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BITS: Basics of sequence databases

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This document provides an introductory training on bioinformatics, focusing on databases and tools essential for everyday use in life sciences research. It discusses sequence databases, including key resources like ENA, GenBank, and DDBJ, highlighting their structure, submission, and retrieval processes. Additionally, it outlines various bioinformatics sub-disciplines and related tools, emphasizing the importance of understanding and interpreting biological data.

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Basic bioinformatics concepts, databases and tools Introduction to the training and Sequence databases Joachim Jacob http://www.bits.vib.be Updated 22 February 2012 http://dl.dropbox.com/u/18352887/BITS_training_material/Link%20to%20mod1-intro_H1_2012_SeqDBs.pdf
Scope Introductory training to Bioinformatics Exploring and understanding databases and software for everyday bioinformatics use If there is any term which is unclear, please stop me and ask me!
Bioinformatics ... Bio all data is derived from living samples Informatics that data is stored and analyzed in and with computers to obtain understanding Extremely broad description, for which however we will extract common principles during the course
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research , sequences
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research
Bioinformatics is present into every aspect of life sciences research
Bioinformatics ... Bio - different types of living samples Informatics - storing and categorizing the information and making it easily accessible - interpreting that information reliably
Bioinformatics … and his companion Bio - different types of living samples Informatics - storing and categorizing the information and making it easily accessible - interpreting that information reliably Statistics - large numbers, observational data
The siblings of Bioinformatics Based on the biological component extracted from life, the measured properties and the ultimate goal of the analysis, different sub-disciplines of bioinformatics exist. DNA RNA proteins metabolites Genomics Transcriptomics Proteomics Metabolomics Epigenomics Structural bioinformatics Systems biology Microbiomics Interactomics Metagenomics Functional genomics Comparative gx
Mere data is worth nothing CGCTACGCATATCGCT Data = symbols - Dasypus novemcinctus Information = data that are processed to be useful; - found in my garden provides answers to "who", "what", "where", and - Part of genome - sequenced on June 2010 "when" questions. Also called metadata. This species seems to be Knowledge: application of data and information; related to my neighbor's pet, because it has also this answers "how" questions sequence Has the same mother Understanding: appreciation of "why" Wisdom http://www.systems-thinking.org/dikw/dikw.htm
? ! Life sciences research as major 'end user' for the data knowledge bioinformatics tools and conclusions 'tool user' Tools and approaches Bioinformatics research, as a specific branch on Biology Computer Statistics the boundary of life science, mathematics and computer science 'tool manufacturer'
This course is organised in several modules Module 1: Sequence databases: what, where, how Module 2: Sequence comparisons: searching, aligning Module 3: Sequence analysis – domains in protein sequences and predicting functionality, standardisation and useful links Module 4: Beyond sequences - additional important data sources Module 5: Genome Browsers - integrating biological data and performing reproducible bioinformatics research in the Galaxy
Overview of the crash course
One tip for the future Be prepared for change... Information is fluid So are bioinfo tools Learn how to accommodate for change Major resources are more stable Important concepts do not change often
Module 1 Sequence databases
Module 1: Sequence databases Sequence databases store DNA and RNA sequences. In Bioinformatics, they are by far (still) the largest collections of biological data, and used by many subdisciplines of bioinformatics. http://www.ebi.ac.uk/embl/Services/DBStats/
... and growing http://www.ebi.ac.uk/embl/Services/DBStats/
Three major nucleotide databanks host primary sequence data European Nucleotide Archive (ENA) at EBI - http://www.ebi.ac.uk/ Division EMBL-bank (European Molecular Biology Laboratory) (single) Trace Archive SRA Archive GenBank at NCBI - http://www.ncbi.nlm.nih.gov/ maintained at NCBI (National Center for Biotechnology Information, (USA) DDBJ (DNA Data Bank of Japan) - http://www.ddbj.nig.ac.jp/ maintained at NIG/CIB (National Institute of Genetics, Center for Information Biology, Mishima, Japan)
These databases are filled with NA sequence information by scientists and consortia Large-scale Individual Patent sequencing scientists Offices ACTGCTGCTA GCTAGCTGAT projects CTATGCTAGC TGTAGCTGAG Primary sequence data each primary sequence = one experiment Primary sequence Basically, all 'source' nucleotide material database Jennifer McDowall - http://www.biotnet.org/training-materials/nucleotide-sequence-databases-ena
