 65

66 Nucleotide Databases Using a Gene-Centric Database DUT [gene] human [organism] Other sections provide information on potential interactions with other gene products, protein functions, a list of all corresponding sequence entries in GenBank and a large variety of links to other databases or NCBI files, etc. 66

67 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV

68 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV

69 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV

70 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV

71 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV

72 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV-1 This clickable picture indicates the identity and respective positions of all the genes. Global summary of the HIV-1 genome 72

73 Nucleotide Databases HIV-1 Working with complete viral genomes: HIV-1 A live map - allows you to zoom in/out on any genome region, down to the nucleotide sequence level. Viruses commonly have the same nucleotide sequence involved in the making of two different aminoacid sequences 73

74 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV

75 Nucleotide Databases Working with complete viral genomes: HIV-1 HIV-1 The Protein List page - you can retrieve the DNA and protein sequences in different formats, GenBank format, or FASTA format. 75

76 Nucleotide Databases Working with complete bacterial genomes at TIGR The Institute for Genome Research (TIGR) is home to a team of scientists who pioneered the field of bacterial genomics. TIGR is founded in 1992 by Craig Venter and is now a part of the J. Craig Venter Institute. In 1995, the scientists of TIGR produced the two first complete bacterial genomes. Since then, they have contributed to more than 700 complete bacterial genomes, with more on the way. TIGR offers a site that is quite complementary to the NCBI resource because it keeps track of all ongoing bacterial genome sequencing projects (not only of the completed ones). TIGR home page:

77 Nucleotide Databases Working with complete bacterial genomes at TIGR 77

78 Nucleotide Databases Working with complete bacterial genomes at TIGR 78

79 Nucleotide Databases Working with complete bacterial genomes at TIGR 79

80 Nucleotide Databases Working with complete bacterial genomes at TIGR 80

81 Nucleotide Databases Working with complete bacterial genomes at TIGR 81

82 Nucleotide Databases Microbes from the environment at DoE The U.S. Department of Energy (DoE) is also a main player in microbial genomics. Its Joint Genome Institute specializes in the study of organisms that are either (a) important for preserving our environment, or (b) offering some new perspective in solving the incoming worldwide energy crisis (such as cheap ways of producing hydrogen). DoE home page: 82

83 Nucleotide Databases Microbes from the environment at DoE 83

84 Nucleotide Databases Microbes from the environment at DoE 84

85 Nucleotide Databases Microbes from the environment at DoE

86 Nucleotide Databases 86

87 Nucleotide Databases Microbes from the environment at DoE 87

88 Nucleotide Databases Microbes from the environment at DoE 88

89 Nucleotide Databases Microbes from the environment at DoE A live display of the microbe gene content in the range 1 to 500,

90 Nucleotide Databases Exploring the Human Genome Human Genome 3 billion nucleotides spread over 23 chromosomes. If you want to make sense of human data, you must have clear ideas on the current state of the data: The complete nucleotide sequence of the human genome is now at hand. This sequence was obtained in raw format; the next challenge is the annotation of the raw data, creating a detailed and accurate FEATURES table of the human genome. Throughout the world, new information is generated daily on human gene properties and functions, using a wide array of techniques. 90

91 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl Ensembl is a joint project between the European Bioinformatics Institute (EBI) and the Sanger Institute. Together they ve developed an integrated database and software system to produce and maintain automatic annotations for the genomes of animals with a special attention to our closest relatives: the vertebrates. Ensembl home page: 91

92 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl 92

93 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl 93

94 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl A schematic image of the various human chromosomes 94

95 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl the Chromosome 15 data subset 95

96 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl 96

97 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl GenBank Entry U90223 for human dutpase gene 97

98 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl 98

99 Nucleotide Databases Exploring the Human Genome : the Internet home page of Ensembl Human DUT ID card - everything you ever wanted to know about this gene can be found. 99

100 Tools for Nucleotide Sequences Establishing the G+C content of your sequence The GC content of a molecule of DNA is the percentage of the total nitrogenous base in the DNA that is either guanine or cytosine. GC content is a very interesting property of DNA sequences because it is correlated to repeats and gene deserts. DNA with high GC-content is more stable than DNA with low GC-content. In PCR experiments, the GC-content of primers are used to predict their annealing temperature to the template DNA. A higher GC-content level indicates a higher melting temperature. ORIGIN cagagaaaat caaaaagcag gccacgcagg accccgatat cgtcgcaggc gttgccgcac ttgccgccga aacaaataat gtggaagaat acgcccggca aaaacgtatc cgtaaaaacc ttgatctgat ctgcgcgaac gatgtttccc // 100

