Introduction to Bioinformatics analysis of Metabarcoding data
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- Amber Bryant
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1 Introduction to Bioinformatics analysis of Metabarcoding data Theoretical part Alvaro Sebastián Yagüe
2 Experimental design Sampling Sample processing Sequencing Sequence processing
3 Experimental design Sampling Sample processing Sequencing Sequence processing
4 What do we want to sequence? ChIP-seq Whole genome sequencing (WGS) Amplicon sequencing (AS) Whole exome sequencing (WES) RNA-seq Mass spectrometry
5 How do we want to sequence? Green,E.D. (2001) Strategies for the systematic sequencing of complex genomes. Nat. Rev. Genet., 2,
6 Metagenomics - Shotgun sequencing
7 Metagenomics - High-throughput sequencing DNA extraction DNA fragmentation and sequencing Read mapping to reference genome De novo assembly Chimeras!
8 Metabarcoding - Amplicon sequencing 1. PCR amplification and sample tagging Barcode 1 Barcode 2 Barcode 3 2. Sequencing of PCR products 3. De-multiplexing of reads Barcode 1 Barcode 2 Barcode 3 Samples
9 Metabarcoding vs Metagenomics Kress, W. J., & Erickson, D. L. (2008). DNA barcodes: genes, genomics, and bioinformatics. Proceedings of the National Academy of Sciences of the United States of America, 105(8),
10 Metabarcoding vs Metagenomics
11 Experimental design Sampling Sample processing Sequencing Sequence processing
12 Where? Variability in abundance within soil and plant. Consider vertical and horizontal distribution of fungi. When? Temporal dynamics over short and long term. For complete community census, sample across multiple seasons. How many? How much? Perform power analysis to determine optimal sample size and quantity. Arbuscular mycorrhizal fungi Read about the sampling methods used in previous literature Hart,M.M. et al. (2015) Navigating the labyrinth: A guide to sequence-based, community ecology of arbuscular mycorrhizal fungi. New Phytol., 207,
13 Experimental design Sampling Sample processing Sequencing Sequence processing
14 Sample preservation Sample preservation methods may result in a significant loss of DNA. Snap-freezing in liquid nitrogen (fast and convenient in the lab but not in field) Other methods: Ethanol storage, silica-gel drying, freeze-drying, oven-drying at low heat, storage in DNA extraction buffer DNA/RNA isolation Traditional phenol/chloroform extraction. Modern extraction kits. Researcher fatigue may result in later samples being handled less efficiently. Samples processed early in the protocol will be exposed to variable conditions longer. Internal controls Internal standards, as a initial known quantity of DNA, will provide a measure of DNA yield. Especially important for samples that originate from different environments. Should be used to quantify DNA/RNA recovery. Will validate the accuracy of results from further analyses. A blank sample (negative control) will help to control contaminations during the process. Sometimes our DNA of interest will be rare compared with other DNAs present in the samples Hart,M.M. et al. (2015) Navigating the labyrinth: A guide to sequence-based, community ecology of arbuscular mycorrhizal fungi. New Phytol., 207,
15 Experimental design Sampling Sample processing Sequencing Sequence processing
16 What do we want to sequence? Metabarcoding or Amplicon sequencing (AS)
17 Metabarcoding - Amplicon sequencing Choose barcodes, design primers and add tags PCR amplification and sequencing Fwd Tag Rev Tag Barcode 1 Barcode 2 Barcode 3
18 Barcodes, markers and tags Fwd Tag DNA barcode / marker PCR PRODUCT Rev Tag A DNA BARCODE is a standardized short sequence of DNA ( bp) that in principle should be easily generated and characterized for all species on the planet. A massive on-line digital library of barcodes will serve as a standard to which any DNA barcode sequence of an unidentified environmental sample from sea, soil, air, etc. can be matched. Savolainen et al A GENETIC MARKER is a specific gene or DNA sequence that produces a detectable trait with a known location on a chromosome and that can be used to study family and population, identification of cells, species or individual. So a DNA barcode is a type of genetic marker. A DNA TAG is A unique short DNA sequence that identifies unambiguously a sample. DNA tags are usually ligated after PCR amplification or directly included in one or both primers.
19 Which barcode to choose? A perfect barcode should be present in all the organisms, in all the cells have variable sequence among different species be conserved among individuals of the same species be easy to amplify with conserved flanking sites be not too long for sequencing
20 Which barcode to choose? Ribosomes contain two major rrnas and 50 or more proteins. The ribosomal RNAs form two subunits, the large subunit (LSU) and small subunit (SSU). rrna is one of only a few gene products present in all organisms and in all cells. For this reason, genes that encode the rrna (rdna) are very good barcodes to identify an organism's taxonomic group, calculate related groups, and estimate rates of species divergence.
21 Which barcode to choose? small subunit large subunit prokaryotic rrna operon 16S (SSU) trna 23S (LSU) 5S eukaryotic rrna operon 18S (SSU) 5.8S ITS1 ITS2 28S (LSU) 5S tandem repeats internally transcribed spacers Type LSU SSU prokaryotic eukaryotic 5S bp 23S bp 5S bp 5.8S bp 28S bp 16S bp 18S bp
22 Which barcode to choose? small subunit large subunit prokaryotic rrna operon 16S (SSU) trna 23S (LSU) 5S eukaryotic rrna operon 18S (SSU) 5.8S ITS1 ITS2 28S (LSU) 5S tandem repeats internally transcribed spacers The ribosomal operon offers the greatest resolution when used as a whole. Unfortunately, the ribosomal operon is in excess of 5500 bp (prokaryotic), which is intractable for Sanger sequencing and for current NGS technologies.
