User Manual for Sigma V1.0.2
Contents
1. v version t multi threads lt int gt number of threads default 1 Outputs sigma_ou sigma_ou sigma ou sigma ou a CINGNE TOIL 1EXAE SONSCIEO y Esso gvector html o AE ERE iar er tr ol OR ls 5 01 Sigma is a wrapper to call two modules sigma build model and sigma solve model If you only need the probabilistic matrix model Q matrix just run the sigma build model sigma build model Page 13 sigma build model p Usage sigma build model options c lt config file path gt w lt working directory gt Inputs se Conrig iile peda Cereme SalCuilel COntEALe sico 2 working directory default current running directory Options h help v version Outputs sigma _out qmatrix txt Then you can solve the model by sigma solve model module You can input the Q matrix directly sigma solve model sigma solve model h Usage sigma solve model options c lt config file path gt w lt working directory gt Inputs i comelg lle peda ceraults sale Ellie Cie 2 working directory default current running directory Options h help v version i input qmatrix lt string gt provide q matrix filename directly t multi threads lt int gt number of threads default 1 Outputs sigma out gvector txt sigma _out gvector html suce Giit o OPE 5 wee Sigma output Finally Sigma summarizes results in two types of formats a HTML forma2. SA Parameters for variants calling SS SS SS SS SS Variants Calling Filtering Genome Name Escherichia coli 0104 H4 2011C 3493 uidl76127 Then run as below sigma target reads options c lt config file parha w lt working directory gt The filtered reads output for the target genome should be reported in the genome directory with name as below genome basename filtered bam Then follow variant calling procedures of SAMtools http samtools sourceforge net mpileup shtml We also support a script to filter out ambiguous variants ver illes oye Usage Weir elle 97 oprions L lt iague ver rile gt a lt ouigobue ver mlle gt Options q lt int or float gt filter out below quality phred scaled score Ex q 20 is phred 20 1 error quality iE lt XIELQEWE gt filter out below allele frequency 0 0 1 0 Ex f 0 5 filter out a Case that has 4 alleles out of 10 reads depth M report homozygous FQ value is negative from samtools h help v version Page 19
3. Als aay MES L usr lib64 gcc x86 64 suse linux 4 7 x86 64 suse linux lib L usr lib64 gcc x86 64 suse linux 4 7 epope llapack Scie icons imolas pthread dinm oE oneen lquadmath After you update the Sigma Makefile you can build the binaries from the command line make It is computationally intensive to align hundreds of millions metagenomic reads into thousands of genomes Sigma provides MPI Message Passing Interface modules a parallel computation communication feature to distribute the computation across nodes in distributed memory systems from small clusters to supercomputers To use this feature the system should have MPI protocol such as MPICH or OpenMPI We tested Sigma in both MPI features If you have MPI protocol in your system just build Sigma MPI binaries from the command line make mpi Make sure your shell search path includes the directory containing mpicxx or mpiCC to compile Sigma MPI You should update the MPTFLAG based on your MPI compiler wrapper in the Makefile Page 6 Sigma Configuration File Create configuration file for each experiment Sigma provides a configuration file to help users easily setup input output and parameters for running Sigma We recommend users to create the configuration file for each experiment to keep the history of the experiment We provide a template of the configuration file in the package For example if you want to make a directory for a