Primary NA sequence can be produced by Sanger-based technologies or NGS technologies Sanger sample Low output in number of seqs, high quality, 400-850 bp. Read profiles in .abi format. Stored in Trace Archive. RNA DNA RT NGS Different technologies. Extremely high output rate, low cDNA quality, 30 bp – 600 bp. Reads in .fastq format, stored in the SRA. These techniques can only read DNA strands, so RNA needs first to be converted to cDNA with reverse transcriptases prior to loading to the machines. Sanger overview: http://www.bio.davidson.edu/Courses/Molbio/MolStudents/spring2003/Obenrader NGS overview: http://seqanswers.com/forums/showthread.php?t=3561
Overview major DNA reading technologies Dennis Wall, NGS Data Analysis and Computation I course, Wall Lab
In the primary sequence dbs a major distinction can be made in two major categories High quality single submission (Sanger) - gene sequence (genomic – 'STD' data class) - mRNA sequence (via cDNA – 'STD') - BAC/YAC/cosmid sequences - genome sequencing projects (contigs, assemblies, WGS) DNA cDNA RNA - genome markers, STS (sequence tagged sites, unique short sequences from a genome) Low quality batch submissions - Expressed Sequence Tags (EST) - Genome Survey Sequences (GSS) - high-throughput sequence data (e.g. NGS) http://www.ebi.ac.uk/ena/about/formats
The batch submissions originate mostly from sequencing centers Large-scale sequencing projects chromosome fragment sequencing library submission sequence reads e.g. whole genome shotgun submission assemble sequence submission annotation cyp30 cyp309 insv cg343
Each primary database stores their sequences and batch submissions in their own way... - NCBI: ESTs are stored in dbEST (separate database) - ENA: ESTs are part of EMBL-bank in 'EST' data class Similar for GSS (see dbGSS at NCBI) ESTs : expressed sequence tag, often partial sequence derived from RNA in batch. See example >est1 ATCGACTAGCATCA sample >est2 TCGACTAGCGACTA RNA-seq >est3 RNA CAGCATCATCGAC
http://www.biotnet.org/sites/biotnet.org/files/documents/17/2010_ena_v2.0.ppt Batch submissions are marked and/or stored differently than single submissions Data class ESTs are ENA-Annotation: also batch submissions Feature annotation 1) EMBL-Bank ENA-Assembly: Assembly information Batch submissions ENA-Reads: 2) Trace Archive Sequencing and - Raw data (capillary sequencing) sampling information 3) Sequence Read Archive - Raw data (Next Gen sequencing) TIER CLASS TYPE ENA structure
The 'normal' submissions are a minority in primary sequence databases http://www.ebi.ac.uk/ena/about/statistics#embl_bases_per_dataclass
Primary sequence dbs are synchronised and every sequence receives a unique identifier All database maintainers assign and share a unique accession number (AC) to each sequence – besides their own ID number – (info at NCBI). Sequences can get updated, and the accession number is extended with a version number, e.g. .1 (see SVA) Example of acc number: BC010109.2 http://www.insdc.org/ Collaboration on GenBank DDBJ Features, taxonomy,... + SRA Synchronized International nucleotide Sequence databases collaboration daily All use the same - Accession Ids ENA - Project Ids - Feature tables (see later) http://en.wikipedia.org/wiki/Accession_number_(bioinformatics)
One sequence entry contains three categories of different types of information 1. Info about sequence, submitters and literature (metadata) 2. Annotations of the sequence (metadata related to the seq) 3. Stretch of ATGC / AUGC sequence (the 'data', at the bottom) • A sequence record is called 'annotated' when biological information is added and linked to a position in the sequence • Annotations, also called 'features', are abbreviated as codes, which can be found in the Feature Tables http://www.ebi.ac.uk/embl/Documentation/FT_d
This sequence information can be written in different formats (plain) Text format, e.g. GenBank 1. General info Official shared accession Genbank specific identifier (just sums up with each new) A lot of different identifiers! ~number of databases → conversion tools can translate identifiers needed (see exercises) *In humans: HUGO Nomenclature committee determines the right gene name http://mobyle.pasteur.fr/cgi-bin/portal.py#tutorials::seqfmt
2. Annotation db_xref = cross references, = links to records of other databases which are related to this record (see later). The format dbname:identifier Feature name Qualifier name
3. Sequence Each protein sequence receives also an accession number
Other sequence formats Fasta (minimal metadata, basically only sequence) >genename And a description ATCGATGCAGCTATATCCTCGCGATCAGC CGGACAGCTCTCGAGCGCATCGACGACGAC ASN.1 Abstract Syntax Notation (ASN.1) EMBL :all info as in gb, online referred to as 'plain text' XML Fastq : sequence info and base 'call' quality Important 'Format' has nothing to do with which program you save your file! You don't have a choice: it needs to be 'plain text format' (.txt - not a file which can be opened with MS Word such as .doc or .rtf files). Wordpad is a good choice for this. 'Format' in bioinfo is all about how the information is structured and written down in the plain text file. http://emboss.sourceforge.net/docs/themes/SequenceFormats.html