23 Which barcode to choose? Kress,W.J. et al. (2014) DNA barcodes for ecology, evolution, and conservation. Trends Ecol. Evol., 30, Bacteria/ 16S RIF Archae CO1: cytochrome c oxidase subunit 1 ITS: internally transcribed spacer LSU: large subunit rrna D1/D2/D3: divergent domains RIF: DnaA replication initiation factor
24 Which barcode to choose? Ideally, a single DNA barcode (also called marker) would be used to recognize organisms at organizational levels from genotype to kingdom. In reality, there is no de facto best sequence target that would achieve all aims. Discriminating taxa at the species level requires a more variable sequence (barcode) than at the genus or family level. Most studies have focused only on identifying taxa, but protein-encoding genes with known functions may become important functional barcodes for future community surveys. Hart,M.M. et al. (2015) Navigating the labyrinth: A guide to sequence-based, community ecology of arbuscular mycorrhizal fungi. New Phytol., 207,
25 Primer design and barcoding Fwd Tag Rev Tag 16S rrna V3-V5 region Barcode Forward primer Reverse primer Microbial 16S rrna V3-V5 region CCGTCAATTCMTTTRAGT CTGCTGCCTCCCGTAGG Sample Forward tag Reverse tag S001 AACGCG AAGACA S002 TCACTC CGTCAC S003 CTTGGT TTGAGT S004 TGGAAC TAACAT S005 CGAATC GGTCGA
26 Which sequencing technology to choose? Sanger ABI Ion Torrent 454 Illumina -400 Illumina MiSeq 2nd 2 x x 10 7 (paired) 4-55 h 0.15 Rhoads,A. and Au,K.F. (2015) PacBio Sequencing and Its Applications. Genomics, Proteomics Bioinforma., 13,
27 What do we have after sequencing? Each sequencing technology outputs different kind of results. Technology Output format Description Sanger ABI ABI It contains the 'trace data' i.e. the probabilities of the 4 bases along the sequencing run, together with the sequence, as deduced from that data. 454 (Roche) SFF Binary format that provides flowgrams or measurements that estimate the length of the next homopolymer stretch in the sequence (i.e., in "AAATGG", "AAA" is a 3-mer stretch of A's). Ion Torrent Illumina BAM FASTQ Binary form of the SAM format. It contains the information for each sequence about where/how it aligns or not to a reference. Text-based format for storing both sequences and its corresponding quality scores. The most accepted NGS standard format is FASTQ.
28 FASTQ read format There are many tools to convert the different formats to FASTQ, e.g.: A FASTQ file has the following look: 1 st IDENTIFIER 2 nd line: SEQUENCE 3 rd line: + OPTIONAL 4 th line: TCACTCGAGTGTCATTTCTCCAACGGGACGGAGCGGGTGCGGTTCCTGGAGAGAC + CCGGAAGAGTGTCATTTCTCCAACGGGACGGAGCAGATACGGTTCCTGGACAGAT + 1:N:0:1 AACCGAGAGTGTCATTTCTCCAACGGGACGGAGCGGGTGCGGTTCCTGGACAGAT + GGHHGHHADGFGGDCFGDF@CEGCFFBGEFFHGFF<CFHFGDGDGE0/A>/HF@E
29 FASTQ read format There are many tools to convert the different formats to FASTQ, e.g.: A FASTQ file has the following look: 1 st IDENTIFIER 2 nd line: SEQUENCE 3 rd line: + OPTIONAL 4 th line: TCACTCGAGTGTCATTTCTCCAACGGGACGGAGCGGGTGCGGTTCCTGGAGAGAC + A2FHGGGGGGGGG?FE//><CGC>..-@CCD.<0=.<-;DGAACFBFB0?DGGGC 37 Each ASCII char represents 17a Phred quality score: 32 39
30 Sequencing quality score Phred quality score: Phred = 50 Phred = 10 Phred = 0 Phred Probability of incorrect base call 10 1 in 10 90% 20 1 in % Base call accuracy 30 1 in % 40 1 in 10, % 50 1 in 100, % 60 1 in 1,000, % Phred Wikipedia
