Sequence databases

1. Sequence databases Daniel Svozil

2. Primary sequence databases • All published genome sequences are available over the internet • requirement of every scientific journal • Main resources (primary databases, big three): • NCBI database (GenBank) (www.ncbi.nlm.nih.gov) • European Molecular Biology Laboratory (EMBL) database (www.ebi.ac.uk/embl) • DNA Database of Japan (DDBJ) (www.ddbj.nig.ac.jp) • DDBJ/EMBL/GenBank – form The International Nucleotide Sequence Database Collaboration (INSDC, http://insdc.org) • Contain all publicly available nucleotide sequences and their protein translations. • They exchange data nightly, so contain essentially the same data.

3. GenBank • http://www.ncbi.nlm.nih.gov/genbank/, Nucleotide in drop-down menu • local copy of DB – release (every 2 month in GenBank) • 15 February 2012, release 188.0 • 137,384,889,783 bases, from 149,819,246 reported sequences, cca 100,000 organisms • exponential growth, doubling every 18 months

4. Direct submissions from individual laboratories, as well as bulk submissions from large-scale sequencing centers • disadvantage • Primary database contain experimental results (with some interpretation – annotation) but are not curated. • There is no guarantee about data quality. • Curated reviews are found in secondary databases. • Sequences are identified by an accession number. • Unique, reported in scientific papers describing that sequence, combination of letters and numbers • e.g. X01714 (1+5 variety), GL000191 (6+2 variety)

5. GenBank - prokaryotic gene • Prokaryotes • genome: circular DNA • genome size: 0.6-8 Mb • gene density: 1 gene per 1 kb • 70% coding for proteins • no overlap between genes • genes transcribed right after the promoter • genes are single piece (no splicing)

6. Low variability in prokaryotic promoters. Typical promoter: Pribnow box, -10, T80A95T45A60A50T96 • Protein sequences are derived by translating the longest open reading frame ORF(from ATG to STOP) spanning the gene-transcript sequence. • The mRNA sequence gets translated into a protein after a special signal, called the Ribosome Binding Site (RBS). Bioinformatics for Dummies

7. TATA sense strand Transcription runs in the 5’ → 3’ of newly synthesized RNA strand. Is assymetric – only one DNA strand is transcribed ([-], template, non-coding, antisense) [+], nontemplate, coding, sense – sequence identical with the RNA coding, sense – term is related to the resulting protein (mRNA is coding for protein, it makes sense by determining the amino-acid sequence) 1st nucleotide of transcribed RNA corresponds to DNA nucleotide +1. Sequence before this point (i.e. opposite to the flow of transcription) – upstream (-) Sequence behind this point (i.e. in the direction of transcription) – downstream (+) antisense strand Tara Robinson, Genetics for Dummies

8. GenBank - prokaryotic gene • http://www.ncbi.nlm.nih.gov/genbank/ • Search for X01714. • GenBank entry is refered to as flat-file format. • It’s called so because you can read it in linear fashion, it does not involve indexes, pointers (well, actually it contains several hyperlinks, it is not 100% flat).

9. The header • LOCUS –the locus name (an arbitrary name), sequence length, molecule type, division code (classification), date of last modification. • DEFINITION –short definition of the gene. • ACCESSION – refered to as the primary accession number. • VERSION – accession.version, GI (geninfo identifier, GenBank specific, accession.version is now preferred) • KEYWORDS – list of terms broadly characterizing the entry, historical reasons, no controlled vocabulary, not used in new records • SOURCE– common name of the organism.

10. The header • ORGANISM– formal scientific name for the source organism (genus and species) and its lineage, based on the phylogenetic classification scheme used in the NCBI Taxonomy Database http://www.ncbi.nlm.nih.gov/Taxonomy/ • SOURCE vs. ORGANISM: baker's yeast vs. Saccharomyces cerevisiae, search for each of these will yield the same results • REFERENCE – at least one • COMMENT – optional, some comment 

11. The features table

12. The features table • Direct representation of the biological information in the record. • Feature key (which biological property), location information (where the feature is located), additional qualifiers. • source – mandatory, origin of specific regions of the sequence, useful when you want to distinguish cloning vectors from host sequences. • promoter – coordinates of a promoter element. In X01714, a –35 region is in 286..291, another promoter -10 region • misc_feature – in this case the putative location of the transcription start (mRNA synthesis) • RBS (Ribosome Binding Site) – the location of the last upstream element

13. The features table • CDS (CoDing Segment) – describes the gene’s open reading frame (ORF): • The first line indicates the coordinates of the ORF from its initial ATG to the last nucleotide of the first stop codon TAA (343 to 798). • Each of the following lines (indented at the same level) 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 (/transl_table), and a number of IDs for the protein sequence. • /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.