http://www.biotnet.org/sites/biotnet.org/files/documents/17/2010_ena_v2.0.ppt Degree of annotation differs between entries Batch submitted sequences are ENA-Annotation: annotated poorly, single Feature annotation submissions are annotated better Good seq 1) EMBL-Bank annotations ENA-Assembly: Assembly information ENA-Reads: 2)Experiment information Trace Archive is- of most(capillary sequencing) Raw data importance in Sequencing and sampling information batch submissions (e.g. 3) Sequence Read which which species, Archive - Raw data (Next Gen sequencing) technique, ...) TIER CLASS TYPE ENA structure
SRA contains batch submitted records of which experiment information is of most importance Since the sequences are barely (not) annotated, is experiment description important: which machine, which organism, which tissue, which developmental stage, disease, treatment, …
How to get sequences into the db, and back out Submit Retrieve Always submit your sequence data (mostly One or few sequences obliged by journals) and include your ACC number in articles (not any other number). → Use one of the numerous webbased tools GenBank: Entrez EMBL: EB-eye MRS: developed for easy Sequin (GenBank retrieval stand alone) retrieve Many sequences (Batch Bankit (GenBank submit web tool) retrieval) Webin (EMBL → use ftp (file transfer protocol) online submission) → use perl (flexible pro- gramming language) → BioMart http://www.biomart.org/
Example of a primary NA sequence record (ENA) http://www.ebi.ac.uk/ena/about/formats
Example of a primary NA sequence record (ENA) Text format Code usable for Data linked to that searching code http://www.ebi.ac.uk/ena/about/formats
Primary sequence data contains a lot of redundancy! Chromosome sequence Several gene sequences from different labs EST sequences from transcripts cDNA sequence Al match to the same gene. Often you end up in your database search with all these sequences... A lot of redundancy!
The primary sequences are the basis for analyses that generate derived sequence data Scientists/Consortia → primary databases – Source for further analyses. Which? • Create protein sequences • Curate the sequence database • Assemble genomes • Searching similarities • Aggregate information about one gene • … Results stored in derived databases
Protein databases come in two kinds
The most important protein db is UniProt and contains 'automatic' and manual entries UniProt Knowledge Base - 'the best annotated protein database of the world' http://www.uniprot.org/
The most important protein db is UniProt and contains 'automatic' and manual entries
Refseq - The NCBI way to reduce redundancy in primary sequence data RefSeq is NCBI 'Reference Sequences' (prot and nuc) Redundancy from primary sequence data is reduced both automatically and by manual annotation of NA and protein sequences. 'one natural biological molecule = one entry'. Links back to the original primary sequences. Hugely popular and a basis for a lot of analyses. Click to apply refseq filter in entrez search http://www.ncbi.nlm.nih.gov/RefSeq/
RefSeq has its own identifiers, not to be mixed up with accession numbers Refseq entry codes looks similar as ACC numbers (but are not ACC numbers – underscore!); and RefSeq is also in GenBank format. Note: in 'Features' section one can find the raw sequences from what is was derived. (typical mistake: search with refseq code in uniprot) NC_* (curated) complete genomic element (chromosome, plasmid,...) NT_* (automated) intermediate assembly from BAC NZ_* (automated) incomplete genomic sequence from WGS NW_* (automated) intermediate assembly from WGS NG_* (curated) incomplete genomic element corresponding to gene NM_* (curated) mRNA NR_* (curated) non-coding RNA or predicted transcript of pseudogene NP_* (curated) protein ZP_* (automated) protein predicted from WGS sequence (NZ_*) YP_* (curated) other predicted protein sequences from NCBI Genome Annotation Pipeline XM_* (automated) mRNA XR_* (automated) non-coding RNA or predicted transcript of pseudogene XP_* (automated) protein http://www.ncbi.nlm.nih.gov/RefSeq/key.html http://www.ncbi.nlm.nih.gov/RefSeq/
UniRef – UniProt redundancy reducing system for proteins sequences Non redundant protein sequences from UniProt ~ refseq Hiding redundant sequences by clustering them • UniRef100 = complete identical sequences • UniRef90 = 90% identical sequences • UniRef50 = 50% identical sequences See http://www.uniprot.org/help/uniref
NCBI's Gene – summarizes gene information including sequence information from primary dbs Example of the gene NPR1 from A. thaliana
UniGene – summarizes transcriptomic information around genes