31 454 and IonTorrent Reads do not have fix lengths: Read type and lengths >G4S72XW01AM8OM rank= x=147.0 y= length=89 TTCTCGACGATTCCTGCAGCAGATGATAGTTACATAGTCCAGGCAAGTTCTGCAAGCAGTTCAAAGCGGAGAGTTAAGCCGAATGAAGT >G4S72XW01ALTYX rank= x=131.0 y= length=86 CCGTCCACGATTCCTGCAGCAGATGATAGTTACATAGTCCAGGCAAGTTCTGCAAGCAGTTCAAGCGGAGAGTTAAGCCGAATCGA >G4S72XW01AVHCV rank= x=241.0 y= length=89 TTCTCGACGATTCCTGCAGCAGATGATAGTTACATAGTCCAGGCAAGTTCTGCAAGCAGTTCAAAGCGGAGAGTTAAGCCGAATACGTG >G4S72XW01AQG7N rank= x=184.5 y= length=65 CCGTCCATCTCCGTGTCCCGGCCCGTATCGCCTCCCTACTGTGCTTGAACACCCTGCGCTACGTG >G4S72XW01ANE2G rank= x=149.5 y= length=227 TTGCAAGCAGGTTGCTCAGGCCCACTTGGTCACTCTGTGCATTGCCCTTGGCAATCCGTGTGTTCCGTTTCCAATACCCCGGCCCCTCCTGCTCTATCCATGGC GCTCGCGGCTCCATCCTCGGCTTCGGGGCGTCGCTGTCAAAGCGCACGAACTGCGTGTCCTCCACATAGCCCACTTCCATATGCCGGGGCTCCCCTCCGGGGCC GGGACACGGAGGTACACTT >G4S72XW01ALTSD rank= x=131.5 y= length=260 TTCTCGGAGTGTCATTTCTCCAACGAGACGGAGCTGGTGCGGTTCCTGGAAAGATACATCTACAACCGGGAGGAGTACGTGCGCTTCGACAGCGACGTGGGGGA GTACCGCGCGGTGAGCGAGCTGGGGCGGCGGTCAGCCGAGTACTGGAACAGACAGAAGGACCTCCTGGAGCAGAAGCGGGGACAGGTGGACAACTACTGCCGAC ACAACTATGGGGTTGGTGAGAGCTTCACTGTGGAGCGGAGAGTTGACTGCTT >G4S72XW01APU23 rank= x=177.5 y= length=54 CCGCTCTCCGTGTCCCGGCCCTGAGCTATGTGCTTGAACACCCTGCGCGCTGGA >G4S72XW01AL62B rank= x=135.5 y= length=221 TTCTCGACCTCCGTGTCCCGGCCCGGAGGGAGCCCGGCATATGGAAGTGGGCTATGTGGAGGACACGCAGTTCGTGCGCTTTGACAGCGACGCCCCGAAGCCGA GGATGGAGCCGCGAGCGCCATAGATAGAGCAGGGGGCCGGGTAGTTGAACGGAACACACGGATTGCCAAGGGCGAATGCACAGAGTGACCAAGTGGGCCTGAGC AACCTGCGTTCCA >G4S72XW01AR49R rank= x=203.0 y= length=87 CCGTCCACGATTCCTGCAGCAGATGATAGTTACATAGTCCAGGCAAGTTCTGCAAGCAGTTCAAAGCGGAGAGTTAAGCCGAATCGA >G4S72XW01AO8U8 rank= x=170.0 y= length=229 TTGCAAGCGCAGGGTGTTCAAGCACATTCGGTGATTCTGTGCAGAGTTCTCGGAAATCCGTGTGTTTTGCTCCCAATACTCCGGACCCTCCAGCCCCATCCACG GCGCCCGCGGCTCCTGTCTGGGATTCTTTGCGTCGCTGTCGAAGCGCATGAACTGGGTGTCGTCCACGTAGCCCACGGAGATGAAGCGGGACTCCCGGAGGCCG GGCCGGGACACGGAGATGTAG
32 Read type and lengths Illumina Reads are fix length but usually are paired (two files): R1 file: >M01530:20: A89BL:1:1101:14684:1732 1:N:0:1 CTTGGTGAGTGTCATTTCTCCAACGGGACGGAGCGGGTGCGGCTCCTACACAGATACATCTACAACCGGGAGGAGGTCTCGC >M01530:20: A89BL:1:1101:18776:1733 1:N:0:1 TCAGAGGAGCGGGGGTCTCCACACCATACAATATGTTACCGGCTGTGACCTCCTGTCCGACGGGAGCGTCCGTGGATCCTT >M01530:20: A89BL:1:1101:15484:1734 1:N:0:1 GGGGACGTCGGCATGTTCTGGTTCCGATAAACAGACACTATTTACCGCCGCTACCGCGGGCAACGGCGACAACTTCACCA >M01530:20: A89BL:1:1101:18291:1819 1:N:0:1 AGTGTTGAGTGTCATTTCTCCAACGGGACGGAGCGGATACGGTTCCTGGACAGATACTTCTACAACCGGGAGGAGTACGTGCG R2 file: >M01530:20: A89BL:1:1101:14684:1732 2:N:0:1 TTGAGTTCACCTCTCCGCTCCACAGTGAAGCTCTCGACAACCCCATAGTTGTGTCTGCACACAGTGTCCACCTCGGCCCGC >M01530:20: A89BL:1:1101:18776:1733 2:N:0:1 AACCGATGCGCTCCAGCTCCTTCTGCCCGTATCCGACGTATTTCTGGAGCTCTTCCGGGCACTCGTGCTTCAGGTAATTCG >M01530:20: A89BL:1:1101:15484:1734 2:N:0:1 TCAGCAGTTTAATCACTGTTGCACTGGTCAACACTGGAATGGCGAGGCGCTGTACTTCTTCCAACAGCACTTTCACCATTAA >M01530:20: A89BL:1:1101:18291:1819 2:N:0:1 GAACTATCACCTCTCCGCTCCACAGTGAAGCTCTCAACAACCCCGTAGTTGTGTCGGCAGTAGTTGTCCACCGTGGCCCGC
33 Read type and lengths Illumina
34 Read type and lengths Illumina And read ends may overlap: R1 file / R2 file Reverse complementary >M01530:20: A89BL:1:1101:14684:1732 1:N:0:1 CTTGGTGAGTGTCATTTCTCCAACGGGACGGAGCGGGTGCGGCTCCTACACAGATACATCTACAACCGGGAGGAGGTCTCGC >M01530:20: A89BL:1:1101:14684:1732 2:N:0:1 GTCATTTCTCCAACGGGACGGAGCGGGTGCGGCTCCTACACAGATACATCTACAACCGGGAGGAGGTCTCGCCGTACGGCTA >M01530:20: A89BL:1:1101:18776:1733 1:N:0:1 TCAGAGGAGCGGGGGTCTCCACACCATACAATATGTTACCGGCTGTGACCTCCTGTCCGACGGGAGCGTCCGTGGATCCTT >M01530:20: A89BL:1:1101:18776:1733 2:N:0:1 GGTCTCCACACCATACAATATGTTACCGGCTGTGACCTCCTGTCCGACGGGAGCGTCCGTGGATCCTTCTAAGCCTCAGGCC >M01530:20: A89BL:1:1101:15484:1734 1:N:0:1 GGGGACGTCGGCATGTTCTGGTTCCGATAAACAGACACTATTTACCGCCGCTACCGCGGGCAACGGCGACAACTTCACCA >M01530:20: A89BL:1:1101:15484:1734 2:N:0:1 GTCGGCATGTTCTGGTTCCGATAAACAGACACTATTTACCGCCGCTACCGCGGGCAACGGCGACAACTTCACCACAGCGG >M01530:20: A89BL:1:1101:18291:1819 1:N:0:1 AGTGTTGAGTGTCATTTCTCCAACGGGACGGAGCGGATACGGTTCCTGGACAGATACTTCTACAACCGGGAGGAGTACGTGCG >M01530:20: A89BL:1:1101:18291:1819 2:N:0:1 TGTCATTTCTCCAACGGGACGGAGCGGATACGGTTCCTGGACAGATACTTCTACAACCGGGAGGAGTACGTGCGATTACCGCT
35 454 and IonTorrent Sequencing errors 1% of sequencing errors, mostly indels in homopolymer regions. Deletion Substitution Insertion Sometimes there are more reads with errors than without!!!!!