4. Inference of Genomes from Metagenomic Analysis for Biosurveillance Bioinformatics 2014 Obtaining and Installing the Program Prerequisites Install Bowtie2 and SAMtools The inputs for Sigma are a database of reference genomes and metagenomic sequences In the first step Sigma uses a short read alignment algorithm Bowtie 2 to align all metagenomic reads onto every reference genome allowing up to a given number of mismatches per read Bowtie2 outputs alignment results in SAM Sequence Alignment Map format To minimize disk usage and space Sigma converts the SAM format to BAM Binary form of the SAM format on the memory buffer using SAMtools Current Sigma was experimented with Bowtie2 2 1 0 and SAMtools 0 1 19 Sigma was designed to work with any other short read alignment algorithm that uses FASTQ FASTA formats for read input and SAM BAM formats for alignment output but we strongly recommend to users to use Bowtie2 After you download and install the latest stable version of Bowtie2 and SAMtools user should update system path or provide the installed directories of the tools in the Sigma configuration file Install Sigma Binaries were built and tested in the Intel architectures x86_64 running Linux Unix environment Sigma package was developed using C C source codes were compiled and linked by g gcc version 4 7 2 Please download the last version of Sigma at http sigma omicsbio org download You can decompress it fr
5. indexed once users do not need to re index the genomes again The indexed files are generated in each genome directory The basename of the index is the reference genome name Bowtie2 does not support parallel indexing of the genomes Sigma provides two types of parallelization indexing wrappers to scale up from multi core workstations to distributed memory clusters One sigma index genomes is for multi core computers using multi processing and the other sigma index genomes mpi is for distributed memory clusters using MPI protocol sigma index genomes sigma index genomes h Usage sigma index genomes options c lt config file path gt w lt working directory gt Inputs ie contig kale peda Cderaults Sima Cleo Qee if config file is not specified the program will search it in the working directory include bowtie search options and more 2 working directory default current running directory if working directory is not specified the program will work in the current directory results will be generated in working directory Options h help v version p multi processes lt int gt number of multi processes default 1 Outputs Bowtie2 index files for each genome will be generated in the genome directory Page 9 sigma index genomes mpi sigma index genomes mpi h Usage sigma index genomes mpi options c lt config file path gt w lt working directory gt Inputs H
6. preprocessed by identifying and masking human reads removing duplicated reads and trimming low quality bases Detailed description for the preprocessing of the HMP datasets is available NGS QC Toolkit FASTX Toolkit and sickle can be used for quality processing How to provide metagenomic reads to Sigma User needs to provide the preprocessed metagenomic reads set in the configuration file Sigma automatically detects FASTQ FASTA format from the input data If you have paired end input you should provide each mate file in the Sigma configuration file The current Sigma cannot work with both paired end inputs and single end inputs So user should provide either paired end inputs or single end inputs Multiple input sets should be separated by comma e g HMPO1_pe_1 fq HMPO2_pe_1 fq Page 8 Provide metagenome NGS read s path using comma separation files You should select only one paired end read s OR single end read s You should comment the unselected option For paired end reads Paired End Reads 1 home user database HMP HMP01 pe 1 fq Paired End Reads 2 home user database HMP HMP01 pe 2 fq For single end reads Single End Reads Index reference genomes before aligning reads to genomes Before aligning metagenomic reads onto reference genomes using Bowtie2 the reference genomes should be indexed using Bowtie2 indexer bowtie2 build for fast and memory efficient aligning If the reference genomes are
7. Download external code S cd IPOPTDIR ThirdParty Blas get Blas Sci Mapa cl get Lapack cd Mumps get Mumps Page 4 HSL and Mumps linear solver libraries are compatible with Sigma The Sigma binary however only contains Mumps library for licensing issue of HSL User can install personal license HSL library for using IPOPT in Sigma 4 Install IPOPT S mkdir IPOPTDIR build S cd SIPOPTDIR build eomiriguz S make make install 5 Provide IPOPT path to Sigma Open the Sigma Makefile and update two fields INCL and LDFLAGS The INCL path should be SIPOPTDIR build include coin You can get the library path from SIPOPTDIR build share coin doc Ipopt ipopt addlibs cpp txt Example of Makefile as below Page 5 HEHE HEH HE HH HH HT HHH EE EE EE EE EE EE EEE EE EH HE HH HH EH Please change values based on your system environment AE FE aE AE AE AE AE ae ae aE AE AE FE FE aaa aaa C Compiler CC Cira Open MPI Compiler MPICC mpiCC C Compile Flag CFLAGS c Wall GCC OpenMP Flag OMPFLAG fopenmp Statie owai llel STFLAG static Include directories INCL I home thm Software CoinIpopt build include coin Library DFLAGS L home thm Software CoinIpopt build 1lib64 L usr 1ib64 gcc x86 64 suse linux 4 7 L usr lib64 gcc x86_64 suse ipsa O a Uae eal Als ae gle il o