And a lot more derived databases with sequence information exist Repbase : repeats (Alu, …), maintained by Jerzy Jurka at the Genetic Information Research Institute (Mountain View CA, USA). CENSOR server allows to "clean" sequences. http://www.girinst.org/repbase MiRBase → published miRNA sequences http://www.mirbase.org/ Eukaryotic promoter database http://www.epd.isb-sib.ch/ UniVec GenBank subset + some sequences from commercial sources - ftp://ftp.ncbi.nih.gov/pub/UniVec/
The most important sequence databases overview Integrated Prim seq data Search Derive Curat d ed Portals GB GenPept RefSeq Entrez ENA trEMBL ENA search EB-eye DDBJ UNIPROT SwissProt UniProt
Common gene annotations on sequences Genome sequence: e.g. Chr6 Enhancers/promotors terminator Intron Gene sequence exon mRNA AAAAAAAAAAAAA 5'UTR CDS 3'UTR poly(A) tail protein Genetic code tables
Searching the database for your gene of interest First you have to determine for yourself which information you want - NA sequences vs. protein sequences - If NA, genomic sequences, or RNA derived - All possible sequences that exists, or curated ones - Protein sequences of which quality - ...
Entrez is a starting point for searches at NCBI http://www.ncbi.nlm.nih.gov/sites/gquery
Visualising the db_xrefs in records at NCBI
ENA has its text-search portal http://www.ebi.ac.uk/ena/
Results from an ENA search are organised following the ENA database structure
UniProt has a simple search box leading to a sophisticated search results page
Complex searches can be achieved by using the index codes in the database e.g. “oc=Primates and de=complete and de=cds and de=MHC” Code usable for Could answer: give me searching all coding sequence of MHC available in primates.
Meta-search tools can search different sequence databases at once. MRS Open Source, developed by Maarten Hekkelman at Radboud U. (Nijmegen, the Netherlands). Allows searching in different databases at once, and provides also statistics on the databases. Alternatives: ACNUC, SRS
Logical operators Searching involves making combinations of conditions. Here the difference between a logic and, or and not explained by venn diagrams. Q1 AND Q2 & Q1 NOT Q2 ! Q1 OR Q2 |
Hands-on! Every module ends with an exercise session. We will now explore how data is stored in different sequence databases. You get …. minutes for this exercise. Afterwards, we summarizes some of the difficulties some of you might have experienced.
Summary This course is organised in several modules Module 1: Sequence databases Three major nucleotide databanks host primary sequence data These databases are filled with NA sequence information by scientists and consortia The batch submissions originate mostly from sequencing centers Each primary database stores their sequences and batch submissions in their own way... Batch submissions are marked and/or stored differently than single submissions The 'normal' submissions are a minority in primary sequence databases Primary sequence dbs are synchronised and every sequence receives a unique identifier One sequence entry contains three categories of different types of information This sequence information can be written in different formats Degree of annotation differs between entries SRA contains batch submitted records of which experiment information is of most importance How to get sequences into the db, and back out Primary sequence data contains a lot of redundancy! The primary sequences are the basis for analyses that generate derived sequence data Protein databases come in two kinds The most important protein db is UniProt and contains 'automatic' and manual entries Refseq - The NCBI way to reduce redundancy in primary sequence data RefSeq has its own identifiers, not to be mixed up with accession numbers UniRef – UniProt redundancy reducing system for proteins sequences NCBI's Gene – summarizes gene information including sequence information from primary dbs UniGene – summarizes transcriptomic information around genes And a lot more derived databases with sequence information exist Searching the database for your gene of interest Entrez is a starting point for searches at NCBI Visualising the db_xrefs in records at NCBI ENA has its text-search portal Results from an ENA search are organised following the ENA database structure UniProt has a simple search box leading to a sophisticated search results page Complex searches can be achieved by using the index codes in the database Meta-search tools can search different sequence databases at once. Hands-on!

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BITS: Basics of sequence databases

  • 1. Basic bioinformatics concepts, databases and tools Introduction to the training and Sequence databases Joachim Jacob http://www.bits.vib.be Updated 22 February 2012 http://dl.dropbox.com/u/18352887/BITS_training_material/Link%20to%20mod1-intro_H1_2012_SeqDBs.pdf
  • 2. Scope Introductory training to Bioinformatics Exploring and understanding databases and software for everyday bioinformatics use If there is any term which is unclear, please stop me and ask me!