14. The sequence section • Starts with ORIGIN, ends with // • Each line contains 60 nucleotides • 1st nucleotide gets number 1 • Save in FASTA format (Display/FASTA (text)) • single line description starts with >, should be shorter than 80 characters • *.fasta, *.fa, *.seq, *.fsa

15. GenBank - eukaryotic gene • Eukaryotes • genome: multiple pieces – chromosomes • genome size: 10 Mb – 670 Gbp • gene density: 1 gene per 100 kb in human, much lower than prok. • genome is not efficient – less than 5% codes for proteins in human • genes on opposite DNA strands might (rarely) overlap • genes transcribed right after the promoter, but sequence elements located far away can have a strong influence on this process • splicing – exons + introns • alternative splicing (1 gene = more proteins, 30 000 genes in human result in 90 000 proteins)

16. Tara Robinson, Genetics for Dummies

17. GenBank - eukaryotic gene • http://www.ncbi.nlm.nih.gov/genbank/ • Search for U90223. • I carefully chose mRNA sequence, not genomic sequence. Thus this entry is not that complex, no exons etc. • sig_peptide – location of a mitochondrial targeting sequence • mat_peptide – exact boundaries of the mature peptide

18. GenBank - eukaryotic gene • http://www.ncbi.nlm.nih.gov/genbank/ • Search for AF018430 – gene from which the previous mRNA sequence originated. • This sequence is still rather simple, but it already contains eukaryotic-specific entries.

19. SEGMENT: This field relates to the mosaic structure of eukaryotic genes. It 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 • The source section contains a /map section. For AF018430, it indicates that the sequence belongs to chromosome 15, and was more precisely mapped on the long arm (q) of this chromosome, within th q21.1 cytogenetic band.

20. gene – describe precisely the reconstruction of the various mRNAs spread over several separate entries • order – exon splicingrecipe: take nucleotides from positions 1 to 1735 from entry AF018429, add nucleotides from positions 1 to 1177 from the current entry, … • The < indicates that the gene might actually start before the indicated position, the > indicates that the gene might actually continue beyond the indicated position. • mRNA – alternative splicing

21. Bioinformatics for Dummies

22. exon – the position of the sole exon present in this sequence • search AF018432 – multiple exons in a single entry • You get accession numbers by reading articles reporting about the sequence. • After you’ve accessed the first GenBank entry relevant to your work, you can retrieve other related genes. • search U90223 • Display – Summary – Related sequences • Retrieved: various mRNA forms and partial sequences, partial genomic sequences (around exons), and two large (154kb and 192kb) sequences of the 15q21.1 genomic region. There are some monkey sequences as well!

23. Sample GenBank Record http://www.ncbi.nlm.nih.gov/Sitemap/samplerecord.html

24. No accession number • GenBank is not the best database for keyword-based searches (gene-centric databases are). • Querying database by gene or protein keywords is still possible, but not that reliable. • you want to find the nucleotide sequence encoding the human dUTPase • sarch for human [organism] AND dUTPase [Protein name] • find related sequences to AF018432. How many entries? • dUTPase is a nickname for which protein? • “dUTP pyrophosphatase” • sarch for human [organism] AND “dUTP pyrophosphatase” [Title] • Are the resulting entries same as in the previous search? • This illustrates the general difficulty in retrieving all entries relevant to a given subject, due to inconsistent usage of synonymous terms.

25. many of the resulting entries are ESTs • Limits – Exclude ESTs

26. RefSeq • Many sequences are more than once in GenBank – redundancy. • NCBI developed RefSeq collection – a curated secondary database • aim: provide comprehensive, integrated, nonredundant set of sequences • For each model organism, RefSeq provides separate and linked records for the genomic DNA, the gene transcripts, and the proteins arising from those transcripts. • RefSeq is limited to major organisms for which sufficient data is available (more than 16,000 distinct “named” organisms as of Jan 2012), while GenBank includes sequences for any organism submitted.

27. RefSeq • RefSeq entries – distinct accession number format, “2 + 6” with underscore • e.g. NC_001477 Reference: http://www.ncbi.nlm.nih.gov/RefSeq/key.html

28. Practise Search the nucleotide domain of Entrez for breast cancer. Provide the following information: • The number of Core nucleotide sequence records associated with breast cancer • Number of the above sequence records that are from the RefSeq database • Number of Core nucleotide sequences associated with breast cancer that are mRNAs • Number of the above mRNA sequence records that are in RefSeq and the words breast cancer appears in their titles • Number of Human gene BRCA1 RefSeq mRNA sequence records with the words breast cancer in their titles

29. Practise • Accession numbers of the mRNA records for human BRCA1a gene in RefSeq • Total number of nucleotides reported in 1st transcript variant • Exact chromosomal location of BRCA1a on the human genome • Number of times this sequence was updated • Total number of amino acids that this mRNA encodes • Identify the last amino acid in the encoded protein • Total number of Exons and Introns in this gene variant (BRCA1a)

30. Practise • BRCA1a encodes full length BRCA1 protein (isoform 1). How many variants (isoforms) there exist for BRCA1? Also provide the accession numbers of their mRNA sequences in RefSeq. • How is the “isoform 2” different from “isoform 1”? • Exact location (nucleotide position) of the BRCA1a start codon • Exact location and sequence of the stop codon in BRCA1a • What is the sequence of polyA signal for BRCA1a?

32. Querying the NCBI database

33. Gene-centric databases • Sequence databases are great tools when you want to come up with a bibliography for a particular sequence. • However, they do not provide easy access to sequence data when your query deals with broader issues related to a gene or function. • The second-generation nucleotide-sequence databases have adopted a more gene-centricperspective. • all the sequence information relevant to a given gene is made accessible at once • NCBI Entrez Gene

34. Genome-centric databases • Nucleotide sequences are routinely determined at the whole genome or chromosome scale – at least for microorganisms • We now have information not only about individual gene sequences, but also e.g. about their relative positions or strand orientation. • To take advantage of this more global information, researchers have had to design state-of-the-art genome-centricsequence-information management systems that can connect specialized sequence collections with browsing tools.