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




454 SEQUENCING

            SOLEXA / ILLUMINA

                                SOLID
454 SEQUENCING



SEQUENCING BY SYNTHESIS

PYROSEQUENCING

> 400 BASEPAIRS IN A SINGLE READ
454 SEQUENCING
454 SEQUENCING
454 SEQUENCING
454 SEQUENCING




REPEATS OF SINGLE NUCLEOTIDES ARE DETECTED BY
SIGNAL STRENGTH

WORKS FOR UP TO 8 CONSECUTIVE BASES
SOLEXA / ILLUMINA



AGAIN: SEQUENCING BY SYNTHESIS

ANOTHER DETECTION-APPROACH

UP TO 100 BASEPAIRS IN A SINGLE READ
SOLEXA / ILLUMINA
...
C
A
T
C
G
G
...
SOLEXA / ILLUMINA
...
C
A              C
                           A
T
C                  T
G                      G

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

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

G     C
...
ADVANTAGES OF NGS



CAN RUN IN PARALLEL

PREPERATION CAN BE AUTOMATED

MUCH CHEAPER WHEN COMPARED TO TRADITIONAL
SEQUENCI...
TRANSCRIPTOME ANALYSIS


ALLOWS FOR EXPRESSION CHANGES IN:

  DIFFERENT CELL TYPES

  DIFFERENT CONDITIONS OF THE ENVIRONM...
TRANSCRIPTOME ANALYSIS




CAN BE USED TO IDENTIFY NEW GENES

CAN BE APPLIED TO NON-MODEL ORGANISMS
HOW TO ANALYSE
         TRANSCRIPTOMES


        FIRST STEP: GET THE DATA

TRADITIONALLY: EXPRESSED SEQUENCE TAGS (ESTS)

...
ESTS



DONE USING SHOTGUN-SEQUENCING

TAKES CLONES OF EXPRESSED MRNA

CHEAP TO PRODUCE
RNA-SEQ



SAME PRINCIPLE:

  GET AVAILABLE MRNA

  THEN SEQUENCING IN PARALLEL VIA NGS
RNA-SEQ



SAME PRINCIPLE:

  GET AVAILABLE MRNA

  THEN SEQUENCING IN PARALLEL VIA NGS



         RNA-SEQ == EST + NGS
HOW TO ANALYSE
        TRANSCRIPTOMES


ASSEMBLY OF READS

 DETECTION OF SNPS

 GENE ANNOTATION

 DETECTION OF OPEN READIN...
ASSEMBLY


       AVAILABLE TOOLS:

CAP3

MIRA

...
CAP3




SMITH-WATERMAN TO CLIP BAD ENDINGS

GLOBAL ALIGNMENT TO FIND FALSE OVERLAPS
MIRA


COMBINES ASSEMBLY & SNP-DETECTION

USES:

  TRACE FILES

  TEMPLATE INSERT INFORMATION

  REDUNDANCY
MIRA


FAST READ COMPARISON TO DETECT POTENTIAL
OVERLAPS

CONFIRMS OVERLAPS USING SMITH-WATERMAN AND
CREATES ALIGNMENTS

A...
MIRA
THE WORKFLOW
MIRA



RESULTS:

 CONSENSUS CONTIGS MADE OF READS THAT
 OVERLAP

 SNPS THAT ARE CALLED DURING ASSEMBLY PROCESS
SNP DETECTION



TOOLS:

  MIRA

  QUALITYSNP

  AND SOME MORE
QUALITYSNP



USES CAP3-FILES

INPUT: CLUSTERS OF POTENTIAL HAPLOTYPES

CALCULATES SIMILARITY BETWEEN SEQUENCES TO
CONSTRU...
QUALITYSNP



REMOVES HAPLOTYPES THAT CONSIST OF ONLY ONE
SEQUENCE

DETECTS SYNONYMOUS AND NON-SYNONYMOUS SNPS

PROVIDES A...
HOMOLOGY DETECTION




ALLOWS TO FIND GENES THAT SHARE AN ANCESTOR

USUALLY ONE SEARCHES AGAINST A DATABASE
HOMOLOGY DETECTION


DIFFERENT KIND OF SEARCHES:

  PROTEIN AGAINST PROTEIN

  NUCLEOTIDE AGAINST NUCLEOTIDE

  PROTEIN AG...
HOMOLOGY DETECTION


TOOLS:

  BLAST

  FASTX / FASTY

  HMMER

  PATTERNHUNTER
BLAST



AVAILABLE FOR ALL TYPES OF COMPARISONS

ONE OF THE OLDEST ALGORITHMS

WIDELY USED

SPEED OVER SENSITIVITY
FASTX / FASTY



PARTS OF FASTA

COMPARE NUCLEOTIDES AGAINST PROTEINS

DETERMINES A HYPOTHESIZED CODING REGION (HCR)