101 Tools for Nucleotide Sequences Establishing the G+C content of your sequence : Genomatix 101

102 Tools for Nucleotide Sequences Establishing the G+C content of your sequence : Genomatix 102

103 Tools for Nucleotide Sequences Establishing the G+C content of your sequence : Genomatix 103

104 Tools for Nucleotide Sequences Establishing the G+C content of your sequence : Genomatix 104

105 Tools for Nucleotide Sequences Establishing the G+C content of your sequence : Genomatix 105

106 Tools for Nucleotide Sequences Counting long words in DNA sequences : Wordcount 4 different nucleotides 16 different dinucleotides 64 different trinucleotides (3-tuples) 256 different 4-tuples 1024 different 5-tuples 4096 different 6-tuples (hexamer) Identifying hexamers (6-tuples) with unexpected high frequencies in a set of sequences (such as promoter regions) is often the starting point for discovering regulatory sequence motifs. The EMBOSS server (EBI), offers an online version of the program wordcount that allows you to compute the word frequency in your DNA sequence for any size

107 Tools for Nucleotide Sequences Counting long words in DNA sequences : : Wordcount 107

108 Tools for Nucleotide Sequences gongjing@sdu.edu.cn 108

109 Tools for Nucleotide Sequences 109

110 Tools for Nucleotide Sequences Finding Protein-Coding Regions Protein-coding genes have vastly different structures in microbes and multi cellular organisms. In microbes, each protein is encoded by a simple DNA segment, from start to end, called an open reading frame (ORF). In animal and plant genes, proteins are encoded in several pieces called exons, separated by non-coding DNA segments called introns. prokaryotes (archaea) eukaryotes 110

111 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI 111

112 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI 112

113 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI 113

114 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI 2 1 AE AE : Rickettsia conorii genome (bacterium) 114

115 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI 115

116 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI

117 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI or 117

118 Tools for Nucleotide Sequences Finding Protein-Coding Regions : ORF Finder at NCBI This program is also good for finding protein-coding regions for higher organisms, if your sequence is a cdna. cdna don t include introns and they have a simple, microbe-like ORF structure. 118

119 Tools for Nucleotide Sequences Finding Protein-Coding Regions : GeneMark The simplest ORF finding programs can probably correctly identify 85% percent of the protein-coding regions you may be interested in. However, in some cases, you may need to : Finding very short proteins Resolving uncertain cases where overlapping ORFs are predicted in different reading frames, on the direct and reverse strand, for instance Pinpoint the exact Start codon (the most distal ATG isn t always the correct one) GeneMark - searches for coding regions using a criterion that s a bit more sophisticated than it has to be an uninterrupted reading frame longer than a certain length. This program also takes into account the statistical properties of your sequence and associates some sort of a probabilistic quality index to each candidate s ORFs. 119

120 Tools for Nucleotide Sequences Finding Protein-Coding Regions : GeneMark 120

121 Tools for Nucleotide Sequences Finding Protein-Coding Regions : GeneMark 121

122 Tools for Nucleotide Sequences PDF 122

123 Tools for Nucleotide Sequences Finding Protein-Coding Regions : MZEF If you re looking at a human genomic sequence, your first question should be: Do I have a protein-coding exon somewhere in there? In eukaryotic DNA sequence, exons are separated by non-coding introns. According to what molecular biologists have worked out, a protein coding exon is an ORF flanked by two specific signals known as splice sites. Several programs exist that can recognize these exons. MZEF - developed by Dr. Michael Zhang at Cold Spring Harbor Lboratory on beautiful Long Island (USA)

124 Tools for Nucleotide Sequences Finding Protein-Coding Regions : MZEF 124

125 Tools for Nucleotide Sequences Finding Protein-Coding Regions : MZEF 125

126 Tools for Nucleotide Sequences Finding Protein-Coding Regions : MZEF 126

127 Tools for Nucleotide Sequences Finding Protein-Coding Regions : MZEF gongj@informatik.u 127

128 Tools for Nucleotide Sequences Finding Protein-Coding Regions : MZEF 128

129 Tools for Nucleotide Sequences Finding Protein-Coding Regions Beijing Gene Finder (BGF) - (eukaryotes) GeneFinder - GENEID - Genlang - GENSCAN - (eukaryotes) Glimmer - (prokarytoes, archaea) GlimmerM - (eukaryotes) GrailEXP

