bem vindos ao setulab! - iq.usp.br · pdf file grafo de de bruijn ... as the ship steams...
TRANSCRIPT
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Metagenômica
João Carlos Setubal
2017
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Reads vs. contigs
• Reads: o que sai da máquina sequenciadora
• Contigs: resultado da montagem
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JC Setubal 3
DNA
A comunidade
SEQ BIOINFO
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Reads
• Essencial usar reads para análises de abundância
• Também é melhor usar reads para identificação taxonômica por causa do possível quimerismo
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Contigs
• Bons para achar genes
– Mais provável achar ORFs completas em contigs
• Para simples presença de genes quimerismo não é um problema sério
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Taxonomia
• Filo: proteobacteria
– Classe: proteobacteria gama
• Ordem: xanthomonadales
–Família: xanthomonadácea
»Gênero: xanthomonas
• Espécie: citri
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OTU
• Unidade taxonômica operacional
• Se for conhecida, leva um rótulo padronizado
• Mas pode ser desconhecida
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Identificação taxonômica
• Estrutura da comunidade microbiana
– Descoberta de quais OTUs conhecidos estão presentes na amostra
– Descoberta de novos OTUs
– Os “conhecidos” podem ser na verdade novas OTUs parentes próximas de reais conhecidos
– Quem são os agentes principais?
• Nem sempre são os mais abundantes
• Dinâmica temporal da comunidade
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Dados (sequências)
• 16S
– Primers específicos
• Dados de DNA total (WMS)
– Maior parte das sequências vem de genes codificadores de proteínas, mas também tem 16S
• Qual escolher depende dos objetivos do projeto
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Muitas variáveis
Variável valores
Data type WMS, 16S amplicon
Sequencing technology Illumina, 454, Ion, pacBio
WMS Reference database NR, NT, M5NR
16S Reference database Greengenes, RDP, Silva
WMS identification program Many programs available
Taxonomy level Phylum, class, order, family, genus, species
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Muitos programas disponíveis!
Bazinet & Cummings, 2012
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Muitas fontes de erro
• Amostragem
• Preparação da biblioteca
• Sequenciamento
• Tamanho da sequência (pode ser curta demais)
• Algoritmo de identificação
• Viéses dos bancos de dados
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Binning e classificação
• Binning
– Juntamos em diferentes caixinhas as sequências que são parecidas entre si
• Não sabemos o que contém cada caixinha
• OTU1, OTU2, OTU3, etc
• Classificação
– Procuramos associar um rótulo taxonômico a cada caixinha (ou a cada read ou fragmento)
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Análise de abundância
• Que organismos ou funções são mais abundantes num nicho?
• Usar contagem de reads como indicador de abundância
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Classificação de reads de DNA total
• Similaridade com sequências de origem conhecida
– BLAST
• Propriedades intrínsicas de cada sequência
– Assinaturas genômicas
• Apropriado para binning
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Classificação com base na frequência
de palavras de k bases k = 4: AAAA, AAAC, AAAG, AAAT, CAAA, etc…
Dada uma janela de x kb, podemos contar as ocorrências de cada uma dessas palavras dentro da janela
Exemplo:
AGATTAGCGACTATTATAGCCTAGATCGATCATTACC
AGAT ocorre 2 vezes
ATTA ocorre 3 vezes
etc
Palavras de k bases: k-mers (kâmeros)
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Matriz de frequências
janela AAAA AAAC AAAG AAAT ACAA ACAC ACAG ACAT
1 15 2
2 16 3
3 14 0
4 13 2
5 15 4
6 12 0
7 18 1
8 17 3
9 16 1
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Zho
u, O
lman
, Xu
, BM
C B
ioin
form
ati
cs, 2
00
9
Genome “barcodes”
E. coli K12 E. coli O157
Burkholderia pseudomallei
Pyrococcus furiosus random
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Não funciona bem com fragmentos curtos
Fragment size, bp
Accuracy, %
Zhou et al, 2009 simulated data
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Exercício
• S1 = TTCTACTACT
• S2 = TTGTACTAGG
• S3 = ACTTCTACTA
• Contar palavras de tamanho 2