FAST...
HMMER



PROTEIN-QUERIES AGAINST PROTEIN-DATABASE

USES HIDDEN MARKOV MODELS

MAPS SMITH-WATERMAN PARAMETERS ONTO A
PROBAB...
PATTERNHUNTER


NUCLEOTIDE-QUERIES AGAINST OTHER NUCLEOTIDE-
SEQUENCES

USES NON-CONSECUTIVE SEEDS FOR INCREASED
SENSITIVI...
ORF DETECTION



READING FRAMES CAN BE DETECTED IN EST-DATA

ALLOWS TO SCREEN FOR PREVIOUSLY UNKNOWN
GENES

ALLOWS TO GIVE...
ORF DETECTION



TOOLS:

  ESTSCAN

  ORFPREDICTOR

  ...
ESTSCAN



USES HIDDEN MARKOV MODELS

ROBUST FOR FRAMESHIFT ERRORS

SENSITIVE ( 5 % FN, 18 % FP)
ORFPREDICTOR



WEB-BASED

USES BLASTX AS GUIDELINE IF POSSIBLE

USES A DEFINED RULESET FOR DEFINING ORFS
ORFPREDICTOR
GENE ANNOTATION




BLAST2GO VIA GENE ONTOLOGY

FINDS HOMOLOG GENES TO ANNOTATE FUNCTIONS OF
GENE OF INTEREST
GENE ONTOLOGY



3 ONTOLOGIES:

  MOLECULAR FUNCTION

  CELLULAR COMPONENTS

  BIOLOGICAL PROCESS
CONCLUSIONS


NGS PROVIDES A FAST AND CHEAP WAY TO GENERATE
DATA

TONS OF TOOLS EXIST TO ANALYSE TRANSCRIPTOME
DATA

ALL T...
Upcoming SlideShare
Loading in...5
×

Next Generation Sequencing & Transcriptome Analysis

15,017

Published on

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

Published in: Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
15,017
On Slideshare
0
From Embeds
0
Number of Embeds
9
Actions
Shares
0
Downloads
556
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Next Generation Sequencing & Transcriptome Analysis