130 Tools for Nucleotide Sequences Information Page 130

131 Classification of Biological Databases Nucleotide Database Protein Database >2000 Primary Nucleotide Database Primary Protein Database Protein Sequence DB Protein Structure DB INSDC UniProt Secondary Nucleotide Database Secondary Protein Database Specific Database 131

132 Protein Databases Introduction to Bioinformatics Primary Protein Sequence Databases UniProt Knowledgebase (UniProtKB) is a central protein database of ExPASy maintained by Swiss Institute of Bioinformatics (SIB) and European Bioinformatics Institute (EBI), consisting of two sections: UniProtKB/Swiss-Prot - a reviewed, manually annotated, nonredundant protein sequence database. It combines information extracted from scientific literature and biocurator- evaluated computational analysis. UniProtKB/TrEMBL - contains high-quality computationally analyzed records, which are enriched with automatic annotation. It was introduced in response to increased dataflow resulting from genome projects, as the time- and labourconsuming manual annotation process of UniProtKB/Swiss-Prot could not be broadened to include all available protein sequences. 132

133 Protein Databases Introduction to Bioinformatics Primary Protein Sequence Databases The Protein Information Resource (PIR), is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies. In 2002, PIR along with its international partners, EBI and SIB, were awarded a grant from NIH to create UniProt, a single worldwide database of protein sequence and function, by unifying the PIR-PSD, Swiss-Prot, and TrEMBL databases. 133

134 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry Proteins are much simpler objects than genes. Proteins correspond to relatively small sequences (350 amino acids long, on the average). Unlike genes, proteins have clear beginnings and clear ends. Proteins are defined on a single strand. Whatever modifications occur between the ORF sequence and the mature protein, the amino acids they contain remain in the same order. Use the human epidermal growth factor receptor (EGFR) as an example. UniprotKB/Swiss-Prot home page (ExPASy) : 134

135 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry human epidermal growth factor receptor (EGFR)

136 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry 136

137 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry General Information Entry Name, Accession Number, Secondary Accession Number, Last Modification Date. 137

138 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry The E.C. number ( ) encodes the biochemical reaction that this protein performs. E.C. stands for Enzyme Nomenclature Committee. It can provides you a complete understanding of this protein enzymatic function. 138

139 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry 139

140 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry 140

141 Protein Databases Introduction to Bioinformatics P Reading a UniprotKB/Swiss-Prot Entry This section provides a simple list of terms relevant to your current protein. Clicking any one of these keywords brings out a list of all Swiss-Prot entries that contain the same term. With the increasing size of the database, it seems that this type of query is not useful anymore. 141

142 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry Other proteins it interacts with P

143 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P Description of the existence of related protein sequence(s) produced by alternative splicing of the same gene, alternative promoter usage, ribosomal frame-shifting or by the use of alternative initiation codons. 143

144 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P

145 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P or 145

146 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P or 146

147 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P

148 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P Only the entry, whose 3D structure was experimentally determined and submitted to the PDB, has this secondary structure annotation. (No computational result here!) 148

149 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P

150 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P The Cross-References section contains links to entries in other databases that contain some information about this protein. 150

151 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P Information Page 151

152 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P Each line begins with a two-character line code, which indicates the type of data contained in the line. 152

153 Protein Databases Introduction to Bioinformatics Reading a UniprotKB/Swiss-Prot Entry P Line code Content Occurrence in an entry Line code Content Occurrence in an entry ID AC DT DE GN OS OG OC Identification Accession number(s) Date Description Gene name(s) Organism species Organelle Organism classification Once; starts the entry Once or more Three times Once or more Optional Once or more Optional Once or more RG RA RT RL CC DR Reference group Reference authors Reference title Reference location Comments or notes Database crossreferences Once or more (Optional if RA line) Once or more (Optional if RG line) Optional Once or more Optional Optional OX Taxonomy cross-reference Once PE Protein existence Once OH Organism host Optional KW Keywords Optional RN Reference number Once or more FT Feature table data Once or more RP Reference position Once or more SQ Sequence header Once RC RX Reference comment(s) Reference cross-reference(s) Optional Optional blanks // Sequence data Termination line Once or more Once; ends the entry 153

