MetAmp: a tool for Meta-Amplicon analysis User Manual Ilya Y
Contents
1. GATGAACGCTGGCGGCGTGCCTAATACATGCAAGT AT AAA HHHIAAAAAAAAAA If you use Illumina sequence data you also have to merge overlapping paired end reads Later I will provide the scripts that can do these things above but for now you can simply use scripts provided at www drive5 com 4 2 Data denoising Data denoising is necessary for downstream analysis because it removes the majority of foreign nucleotides such as barcodes and primers and low quality regions We suggest you to use Seqy Clean tool for this operation since it is the most comprehensive tool for sequence denoising You can download SeqyClean here Using SeqyClean is simple and straightforward Nevertheless we provided a special script that does all the data de noising 4 3 Analysis workflow 1 Building a reference topology of microbial populations where pairwise distances are com puted from applying pairwise alignment of complete reference 16S gene sequences from RDP database see Figure 1 a top plane We use the percent sequence identity in order to compute the distances between sequences 2 Computing a guide amplicon topology for each reference 16S sequence To do this we extract marker regions for example V1 V3 and V6 V9 from reference complete 16S se quences reference points and place them on to the plane with the same methodology that was used for buildin2. Grant DBI0939454 13
3. in both orientations forward and reverse complemented e In addition we supply a dataset that contains 1500 microbial 16S sequences extracted from the Gold Dataset e Evaluation data set contains a smal set of reference sequences 21 sequences and em pirical amplicon sequences data even zip and data staggered zip Amplicon sequences contain sequence data from Human Mock Community More information provided here http www hmpdacc org HMMC 11 Evaluation Name Description even Even community SRRO53818 SRRO72220 SRRO72239 staggered Staggered community SRRO72221 SRRO72223 SRRO72237 gold21 fasta contains 21 reference 16S sequences gold21_V13V31 fasta contains 21 reference marker sequences 1 3 regions gold21_V35V53 fasta contains 21 reference marker sequences 3 5 regions gold21_V69V96 fasta contains 21 reference marker sequences 6 9 regions The even and staggered are the Human mock communuty HMP pyrosequence data SRX021555 http www ncbi nlm nih gov sra term SRX021555 Detailed de scription of these datasets and sequencing protocols is under the following link www hmpdacc org HMMC 5 0 1 Directory map and descriptions of folders MetAmp data gold21 gold1500 gold5000 sre Packages blastparser bin python_scripts data contains reference sequences evaluation data and other data that can potentially be usefu
4. r REF16S ref f E2 r3 r4 r5 r6 r r8 REET refl REF2 ref2 REF3 ref3 REF4 ref4 REF5 ref5 REF6 ref6 REF7 ref7 REF8 ref8 show this help m output OUTPUT Output directory REF16S Output file with REF 1 ssage and exit keywords Reference marker REF2 Reference marker sequences for REF 3 sequences for Reference marker REF 4 Reference marker sequences for REES Reference marker sequences for REF 6 sequences for Reference marker REF 7 Reference marker sequences for REF 8 sequences for marker marker marker marker marker marker marker type type type type type type type 1 2 3 4 5 6 7 Reference marker sequences for marker type 8 r9 REF9 ref9 REFY Reference marker sequences for marker type 9 11 LIB1 lib1 LIBI Amplicon sequences for marker 1 12 LIB2 lib2 LIB2 Amplicon sequences for marker 2 13 LIB3 lib3 LIB3 Amplicon sequences for marker 3 14 LIB4 lib4 LIB4 Amplicon sequences for marker 4 15s bE BS yo SkhibS LIBS Amplicon sequences for marker 5 16 LIB6 lib6 LIBO Amplicon sequences for marker 6 17 LIB7 lib7 LIB Amplicon sequences for marker 7 18 LIB8 lib8 LIB8 Amplicon sequences for mar
5. 20 _V13V31_relabeled fastq amplicon emprirical reads relabeled means that a barcode sequences were attached to each read label e o test output directory with all analysis results Three variable regions python metamp py r data gold21 gold21 fasta r1 data gold21 gold21_V13V31 fasta 11 data even SRRO072220_V13V3l1l_relabeled fastq r2 data gold21 gold21_V35V53 fasta 12 data even SRRO72220_V35V53_relabeled fastq r3 data gold21 gold21_V69V96 fasta 13 data even SRRO72239 V69V96_relabeled fastq o test In this case e r2 data gold gold21_V35V53 fasta a reference file that contains marker re gion V3 5 sequences extracted from gold21 fasta whole 16S in forward and reverse com plement e 12 data even SRRO72220_V35V53_relabeled fastq amplicon empirical reads e r3 data gold gold21_V69V96 fasta a reference file that contains marker re gion V3 5 sequences extracted from gold21 fasta whole 16S in forward and reverse com plement e 13 data even SRR0O72239 V69V96_relabeled fastq amplicon empirical reads To get help from the program you can use python metamp py h Spython metamp py h usage metamp py o output r ref lt reference 16S seqs gt refl lt reference marker seqs gt libl lt your amplicon library gt options optional arguments Shs 0o OUTPUT help