36 Sequencing errors Illumina <1% of sequencing errors, mostly random substitutions. Substitution As errors are random, the consensus sequence will be correct.
37 PCR errors Other errors Most commercially available Taq polymerases introduce errors at the rate of 1 point mutation every 1000 nts. Solution: higher fidelity polymerases such as Pfu or Phusion High-Fidelity generating times fewer errors respectively. Chimeras Chimeras are sequences formed from two or more biological sequences joined together. Solutions: - Reduce the number of PCR cycles. - Increase the annealing temperature.
38 Error correction strategies Filtering: removes suspicious reads Problem: we can lost most of the reads, and with them most of the information. Also we can discard correct reads by error. Clustering: corrects erroneous reads Problem: it can be hard to discriminate among erroneous reads and correct ones.
39 Experimental design Sampling Sample processing Sequencing Sequence processing
40 Operational taxonomic unit (OTU) Theoretically, an OTU is a taxonomic level of sampling selected by the user to be used in a study, such as individuals, populations, species, genera, or bacterial strains (Sokal and Sneath, 1963). Practically, an OTU is a cluster of similar sequence variants of the barcode (16S, ITS, etc.). Each of these cluster is intended to represent a taxonomic unit of a bacteria species or genus depending on the sequence similarity threshold. An OTU cluster is usually defined by variants with a 97% of sequence identity. Stackebrandt and Goebel (1994) BUT Some species have genes that are >97% similar, giving merged OTUs containing multiple species. A single species may have paralogs that are <97% similar, causing the species to be split across two or more OTUs. Some clusters, even a majority, may be spurious due to artifacts including read errors and chimeras.
41 Susan Huse. STAMPS 2016 Course. Introduction to OTU Clustering.
42 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR, MICCA and AmpliTAXO Read pre-processing Filtering of artefacts De-multiplexing Alignment to references De-replication Taxonomy annotation Clustering of variants Downstream analysis Oulas,A. et al. (2015) Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinform. Biol. Insights, 9,
43 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR, MICCA and AmpliTAXO Read pre-processing De-multiplexing De-replication Clustering of variants 1. Read pre-processing If reads are paired-end type (e.g. Illumina), an initial step consists of merging overlapping paired reads into single reads is required. Anomalous reads are removed and when reads have different lengths (e.g. 454) they are also trimmed to a fix length to make easier further processing (alignment and clustering). 2. De-multiplexing Organizes the multiplexed reads into amplicons (single PCR products) based on the different barcodes (primers) and tags (samples) used. 3. De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). 4. Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs.
44 If reads are paired-end type (e.g. Illumina), an initial step consists of merging overlapping paired reads into single reads is required. Illumina paired read with overlap: Read pre-processing Illumina paired reads with staggered overlap:
45 Read pre-processing Detection and removal of suspicious reads. Primer: CCGTCAATTCMTTTRA Barcode: AATGGTAC >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATACAGTTTCCAATG >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCACAGTTTCCAAAGCAGTTCCGGGGTTGGG >GQY1XT001AK4J0 TCTAGCCGCACAGTTTCAAAAGCACTCCCAGGGTT >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGACGTTGCCCCCCGTTTACTGTGCGGACTACCAGTCGCACTCAAGGCCCCCAGTTTCAACGG >GQY1XT001BDDE9 AATGGTACCCGTCAATTCCTTTAATCTTGCGGGTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTTACACAGTTTCCAGAG >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCTTTACGGCGTGGACTACCAGGCGCCCTCCAGCCCGGCAGTTTCCAGTGCAGTCCCGGGGTT >GQY1XT001BKRP5 AATGGTACCCGTCAATTCATTTAATCTCTCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCC >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTAACCTTGCGGGGTTTTACCGCGTGGACTACCAGGCGCCCTCAAGAAGAACAGTTTTGAACGCAGCTATGGGTT >GQY1XT001CIW3P AATGGTACCCGTCAATTCATTTGACGTTGCCTCTCGTTTACTGCGTGGACTACCAGTCGCACTCAAGGCCCCCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTAACGTTGCCCCCGTTACTGCGTGGACTACCAGGGGCAATCAAGACTGCCA
46 Read pre-processing Detection and removal of suspicious reads. Primer: CCGTCAATTCMTTTRA Barcode: AATGGTAC >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATACAGTTTCCAATG >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCACAGTTTCCAAAGCAGTTCCGGGGTTGGG >GQY1XT001AK4J0 TCTAGCCGCACAGTTTCAAAAGCACTCCCAGGGTT >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGACGTTGCCCCCCGTTTACTGTGCGGACTACCAGTCGCACTCAAGGCCCCCAGTTTCAACGG >GQY1XT001BDDE9 AATGGTACCCGTCAATTCCTTTAATCTTGCGGGTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTTACACAGTTTCCAGAG >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCTTTACGGCGTGGACTACCAGGCGCCCTCCAGCCCGGCAGTTTCCAGTGCAGTCCCGGGGTT >GQY1XT001BKRP5 AATGGTACCCGTCAATTCATTTAATCTCTCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCC >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTAACCTTGCGGGGTTTTACCGCGTGGACTACCAGGCGCCCTCAAGAAGAACAGTTTTGAACGCAGCTATGGGTT >GQY1XT001CIW3P AATGGTACCCGTCAATTCATTTGACGTTGCCTCTCGTTTACTGCGTGGACTACCAGTCGCACTCAAGGCCCCCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTAACGTTGCCCCCGTTACTGCGTGGACTACCAGGGGCAATCAAGACTGCCA