  • 3. Bioinformatics ... Bio all data is derived from living samples Informatics that data is stored and analyzed in and with computers to obtain understanding Extremely broad description, for which however we will extract common principles during the course
  • 4. Bioinformatics is present into every aspect of life sciences research
  • 5. Bioinformatics is present into every aspect of life sciences research
  • 6. Bioinformatics is present into every aspect of life sciences research , sequences
  • 7. Bioinformatics is present into every aspect of life sciences research
  • 8. Bioinformatics is present into every aspect of life sciences research
  • 9. Bioinformatics is present into every aspect of life sciences research
  • 10. Bioinformatics is present into every aspect of life sciences research
  • 11. Bioinformatics is present into every aspect of life sciences research
  • 12. Bioinformatics is present into every aspect of life sciences research
  • 13. Bioinformatics ... Bio - different types of living samples Informatics - storing and categorizing the information and making it easily accessible - interpreting that information reliably
  • 14. Bioinformatics … and his companion Bio - different types of living samples Informatics - storing and categorizing the information and making it easily accessible - interpreting that information reliably Statistics - large numbers, observational data
  • 15. The siblings of Bioinformatics Based on the biological component extracted from life, the measured properties and the ultimate goal of the analysis, different sub-disciplines of bioinformatics exist. DNA RNA proteins metabolites Genomics Transcriptomics Proteomics Metabolomics Epigenomics Structural bioinformatics Systems biology Microbiomics Interactomics Metagenomics Functional genomics Comparative gx
  • 16. Mere data is worth nothing CGCTACGCATATCGCT Data = symbols - Dasypus novemcinctus Information = data that are processed to be useful; - found in my garden provides answers to "who", "what", "where", and - Part of genome - sequenced on June 2010 "when" questions. Also called metadata. This species seems to be Knowledge: application of data and information; related to my neighbor's pet, because it has also this answers "how" questions sequence Has the same mother Understanding: appreciation of "why" Wisdom http://www.systems-thinking.org/dikw/dikw.htm
  • 17. ? ! Life sciences research as major 'end user' for the data knowledge bioinformatics tools and conclusions 'tool user' Tools and approaches Bioinformatics research, as a specific branch on Biology Computer Statistics the boundary of life science, mathematics and computer science 'tool manufacturer'
  • 18. This course is organised in several modules Module 1: Sequence databases: what, where, how Module 2: Sequence comparisons: searching, aligning Module 3: Sequence analysis – domains in protein sequences and predicting functionality, standardisation and useful links Module 4: Beyond sequences - additional important data sources Module 5: Genome Browsers - integrating biological data and performing reproducible bioinformatics research in the Galaxy
  • 19. Overview of the crash course
  • 20. One tip for the future Be prepared for change... Information is fluid So are bioinfo tools Learn how to accommodate for change Major resources are more stable Important concepts do not change often
  • 21. Module 1 Sequence databases
  • 22. Module 1: Sequence databases Sequence databases store DNA and RNA sequences. In Bioinformatics, they are by far (still) the largest collections of biological data, and used by many subdisciplines of bioinformatics. http://www.ebi.ac.uk/embl/Services/DBStats/
  • 23. ... and growing http://www.ebi.ac.uk/embl/Services/DBStats/
  • 24. Three major nucleotide databanks host primary sequence data European Nucleotide Archive (ENA) at EBI - http://www.ebi.ac.uk/ Division EMBL-bank (European Molecular Biology Laboratory) (single) Trace Archive SRA Archive GenBank at NCBI - http://www.ncbi.nlm.nih.gov/ maintained at NCBI (National Center for Biotechnology Information, (USA) DDBJ (DNA Data Bank of Japan) - http://www.ddbj.nig.ac.jp/ maintained at NIG/CIB (National Institute of Genetics, Center for Information Biology, Mishima, Japan)
  • 25. These databases are filled with NA sequence information by scientists and consortia Large-scale Individual Patent sequencing scientists Offices ACTGCTGCTA GCTAGCTGAT projects CTATGCTAGC TGTAGCTGAG Primary sequence data each primary sequence = one experiment Primary sequence Basically, all 'source' nucleotide material database Jennifer McDowall - http://www.biotnet.org/training-materials/nucleotide-sequence-databases-ena