6. Internet connection 1 Download or clone MetAmp from our GitHub repository http github com izhbannikov MetAmp for example git clone http github com izhbannikov MetAmp 2 In Terminal cd to the MetAmp directory and execute Makefile Smake In some cases you have to be a system administrator so you need to compile MetAmp with the following command sudo make First the installation script will check for required tools R Python and gcc Then it builds and installs required packages and data for evaluation You can also run make install to avoid checking stage 3 Installation summary Sgit clone http github com izhbannikov MetAmp Scd MetAmp Smake 3 Quick start You have to provide reference libraries your amplicon data and output directory where all analysis results will be strored One marker region python metamp py r dat ta gol d2 go d2 r1 da ta gol d2 gol d2 sfastar _V13V31 fasta 11 data even SRR072220_V13V3l_relabeled fastq o test Here e metamp py program name e r data gold21 gold21 fasta passing the reference file that contains whole 16S sequences in forward and reverse complement e r1 data gold gold21_V13V31 fasta passing the reference file that contains marker region V1 3 sequences extracted from gold21 fasta whole 16S in forward and reverse complement e 11 data even SRRO722
7. November 12 2014 MetAmp a tool for Meta Amplicon analysis User Manual Ilya Y Zhbannikov Janet E Williamst James A Foster 3Institute for Bioinformatics and Evolutionary Studies University of Idaho Moscow ID 83844 3051 Department of Biological Sciences University of Idaho Moscow ID 83844 3051 Department of Bioinformatics and Computational Biology University of Idaho Moscow ID 83844 3051 Keywords Bioinfomatics 16S genes microbial studies meta analysis data mining Correspondence Ilya Y Zhbannikov Department of Bioinformatics and Computational Biology University of Idaho Moscow ID 83844 30521 Email ilyaz uidaho edu foster uidaho edu 1 Description MetAmp tool was developed to analyze microbial amplicon data by combining several marker regions from 16S rRNA gene Such marker regions serve as unique identifications for species There are nine variable regions in total in bacterial 16S rRNA gene and even more marker regions because marker region can contain itself several variable regions MetAmp was developed by Ilya Y Zhbannikov ilyaz uidaho edu i zhbannikov mail ru James A Foster foster uidaho edu and Janet Williams janetw uidaho edu 2 Installation MetAmp is easy to install However you must have R www r project org Python 2 7 6 at least because older versions may not support some features www python org GCC https gcc gnu org and make sure that you have a stable
8. es for proteins 6 Not used 7 Not used 8 Compressed alignment or the symbol equals sign 9 Label of query sequence always present 10 Label of target sequence H records only Record Description H Hit Represents a query target alignment S Centroid clustering only C Cluster record clustering only N No hit 3 2 2 OTU table An OTU table where rows are OTUs and colums are barcodes by default otu table txt Example OTUId TCAG OTU_8 123 OTU_9 158 OTU_2 341 3 2 3 Coordinates A text file that contain coordinates for each point including reference points by default coordinates crd 3 2 4 Log A text file containing all messages during the analysis process by default Llog t xt 4 MetAmp workflow In the following sections we present key stages of our meta amplicon analysis pipeline 4 1 Input data Input data can be any of the following NGS libraries Roche 454 Ion Torrent Ilumina 7 Roche 454 and Ion Torrent provide single end sequences in special binary Sequence Flowgram Format SFF Before using SFF files you have to convert them into FASTQ format Illumina paired end reads should be merged into longer single end sequences Important Before analysis you may need to perform re labeling of your read headers a read header should contain barcodelabel see more at www driveS com for example readl barcodelabel TCAG
