Amplicon Sequencing. Dr. Orla O Sullivan SIRG Research Fellow Teagasc

Transcription

1 Amplicon Sequencing Dr. Orla O Sullivan SIRG Research Fellow Teagasc

2 What is Amplicon Sequencing? Sequencing of target genes (are regions of ) obtained by PCR using gene specific primers.

3 Why do we do it? THE PLATE COUNT ANOMALY: Roughly one out of every 100 microbes can be cultivated (ASM blog; Dec 2014) Amplicon Sequencing provides a snapshot of microbial populations in environment at time of sequencing

4 Target genes Traits Ubiquitous Discriminating Slow evolving Good database available Examples 16S rrna gene ( bacteria) 18S rrna gene ( eukaryotes) ITS ( fungi) Amplicon sequencing not suitable for groups such as protists and viruses which are extremely diverse and have little Target particular species ( rpob; Bifidobacteria) sequence information available Decarboxylases ( cheese)

5 Platforms and outputs 454 GS FLX : 800K amplicon reads ( up to 500 bp reads ) Ion PGM ( 318 chip) : 5.5 million amplicon reads Illumina MiSeq ( V4 chemistry): 17million amplicon reads ; 2x 300bp reads Illumina HiSeq: 300 million -2 billion reads; 2 x 250bp / 2 x 125 bp reads ( run mode dependant)

6 Before you start What is your objective? e.g. to ascertain what microbial populations are in an environment What populations do you want to target? e.g. fungal/bacterial/ archaea ; this will determine primer choice What is your sample? e.g. soil, water, cheese, kefir, faeces (mouse, rat, adult, human, fermenter); this will affect extraction protocols What is in your sample? Is it a low or high diversity sample; this will affect how much sequencing depth you need Also number of unique indices available is a limiting factor Plan, Plan, Plan!!! Some caveats Also sequence dead DNA Can miss minor populations Use the same platform and primers for each study

7 16S rrna Advantages Ubiquitous Well annotated databases Well studied and widely used Discriminating Disadvantages Can be in multiple copies GC bias Cannot always get to species level (bp issue) Variable regions can differ in results Primers can be designed to match a single variable region or to span 2 variable regions. Sequence platform and read length will help determine this.

8 Terminology Clustering: grouping sequences into clusters ( or bins) bases on percent similarity (commonly 97%). Each bin/cluster is termed an OTU. OTU: operational taxonomical unit; It is the most commonly used microbial diversity unit. Whilst sometimes used as a proxy for species it is a distinct entity. Chimera: PCR artefact Barcode/index: short piece of DNA added to each read that is sample specific; allows for multiplexing of samples Multiplexing: pooling multiple DNA samples together for sequencing. During downstream analysis samples Will be demultiplexed ( separated by sample) based on the barcodes. Biom tables: Matrix containing counts of OTUS and corresponding meta-data ( e.g.. Taxonomy) Mapping file: tab-delimited file which assigns samples to groups based on index and name

9 FastQ format 1:N:0:1 GAATAACTTTATCATTTTTATATACAGGTACAACAATCGCTTCTTTTACGAACTC + CCCCCFGFGGGGGGGGGGGGGGGGGGGGGGG9FGDGGGGGFGGFDFAG Sequence Identifier breakdown (split by : ) M01383 unique instrument name RunID A5KT8 Flowcell ID 1 Lane on flow cell 1101 tile number within the flowcell lane x coordinate of the cluster within the tile y coordinate of the cluster within the tile 1 Read number - (either one or two) N - Filter flag (either Y or N) 0 Filler? 1 - Sample Number Sequence identifier Sequence Quality Scores

10 FastQC 1:N:0:1 GAATAACTTTATCATTTTTATATACAGGTACAACAATCGCTTCTTTTACGAACTC + CCCCCFGFGGGGGGGGGGGGGGGGGGGGGGG9FGDGGGGGFGGFDFAG Quality scores Phred scores. Q = -10log10(e) where e= estimated probability of the base call being wrong Illumina quality scores typically in format of phred+33 and converted to ASCII character. Example for first base in the sequence above : Base Quality score of 34 becomes = 67 and is denoted by C meaning that we have between 99.9% and 99.99% call accuracy at this base. *NB* Most downstream analysis tools (trimmers, aligners, variant callers) expect quality scores in certain format so it is vital you know how your scores are encoded and to keep them consistent. Sequence identifier Sequence Quality Scores

11 Sample Workflow Trimming, demultiplexing and joining (trim_galore, trimmomatic, flash) Quality Checks (fastqc, Qiime, Mothur, R) Cluster (Usearch, cdhit, swarm, uclust, sortmerna,blast) Chimera checking/denoising (usearch,chimeraslayer, AmpliconNoise, PyroNoise, Denoiser) Align (PyNAST, Infernal,MUSCLE ) Tree-drawing (FastTree) OTU biom table Taxonomic assignment Diversity analysis Commonly used complete workflows Qiime (linux) Mothur (linux) R (numerous packages for each stage) Usearch (linux) Illumina Basespace Greengenes