8. User Manual for Sigma V1 0 2 Tae Hyuk Ahn Juanjuan Chai and Chongle Pan October 1 2014 Table of Contents INTO UN crias 3 Whats SIMA Gora its 3 A A 3 Obtaining and Installing the PrograM cccccccnnonoononcnnnnnnnnonnonnnnnnonnnnononnnnnnnnnnnnnonnnnnnnnnnnnnnno nn nnnnnnnnnnnnnonannns 3 Prerequisites Install Bowtie2 and SAMTtOOIS cccecsccccssssececsesececeeaececseaeeeceeaeeecseaeeeeseaeeeesenaeeeeseaaes 3 Install SM ind di ada eaetees 3 Building Sigmatrom source Codes mecaciinaiaia laca 4 Sigma Configuration File coccion a a eaae E ESAE aa EEEE EEA AREATA S 7 Create configuration file for each experiment ccesccesssecssseecssecessseceseecsseeecsaeeeeaeeseseecseeeesseesseeesenees 7 Configuration tile setting serorei aeaee da 7 Aligning Metagenomic Reads onto Reference Genome DatabasSe cccococnncconocanoconnnncnonnnnncnannnnncnnnnannnnnnnons 7 How to prepare reference database ccccccssccssscecsnscesssecsessecseeeessaecsessecseeeecseeesaeeseseesseesessaesesaeeseneeees 7 Quality control of metagenomic reads ccccccccssscccsesssceceessecececsaeeeceesaeeeceesseseceesaeeeceesaeeeceesaeeeceeaeeeeeees 8 How to provide metagenomic reads to SiGMA ccccsssccccecessessssececececsssesnsaeeececssesseasaeeeescesseseaeaeeeeeens 8 Index reference genomes before aligning reads to genomes ssccssssecesscecsnseessueeseseeceeeeessaeeesaeessneeess 9 SISMaA INGEX BENOMES eo a oeeae e aa Fea aane
9. elihood ratio test by assuming a chi square distribution for the likelihood ratio test statistic Variant Calling Why does variant calling require in metagenomics analysis Sigma enables strain variant calling by assigning metagenomic reads to their most likely reference genomes When an outbreak spreads or a pathogen re emerges it is important to determine the sequence variations between the genomes of this pathogen in the field collected samples and its reference genomes in the database This allows accurate reconstruction of the transmission sources of the outbreak and the evolutionary history of the pathogen Thus biosurveillance further requires information on the single nucleotide polymorphisms SNPs of a detected pathogen to distinguish different variant populations Identification of variants is straightforward for isolate genome sequencing by mapping all reads to a reference genome However in metagenome sequencing one cannot simply map all metagenomic reads to a reference genome because some of the aligned reads may originate from different microorganisms which could introduce false variations Sigma solves this problem by assigning each read to its most likely originating genome based on the Sigma probabilistic model which allows subsequent variant calling by variants calling software such as SAMTools Page 18 How to run variant calling Update Sigma configuration file to provide the target genome for variant calling Ma
10. en aaeeea aeaii iaa kenkien 9 SIEM A INAEX BENOMESHMP cwscsssscecccusscvesvavsonnceevsccccustvsnsdcubsiceanevssesecavbeceunebuseaccussonesnavsseaeeuveeseansdveeaaentets 10 Aligning read to genome Senice arsed ianiai sici n 10 feae sA o E EE EA E A E EE AEA 11 sigma align reads Mii iia 12 Genome Identification using Maximum Likelihood Estimation c cccssccssscecsscesssecseseeceseeecseeessaeessseeees 12 How to build a probabilistic model and solve tP ooooocccconoccccnonocanonananoncnananancnnonnnncnonn nn ncnnnn nn nnonnn nn ncnnnnnss 12 How to AN 12 Page 1 sigma b ild mod srie aa AA AA Adidas 13 AE A AN 14 Sigma OU Putin 14 Statistical Confidence Measures dic 15 What ls BOOtStrap PING ei da ida 15 How to rN Bootstrapping ra dl e Nc ato ite A e ea 15 SIS a DOOTSE AP it 15 SIBMa DOOTSEA P PI iii nt acts esdnesedaate cess cbusoduntene it tai 16 BOOtstrapping results heels a a et htt o ern sa eel A cts Leto db Ad 16 What is likelihood ratio test Jackknife 0 0 ccccccccsssccecssssececsessesecsssaececsessececsesaececeesaececsesaesecsesaeeeeseaaes 17 How to run likelihood ratio test eee cecccecessceesseeeesaeceecaeceeneeesaeeeeaaeceeaaesaeeeesaeceeaaeseeaeesaeeseaaeeeeaaesenees 17 SEM TAC tit AS A AA A AA A A A taa 17 TEMATICA Maat 18 Likelihood ratio testresults ai coco ereire aoada aara naaraana aa aana da Slbnadecpaccevibcntuanteeddacadnosansteessddedt 18 Variant Callin Eocene oranan A A toda 18 Why does va