47 When reads have different lengths they are trimmed to a fix length to make easier further processing (alignment and clustering). Primer: CCGTCAATTCMTTTRA Barcode: AATGGTAC Read pre-processing >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATACAGTTTCCAATG >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCACAGTTTCCAAAGCAGTTCCGGGGTTGGG >GQY1XT001AK4J0 TCTAGCCGCACAGTTTCAAAAGCACTCCCAGGGTT >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGACGTTGCCCCCCGTTTACTGTGCGGACTACCAGTCGCACTCAAGGCCCCCAGTTTCAACGG >GQY1XT001BDDE9 AATGGTACCCGTCAATTCCTTTAATCTTGCGGGTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTTACACAGTTTCCAGAG >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCTTTACGGCGTGGACTACCAGGCGCCCTCCAGCCCGGCAGTTTCCAGTGCAGTCCCGGGGTT >GQY1XT001BKRP5 AATGGTACCCGTCAATTCATTTAATCTCTCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCC >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTAACCTTGCGGGGTTTTACCGCGTGGACTACCAGGCGCCCTCAAGAAGAACAGTTTTGAACGCAGCTATGGGTT >GQY1XT001CIW3P AATGGTACCCGTCAATTCATTTGACGTTGCCTCTCGTTTACTGCGTGGACTACCAGTCGCACTCAAGGCCCCCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTAACGTTGCCCCCGTTACTGCGTGGACTACCAGGGGCAATCAAGACTGCCA
48 When reads have different lengths they are trimmed to a fix length to make easier further processing (alignment and clustering). Primer: CCGTCAATTCMTTTRA Barcode: AATGGTAC Read pre-processing >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATACAGTTTCCAATG >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCACAGTTTCCAAAGCAGTTCCGGGGTTGGG >GQY1XT001AK4J0 TCTAGCCGCACAGTTTCAAAAGCACTCCCAGGGTT >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGACGTTGCCCCCCGTTTACTGTGCGGACTACCAGTCGCACTCAAGGCCCCCAGTTTCAACGG >GQY1XT001BDDE9 AATGGTACCCGTCAATTCCTTTAATCTTGCGGGTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTTACACAGTTTCCAGAG >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCTTTACGGCGTGGACTACCAGGCGCCCTCCAGCCCGGCAGTTTCCAGTGCAGTCCCGGGGTT >GQY1XT001BKRP5 AATGGTACCCGTCAATTCATTTAATCTCTCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCCCCCTTTCCCCCCCCCC >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTAACCTTGCGGGGTTTTACCGCGTGGACTACCAGGCGCCCTCAAGAAGAACAGTTTTGAACGCAGCTATGGGTT >GQY1XT001CIW3P AATGGTACCCGTCAATTCATTTGACGTTGCCTCTCGTTTACTGCGTGGACTACCAGTCGCACTCAAGGCCCCCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTAACGTTGCCCCCGTTACTGCGTGGACTACCAGGGGCAATCAAGACTGCCA
49 When reads have different lengths they are trimmed to a fix length to make easier further processing (alignment and clustering). Primer: CCGTCAATTCMTTTRA Barcode: AATGGTAC Read pre-processing >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGACGTTGCCCCCCGTTTACTGTGCGGACTACCAGTCGCACTCAAGGCCCC >GQY1XT001BDDE9 AATGGTACCCGTCAATTCCTTTAATCTTGCGGGTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTTACA >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCTTTACGGCGTGGACTACCAGGCGCCCTCCAGCCCGGC >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTAACCTTGCGGGGTTTTACCGCGTGGACTACCAGGCGCCCTCAAGAAGAAC >GQY1XT001CIW3P AATGGTACCCGTCAATTCATTTGACGTTGCCTCTCGTTTACTGCGTGGACTACCAGTCGCACTCAAGGCCCC >GQY1XT001A731D AATGGTACCCGTCAATTCATTTAACGTTGCCCCCGTTACTGCGTGGACTACCAGGGGCAATCAAGACTGCCA
50 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR, MICCA and AmpliTAXO Read pre-processing De-multiplexing De-replication Clustering of variants 1. Read pre-processing If reads are paired-end type (e.g. Illumina), an initial step consists of merging overlapping paired reads into single reads is required. Anomalous reads are removed and when reads have different lengths (e.g. 454) they are also trimmed to a fix length to make easier further processing (alignment and clustering). 2. De-multiplexing Organizes the multiplexed reads into amplicons (single PCR products) based on the different barcodes (primers) and tags (samples) used. 3. De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). 4. Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs.
51 De-multiplexing Organizes the multiplexed reads into amplicons (single PCR products) based on the different barcodes (primers) and tags (samples) used. 1 Amplicon = 1 Sample
52 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR, MICCA and AmpliTAXO Read pre-processing De-multiplexing De-replication Clustering of variants 1. Read pre-processing If reads are paired-end type (e.g. Illumina), an initial step consists of merging overlapping paired reads into single reads is required. Anomalous reads are removed and when reads have different lengths (e.g. 454) they are also trimmed to a fix length to make easier further processing (alignment and clustering). 2. De-multiplexing Organizes the multiplexed reads into amplicons (single PCR products) based on the different barcodes (primers) and tags (samples) used. 3. De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). 4. Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs.
