MAPseq v1.0 (14 October 2016) by Joao F. Matias Rodrigues, Thomas S.B. Schmidt, Janko Tackmann, and Christian von Mering Institute of Molecular Life Sciences, University of Zurich, Switzerland Matias Rodrigues JF, Schmidt TSB, Tackmann J, Mering C von. MAPseq: bringing speed, accuracy and consistency to metagenomic ribosomal RNA analysis. submitted. ================================================= Table of contents 1. Installation 2. MAPseq usage instructions 3. File output 4. History - MAPseq is a set of fast and accurate sequence read classification tools designed to assign taxonomy and OTU classifications to ribosomal RNA sequences. This is done by using a reference set of full-length ribosomal RNA sequences for which known taxonomies are known, and for which a set of high quality OTU clusters has been previously generated. For each read, the best guess and correspoding confidence in the assignment is shown at each taxonomic and OTU level. - For bugs and more information contact: Joao F. Matias Rodrigues 1. INSTALLATION ================================================= You can get the source code or binary packages at: http://meringlab.org/software/mapseq/ i) Installing the binary package To install the binary package simply unpack the contents of the mapseq tar.gz file, i.e.: tar -xvzf mapseq-1.0-bin.tar.gz or tar -xvzf mapseq-1.0-bin-macosx.tar.gz # for the MacOSX version The mapseq binary will be located in the mapseq-1.0-bin directory. You may move the whole directory to another location. Moving the binary elsewhere will break the installation though, as the data files and libraries are searched for in relation to the binary's path. ii) Installing the ubuntu/debian package To install the ubuntu/debian package you need admin priviledges. Simply run the following command: sudo dpkg -i mapseq_1.0-1_amd64.deb After installation the mapseq executable can be called by simply typing "mapseq" on the shell. iii) Installing from source First make sure you have the gcc compiler / build tools installed. On Ubuntu you would need to run "apt-get install build-essential" with admin priviledges. ./bootstrap # this step is only needed if you cloned the repository, you will also need to install autotools/autoconf packages ./configure make make install In the directory where you unpacked the package contents. Alternatively, if you want the program to be installed to another location instead of the default system wide /usr/local/ directory, you can change the ./configure command to: ./configure --prefix=$HOME/usr This would install the program binaries to a directory usr/bin inside your home directory (i.e.: $HOME/usr/bin/mapseq), after you type the command "make install". 2. MAPseq usage instructions ================================================= a) USING MAPseq ================================================= MAPseq takes as input a fasta file with raw sequence data which should have been previously demultiplexed and quality filtered usually from a fastq file produced by the sequencing machine. If the input sequences can be found in the file "rawseqs.fa". Then to classify the reads one simply has to run the following command: mapseq rawseqs.fa > rawseqs.fa.mseq This will classify all the sequences found in rawseqs against the standard reference dataset provided with MAPseq. You can change the number of threads that MAPseq uses with the -nthreads argument. 3. FILE OUTPUT ================================================= In the results output, each line indicates a classification of the read. Two output formats can be chosen ("simple" or "confidences") using the --outfmt option. The default "simple" format gives the alignment information plus the taxonomy assignment for which the combined confidence at least 0.5. For example: query1 FJ560320:1..876 301 0.7369985 301 0 0 0 301 305 606 Archaea Archaea;F94;G275 The "confidences" format outputs the confidence values for each of the taxonomic levels. For example: SRR044946.347 GQ156763:1..1446 548 0.91985428 505 22 22 1 540 263 800 0.99072355 20 Bacteria 1 1 Firmicutes 0.55452305 1 Clostridia 0.55452305 1 Clostridiales 0.55452305 1 Ruminococcaceae 0.31190208 0.3119020760059357 Ruminococcus 0 0.2104288786649704 Ruminococcus gnavus 0 0.0604640431702137 Bacteria 0.58272612 1 F6159 0.22964814 1 G35588 0 1 S61033 0 0.7381679934055649 SS52094 0 0.2980887881680916 Each field is tab separated and indicates the following: Field 1 Query sequence id 2 Reference sequence id (highest alignment score) 3 Alignment bitscore 4 Pairwise identity 5 Matches 6 Mismatches 7 Gaps 8 Query start pos 9 Query end pos 10 Reference start pos 11 Reference end pos 12 [empty] After the first empty field the taxonomy classifications and confidences are shown, every taxonomy classification is separated by an empty field. Although different fasta reference and taxonomy databases can be specified by the user, by default mapseq maps reads to the NCBI taxonomy and to OTU taxonomies NCBI taxonomy fields: 13,14,15 kingdom, combined confidence (score+cutoff), score confidence 16,17,18 phylum, combined confidence, score confidence 19,20,21 class, combined confidence, score confidence 22,23,24 order, combined confidence, score confidence 25,26,27 family, combined confidence, score confidence 28,29,30 genus, combined confidence, score confidence 31,32,33 species, combined confidence, score confidence 34 [empty] MAP_hOTUs fields: 35,36,37 kingdom, combined confidence (score+cutoff), score confidence 38,39,40 90% otus, combined confidence, score confidence 41,42,43 96% otus, combined confidence, score confidence 44,45,46 98% otus, combined confidence, score confidence 47,48,49 99% otus, combined confidence, score confidence The combined confidence is computed based on a score confidence, used to control misclassification errors, and a identity cutoff confidence, used to ensure that the query isnt misclassified due to the inexistence of a sequence representative in the database of the true classification. The score confidence is calculated by comparing the identity of the assigned taxonomy to the identity of the first sequence not matching the assigned taxonomy. The identity cutoff confidence uses preoptimized cutoffs at each taxonomic level to calculate the confidence that the query is not too divergent from the assigned taxonomy. We recommend using a combined confidence cutoff of 0.4, or 0.5 as this value yielded the highest F1/2-score for MAPseq in our benchmarks. Please see our article for further information. ================================================= 4. HISTORY 1.0 (14 October 2016) - First release of MAPseq