9. ference sequences The same approach is used for guide sequences originated from corresponding whole 16S gene sequences Reference and guide points are then placed on to the planes with multidimensional scaling b The empirical amplicon sequences hollow blue circles are places on to the bottom plane along with the guide circles hollow green circles and with the same metric Such guide sequences are then mapped back to the reference 16S plane carrying empirical sequences solid blue circles c Then this is repeated for each of marker variable region 10 4 4 Output files e Clustering results clstr by default it is clusters clstr but the name can be changed by editing clust_filename parameter in config R file e An OTU table where rows are OTUs and colums are barcodes by default otu_table txt e A text file that contain coordinates for each point including reference points by default coordinates crd e A text file containing all messages during the analysis process by default log t xt 5 Provided data We provided some data that can be used in your projects For evaluation purposes we provided small data set Description of provided data is given below e gold folder contains Gold Dataset gold fa containing the ChimeraSlayer reference database in the Broad Microbiome Utilities http microbiomeutil sourceforge net version microbiomeutil r20110519 This contains more than 5000 sequences
10. g the reference topology of complete 16S see Figure 1 a bottom plane hollow green circles 3 add empirical amplicon sequences in fact amplicon consensus sequences obtained through amplicon sequencing of microbial data Figure 1b bottom filled blue circles This topology is the same empirical topology as if the empirical data would be clustered with existing methods 4 Normalized empirical topologies are formed foreach marker through mapping of each em pirical point back to the reference 16S sequence topology Mapping between the reference topology and guide amplicon topology is achieved with affine transformation Guide se quences are so match with the corresponding reference 16S sequences from where they were obtained from see Figure la top solid green circles and in turn carry the empirical amplicon sequences or consensus amplicon sequences back to the 16S plane 5 Repeating stages 2 4 for each variable region 6 Clustering and building OTUs 7 Final statistics Reference Sequences janaa Sees Seas r Tass 3 Normalized Reads Transformed Reference Empirical Reads a b Marker 1 Marker 2 x TE O 7 o N 6 N Ta O ur N f O Oo Marker 3 Marker 4 c Figure 1 Ilustration of meta amplicon analysis algorithm a Computing reference top plane and guide bottom plane topologies Pairwise sequence dissimilarity is used to compute distances between re
11. ker 8 19 LIB9 lib9 LIB9 Amplicon sequences for marker 9 qual QUAL qual QUAL Quality score threshold default 15 minlen MINLEN minlen MINLEN Minimum read length default 250 3 1 Test data The following test data sets were used e data even e data staggered The even and staggered are the Human mock community pyrosequence data SRX021555 even and staggered community 21 microbial species and one eucariotic species 3 2 Results 3 2 1 Clustering results By default it is clusters clstr but the name can be changed by changing clust_filename parameter in config R file This file has the uc format and de scribed athttp drive5 com usearch manual ucout html Below we provide an example and copy the format description trom www drive5S com H 0 250 99 6 0 0 250M read11 barcodelabel TCAG OTU_1 H a 250 100 0 0 0 250M read6 barcodelabel TCAG OTU_2 H 1 250 100 0 0 0 250M read12 barcodelabel TCAG OTU_2 H 5 250 100 0 0 0 250M read7 barcodelabel TCAG OTU_4 H 22 250 98 8 0 0 250M read22 barcodelabel TCAG OTU_20 Description of uc format is directly taked from http drive5 com usearch manual ucout html Field Description 1 Record type S H C or N see table below 2 Cluster number 0 based 3 Sequence length S N and H or cluster size C 4 For H records percent identity with target 5 For H records the strand or for nucleotid
12. l data gold contains gold reference sequences from Broad Institute data gold1500 contains 1500 gold reference sequences extracted from the previous gold dataset data gold21 contains 21 gold reference sequences extracted from the previous gold dataset This dataset was used to evaluate MetAmp src contains source files Packages contains installation files 12 Packages blastparser a parser for blastn files bin binary files alignment and clustering programs python scripts Python scripts from www drive5 com 6 Parameters You can adjust parameters in file config R It contains parameters such as path to temporary directory names of the output files etc Below we describe those parameters in details 6 1 config R usearch and usearch7 paths to alignment tool we use USEARCH see www drive5 com R_LIBS Path to installed BLASTParser package BLASTParser parses output files from USEARCH and creates distance matrices clust_filename Filename of file with final clusters otu_table filename Filename of final OTU table coord filename Filename of output file with NMDS coordinates of each reference and empirical point see Analysis workflow chime_ref Chimeric reference database tmp_dir A directory that contains temporary files keep_tmp files Keep or not temporary files 7 Acknowledgements This work was made possible by NIH Grants P20GM16448 and P20GM16448 and NSF
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