  1. 1. NEXT GENERATION SEQUENCING
  2. 2. NEXT GENERATION SEQUENCING AND HOW TO USE THE DATA GENERATED FOR TRANSCRIPTOMICS
  3. 3. METHODS
  4. 4. METHODS 454 SEQUENCING SOLEXA / ILLUMINA SOLID
  5. 5. 454 SEQUENCING SEQUENCING BY SYNTHESIS PYROSEQUENCING > 400 BASEPAIRS IN A SINGLE READ
  6. 6. 454 SEQUENCING
  7. 7. 454 SEQUENCING
  8. 8. 454 SEQUENCING
  9. 9. 454 SEQUENCING REPEATS OF SINGLE NUCLEOTIDES ARE DETECTED BY SIGNAL STRENGTH WORKS FOR UP TO 8 CONSECUTIVE BASES
  10. 10. SOLEXA / ILLUMINA AGAIN: SEQUENCING BY SYNTHESIS ANOTHER DETECTION-APPROACH UP TO 100 BASEPAIRS IN A SINGLE READ
  11. 11. SOLEXA / ILLUMINA ... C A T C G G ...
  12. 12. SOLEXA / ILLUMINA ... C A C A T C T G G G ...
  13. 13. SOLEXA / ILLUMINA ... C A A T C T G G G C ...
  14. 14. SOLEXA / ILLUMINA ... C A A T C T G G G C ...
  15. 15. ADVANTAGES OF NGS CAN RUN IN PARALLEL PREPERATION CAN BE AUTOMATED MUCH CHEAPER WHEN COMPARED TO TRADITIONAL SEQUENCING
  16. 16. TRANSCRIPTOME ANALYSIS ALLOWS FOR EXPRESSION CHANGES IN: DIFFERENT CELL TYPES DIFFERENT CONDITIONS OF THE ENVIRONMENT DISEASES DIFFERENT DEVELOPMENTAL STAGES
  17. 17. TRANSCRIPTOME ANALYSIS CAN BE USED TO IDENTIFY NEW GENES CAN BE APPLIED TO NON-MODEL ORGANISMS
  18. 18. HOW TO ANALYSE TRANSCRIPTOMES FIRST STEP: GET THE DATA TRADITIONALLY: EXPRESSED SEQUENCE TAGS (ESTS) USING NGS: RNA-SEQ
  19. 19. ESTS DONE USING SHOTGUN-SEQUENCING TAKES CLONES OF EXPRESSED MRNA CHEAP TO PRODUCE
  20. 20. RNA-SEQ SAME PRINCIPLE: GET AVAILABLE MRNA THEN SEQUENCING IN PARALLEL VIA NGS
  21. 21. RNA-SEQ SAME PRINCIPLE: GET AVAILABLE MRNA THEN SEQUENCING IN PARALLEL VIA NGS RNA-SEQ == EST + NGS
  22. 22. HOW TO ANALYSE TRANSCRIPTOMES ASSEMBLY OF READS DETECTION OF SNPS GENE ANNOTATION DETECTION OF OPEN READING FRAMES DETECTION OF HOMOLOGOUS GENES
  23. 23. ASSEMBLY AVAILABLE TOOLS: CAP3 MIRA ...
  24. 24. CAP3 SMITH-WATERMAN TO CLIP BAD ENDINGS GLOBAL ALIGNMENT TO FIND FALSE OVERLAPS
  25. 25. MIRA COMBINES ASSEMBLY & SNP-DETECTION USES: TRACE FILES TEMPLATE INSERT INFORMATION REDUNDANCY
  26. 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. 27. MIRA THE WORKFLOW
  28. 28. MIRA RESULTS: CONSENSUS CONTIGS MADE OF READS THAT OVERLAP SNPS THAT ARE CALLED DURING ASSEMBLY PROCESS
  29. 29. SNP DETECTION TOOLS: MIRA QUALITYSNP AND SOME MORE
  30. 30. QUALITYSNP USES CAP3-FILES INPUT: CLUSTERS OF POTENTIAL HAPLOTYPES CALCULATES SIMILARITY BETWEEN SEQUENCES TO CONSTRUCT HAPLOTYPES AND REMOVES PARALOGS
  31. 31. QUALITYSNP REMOVES HAPLOTYPES THAT CONSIST OF ONLY ONE SEQUENCE DETECTS SYNONYMOUS AND NON-SYNONYMOUS SNPS PROVIDES A WEB-FRONTEND CALLED HAPLOSNPER
  32. 32. HOMOLOGY DETECTION ALLOWS TO FIND GENES THAT SHARE AN ANCESTOR USUALLY ONE SEARCHES AGAINST A DATABASE
  33. 33. HOMOLOGY DETECTION DIFFERENT KIND OF SEARCHES: PROTEIN AGAINST PROTEIN NUCLEOTIDE AGAINST NUCLEOTIDE PROTEIN AGAINST NUCLEOTIDE NUCLEOTIDE AGAINST PROTEIN
  34. 34. HOMOLOGY DETECTION TOOLS: BLAST FASTX / FASTY HMMER PATTERNHUNTER
  35. 35. BLAST AVAILABLE FOR ALL TYPES OF COMPARISONS ONE OF THE OLDEST ALGORITHMS WIDELY USED SPEED OVER SENSITIVITY
  36. 36. FASTX / FASTY PARTS OF FASTA COMPARE NUCLEOTIDES AGAINST PROTEINS DETERMINES A HYPOTHESIZED CODING REGION (HCR) FASTX IS FASTER, FASTY IS MORE ACCURATE
  37. 37. HMMER PROTEIN-QUERIES AGAINST PROTEIN-DATABASE USES HIDDEN MARKOV MODELS MAPS SMITH-WATERMAN PARAMETERS ONTO A PROBABILISTIC MODEL IMPROVES ACCURACY
  38. 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. 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. 40. ORF DETECTION TOOLS: ESTSCAN ORFPREDICTOR ...
  41. 41. ESTSCAN USES HIDDEN MARKOV MODELS ROBUST FOR FRAMESHIFT ERRORS SENSITIVE ( 5 % FN, 18 % FP)
  42. 42. ORFPREDICTOR WEB-BASED USES BLASTX AS GUIDELINE IF POSSIBLE USES A DEFINED RULESET FOR DEFINING ORFS
  43. 43. ORFPREDICTOR
  44. 44. GENE ANNOTATION BLAST2GO VIA GENE ONTOLOGY FINDS HOMOLOG GENES TO ANNOTATE FUNCTIONS OF GENE OF INTEREST
  45. 45. GENE ONTOLOGY 3 ONTOLOGIES: MOLECULAR FUNCTION CELLULAR COMPONENTS BIOLOGICAL PROCESS
  46. 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“
  1. Gostou de algum slide específico?

    Recortar slides é uma maneira fácil de colecionar informações para acessar mais tarde.

×