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Kraken
• Wood & Salzberg: Genome Biology, 2014
• Ideia: um banco de dados com k-mers e o LCA (ancestral comum mais baixo/próximo) de todos os organismos que contém aquele k-mer
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myTaxa
• Luo, Rodriguez-R, Konstantinidis: Nucleic Acids Research, 2014
• The distinguishing aspect of MyTaxa is that it employs all genes present in an unknown sequence as classifiers, weighting each gene based on its (predetermined) classifying power at a given taxonomic level and frequency of horizontal gene transfer
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Uma comparação usando dados reais
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Dados de entrada
Variable value
dataset ZC4, day 15 (metazoo)
Data type WMS
Sequencing technology Illumina miSeq
WMS program Kraken [Wood & Salzberg, 2014]
WMS Reference database: Kraken database
Taxonomy level Phylum
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Resultado
Unclassified 85%
Classified 15%
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Dentre os reads classificados como bactérias (99%)
Proteobacteria, 29%
Actinobacteria, 27%
Bacteroidetes, 25%
Firmicutes, 11%
Chloroflexi, 4% other, 4%
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Outro tipo de análise
Variable value
dataset ZC4, day 15
Data type 16S
Data type details V3 and V4, read size ~416 bp
Sequencing technology Illumina (miSeq)
16S analysis pipeline Qiime (RDP classifier + Greengenes)
Taxonomy level Phylum
Qiime (Caporaso et al., 2010)
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Resultados
Unclasssified, 23%
Classified, 77%
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Dentre os classificados
Firmicutes, 39%
Proteobacteria, 25%
Bacteroidetes, 14%
Actinobacteria, 12%
Acidobacteria, 3%
Verrucomicrobia, 2%
Chloroflexi, 2% other, 3%
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WMS e 16S
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Proteobacteria Actinobacteria Bacteroidetes Firmicutes Chloroflexi
WGS
16S
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Comentários
• Concordância qualitativa
– boa
• Concordância quantitativa (abundância relativa)
– fraca
• esp. Firmicutes
• Kraken deixa muitos reads não classificados
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Vamos mudar o programa identificador
• myTaxa
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Resultados
Unclassified, 27%
Classified, 73%
Unclasssified, 23%
Classified, 77%
Kraken 16S
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Dentre os classificados como bactéria (99%)
Proteobacteria, 29%
Firmicutes, 27%
Actinobacteria, 16%
Bacteroidetes, 15%
Chloroflexi, 5%
Deinococcus-Thermus, 2%
Cyanobacteria, 1%
Planctomycetes, 1%
Acidobacteria, 1%
other, 3%
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Prokaryotes in Genbank
filo # genomas %
Actinobacteria 4059 13
Bacteroidetes/chlorobi 932 3
Cyanobacteria 340 1
Firmicutes 9628 31
Proteobacteria 14268 46
Spirochaetes 525 2
Others 1500 5
Source: Land et al. 2015
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Comparação de todos os resultados
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Proteobacteria Actinobacteria Bacteroidetes Firmicutes Chloroflexi
Kraken
myTaxa
16S
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Réplica
• Outro conjunto de dados: ZC4, day 99
• Mesmas metodologias
– Kraken
– myTaxa
– 16S
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Resultados
Unclass 90%
10% Unclass 30%
Classified, 70%
Unclassified, 28%
Classified, 72%
kraken 16S
myTaxa
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comparação
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Proteobacteria Actinobacteria Firmicutes Bacteroidetes Chloroflexi Acidobacteria Planctomycetes
kraken
myTaxa
16S
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Conclusões
• WMS vs. 16S
– 16S pode perder OTUs pela especificidade dos primers
• Menos sensibilidade
– É mais difícil chegar ao nível de espécie
– Identificações positivas são confiáveis
• Especificidade boa
– WMS tem melhor sensibilidade (pega tudo)
• Mais sensível aos vieses dos bancos
• myTaxa é um programa melhor do que kraken
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Vamos mudar o nível taxonômico
• Ordem
• Dados: ZC4, day 99
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Fica mais complicado…
• RDP muitas vezes diz que reads pertencem a uma ordem desconhecida