154 Protein Databases Introduction to Bioinformatics Primary Protein Structure Databases The Protein Data Bank (PDB) is a repository for the 3D structural data of large biological molecules, such as proteins and nucleic acids (mainly proteins). The data, typically obtained by X-ray crystallography or NMR spectroscopy and submitted by biologists and biochemists from around the world. The structures in PDB are freely accessible on the Internet via the websites of its member organizations (PDBe, PDBj, and RCSB). The PDB is overseen by an organization called the Worldwide Protein Data Bank (wwpdb). Secondary Protein Structure Databases The Structural Classification of Proteins (SCOP) and CATH Protein Structure Classification (CATH) categorize structures according to type of structures stored in PDB and assumed their evolutionary relations. 154

155 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X 3H6X E coli dutpase protein 155

156 Protein Databases 3H6X 156

157 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X 记事本 157

158 158

159 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Title Section This section contains records used to describe the experiment and the biological macromolecules present in the entry. Keywords in this section include: HEADER, OBSLTE, TITLE, SPLIT, CAVEAT, COMPND, SOURCE, KEYWDS, EXPDTA, AUTHOR, REVDAT, SPRSDE, JRNL, REMARK. 159

160 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X HEADER identifies a PDB entry through the idcode field. This record also provides a classification for the entry. Finally, it contains the date when the coordinates were deposited to the PDB archive. 160

161 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X TITLE contains a title for the entry. It is also the title of the cited publication. 161

162 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X COMPND describes the macromolecular contents of an entry. 162

163 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X SOURCE specifies the biological and chemical source of each biological molecule in the entry. 163

164 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X KEYWDS contains a set of terms relevant to the entry, which can be used for keyword search across databases. 164

165 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X EXPDTA identifies the experimental technique used: X-RAY DIFFRACTION FIBER DIFFRACTION NEUTRON DIFFRACTION ELECTRON CRYSTALLOGRAPHY ELECTRON MICROSCOPY SOLID-STATE NMR SOLUTION NMR SOLUTION SCATTERING 165

166 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X AUTHOR contains the names of the people responsible for the contents of the entry. 166

167 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X REVDAT contains a history of the modifications made to an entry since its release. 167

168 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X JRNL contains the primary literature citation that describes the experiment which resulted in the deposited coordinate set. 168

169 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X REMARK presents experimental details, annotations, comments, and information not included in other records. 169

170 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Primary Structure Section This section contains the sequence of residues in each chain of the macromolecule(s). DBREF, SEQADV, SEQRES, MODRES 170

171 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Heterogen Section This section contains the complete description of nonstandard residues in the entry. 171

172 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Secondary Structure Section This section describes helices, sheets, and turns found in protein and polypeptide structures. 172

173 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Connectivity Annotation Section This section specifies the existence and location of disulfide bonds and other linkages. 173

174 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Crystallographic and Coordinate Transformation Section This section describes the geometry of the crystallographic experiment and the coordinate system transformations. 174

175 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein Atom index 3H6X The most important part!!! X Y Z Coordinate Section This section contains the collection of atomic coordinates. Residue index 175

176 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Connectivity Section This section provides information on atomic connectivity. 176

177 Protein Databases Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Bookkeeping Section This section provides some final information about the file itself. A *.pdb file always ends with this END. 177

178 Protein Databases VMD Introduction to Bioinformatics Reading a PDB Entry: E. coli dutpase protein 3H6X Maestro 3H6X.pdb Pymol Chapter 4 Structure 178

179 Protein Databases Introduction to Bioinformatics Finding out more about your protein : RESID RESID, the post-translational modification database maintained by John Garavelli at the European Bioinformatics Institute (EBI). 179

180 Protein Databases Introduction to Bioinformatics Finding out more about your protein : RESID RESID, the post-translational modification database maintained by John Garavelli at the European Bioinformatics Institute (EBI). Myristoylation ( 豆蔻酰化 ) 180

181 Protein Databases Introduction to Bioinformatics Finding out more about your protein : RESID RESID, the post-translational modification database maintained by John Garavelli at the European Bioinformatics Institute (EBI). 181

182 Protein Databases Introduction to Bioinformatics Finding out more about your protein : RESID 182

183 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG KEGG - the Kyoto Encyclopedia of Genes and Genomes, was initiated by the Japanese human genome program in KEGG can be regarded as a "computer representation" of the biological system. Its collection includes genomes, enzymatic pathways, and biological chemicals. The PATHWAY database records networks of molecular interactions in the cells, and variants of them specific to particular organisms. Home page of KEGG : 183

184 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG 184

185 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG 185

186 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG 186

187 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG 187

188 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG Enzyme Pathway Compound 188