12 Quality checking Remove reads that: 1) Have mismatched primers/adapter sequences 2) Do not overlap/join ( for paired-end runs) 3) Too short 4) Runs of low quality reads ( quality cut off is platform dependant) 5) Runs of homopolymers ( NB: these can be correct in ITS sequencing so need to modify)

13 Clustering ( OTU assignment) De-novo Clustering reads are clustered against one another without a reference sequence Must be used for marker genes without reference databases. Pros: All reads are clustered Cons: Speed. Closed-reference Clustering Used if amplicons don t overlap ( e.g. V2 and V4 of 16S rrna) Must have a reference database Reads with no hit in the reference database are discarded Pros: Speed; improved trees and alignments Cons: Can lose novel sequences Open-reference Clustering Combination of above; reads with no hit to database are subsequently clustered de-novo Pros: All reads are clustered, speed Cons: Speed!! If you have a lot of reads for de-novo

14 Taxonomic assignment What is it? Comparing OTUs against a reference database to assign a taxa. Methods: BLAST Uclust RDP Classifier ( 16S rrna only) SortmeRNA (16S rrna only) Databases: Silva ( 16S and 18S) Greengenes ( 16S rrna) RDP ( 16S rrna) UNITE/ ITSone ( ITS) NR/NT ( marker genes) Caveats: Use the right database Update databases Updating mid-project can have affect Database quality

15 Taxonomic levels Kingdom Phylum Class Order Family Genus Species Bacteria Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus Lb. casei 100% 95% OTU_1169 Bacteria; Firmicutes; Clostridia; Clostridiales; Ruminococcaceae; Faecalibacterium 0.0 OTU_2996 Bacteria; Firmicutes; Clostridia; Clostridiales; Lachnospiraceae 1e-176 EU % FJ OTU hits two or more genera with same % identity and e-value so can t discriminate so assignment stops at family level. OTU_2483 No blast hit None None

16 Taxonomy: MEGAN

17 Taxonomy: statistics Statistics are study and data dependant Packages : R, SPSS, GraphPad, SAS Simple comparison between groups: Mann-Whitney, Kruskal-Wallis, Wilcoxon (paired), Friedman (paired) Correlations with metadata: Spearman Rank, Pearson, Mixed Models ( paired), Linear regression ( confounders) Identifying discriminating OTUs Random Forests

18 Taxonomy: results Proteobacteria Bacteroidetes Firmicutes Lentisphaerae Tenericutes Actinobacteria Cyanobacteria Verrucomicrobia RF3 Spirochaetes Fusobacteria other Athletes 1% 7% 47% 44% BMI<25 Control 1% 1% BMI>28 Control 1%1% 6% 6% 34% 40% 52% 58%

19 Taxonomy: results 100% 80% 60% 40% 20% 0%

20 Taxonomy results

21 Diversity analysis Alpha diversity- Within Sample diversity How many unique/different OTUs in a sample? Beta diversity- between sample diversity How many OTUs are shared between samples? Of late diversity has been mooted as more important than composition particularly for gut populations. Decreased diversity associated with disease states.

22 Alpha diversity metrics Observed species: number of different sequences in a sample Phylogenetic diversity: incorporates phylogenetic difference between species. It is defined and calculated as "the sum of the lengths of all those branches that are members of the corresponding minimum spanning path". Simpson : measure the degree of concentration when individuals are classified into types. So how much coverage is there Chao1: (richness) Species richness is simply a count of species; it does not take into account the abundances of the species or their relative abundance distributions Shannon: (Evenness) takes into account both abundance and evenness of species present in the community; species evenness quantifies how equal the abundances of the species are.

23 Rarefaction Rarefaction analysis tells you if you have sufficient sequencing depth. Plot should plateau. In-silico sub sampling of data Can repeat for all alpha diversity metrics.

24 Alpha diversity :EXAMPLES *** * *** ** Correlation coefficient: p value: Protein Protein Correlation coefficient: p value: Phylogenetic diversity Shannon index

25 Beta diversity metrics Calculate the distance between a pair of samples Build up a distance matrix Matrix visualised in number of ways e.g.; network, PCoA, UPGMA tree Numerous metrics used to estimate distance e.g. Unifrac ( dissimilarity measure): measure phylogenetic distance between sets of OTUs in a tree weighted Unifrac takes into account relative abundance of OTUs unweighted Unifrac no relative abundance. - bray curtis dissimilarity: compares counts of OTUs between samples taking relative abundances into account

26 Data repositories All data should be submitted to one of several databases: SRA /EBI DDJB NCBI The very least you submit is raw reads and sample information. Can include as much metadata as you wish

27 Questions????