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SGA - String Graph Assembler SGA is a de novo assembler for DNA sequence reads. It is based on Gene Myers' string graph formulation of assembly and uses the FM-index/Burrows-Wheeler transform to efficiently find overlaps between sequence reads. The core algorithms are described in this paper: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/12/i367 *** Compiling SGA SGA dependencies: -google sparse hash library (http://code.google.com/p/google-sparsehash/) -the bamtools library (https://github.com/pezmaster31/bamtools) -zlib (http://www.zlib.net/) -(optional) the hoard memory allocator (http://www.hoard.org/) Additionally, the pipeline python scripts use the following modules. These are not required to build SGA but must be available if you want to use the relevant python helper scripts: -pysam (http://code.google.com/p/pysam/) -ruffus (http://www.ruffus.org.uk/) If you cloned the repository from github, run autogen.sh from the src directory to generate the configure file: ./autogen.sh If bamtools and the sparsehash have been installed in standard locations (like /usr/local) you can run configure without any parameters then run make: ./configure make If bamtools or the sparsehash are installed elsewhere, you can specify their locations as follows: ./configure --with-sparsehash=/home/jsimpson/ --with-bamtools=/home/jsimpson/software/bamtools These directories should be the root of the install (in other words, the directories have include/ and lib/ as subdirectories contained the header files and libraries, respectively). The program uses pthread to parallelize most steps of the assembly. The use of a concurrent memory allocator like hoard can drastically improve running time. If you would like to enable use of the hoard memory allocator, specify the path to hoard as follows: can be specified as above: ./configure --with-hoard=/home/jsimpson/hoard *** Installing sga Running make install will install sga into /usr/local/bin/ by default. To specify the install location use the --prefix option to configure: ./configure --prefix=/home/jsimpson/ && make && make install This command will copy sga to /home/jsimpson/bin/sga *** Running SGA SGA consists of a number of subprograms, together which form the assembly pipeline. The subprograms can also be used to perform other interesting tasks, like read error correction or removing PCR duplicates. Each program and subprogram will print a brief description and its usage instructions if the --help flag is used. To get a listing of all subprograms, run sga --help. The bin/sga-pipeline script implements a simple assembly pipeline and is a good place to start. It is implemented in python using the ruffus library (http://ruffus.org.uk). The most important options (--min-overlap, --error-rate) are exposed in the pipeline script but tweaking the parameters of the individual subprograms may give better results. The major subprograms are: * sga preprocess READS > out.fastq Prepare reads for assembly. It can perform optional quality filtering/trimming. By default it will discard reads that have uncalled bases ('N' or '.'). If you wish to keep these reads, use the --permuteN flag which will randomly change any uncalled bases to one of [ACGT]. It is mandatory to run this command on real data. If your reads are paired, the --pe-mode 1 option should be specified. The paired reads can be input in two files (sga preprocess READS1 READS2) where the first read in READS1 is paired with the first read on READS2 and so on. Alternatively, they can be specified in a single file where the two reads are expected to appear in consecutive records. By default, output is written to stdout. * sga index READS Build the FM-index for READS, which is a fasta or fastq file. The -d option can be used to limit the amount of memory consumed at the cost of higher running time. Typical values of -d are 2000000 or 4000000. This program is threaded (-t N). * sga correct READS Perform error correction on READS file. Overlap and kmer-based correction algorithms are implemented. By default, a hybrid algorithm is used which first attempts to correct the reads using long kmers. This method of correction is fast and will get rid of most singleton errors. The reads that cannot be corrected using kmers are corrected by finding inexact overlaps from which a multiple alignment and consensus sequence is computed. Any remaining uncorrected reads can be discarded by specifying the --discard flag. Many options exist for this program, see --help. Substantially improved results can be found by changing the --min-overlap, --error-rate, --kmer-size and --kmer-threshold parameters. This program is threaded (-t N). By default, the corrected reads will be output to READS.ec.fa. * sga rmdup READS Remove duplicate sequences from READS file. This is useful for removing PCR/optical duplicates. The --error-rate parameter controls the edit percentage that is allowed to consider two reads to be identical. This program automatically regenerates the FM-index without the duplicated reads. * sga overlap -m N READS Find overlaps between reads to construct the string graph. The -m parameter specifies the minimum length of the overlaps to find. By default only non-transitive (irreducible) edges are output and edges between identical sequences. If all overlaps between reads are desired, the --exhaustive option can be specified. This program is threaded. The output file is READS.asqg.gz by default. * sga assemble READS.asqg.gz Assemble takes the output of the overlap step and constructs contigs. The output is in contigs.fa by default. Options exist for cleaning the graph before assembly which will substantially increase assembly continuity. See the --cut-terminal, --bubble, --resolve-small options. *** Workflow examples Refer to the wiki on the sga github page for usage examples. *** Data quality issues Sequence assembly requires high quality data. It is worth assessing the quality of your reads using tools like FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) to help guide the choice of assembly parameters. Low-quality data should be filtered or trimmed. Very highly-represented sequences (>1000X) can cause problems for SGA. This can happen when sequencing a small genome or when mitochondria or other contamination is present in the sequencing run. In these cases, it is worth considering pre-filtering the data or running an initial 'rmdup' step before error correction. *** History The first SGA code check-in was August, 2009. The algorithms for directly constructing the string graph from the FM-index were developed and implemented in the fall of 2009. The initial public release was October 2010. *** Third party code SGA uses Bentley and Sedgwick's multikey quicksort code that can be found here: http://www.cs.princeton.edu/~rs/strings/demo.c It also uses zlib, the google sparse hash and gzstream by Deepak Bandyopadhyay and Lutz Kettner (see Thirdparty/README). SGA also uses the stdaln dynamic programming functions written by Heng Li. *** Credits Written by Jared Simpson. The algorithms were developed by Jared Simpson and Richard Durbin.