11. ference genome databases were constructed from RefSeq downloaded from NCBI ftp site ftp ftp ncbi nlm nih gov genomes Bacteria all fna tar gz Page 7 mkdir RefSeq cd RefSeq wget ftp ftp ncbi nlm nih gov genomes Bacteria all fna tar gz eee Seance elio tnes Lele CZ UNNE In November 2013 NCBI RefSeq database includes 2731 bacterial and archaeal complete genomes 2 7GB compressed 9 1GB decompressed Sigma uses a hierarchical structure for the reference genome database as set up by RefSeq a root directory containing many sub directories of FASTA files Each sub directory corresponds to a genome and the directory names are used by Sigma as the genome names because they are unique identifiers The FASTA files in a sub directory correspond to multiple chromosomes and plasmids of a genome Genomes can be added or removed from the reference genome database by simply adding or removing their sub directories in the root directory The root directory is specified in the configuration file of Sigma Data_Info Reference genome directory required database hierarchy database directory genome directory fasta file genome directory fasta file fasta file genome directory fasta file Festa Elle Reference Genome Directory home user database refseq Quality control of metagenomic reads We recommend users to preprocess the reads to ensure data quality For example HMP WGS reads were
12. istic model The probabilistic model is described in the Results section of the paper in detail The relative abundances of genomes are estimated from the Q matrix using maximum likelihood estimation MLE implemented in C Sigma solves the nonlinear programming NLP problem for MLE using the Ipopt library Most of the computing time for NLP is spent in the objective function evaluation step which involves non trivial calculation on the large Q matrix To speed up the calculation we parallelized the objective functional evaluation step using multi threading with OpenMP Open Multi Processing How to run Sigma Before you run the Sigma you may check the configuration file for setting model probability such as mismatch probability for one base pair Default 0 05 equals 5 and minimum relative abundance rate to report 0 01 Page 12 Model Probability Mismatch probability for one base pair Default 0 05 equal Mismatch Probability 0 05 Minimum relative abundance rate to report Default Minimum Relative Abundance 0 01 You can run the Sigma with t multi threads option sigma t 16 w home user test HMP01 We do not provide MPI version for this module sigma S sigma h Usage Sigma options c lt config file path gt w lt working directory gt Inputs se Conrig iile parn Cereme gicme ConTiG Ere 2 working directory default current running directory Options h help
13. n experiment run01 then mkir seua cp SigmaDIR sigma config cfg run01 Configuration file setting The configuration file follows the INI format that is a simple text file with a basic structure composed of sections and properties Every key has a name and a value delaminated by an equals sign is used for section name e g Section Name Pounds at the beginning of the line indicate a comment Comment lines are ignored For example if you want to provide the specific paths of prerequisite software then setup the configuration file as below HEHEHE EEE HHEE HEE HEE HEE HEE HEE Parameters for Sigma HPT EEE HEE HEE HEE HEE HH HEE Program Info Provide bowtie2 and samtools software directory path If you don t know the exact path just comment the below line with Then Sigma will search the programs automatically from env path Bowtie Directory home user software bowtie2 2 1 0 Samtools Directory home user software samtools 0 1 9 The current configuration file has 5 sections Program_Info Data_Info Bowtie_Search Model Probability and Statistics The keys and values for each section of the configuration are described in the each section of the manual Aligning Metagenomic Reads onto Reference Genome Database How to prepare reference database The reference genome database for Sigma may contain dozens to thousands genomes In this study the re