  • 26. Primary NA sequence can be produced by Sanger-based technologies or NGS technologies Sanger sample Low output in number of seqs, high quality, 400-850 bp. Read profiles in .abi format. Stored in Trace Archive. RNA DNA RT NGS Different technologies. Extremely high output rate, low cDNA quality, 30 bp – 600 bp. Reads in .fastq format, stored in the SRA. These techniques can only read DNA strands, so RNA needs first to be converted to cDNA with reverse transcriptases prior to loading to the machines. Sanger overview: http://www.bio.davidson.edu/Courses/Molbio/MolStudents/spring2003/Obenrader NGS overview: http://seqanswers.com/forums/showthread.php?t=3561
  • 27. Overview major DNA reading technologies Dennis Wall, NGS Data Analysis and Computation I course, Wall Lab
  • 28. In the primary sequence dbs a major distinction can be made in two major categories High quality single submission (Sanger) - gene sequence (genomic – 'STD' data class) - mRNA sequence (via cDNA – 'STD') - BAC/YAC/cosmid sequences - genome sequencing projects (contigs, assemblies, WGS) DNA cDNA RNA - genome markers, STS (sequence tagged sites, unique short sequences from a genome) Low quality batch submissions - Expressed Sequence Tags (EST) - Genome Survey Sequences (GSS) - high-throughput sequence data (e.g. NGS) http://www.ebi.ac.uk/ena/about/formats
  • 29. The batch submissions originate mostly from sequencing centers Large-scale sequencing projects chromosome fragment sequencing library submission sequence reads e.g. whole genome shotgun submission assemble sequence submission annotation cyp30 cyp309 insv cg343
  • 30. Each primary database stores their sequences and batch submissions in their own way... - NCBI: ESTs are stored in dbEST (separate database) - ENA: ESTs are part of EMBL-bank in 'EST' data class Similar for GSS (see dbGSS at NCBI) ESTs : expressed sequence tag, often partial sequence derived from RNA in batch. See example >est1 ATCGACTAGCATCA sample >est2 TCGACTAGCGACTA RNA-seq >est3 RNA CAGCATCATCGAC
  • 31. http://www.biotnet.org/sites/biotnet.org/files/documents/17/2010_ena_v2.0.ppt Batch submissions are marked and/or stored differently than single submissions Data class ESTs are ENA-Annotation: also batch submissions Feature annotation 1) EMBL-Bank ENA-Assembly: Assembly information Batch submissions ENA-Reads: 2) Trace Archive Sequencing and - Raw data (capillary sequencing) sampling information 3) Sequence Read Archive - Raw data (Next Gen sequencing) TIER CLASS TYPE ENA structure
  • 32. The 'normal' submissions are a minority in primary sequence databases http://www.ebi.ac.uk/ena/about/statistics#embl_bases_per_dataclass
  • 33. Primary sequence dbs are synchronised and every sequence receives a unique identifier All database maintainers assign and share a unique accession number (AC) to each sequence – besides their own ID number – (info at NCBI). Sequences can get updated, and the accession number is extended with a version number, e.g. .1 (see SVA) Example of acc number: BC010109.2 http://www.insdc.org/ Collaboration on GenBank DDBJ Features, taxonomy,... + SRA Synchronized International nucleotide Sequence databases collaboration daily All use the same - Accession Ids ENA - Project Ids - Feature tables (see later) http://en.wikipedia.org/wiki/Accession_number_(bioinformatics)
  • 34. One sequence entry contains three categories of different types of information 1. Info about sequence, submitters and literature (metadata) 2. Annotations of the sequence (metadata related to the seq) 3. Stretch of ATGC / AUGC sequence (the 'data', at the bottom) • A sequence record is called 'annotated' when biological information is added and linked to a position in the sequence • Annotations, also called 'features', are abbreviated as codes, which can be found in the Feature Tables http://www.ebi.ac.uk/embl/Documentation/FT_d
  • 35. This sequence information can be written in different formats (plain) Text format, e.g. GenBank 1. General info Official shared accession Genbank specific identifier (just sums up with each new) A lot of different identifiers! ~number of databases → conversion tools can translate identifiers needed (see exercises) *In humans: HUGO Nomenclature committee determines the right gene name http://mobyle.pasteur.fr/cgi-bin/portal.py#tutorials::seqfmt
  • 36. 2. Annotation db_xref = cross references, = links to records of other databases which are related to this record (see later). The format dbname:identifier Feature name Qualifier name
  • 37. 3. Sequence Each protein sequence receives also an accession number
  • 38. Other sequence formats Fasta (minimal metadata, basically only sequence) >genename And a description ATCGATGCAGCTATATCCTCGCGATCAGC CGGACAGCTCTCGAGCGCATCGACGACGAC ASN.1 Abstract Syntax Notation (ASN.1) EMBL :all info as in gb, online referred to as 'plain text' XML Fastq : sequence info and base 'call' quality Important 'Format' has nothing to do with which program you save your file! You don't have a choice: it needs to be 'plain text format' (.txt - not a file which can be opened with MS Word such as .doc or .rtf files). Wordpad is a good choice for this. 'Format' in bioinfo is all about how the information is structured and written down in the plain text file. http://emboss.sourceforge.net/docs/themes/SequenceFormats.html