• Idem myTaxa
• Estimativas de abundância ficam ainda menos confiáveis do que ao nível de filo
• Existe maior sensibilidade ao contéudo dos bancos utilizados
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MG-RAST/M5NR*
Actinomycetales
Rhizobiales
Clostridiales
Burkholderiales
Bacillales
Myxococcales
Chromatiales
Sphaerobacterales
Xanthomonadales
Bacteroidetes Order II. Incertae sedis
Rhodospirillales
Pseudomonadales
Rhodobacterales
Thermoanaerobacterales
Planctomycetales
Chloroflexales
Gemmatimonadales
Cytophagales
Sphingomonadales
Desulfuromonadales
Alteromonadales
Sphingobacteriales
Flavobacteriales
Oceanospirillales
Solibacterales
Thermales
Kraken
Actinomycetales
Sphaerobacterales
Rhizobiales
Burkholderiales
BacteroidetesOrderII.Inc
Myxococcales
Xanthomonadales
Clostridiales
Bacillales
Alteromonadales
Pseudomonadales
Rhodospirillales
Sphingomonadales
Chromatiales
Rhodobacterales
Rhodocyclales
Solirubrobacterales
Caulobacterales
Enterobacteriales
Thermales
Deinococcales
Desulfovibrionales
Desulfuromonadales
Acidimicrobiales
Neisseriales
Oceanospirillales
16S
Actinomycetales
Bacillales
Myxococcales
Xanthomonadales
Rhizobiales
*Meyer et al. 2008
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Análise de abundância
• Abundância relativa
– Curvas de rarefação
• Variação
– temporal
– espacial
– Entre diferentes condições
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Curvas de rarefação (ou saturamento)
n. especies
n. amostras
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Amostras metazoo
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
2750
3000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000
Nu
mb
er o
f S
pe
cie
s
Number of Sequences
ZC1
ZC2
2,260
2,816
(16S rRNA)
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Schwartz et al. Genome Biology 2012 13:r32 doi:10.1186/gb-2012-13-4-r32
Variação da microbiota intestinal entre bebês amamentados no seio e com
mamadeira
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Variação da abundância relativa no tempo
%
dias
Prejeto metagenoma zoológico - compostagem
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Montagem de genomas
buraco
contig
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Montagem
• Em genomas bacterianos isolados, é um processo razoavelmente bem compreendido
• Em metagenomas há velhas e novas dificuldades
– Mistura de organismos
• Quimeras
• Transferência lateral
– Repetições
– Tamanho dos conjuntos de dados
– Chegando a bilhões de reads
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Exemplo de quimerismo
chlorobium firmicutes euryarch.
proteob.
crenarch.
g1 g2 g3 g4 g5 contig
genes
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Algoritmos de montagem especializados para metagenômica
• Genovo [Laserson, Jojic, Koller 2011]
• Metavelvet [Namiki et al. 2012]
• Differential-coverage binning [Albertsen et al. 2013]
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metavelvet
• Baseado em velvet
• Velvet é baseado em grafos de de Bruijn
http://chessprogramming.wikispaces.com/De+Bruijn+sequence
Sobreposição de k-mers
k = 1
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http://www.homolog.us/blogs/wp-content/uploads/2011/07/i6.png
Grafo de de Bruijn em montagem
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metavelvet
http://metavelvet.dna.bio.keio.ac.jp/
Permite escolha
de mais de um
valor para k
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Na prática, no meu lab
• SoapDeNovo
– Luo et al.: SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 2012, 1:18
– Mais rápido, consegue dar conta de conjuntos grandes de dados, resultados suficientemente bons
• Mira (http://www.chevreux.org/projects_mira.html)
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Anotação funcional
• Pipeline para genomas completos pode ser usado
– Exemplo: IMG/M
• Cobertura
– Quanto cada genoma é coberto pelos reads obtidos
– Ambientes de grande riqueza: cobertura baixa
• Cobertura baixa cria contigs pequenos
– maioria das ORFs são parciais
– Dificulta atribuição de função • Potencial gerador de erros
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Comparação de metagenomas
• Genomicamente
• Taxonomicamente
• Funcionalmente
• Recursos oferecidos pelo IMG/M
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Figure 1. Distribution of the GC content percentage for ZC1 and ZC2 compared with selected metagenomes.