14. o Contig alle pach ceraults Srema Elie CEE 2 working directory default current running directory Options h help v version Outputs Bowtie2 index files for each genome will be generated in the genome directory To run the MPI program you can use mpi run or mpiexec wrapper with np or n option for setting number of processes The executable wrapper is different for your MPI environment of the system so please contact your system administrator to learn how to run MPI programs This is an example how to run the HMP01 experiment working directory is nome user test HMP01 and the configuration file for the experiment is in the working directory with sigma index genomes mpi using 10 processes mpirun np 10 sigma index genomes mpi w home user test HMPO1 Aligning read to genomes Reads are aligned against the reference genome database using Bowtie2 User may specify the maximum number of mismatches per read the range of the inter mate distance and number of threads for the Bowtie2 alignment in the Sigma configuration file Bowtie Search Maximum count of mismatches for one read alignment Default 3 Maximum Mismatch Count 3 The minimum fragment length insert size for valid paired end alignments Default 0 Minimum Fragment _Length 0 The maximum fragment length insert size for valid paired end alignments Default 500 Maximum Fragment Length 1000 N
15. om the command line tar xzvf sigma version tar gz Page 3 After you extract the tar ball please check usage with h option For example cd Sigma bin S sigma h To run Sigma in anywhere without providing location of Sigma user needs to add the Sigma directory into PATH environment variable For example if user uses a bash shell open basrch and add the below line export PATH home user Sigma PATH Building Sigma from source codes Building Sigma from source requires a GNU like environment with GCC GNU Make and other basics Try it cd Sigma src make If you fail to make the binary then you should install IPOPT by yourself Sigma uses the primal dual interior point NLP method implemented in the IPOPT library to solve the probabilistic model Ipopt Interior Point OPTimizer pronounced eye pea Opt is a software package for large scale nonlinear optimization User should download and install the IPOPT package and provide library of IPOPT into the Sigma Sigma was experimented with the latest stable version IPOPT 3 11 Download the IPOPT compressed source at http www coin or org download source Ipopt and install it Please refer the IPOPT document to install it IPOPT requires external packages that are not included in the IPOPT source code distribution Below steps is what we did 1 Download IPOPT source package 2 Extract the package tar xzvf sigma version src tar gz S my Lou oz COM MO OME 3
16. riant calling require in metagenomics analysis ccococononoononcnnononononannnnnnnnnnnnnnnnnnnnncnnnns 18 How to run variant Calling cccessssccccecessesessececececesseseeaeaeeeeecesseseaaeseeeescesseseaseaeeeescesseseaaeseeeessessesenaeees 19 Page 2 Introduction What is Sigma Identification of microbes and accurate estimation of their relative abundance are crucial subjects in metagenomic analysis Existing taxonomic classification methods are unsuitable for metagenomic biosurveillance due to the following three factors First reference genomes including complete genomes of pathogenic bacteria are required Second strain level genome identification from the same species is essential Third statistical confidence evaluation of the metagenomic analysis is recommended We developed Sigma Strain level Inference of Genomes from Metagenomic Analysis a novel sequence similarity based approach for strain level identification of genomes from metagenomic analysis for biosurveillance Nucleotide level alignments analysis using maximum likelihood estimation empowers Sigma to estimate the relative abundances and likelihood ratios of each genome accurately given a list of reference genomes Hybrid parallel architecture of the Sigma allows its computation to be scalable from desktops to supercomputers for different sizes of metagenomic reads and reference datasets How to cite Sigma T H Ahn J Chai and C Pan Sigma Strain level