189 Protein Databases Enzyme relevant Pathways Compound 189

190 Protein Databases 190

191 Protein Databases 191

192 Protein Databases 192

193 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG 193

194 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG Toll-like Receptor (TLR) - recognize pathogen-associated molecular patterns (PAMPs) on invading organisms but not on hosts and are the first line of defense in innate immunity. 194

195 Protein Databases Removing vector sequences 195

196 Protein Databases 196

197 Protein Databases 197

198 Protein Databases Introduction to Bioinformatics Finding out more about your protein : KEGG TLR4 LIPID of LPS MD2 Park et al

199 Protein Databases 199

200 Protein Databases autoimmunity caused by overactivity of TLRs : Systemic Lupus Erythematosus (SLE) 系统性红斑狼疮 Agonist Antagonist 200

201 Protein Databases Introduction to Bioinformatics Finding out more about your protein : ProtParam ProtParam - a program you can use online on the ExPASy server, is a convenient way to estimate every simple physico-chemical property, include the molecular weight, theoretical pi, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index and grand average of hydropathicity. 201

202 Protein Databases Introduction to Bioinformatics Finding out more about your protein : ProtParam 202

203 Protein Databases Introduction to Bioinformatics P Finding out more about your protein : ProtParam 203

204 Protein Databases Introduction to Bioinformatics Finding out more about your protein : ProtParam P

205 Protein Databases Introduction to Bioinformatics Finding out more about your protein : ProtParam P

206 Protein Databases Introduction to Bioinformatics Finding out more about your protein: WebLogo Sequence logos - are a graphical representation of an amino acid or nucleic acid multiple sequence alignment developed by Tom Schneider and Mike Stephens. Each logo consists of stacks of symbols, one stack for each position in the sequence. The overall height of the stack indicates the sequence conservation at that position, while the height of symbols within the stack indicates the relative frequency of each amino or nucleic acid at that position. In general, a sequence logo provides a richer and precise description of, for example, a binding site. 206

207 Protein Databases Introduction to Bioinformatics Finding out more about your protein: WebLogo WebLogo - is a web based application designed to make the generation of sequence logos easy and painless. WebLogo has featured in over 150 scientific publications

208 Protein Databases Introduction to Bioinformatics Finding out more about your protein: WebLogo promoter.seqs

209 Protein Databases Introduction to Bioinformatics Finding out more about your protein: WebLogo promoter.seqs 209

210 Protein Databases Introduction to Bioinformatics Finding out more about your protein: WebLogo promoter.seqs

211 Protein Databases Introduction to Bioinformatics Finding out more about your protein: WebLogo promoter.seqs In the promoter region of genes, we usually found a special fragment, called TATA box (also called Goldberg-Hogness box). The TATA box has the core DNA sequence 5'-TATAAA-3' or a variant. It is usually found as the binding site of RNA polymerase II. 211

212 Protein Databases Introduction to Bioinformatics Finding out more about your protein: MEME Sequence Motif - a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. An example is the N-glycosylation site motif: Asn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro This pattern can be written as N{P}[ST]{P}(Regular expression), where N=Asn, P=Pro, S=Ser, T=Thr; {X} means any amino acid except X; and [XY] means either X or Y. The notation [XY] does not give any indication of the probability of X or Y occurring in the pattern. Observed probabilities can be graphically represented using sequence logos. 212

213 Protein Databases Introduction to Bioinformatics Finding out more about your protein: MEME The MEME Suite - Motif-based sequence analysis tools. The MEME Suite allows you to: discover motifs on groups of related DNA or protein sequences, search sequence databases using motifs, compare a motif to all motifs in a database of motifs. Home page : 213

214 Protein Databases Introduction to Bioinformatics Finding out more about your protein: MEME 214

215 Protein Databases Introduction to Bioinformatics meme.seqs Finding out more about your protein: MEME 215

216 Protein Databases Introduction to Bioinformatics meme.seqs Finding out more about your protein: MEME 216

217 Protein Databases Introduction to Bioinformatics meme.seqs Finding out more about your protein: MEME 217

218 Protein Databases Introduction to Bioinformatics meme.seqs Finding out more about your protein: MEME 218

219 Protein Databases meme.seqs 219

220 Protein Databases Introduction to Bioinformatics Finding out more about your protein The Nuclear Protein Database (NPD) - a searchable database of information on proteins that are localized to the nucleus of vertebrate cells. SignalP 3.0 server - predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. Phospho.ELM - a database of S/T/Y phosphorylation sites. SYSTERS - protein family database of large-scale protein clustering based on sequence similarity More tools and databases, please see Information Page 220

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