  • 39. http://www.biotnet.org/sites/biotnet.org/files/documents/17/2010_ena_v2.0.ppt Degree of annotation differs between entries Batch submitted sequences are ENA-Annotation: annotated poorly, single Feature annotation submissions are annotated better Good seq 1) EMBL-Bank annotations ENA-Assembly: Assembly information ENA-Reads: 2)Experiment information Trace Archive is- of most(capillary sequencing) Raw data importance in Sequencing and sampling information batch submissions (e.g. 3) Sequence Read which which species, Archive - Raw data (Next Gen sequencing) technique, ...) TIER CLASS TYPE ENA structure
  • 40. SRA contains batch submitted records of which experiment information is of most importance Since the sequences are barely (not) annotated, is experiment description important: which machine, which organism, which tissue, which developmental stage, disease, treatment, …
  • 41. How to get sequences into the db, and back out Submit Retrieve Always submit your sequence data (mostly One or few sequences obliged by journals) and include your ACC number in articles (not any other number). → Use one of the numerous webbased tools GenBank: Entrez EMBL: EB-eye MRS: developed for easy Sequin (GenBank retrieval stand alone) retrieve Many sequences (Batch Bankit (GenBank submit web tool) retrieval) Webin (EMBL → use ftp (file transfer protocol) online submission) → use perl (flexible pro- gramming language) → BioMart http://www.biomart.org/
  • 42. Example of a primary NA sequence record (ENA) http://www.ebi.ac.uk/ena/about/formats
  • 43. Example of a primary NA sequence record (ENA) Text format Code usable for Data linked to that searching code http://www.ebi.ac.uk/ena/about/formats
  • 44. Primary sequence data contains a lot of redundancy! Chromosome sequence Several gene sequences from different labs EST sequences from transcripts cDNA sequence Al match to the same gene. Often you end up in your database search with all these sequences... A lot of redundancy!
  • 45. The primary sequences are the basis for analyses that generate derived sequence data Scientists/Consortia → primary databases – Source for further analyses. Which? • Create protein sequences • Curate the sequence database • Assemble genomes • Searching similarities • Aggregate information about one gene • … Results stored in derived databases
  • 46. Protein databases come in two kinds
  • 47. The most important protein db is UniProt and contains 'automatic' and manual entries UniProt Knowledge Base - 'the best annotated protein database of the world' http://www.uniprot.org/
  • 48. The most important protein db is UniProt and contains 'automatic' and manual entries
  • 49. Refseq - The NCBI way to reduce redundancy in primary sequence data RefSeq is NCBI 'Reference Sequences' (prot and nuc) Redundancy from primary sequence data is reduced both automatically and by manual annotation of NA and protein sequences. 'one natural biological molecule = one entry'. Links back to the original primary sequences. Hugely popular and a basis for a lot of analyses. Click to apply refseq filter in entrez search http://www.ncbi.nlm.nih.gov/RefSeq/
  • 50. RefSeq has its own identifiers, not to be mixed up with accession numbers Refseq entry codes looks similar as ACC numbers (but are not ACC numbers – underscore!); and RefSeq is also in GenBank format. Note: in 'Features' section one can find the raw sequences from what is was derived. (typical mistake: search with refseq code in uniprot) NC_* (curated) complete genomic element (chromosome, plasmid,...) NT_* (automated) intermediate assembly from BAC NZ_* (automated) incomplete genomic sequence from WGS NW_* (automated) intermediate assembly from WGS NG_* (curated) incomplete genomic element corresponding to gene NM_* (curated) mRNA NR_* (curated) non-coding RNA or predicted transcript of pseudogene NP_* (curated) protein ZP_* (automated) protein predicted from WGS sequence (NZ_*) YP_* (curated) other predicted protein sequences from NCBI Genome Annotation Pipeline XM_* (automated) mRNA XR_* (automated) non-coding RNA or predicted transcript of pseudogene XP_* (automated) protein http://www.ncbi.nlm.nih.gov/RefSeq/key.html http://www.ncbi.nlm.nih.gov/RefSeq/
  • 51. UniRef – UniProt redundancy reducing system for proteins sequences Non redundant protein sequences from UniProt ~ refseq Hiding redundant sequences by clustering them • UniRef100 = complete identical sequences • UniRef90 = 90% identical sequences • UniRef50 = 50% identical sequences See http://www.uniprot.org/help/uniref
