How to use next generation sequencing in transcriptomics and how to analyse those data.

1. NEXT GENERATION
SEQUENCING

2. NEXT GENERATION
SEQUENCING
AND HOW TO USE THE DATA GENERATED
FOR TRANSCRIPTOMICS

3. METHODS

4. METHODS
454 SEQUENCING
SOLEXA / ILLUMINA
SOLID

5. 454 SEQUENCING
SEQUENCING BY SYNTHESIS
PYROSEQUENCING
> 400 BASEPAIRS IN A SINGLE READ

6. 454 SEQUENCING

7. 454 SEQUENCING

8. 454 SEQUENCING

9. 454 SEQUENCING
REPEATS OF SINGLE NUCLEOTIDES ARE DETECTED BY
SIGNAL STRENGTH
WORKS FOR UP TO 8 CONSECUTIVE BASES

10. SOLEXA / ILLUMINA
AGAIN: SEQUENCING BY SYNTHESIS
ANOTHER DETECTION-APPROACH
UP TO 100 BASEPAIRS IN A SINGLE READ

11. SOLEXA / ILLUMINA
...
C
A
T
C
G
G
...

12. SOLEXA / ILLUMINA
...
C
A C
A
T
C T
G G
G
...

13. SOLEXA / ILLUMINA
...
C
A
A
T
C T
G G
G C
...

14. SOLEXA / ILLUMINA
...
C
A
A
T
C T
G G
G C
...

15. ADVANTAGES OF NGS
CAN RUN IN PARALLEL
PREPERATION CAN BE AUTOMATED
MUCH CHEAPER WHEN COMPARED TO TRADITIONAL
SEQUENCING

16. TRANSCRIPTOME ANALYSIS
ALLOWS FOR EXPRESSION CHANGES IN:
DIFFERENT CELL TYPES
DIFFERENT CONDITIONS OF THE ENVIRONMENT
DISEASES
DIFFERENT DEVELOPMENTAL STAGES

17. TRANSCRIPTOME ANALYSIS
CAN BE USED TO IDENTIFY NEW GENES
CAN BE APPLIED TO NON-MODEL ORGANISMS

18. HOW TO ANALYSE
TRANSCRIPTOMES
FIRST STEP: GET THE DATA
TRADITIONALLY: EXPRESSED SEQUENCE TAGS (ESTS)
USING NGS: RNA-SEQ

19. ESTS
DONE USING SHOTGUN-SEQUENCING
TAKES CLONES OF EXPRESSED MRNA
CHEAP TO PRODUCE

20. RNA-SEQ
SAME PRINCIPLE:
GET AVAILABLE MRNA
THEN SEQUENCING IN PARALLEL VIA NGS

21. RNA-SEQ
SAME PRINCIPLE:
GET AVAILABLE MRNA
THEN SEQUENCING IN PARALLEL VIA NGS
RNA-SEQ == EST + NGS

22. HOW TO ANALYSE
TRANSCRIPTOMES
ASSEMBLY OF READS
DETECTION OF SNPS
GENE ANNOTATION
DETECTION OF OPEN READING FRAMES
DETECTION OF HOMOLOGOUS GENES

23. ASSEMBLY
AVAILABLE TOOLS:
CAP3
MIRA
...

24. CAP3
SMITH-WATERMAN TO CLIP BAD ENDINGS
GLOBAL ALIGNMENT TO FIND FALSE OVERLAPS

25. MIRA
COMBINES ASSEMBLY & SNP-DETECTION
USES:
TRACE FILES
TEMPLATE INSERT INFORMATION
REDUNDANCY

26. MIRA
FAST READ COMPARISON TO DETECT POTENTIAL
OVERLAPS
CONFIRMS OVERLAPS USING SMITH-WATERMAN AND
CREATES ALIGNMENTS
ASSEMBLES READ-PAIRS BY FINDING BEST PATH
CHECKS ASSEMBLIES FOR ERRORS AND BEGINS AGAIN