53 De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BDDE9 AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001CIW3P AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA
54 De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). >GQY1XT001A6MUA AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BTRWS AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BDDE9 AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001CIW3P AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA
55 De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). >GQY1XT001A6MUA DEPTH = 5 AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BTRWS DEPTH = 3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001BBPBR AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001BDDE9 AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001CIUF3 AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001B44ZE AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA >GQY1XT001CIW3P AATGGTACCCGTCAATTCCTTTGATCTTGCGGGCCGTTTACGGCGTGGACTACCAGTCGCACTCGAGCTGCA >GQY1XT001A731D AATGGTACCCGTCAATTCATTTGATCTTGCGGTTCGTTTACGGCGTGGACTACCAGTCGCACTCCAGTCATA
56 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR, MICCA and AmpliTAXO Read pre-processing De-multiplexing De-replication Clustering of variants 1. Read pre-processing If reads are paired-end type (e.g. Illumina), an initial step consists of merging overlapping paired reads into single reads is required. Anomalous reads are removed and when reads have different lengths (e.g. 454) they are also trimmed to a fix length to make easier further processing (alignment and clustering). 2. De-multiplexing Organizes the multiplexed reads into amplicons (single PCR products) based on the different barcodes (primers) and tags (samples) used. 3. De-replication Redundant reads are annotated as unique sequences (variants) and their abundances (depths). 4. Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs.
57 Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs. >*S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTTTAGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGT-TAGGGTGTGGACTAA >#S TACGTTTATCGCATT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGG-TGTGGACTAA >*S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGCCCAACGTTTACAGCGTGGT >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGGTCCCCCACACCTAGTGCCCAACGTTTACAGCGTGGG >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGC-CAACGTTTACAGCGTGGT >*S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGT-TACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGGGGCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT
58 Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs. >*S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTTTAGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGT-TAGGGTGTGGACTAA >#S TACGTTTATCGCATT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGG-TGTGGACTAA >*S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGCCCAACGTTTACAGCGTGGT >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGGTCCCCCACACCTAGTGCCCAACGTTTACAGCGTGGG >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGC-CAACGTTTACAGCGTGGT >*S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGT-TACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGGGGCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT
59 Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs. >*S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTTTAGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGT-TAGGGTGTGGACTAA >#S TACGTTTATCGCATT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGG-TGTGGACTAA >*S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGCCCAACGTTTACAGCGTGGT >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGGTCCCCCACACCTAGTGCCCAACGTTTACAGCGTGGG >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGC-CAACGTTTACAGCGTGGT >*S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGT-TACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGGGGCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT
60 Clustering of variants Variants are clustered based on a user-defined similarity threshold. This step is crucial to group redundant sequences due to sequencing and PCR errors into unique variants that will be representative of single OTUs. >*S DEPTH + 3 TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >*S DEPTH + 2 GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGCCCAACGTTTACAGCGTGGT >*S DEPTH + 2 TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT >#S TACGTTTATCGCGTTTAGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGGGTGTGGACTAA >#S TACGTTTATCGCGTT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGT-TAGGGTGTGGACTAA >#S TACGTTTATCGCATT-AGCTTCGCCAAGCACAGCATCCTGCGCTTAGCCAACGTACATCGTTTAGG-TGTGGACTAA >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGGTCCCCCACACCTAGTGCCCAACGTTTACAGCGTGGG >#S GGCACTTAAAGCGTTAGCTACGGCGCAGAAACCACGGGTGG-CCCCCACACCTAGTGC-CAACGTTTACAGCGTGGT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGG-GCACAACCTCCAAGTCGACATCGT-TACGGCGTGGAT >#S TCGACTTAACGCGTTAGCTCCGGAAGCCACGCCTCAAGGGGCACAACCTCCAAGTCGACATCGTTTACGGCGTGGAT