  • 52. NCBI's Gene – summarizes gene information including sequence information from primary dbs Example of the gene NPR1 from A. thaliana
  • 53. UniGene – summarizes transcriptomic information around genes
  • 54. And a lot more derived databases with sequence information exist Repbase : repeats (Alu, …), maintained by Jerzy Jurka at the Genetic Information Research Institute (Mountain View CA, USA). CENSOR server allows to "clean" sequences. http://www.girinst.org/repbase MiRBase → published miRNA sequences http://www.mirbase.org/ Eukaryotic promoter database http://www.epd.isb-sib.ch/ UniVec GenBank subset + some sequences from commercial sources - ftp://ftp.ncbi.nih.gov/pub/UniVec/
  • 55. The most important sequence databases overview Integrated Prim seq data Search Derive Curat d ed Portals GB GenPept RefSeq Entrez ENA trEMBL ENA search EB-eye DDBJ UNIPROT SwissProt UniProt
  • 56. Common gene annotations on sequences Genome sequence: e.g. Chr6 Enhancers/promotors terminator Intron Gene sequence exon mRNA AAAAAAAAAAAAA 5'UTR CDS 3'UTR poly(A) tail protein Genetic code tables
  • 57. Searching the database for your gene of interest First you have to determine for yourself which information you want - NA sequences vs. protein sequences - If NA, genomic sequences, or RNA derived - All possible sequences that exists, or curated ones - Protein sequences of which quality - ...
  • 58. Entrez is a starting point for searches at NCBI http://www.ncbi.nlm.nih.gov/sites/gquery
  • 59. Visualising the db_xrefs in records at NCBI
  • 60. ENA has its text-search portal http://www.ebi.ac.uk/ena/
  • 61. Results from an ENA search are organised following the ENA database structure
  • 62. UniProt has a simple search box leading to a sophisticated search results page
  • 63. Complex searches can be achieved by using the index codes in the database e.g. “oc=Primates and de=complete and de=cds and de=MHC” Code usable for Could answer: give me searching all coding sequence of MHC available in primates.
  • 64. Meta-search tools can search different sequence databases at once. MRS Open Source, developed by Maarten Hekkelman at Radboud U. (Nijmegen, the Netherlands). Allows searching in different databases at once, and provides also statistics on the databases. Alternatives: ACNUC, SRS
  • 65. Logical operators Searching involves making combinations of conditions. Here the difference between a logic and, or and not explained by venn diagrams. Q1 AND Q2 & Q1 NOT Q2 ! Q1 OR Q2 |
  • 66. Hands-on! Every module ends with an exercise session. We will now explore how data is stored in different sequence databases. You get …. minutes for this exercise. Afterwards, we summarizes some of the difficulties some of you might have experienced.
  • 67. Summary This course is organised in several modules Module 1: Sequence databases Three major nucleotide databanks host primary sequence data These databases are filled with NA sequence information by scientists and consortia The batch submissions originate mostly from sequencing centers Each primary database stores their sequences and batch submissions in their own way... Batch submissions are marked and/or stored differently than single submissions The 'normal' submissions are a minority in primary sequence databases Primary sequence dbs are synchronised and every sequence receives a unique identifier One sequence entry contains three categories of different types of information This sequence information can be written in different formats Degree of annotation differs between entries SRA contains batch submitted records of which experiment information is of most importance How to get sequences into the db, and back out Primary sequence data contains a lot of redundancy! The primary sequences are the basis for analyses that generate derived sequence data Protein databases come in two kinds The most important protein db is UniProt and contains 'automatic' and manual entries Refseq - The NCBI way to reduce redundancy in primary sequence data RefSeq has its own identifiers, not to be mixed up with accession numbers UniRef – UniProt redundancy reducing system for proteins sequences NCBI's Gene – summarizes gene information including sequence information from primary dbs UniGene – summarizes transcriptomic information around genes And a lot more derived databases with sequence information exist Searching the database for your gene of interest Entrez is a starting point for searches at NCBI Visualising the db_xrefs in records at NCBI ENA has its text-search portal Results from an ENA search are organised following the ENA database structure UniProt has a simple search box leading to a sophisticated search results page Complex searches can be achieved by using the index codes in the database Meta-search tools can search different sequence databases at once. Hands-on!