metatranscriptomics: challenges and progress

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Metatranscriptomics: Challenges and Progress Cindi Hoover DOE Joint Genome Institute May 17, 2012

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Metatranscriptomics: Challenges and Progress. Cindi Hoover DOE Joint Genome Institute May 17, 2012. Metatranscriptomics. Metatranscriptome The complete collection of transcribed sequences in a microbial community: Protein-coding RNA (mRNA) Non-coding RNA (rRNA, tRNA, regulatory RNA, etc) - PowerPoint PPT Presentation

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Page 1: Metatranscriptomics: Challenges and Progress

Metatranscriptomics:Challenges and Progress

Cindi Hoover

DOE Joint Genome Institute

May 17, 2012

Page 2: Metatranscriptomics: Challenges and Progress

MetatranscriptomicsMetatranscriptomics

Metatranscriptome

The complete collection of transcribed sequences in a microbial community:

Protein-coding RNA (mRNA) Non-coding RNA (rRNA, tRNA, regulatory RNA, etc)

Metagenome = who’s there?Metatranscriptome = function?

What genes are active in environment?How does gene expression change in response to particular conditions?

Page 3: Metatranscriptomics: Challenges and Progress

Evolution of Metatranscriptome MethodsEvolution of Metatranscriptome Methods

cDNA clone libraries + Sanger sequencing (low throughput)

Microarrays (medium throughput)

RNA-seq enabled by next-generation sequencing technologies (high throughput) Influenced by presence of rRNA

Page 4: Metatranscriptomics: Challenges and Progress

Wet lab Low RNA yield from environmental samples Instability of RNA High rRNA content in total RNA

mRNA = 1-5% of total

http://cybernetnews.com/vista-recovery-disc/

http://www.nwfsc.noaa.gov/index.cfm

Bioinformatics General challenges with short reads and large

data size Small overlap between metagenome and

metatranscriptome, or complete lack of metagenome reference

Main ChallengesMain Challenges

How do you effectively removal rRNA from metatranscriptome samples?

Page 5: Metatranscriptomics: Challenges and Progress

rRNA Removal MethodsrRNA Removal Methods

Method rRNA feature usedInput RNA

Manipulate raw RNA

Before cDNA synthesis

Subtractive hybridization Conserved sequence

HighYes

RNase H digestion

Exonuclease digestion 5’ monophosphate

Gel extraction Size

Biased poly(A) tailing 2o structure Low

During cDNA synthesis

Not-so-random primers Sequence feature Low No

After cDNA synthesis

Library normalization w/ DSN High abundance Low No

Page 6: Metatranscriptomics: Challenges and Progress

Sample-specific probe method Sample-specific probe method

Stewart et al, ISME J (2010) 4, 896–907

One of the first to successfully tackle the rRNA in metatranscriptome problem

PRO: Customized probes are specific to communities of interest

CONS: Very time consuming process; requires >3ug RNA or matched DNA samples

Different batches of probe may give different results

Method has been applied on marine metatranscriptome samples to substantially reduce rRNA.

Page 7: Metatranscriptomics: Challenges and Progress

Epicentre: Ribo-Zero Epicentre: Ribo-Zero TMTM

Essentially a subtractive hybridization

rRNA removal reagent contains oligo probes complementary to rRNA sequences

Magnetic beads bind rRNA-probe complexes and remove them from solution

Process takes ~1-1.5 hours; requires 1ug total RNA

Page 8: Metatranscriptomics: Challenges and Progress

Ribo-Zero TypesRibo-Zero Types

Metabacteria: handles Gram (-) and Gram (+)

Human/Mouse/Rat: also works on fungal samples

Plant LeafPlant Seed/Root

Page 9: Metatranscriptomics: Challenges and Progress

Synthetic metatranscriptomeSynthetic metatranscriptome

Both methods tested on sample Mettr_1:

Organism Amount in pool (ug)

Prochlorococcus marinus pastoris CMP1986

0.1

Pediococcus pentosaceus 6.0

Acinetobacter sp. ADP1 2.5

Cyanobacterium synechocystis PCC 6803

3.0

Synechococcus elongates PCC 7942

0.5

Total Pool 12 ug

Page 10: Metatranscriptomics: Challenges and Progress

Example of Depletion QCExample of Depletion QC

Red = total RNA

Blue = (+) Ribo-Zero A

Green = (+) Ribo-Zero B

Agilent Nano chip: total RNA vs depletion with beta test kit

Page 11: Metatranscriptomics: Challenges and Progress

Initial Mettr_1 DataInitial Mettr_1 Data

Sample Total reads

(million)

% rRNA % Map

Mettr_1 CONTROL (no depletion)

6.08 75.4 4.1

Mettr_1 (+) probe 6.76 19.3 24.3

Mettr_1 Ribo-zero A 7.96 4.1 68.0

Mettr_1 Ribo-zero B 6.82 4.3 69.5

Page 12: Metatranscriptomics: Challenges and Progress

Gene Expression Correlations Ribo-Zero vs. No Depletion

Ribo-Zero does not appear cause bias in gene expression.

Page 13: Metatranscriptomics: Challenges and Progress

Gene Expression Ribo-Zero vs Probe Gene Expression Ribo-Zero vs Probe MethodMethod

Page 14: Metatranscriptomics: Challenges and Progress

Gene Expression Correlations Ribo-Zero Gene Expression Correlations Ribo-Zero ReplicatesReplicates

Page 15: Metatranscriptomics: Challenges and Progress

Ribo-Zero & Cow RumenRibo-Zero & Cow Rumen

Page 16: Metatranscriptomics: Challenges and Progress

Cow Rumen DataCow Rumen Data

Ribo-Zero is effective, even on complex metatranscriptome samples like cow rumen.

Sample % rRNA % Map (rumen) % Other

No depletion control

82.4% 3.4 10.5

Ribo-Zero Metabacteria

15.9 27.7 55.2

Ribo-Zero Metabacteria + Human/Mouse/Rat

4.9 26.7 56.3

Page 17: Metatranscriptomics: Challenges and Progress

SummarySummary

rRNA removal technique is critical to metatranscriptome sequencing success!

Ribo-Zero = efficient rRNA removal method

Highly effective on complex metatranscriptome samples

Ability to customize by mixing rRNA removal solutions

Page 18: Metatranscriptomics: Challenges and Progress

AcknowledgementsAcknowledgements

Cris Kinross

Matt Blow Jeff Martin Weibing Shi Shaomei He Erika Lindquist Feng Chen

Page 19: Metatranscriptomics: Challenges and Progress

Questions?