17. s and or p multi processes options for multi processing Multi threads t option is used for solving IPOPT sigma solve model in parallel and multi processes p is used for running bootstrapping iterations in parallel sigma bootstrap t 8 p 8 w home user test HMPO1 sigma bootstrap Page 15 S sigma bootstrap h Usage sigma bootstrap options c lt config file path gt w lt working directory gt Inputs se Conrig iile parn Cereales SalCuilel Colac alte 40 2 working directory default current running directory Options h help v version p multi processes lt int gt number of multi processes default 1 St Mbt Enreads lt amt gt number of threads default 1 Outputs sigma _out stat_bootstrap txt We also provides MPI version for the bootstrapping Multi threads t option also can be used in the MPI mode This example will launch 50 MPI tasks each with 8 threads total 400 cores required mpirun np 50 sigma bootstrap mpi t 8 w home user test HMP01 sigma bootstrap mpi sigma bootstrap mpi h Usage sigma bootstrap mpi options c lt config file path gt w lt working directory gt Inputs 1 Contig rile parh Ceirewlles silcua CONTEO CEG 2 working directory default current running directory Options h help v version t multi threads lt int gt number of threads default 1 Outputs sigma _out stat_bootstrap txt Boo
18. sigma jackknife h Usage sigma bootstrap options c lt config file path gt w lt working directory gt Inputs il Conrig rile parca Cloicenillics Sicme GOiae se Ere 2 working directory default current running directory Options h help v version p multi processes lt int gt number of multi processes default 1 t multi threads lt int gt number of threads default 1 Outputs sigma out stat_jackknife relative abundance estimation txt sigma out stat_jackknife percentage scaled estimation txt We also provides MPI version for the likelihood ratio test Multi threads t option also can be used in the MPI mode This example will launch 10 MPI tasks each with 8 threads total 800 cores required mpirun np 10 sigma jackknife mpi t 8 w home user test HMPO1 Page 17 sigma jackknife mpi sigma jackknife mpi h Usage sigma Jackknife mpi options c lt config file path gt w lt working directory gt Inputs Ho contig alle pach ceraults Srema Elie CEE 2 working directory default current running directory Options h help v version t multi threads lt int gt number of threads default 1 Outputs sigma _ out stat_jackknife relative abundance estimation txt sigma out stat_jackknife percentage scaled estimation txt Likelihood ratio test results Log scale likelihood ratio for each genome is reported A p value can be calculated using the lik
19. t for result visualization and a text format for further data analysis e sigma_out gvector txt relative abundance estimation output text format e sigma_out gvector html relative abundance estimation output html format The Sigma outputs provide genome alignment results estimated relative abundances and percentage chances of genomes Sigma also provides internal procedure output sigma_out qmatrix txt for Page 14 probabilistic model text file and sigma_out ipopt txt for IPOPT results Users usually do not need to open these internal outputs Statistical Confidence Measure What is Bootstrapping The bootstrap confidence interval estimation measures the uncertainty caused by the stochastic sampling process of metagenomic sequencing Sigma generates a bootstrap Q matrix by randomly taking reads from the original Q matrix with replacement Sigma then performs MLE using the bootstrap Q matrix This process is iterated for many times to generate a set of bootstrap estimates which are used to calculate a percentile confidence interval and relative standard deviation Sigma distributes the parallel processing of bootstrap samples on a cluster using MPI OpenMP How to run Bootstrapping Users can set the number of bootstrapping iterations in the Sigma configuration file sitas taney Number of iterations for bootstrapping Default 100 Bootstrap Iteration Number 200 You can run the Sigma bootstrapping with t multi thread