Martins LF, Antunes LP, Pascon RC, de Oliveira JCF, Digiampietri LA, et al. (2013) Metagenomic Analysis of a Tropical Composting
Operation at the São Paulo Zoo Park Reveals Diversity of Biomass Degradation Functions and Organisms. PLoS ONE 8(4):
e61928. doi:10.1371/journal.pone.0061928
http://127.0.0.1:8081/plosone/article?id=info:doi/10.1371/journal.pone.0061928
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Genome clustering (IMG/M)
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Figure 8. Hierarchical clustering of functional gene groups of ZC1 and ZC2 and seven public metagenomes.
Martins LF, Antunes LP, Pascon RC, de Oliveira JCF, Digiampietri LA, et al. (2013) Metagenomic Analysis of a Tropical Composting
Operation at the São Paulo Zoo Park Reveals Diversity of Biomass Degradation Functions and Organisms. PLoS ONE 8(4):
e61928. doi:10.1371/journal.pone.0061928
http://127.0.0.1:8081/plosone/article?id=info:doi/10.1371/journal.pone.0061928
Categorias COG COGs
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Normalização
• É um assunto que requer competência em estatística
• A seguir são apresentadas duas regras práticas
• Motivação: Metagenômica comparativa requer normalização do número de reads das amostras
• Variação pode ser grande; por exemplo:
– Amostra 1: 200 mil reads
– Amostra 2: 2 milhões de reads
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Método 1
• Determinar a amostra com menor número de reads (suponha nmin)
• Para cada outra amostra
– Selecionar aleatoriamente nmin reads
• Desvantagem
– Pode jogar muito dado fora
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Método 2
• Seja nA o numero de reads da amostra A
• Seja σ o número médio de reads por amostra
• Seja x a contagem da característica de interesse em A (ou seja, x reads tem essa característica)
• Então normalizamos x pela fórmula
– log10𝑥
𝑛𝐴
. σ + 1
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Abundância de funções
• BLASTX de reads contra um banco de COGs Cluster of Orthologous Groups
67
Example of a COG: monoamine oxidase
http://www.ncbi.nlm.nih.gov/books/NBK21090/ eggNOG [Jensen et al. 2008]
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• COGs diferencialmente representados
• Semelhante a genes diferencialmente expressos
• Heat maps, clusterização hierárquica
Abundância relativa espacial
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Projeto Metagenômica do Mar Vermelho
• Colaboração com a American University in Cairo
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Ocean water columns
surface
bottom
samples
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2,200 m
Africa Saudi Arabia
Brine pool Atlantis II
Discovery
Kebrit
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The brine pools are a special niche
• High salinity (10X more than surface water)
• Enriched with heavy metals: iron, manganese, copper,
zinc (1000X more concentrated than normal water)
• High temperatures (70 C)
• High pressure
• No light
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The Oceanus research ship belongs to the Woods Hole Oceanographic Institute.