61 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR and AmpliTAXO Filtering of artefacts Alignment to references Taxonomy annotation Downstream analysis 5. Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants, PCR errors 6. Alignment to references Clustered variants (OTUs) are aligned against a database of reference sequences, e.g. Greengenes, SILVA 7. Taxonomy annotation Taxonomy annotations from databases will be assigned to OTUs. In an ideal scenario, each OTU will correspond to a unique species taxonomy assignment. 8. Downstream analysis OTU table and taxonomy results can be subject of further analyses: alpha diversity measurements and rarefaction plots, beta diversity and ordination plots, taxonomy heatmaps
62 Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants >*16S depth=44 freq=2.42 TTCAGTCGCTCCCCTAGCTTTCGCACTTCAGCGTCAGTTGCCGTCCAGTGAACTATCTTCATCATCGGCATT CCTGCACATATCTACGAATTTCACCTCTACTCGTGCAGTTCCGTCCACCTCTCCAGCACTCTAGCCAAACAG >*16S depth=33 freq=1.82 TTCAATGTTTGCTCCCCACGCTTTCGAGCCTCAGCGTCAGTTACAAGCCAGAGAGCCGCTTTCGCCACCGGT GTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAA >*16S depth=32 freq=1.76 TTCACGATACCCGCACCTTCGAGCTTAAGCGTCAGTGGCGCTCCCGTCAGCTGCCTTCGCAATCGGAGTTCT TCGTCATATCTAAGCATTTCACCGCTACACGACGAATTCCGCCAACGTTGTGCGTACTCAAGGAAACCAGTA >*16S depth=15 freq=0.83 TTCAACGTTCGCTCCCCTGGCTTTCGCGCCTCAGCGTCAGTTTTCGTCCAGAAAGTCGCCTTCGCCACTGGT GTTCTTCCTAATATCTACGCATTTCACCGCTACACTAGGAATTCCACTTTCCTCTCCGATACTCTAGATTGG >#16S depth=12 freq=0.66 TTCAGTCGCTCCCCTAGCTTTCGCACTTCAGCGTCAGTTGCCGTAAGCCAGAGAGCCGCTTTCGCCACCGGT GTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAA >*16S depth=10 freq=0.55 TTTAGTCCTGTTCGCTCCCCACGCTTTCGCTCCTCAGCGTCAGTAACGGCCCAGAGACCCGCCTTCGCCACC GGTGTTCTTCCTGATATCTGCGCATTCCACCGCTACACCAGGAGTTCCAGCCTCCCCTACCGCACTCAAGCC >#16S depth=2 freq=0.11 TTCACGATACCCACGCTTTCGAGCATCAGCGTCAGTTGCGCTACAGTAAGCTGCCTTCGCAATCGGAGTTCT TCGTGATATCTAAGCATTTCACCGCTACACCACGAATTCCGCCTACTTTCGGCGCACTCAAGCCCCCCAGTT >#16S depth=1 freq=0.06 TTCAACGTTCGCTCCCCTGGCTTTCGCGCCTCAGCGTCAGTTTTCGTCCAGAAAGTCGCCTTCGCCACTGGT GTTCTTCCTAATATCTACGCATTTCACCGCTACACTAGGAATTCCACTTTCCTCTCCGATACTCTAGATCAG
63 Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants >*16S depth=44 freq=2.42 TTCAGTCGCTCCCCTAGCTTTCGCACTTCAGCGTCAGTTGCCGTCCAGTGAACTATCTTCATCATCGGCATT CCTGCACATATCTACGAATTTCACCTCTACTCGTGCAGTTCCGTCCACCTCTCCAGCACTCTAGCCAAACAG >*16S depth=33 freq=1.82 TTCAATGTTTGCTCCCCACGCTTTCGAGCCTCAGCGTCAGTTACAAGCCAGAGAGCCGCTTTCGCCACCGGT GTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAA >*16S depth=32 freq=1.76 TTCACGATACCCGCACCTTCGAGCTTAAGCGTCAGTGGCGCTCCCGTCAGCTGCCTTCGCAATCGGAGTTCT TCGTCATATCTAAGCATTTCACCGCTACACGACGAATTCCGCCAACGTTGTGCGTACTCAAGGAAACCAGTA >*16S depth=15 freq=0.83 TTCAACGTTCGCTCCCCTGGCTTTCGCGCCTCAGCGTCAGTTTTCGTCCAGAAAGTCGCCTTCGCCACTGGT GTTCTTCCTAATATCTACGCATTTCACCGCTACACTAGGAATTCCACTTTCCTCTCCGATACTCTAGATTGG >#16S depth=12 freq=0.66 TTCAGTCGCTCCCCTAGCTTTCGCACTTCAGCGTCAGTTGCCGTAAGCCAGAGAGCCGCTTTCGCCACCGGT GTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAA >*16S depth=10 freq=0.55 TTTAGTCCTGTTCGCTCCCCACGCTTTCGCTCCTCAGCGTCAGTAACGGCCCAGAGACCCGCCTTCGCCACC GGTGTTCTTCCTGATATCTGCGCATTCCACCGCTACACCAGGAGTTCCAGCCTCCCCTACCGCACTCAAGCC >#16S depth=2 freq=0.11 TTCACGATACCCACGCTTTCGAGCATCAGCGTCAGTTGCGCTACAGTAAGCTGCCTTCGCAATCGGAGTTCT TCGTGATATCTAAGCATTTCACCGCTACACCACGAATTCCGCCTACTTTCGGCGCACTCAAGCCCCCCAGTT >#16S depth=1 freq=0.06 TTCAACGTTCGCTCCCCTGGCTTTCGCGCCTCAGCGTCAGTTTTCGTCCAGAAAGTCGCCTTCGCCACTGGT GTTCTTCCTAATATCTACGCATTTCACCGCTACACTAGGAATTCCACTTTCCTCTCCGATACTCTAGATCAG
64 Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants >*16S depth=44 freq=2.42 TTCAGTCGCTCCCCTAGCTTTCGCACTTCAGCGTCAGTTGCCGTCCAGTGAACTATCTTCATCATCGGCATT CCTGCACATATCTACGAATTTCACCTCTACTCGTGCAGTTCCGTCCACCTCTCCAGCACTCTAGCCAAACAG >*16S depth=33 freq=1.82 TTCAATGTTTGCTCCCCACGCTTTCGAGCCTCAGCGTCAGTTACAAGCCAGAGAGCCGCTTTCGCCACCGGT GTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAA >*16S depth=32 freq=1.76 TTCACGATACCCGCACCTTCGAGCTTAAGCGTCAGTGGCGCTCCCGTCAGCTGCCTTCGCAATCGGAGTTCT TCGTCATATCTAAGCATTTCACCGCTACACGACGAATTCCGCCAACGTTGTGCGTACTCAAGGAAACCAGTA >*16S depth=15 freq=0.83 TTCAACGTTCGCTCCCCTGGCTTTCGCGCCTCAGCGTCAGTTTTCGTCCAGAAAGTCGCCTTCGCCACTGGT GTTCTTCCTAATATCTACGCATTTCACCGCTACACTAGGAATTCCACTTTCCTCTCCGATACTCTAGATTGG >#16S depth=12 freq=0.66 Chimera TTCAGTCGCTCCCCTAGCTTTCGCACTTCAGCGTCAGTTGCCGTAAGCCAGAGAGCCGCTTTCGCCACCGGT GTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAA >*16S depth=10 freq=0.55 TTTAGTCCTGTTCGCTCCCCACGCTTTCGCTCCTCAGCGTCAGTAACGGCCCAGAGACCCGCCTTCGCCACC GGTGTTCTTCCTGATATCTGCGCATTCCACCGCTACACCAGGAGTTCCAGCCTCCCCTACCGCACTCAAGCC >#16S depth=2 freq=0.11 Contaminations TTCACGATACCCACGCTTTCGAGCATCAGCGTCAGTTGCGCTACAGTAAGCTGCCTTCGCAATCGGAGTTCT TCGTGATATCTAAGCATTTCACCGCTACACCACGAATTCCGCCTACTTTCGGCGCACTCAAGCCCCCCAGTT >#16S depth=1 freq=0.06 TTCAACGTTCGCTCCCCTGGCTTTCGCGCCTCAGCGTCAGTTTTCGTCCAGAAAGTCGCCTTCGCCACTGGT GTTCTTCCTAATATCTACGCATTTCACCGCTACACTAGGAATTCCACTTTCCTCTCCGATACTCTAGATCAG