20. tstrapping results Bootstrapping reports average STD upper confidence bound and lower confidence bound for the estimated relative abundance and percentage chance Page 16 What is likelihood ratio test Jackknife Sigma performs likelihood ratio tests on a target genome with a relative abundance above a user defined threshold The log likelihood of the null hypothesis i e the target genome is absent is calculated using a Q matrix that does not contain the target genome The log likelihood of the alternative hypothesis i e the target genome is present is the same as calculated from the original Q matrix The log likelihood of the null hypothesis should be smaller than that of the alternative hypothesis because the target genome has been estimated to have a non zero relative abundance A p value is calculated using the likelihood ratio test by assuming a chi square distribution for the likelihood ratio test statistic Sigma can perform likelihood ratio tests for many target genomes in parallel on a cluster using MPI OpenMP How to run likelihood ratio test You can run the Sigma likelihood ratio tests with t multi threads and or p multi processes options for multi processing Multi threads t option is used for solving IPOPT sigma solve model in parallel and multi processes p is used for running all reported genomes by Sigma in parallel sigma jackknif t 8 p 8 v home user test HMPO1 sigma jackknife
21. umber of threads for running one bowtie task Default 1 Bowtie Threads Number 4 Page 10 Sigma also provides two types of parallelization aligning wrappers one sigma align reads is for multi core computers using multi processing and the other sigma align reads mpi is for distributed memory clusters using MPI protocol Sigma uses a simple dynamic load balancing strategy in both multi processing and MPI to maximize throughput and minimize wall clock computing time sigma align reads sigma align reads h Usage sigma align reads options c lt config file path gt w lt working directory gt Inputs l Contig lle parch Cloicewilles SalCMilsl CONEA cic 2 working directory default current running directory Options h help v version p multi processes lt int gt number of multi processes default 1 Outputs bam format aligned results are reported in each output genome directory Bowite2 supports parallel searching using threads pthreads library Multi threads has speed up scalability limit that is different to systems but usually 8 to 16 threads in the current technology Multi processes overcome the limit of the multi threads especially when a system has a shared memory with many cores For example if your system has 8 cores you may set the Bowtie2 threads number in the config file Number of threads for running one bowtie task Default 1 Bowtie Threads Number 8 and run aligning
22. wrapper without p option sigma align reads w home user test HMPO1 If your system has 48 cores you may set the Bowtie2 threads number in the config file Number of threads for running one bowtie task Default 1 Bowtie Threads Number 8 and run aligning wrapper with p option sigma align reads p 8 w home user test HMPOL Page 11 This command executes 8 multiple aligning processes simultaneously using 8 threads for each process Therefore 48 cores can be used most efficiently to get maximum speed up scalability and minimum wall clock time sigma align reads mpi sigma align reads mpi h Usage sigma align reads mpi options c lt config file path gt w lt working directory gt Inputs 1 Gomis lle parh Cerewet sica COn gt cic 2 working directory default current running directory Options h help v version Outputs bam format aligned results are reported in each output genome directory Bowtie2 outputs alignment results in SAM Sequence Alignment Map format To minimize disk usage and space Sigma converts the SAM format to BAM Binary form of the SAM format on the memory buffer using SAMtools The alignment output directory sigma_alignments output is generated in the working directory Genome Identification using Maximum Likelihood Estimation How to build a probabilistic model and solve it Sigma parses the alignment results and generates the Q matrix probabil
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