27. MIRA
THE WORKFLOW

28. MIRA
RESULTS:
CONSENSUS CONTIGS MADE OF READS THAT
OVERLAP
SNPS THAT ARE CALLED DURING ASSEMBLY PROCESS

29. SNP DETECTION
TOOLS:
MIRA
QUALITYSNP
AND SOME MORE

30. QUALITYSNP
USES CAP3-FILES
INPUT: CLUSTERS OF POTENTIAL HAPLOTYPES
CALCULATES SIMILARITY BETWEEN SEQUENCES TO
CONSTRUCT HAPLOTYPES AND REMOVES PARALOGS

31. QUALITYSNP
REMOVES HAPLOTYPES THAT CONSIST OF ONLY ONE
SEQUENCE
DETECTS SYNONYMOUS AND NON-SYNONYMOUS SNPS
PROVIDES A WEB-FRONTEND CALLED HAPLOSNPER

32. HOMOLOGY DETECTION
ALLOWS TO FIND GENES THAT SHARE AN ANCESTOR
USUALLY ONE SEARCHES AGAINST A DATABASE

33. HOMOLOGY DETECTION
DIFFERENT KIND OF SEARCHES:
PROTEIN AGAINST PROTEIN
NUCLEOTIDE AGAINST NUCLEOTIDE
PROTEIN AGAINST NUCLEOTIDE
NUCLEOTIDE AGAINST PROTEIN

34. HOMOLOGY DETECTION
TOOLS:
BLAST
FASTX / FASTY
HMMER
PATTERNHUNTER

35. BLAST
AVAILABLE FOR ALL TYPES OF COMPARISONS
ONE OF THE OLDEST ALGORITHMS
WIDELY USED
SPEED OVER SENSITIVITY

36. FASTX / FASTY
PARTS OF FASTA
COMPARE NUCLEOTIDES AGAINST PROTEINS
DETERMINES A HYPOTHESIZED CODING REGION (HCR)
FASTX IS FASTER, FASTY IS MORE ACCURATE

37. HMMER
PROTEIN-QUERIES AGAINST PROTEIN-DATABASE
USES HIDDEN MARKOV MODELS
MAPS SMITH-WATERMAN PARAMETERS ONTO A
PROBABILISTIC MODEL
IMPROVES ACCURACY

38. PATTERNHUNTER
NUCLEOTIDE-QUERIES AGAINST OTHER NUCLEOTIDE-
SEQUENCES
USES NON-CONSECUTIVE SEEDS FOR INCREASED
SENSITIVITY
COMPARES HUMAN GENOME TO MOUSE GENOME IN 20
CPU-DAYS

39. ORF DETECTION
READING FRAMES CAN BE DETECTED IN EST-DATA
ALLOWS TO SCREEN FOR PREVIOUSLY UNKNOWN
GENES
ALLOWS TO GIVE A POTENTIAL PROTEIN SEQUENCE

40. ORF DETECTION
TOOLS:
ESTSCAN
ORFPREDICTOR
...

41. ESTSCAN
USES HIDDEN MARKOV MODELS
ROBUST FOR FRAMESHIFT ERRORS
SENSITIVE ( 5 % FN, 18 % FP)

42. ORFPREDICTOR
WEB-BASED
USES BLASTX AS GUIDELINE IF POSSIBLE
USES A DEFINED RULESET FOR DEFINING ORFS

43. ORFPREDICTOR

44. GENE ANNOTATION
BLAST2GO VIA GENE ONTOLOGY
FINDS HOMOLOG GENES TO ANNOTATE FUNCTIONS OF
GENE OF INTEREST

45. GENE ONTOLOGY
3 ONTOLOGIES:
MOLECULAR FUNCTION
CELLULAR COMPONENTS
BIOLOGICAL PROCESS

46. CONCLUSIONS
NGS PROVIDES A FAST AND CHEAP WAY TO GENERATE
DATA
TONS OF TOOLS EXIST TO ANALYSE TRANSCRIPTOME
DATA
ALL TOOLS HAVE THEIR OWN PROS & CONTRAS
MOST OF THOSE TOOLS ARE UNSUITABLE FOR A
„NORMAL USER“