65 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR and AmpliTAXO Filtering of artefacts Alignment to references Taxonomy annotation Downstream analysis 5. Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants, PCR errors 6. Alignment to references Clustered variants (OTUs) are aligned against a database of reference sequences, e.g. Greengenes, SILVA 7. Taxonomy annotation Taxonomy annotations from databases will be assigned to OTUs. In an ideal scenario, each OTU will correspond to a unique species taxonomy assignment. 8. Downstream analysis OTU table and taxonomy results can be subject of further analyses: alpha diversity measurements and rarefaction plots, beta diversity and ordination plots, taxonomy heatmaps
66 Alignment to references After clustering and filtering variants, the retrieved OTUs are aligned against a database of reference sequences, e.g. Greengenes, SILVA >*16S depth=42 freq=2.31 TTCAACCTTGCGGTCGTACTCCCCAGGCGGAGTGCTTAATGCGTTAGCTGCGGCACTAAACCCCGGAAAGGGTCTAACACCTAGCACTCATCGTT TACGGCGTGGACTACCAGGGTATCTAATCCTGTTTGCTCCCCACGCTTTCGAGCCTCAGCGTCAGTTACAAGCCAGAGAGCCGCTTTCGCCACCG GTGTTCCTCCATATATCTACGCATTTCACCGCTACACATGGAATTCCACTCTCCCCTCTTGCACTCAAGTTAAACAGTTTCCAAAGCGTACTATG GTTAAGCCACAGCCTTTAACTTCAGACTTATCT >*16S depth=12 freq=0.66 TTCAGCCTTGCGGCCGTACTCCCCAGGCGGATTACTTATCGCATTCGCTTCGGCACAGACAGTCTTCCTGCCCACACCCAGTAATCATCGTTTAC GGCCGGGACTACCAGGGTATCTAATCCTGTTCGCTCCCCCGGCTTTCGCACTTCAGCGTCAGTTACCGTCCAGTGAACTATCTTCATCATCGGCA TTCCTGCACATATCTACGAATTTCACCTCTACTCGTGCAGTTCCGTCCACCTCTCCGGTACTCCAGCCTATCAGTTTCAAAGGCAGGCCTGCGGT TGAGCCGCAGGTTTTCACCCCTGACTTGAAAGG vs.
67 Alignment to references
68 Alignment to references Around 95-97% of identity is required in the alignment of an OTU sequence to a database reference
69 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR and AmpliTAXO Filtering of artefacts Alignment to references Taxonomy annotation Downstream analysis 5. Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants, PCR errors 6. Alignment to references Clustered variants (OTUs) are aligned against a database of reference sequences, e.g. Greengenes, SILVA 7. Taxonomy annotation Taxonomy annotations from databases will be assigned to OTUs. In an ideal scenario, each OTU will correspond to a unique species taxonomy assignment. 8. Downstream analysis OTU table and taxonomy results can be subject of further analyses: alpha diversity measurements and rarefaction plots, beta diversity and ordination plots, taxonomy heatmaps
70 Taxonomy annotation Taxonomy annotations from databases will be assigned to OTU sequences.
71 Taxonomy annotation In an ideal scenario, each OTU sequence will have a taxonomy assignment. OTU representative sequences OTU taxonomy assignments Samples and OTU frequencies
72 Analysis pipeline These are the general steps shared by the most used metagenomics analysis tools: UPARSE, QIIME, MOTHUR and AmpliTAXO Filtering of artefacts Alignment to references Taxonomy annotation Downstream analysis 5. Filtering of artefacts Detection and removal of artefactual variants left after clustering: chimeras, contaminants, PCR errors 6. Alignment to references Clustered variants (OTUs) are aligned against a database of reference sequences, e.g. Greengenes, SILVA 7. Taxonomy annotation Taxonomy annotations from databases will be assigned to OTUs. In an ideal scenario, each OTU will correspond to a unique species taxonomy assignment. 8. Downstream analysis OTU table and taxonomy results can be subject of further analyses: alpha diversity measurements and rarefaction plots, beta diversity and ordination plots, taxonomy heatmaps
73 Taxonomy summaries: Downstream analysis
74 Principal Coordinate Analysis (PCoA): Downstream analysis
75 Downstream analysis Alpha diversity measurements and rarefaction plots:
76 Interesting materials Materials from Strategies and Techniques for Analyzing Microbial Population Structure Course SSU Metagenomics (UPARSE) MOTHUR manual QIIME overview tutorial
77
CBC Data Therapy. Metagenomics Discussion
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