Source: Hamza El Dorry
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A CTD determines the essential physical properties of
ocean water. It gives scientists a precise and
comprehensive charting of the distribution and variation
of water temperature, salinity, and density that helps to
understand how the oceans affect life. (Media
Relations, Woods Hole Oceanographic Institution)
As the ship steams slowly ahead, scientists in a lab on the ship
will guide the CTD up and down in the water column,
occasionally sending the instrument an electronic signal to
collect a water sample in a bottle mounted on the instrument's
cage. (© C.A. Linder, WHOI)
A CTD: Conductivity, Temperature, and Depth (CTD) sensors
One of the labs on OCEANUS
Source: Hamza El Dorry
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Data
• Pyrosequencing with Roche 454 (AUC)
• 2 columns (above two different brine pools)
– 5 samples in each column
• Esta aula – data for the column above the brine pool Atlantis II (ATIIC)
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2,200 m
Africa Saudi Arabia
Water column
1,500m
700m
200m
50m
ATIIC brine pool
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First results
• Comparison among several ocean water columns
– 11 locations worldwide
– 24 samples at different depths
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Comparison: gene functions
• Based on COG assignments
– BLASTX against eggNOG [Jensen et al. 2008]
• What functions were present in one site but not in others
• What functions were present at a certain depth but not in others
• Rather than presence/absence
– Differentially represented COGs
– Similar to differentially expressed genes
– Heat maps, hierarchical clustering
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Methodological issues
• Comparative metagenomics
• How to determine whether an assigned COG is differentially represented
– Normalization, statistics
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First result: COGs per water column
site #COGs
ATIIC 483
ALOHA 337
BATS 360
Iquique 174
Total unique 790
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Based on 386 COGs
shared by ATIIC,
Aloha, BATS with
differential
representation
Iquique not included
COGs
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And Iquique?
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Why is Iquique different?
• Humboldt current
• Upwelling phenomenon
– Cold nutrient-rich water is taken to the surface
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http://what-when-how.com/marine-mammals/south-american-aquatic-mammals
http://mynasadata.larc.nasa.gov/glossary.php?&word=upwelling
upwelling
iquique
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PAR values (Photosynthetically active radiation )
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“Photic and aphotic COGs”
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41 COGs with higher
abundance in photic
zones
34 COGs with
higher abundance in
aphotic zones
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Photic COGs
• Photosyntesis
• biosynthesis of light-harvesting pigments
• assimilation of CO2 by photosynthetic bacteria
• Light-induced DNA repair
• oxidative stress response
• N2 fixation
• phosphate metabolism
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Aphotic COGs
• Catabolism of proteins and aminoacids
• Methane oxidation
• sulfate assimilation and metabolism
• selenocysteine metabolism
• terpenoid biosynthesis
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Deoxyribodipyrimidine photolyase (repairs DNA
damage caused by exposure to ultraviolet light, COG0415)
photic
aphotic
“mixed
layer”
?
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Abundance ratios
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Another puzzle: Marmara
Relatively high ratio photic:aphotic COGs for the depth
Overrepresentation of the photolyase COG
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Sea of Marmara
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Conjectured explanation
• The Marmara sea receives saltier water inflow from the Mediterranean through the Dardanelles strait (65m)
• This water is denser therefore it sinks
• Effect: Greater ratio between photic and aphotic COGs even at 1,000m
http://www.sciencedirect.com/science/article/pii/S0034666703001179 Mudie et al., 2004
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Oxygen limitation
• Hypoxic regions in Iquique
• 85, 110, 200m
• Lowest O2 values among all samples
• Are there COGs significantly associated with lack of oxygen?
• We removed 383 photic/aphotic COGs and verified the rest
• Result: 22 differentially represented COGs (11 up, 11 down)
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Gene favored by low oxygen levels: narG, involved in denitrification
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Summary
• A metagenomics-enabled functional profiling analysis
– Light
– Oxygen
• Reference sets of functional biological activities
– diagnosis of the physiological and biochemical capabilities of marine microorganisms
• May help monitor “the oceans’ health”
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http
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Platformas web de processamento
• Laboratórios governamentais
• Serviços padronizados de processamento
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