# SKESA 2.5.0 and SAUTE 1.3.0 ## SKESA: strategic k-mer extension for scrupulous assemblies SKESA is a de-novo sequence read assembler for microbial genomes. It uses conservative heuristics and is designed to create breaks at repeat regions in the genome. This leads to excellent sequence quality without significantly compromising contiguity. If desired, SKESA contigs could be connected into a GFA graph using GFA connector. ## SAUTE: sequence assembly using target enrichment SAUTE is a de Bruijn graph based target enriched de-novo assembler designed for assembling genomic and RNA-seq reads sequenced using Illumina. The result is reported as a GFA graph and two nucleotide fasta sequence files for assemblies in the graph. Location and version: ```console $ which skesa /local/cluster/bin/skesa $ skesa --version skesa --version SKESA 2.5.0 $ which saute /local/cluster/bin/saute $ saute --version saute --version saute 1.3.0 ``` help message: ```console Running skesa or skesa -h or skesa --help gives information about options and produces the following: General options: -h [ --help ] Produce help message -v [ --version ] Print version --cores arg (=0) Number of cores to use (default all) [integer] --memory arg (=32) Memory available (GB, only for sorted counter) [integer] --hash_count Use hash counter [flag] --estimated_kmers arg (=100) Estimated number of distinct kmers for bloom filter (M, only for hash counter) [integer] --skip_bloom_filter Don't do bloom filter; use --estimated_kmers as the hash table size (only for hash counter) [flag] Input/output options: at least one input providing reads for assembly must be specified: --reads arg Input fasta/fastq file(s) for reads (could be used multiple times for different runs, could be gzipped) [string] --use_paired_ends Indicates that single (not comma separated) fasta/fastq files contain paired reads [flag] --sra_run arg Input sra run accession (could be used multiple times for different runs) [string] --contigs_out arg Output file for contigs (stdout if not specified) [string] Assembly options: --kmer arg (=21) Minimal kmer length for assembly [integer] --min_count arg Minimal count for kmers retained for comparing alternate choices [integer] --max_kmer_count arg Minimum acceptable average count for estimating the maximal kmer length in reads [integer] --vector_percent arg (=0.05) Percentage of reads containing 19-mer for the 19-mer to be considered a vector (1. disables) [float (0,1]] --insert_size arg Expected insert size for paired reads (if not provided, it will be estimated) [integer] --steps arg (=11) Number of assembly iterations from minimal to maximal kmer length in reads [integer] --fraction arg (=0.1) Maximum noise to signal ratio acceptable for extension [float [0,1)] --max_snp_len arg (=150) Maximal snp length [integer] --min_contig arg (=200) Minimal contig length reported in output [integer] --allow_snps Allow additional step for snp discovery [flag] Debugging options: --force_single_ends Don't use paired-end information [flag] --seeds arg Input file with seeds [string] --all arg Output fasta for each iteration [string] --dbg_out arg Output kmer file [string] --hist arg File for histogram [string] --connected_reads arg File for connected paired reads [string] Note that --sra_run option is not available if SKESA is built using Makefile.nongs ``` SKESA usage examples: ```console In all the examples below, we are providing 4 cores and have 48 Gb of memory. Example of an assembly that directly accesses SRA for an unpaired read set SRR867211 is: $ skesa --sra_run SRR867211 --cores 4 --memory 48 > SRR867211.skesa.fa Example of an assembly that directly accesses SRA for a paired read set SRR1960353 is: $ skesa --sra_run SRR1960353 --cores 4 --memory 48 > SRR1960353.skesa.fa Example of an assembly that uses separate fastq files for each mate of SRR1703350 is: $ skesa --reads SRR1703350_1.fq,SRR1703350_2.fq --cores 4 --memory 48 > SRR1703350.skesa.fa Example of an assembly that uses interleaved mates for SRR1703350 as fastq input is: $ skesa --reads SRR1703350.fq --use_paired_ends --cores 4 --memory 48 > SRR1703350.skesa.fa Example of an assembly that uses reads from SRA for SRR1695624 and gzipped fasta for SRR1745628 is: $ skesa --sra_run SRR1695624 --reads SRR1745628.fa.gz --use_paired_ends --cores 4 --memory 48 > SAMN03218571.skesa.fa Example of the same assembly as above done with both runs accessed from SRA is: $ skesa --sra_run SRR1695624 --sra_run SRR1745628 --cores 4 --memory 48 > SAMN03218571.skesa.fa ``` SAUTE help: ```console General options: -h [ --help ] Produce help message -v [ --version ] Print version --cores (=0) Number of cores to use (default all) [integer] --estimated_kmers (=1000) Estimated number of distinct kmers for bloom filter (millions) [integer] To avoid expensive rehashing of the de Bruijn graph, SAUTE uses a light-weight counting Bloom filter to find the number of different kmers satisfying the --min_count criterion. Parameter --estimated_kmers provides the number (in millions) of different kmers with any count present in the reads. Overestimating will result in a small waste of memory. Underestimating will trigger iterative Bloom filter recalculation until a good estimate is found. Input/output options : target file, at least one input for reads and output for gfa must be specified: --gfa Output file for GFA graph [string] --all_variants Output file for sequences with all variant combinations [string] --max_variants (=1000) Restricts the number of variants reported in --all_variants per graph [integer] --selected_variants Output file for selected sequences representing all graph elements [string] --targets Input file with reference sequences [string] --sra_run Input sra run accession (could be used multiple times for different runs) [string] --reads Input fasta/fastq file(s) for reads (could be used multiple times for different runs, could be gzipped) [string] --use_paired_ends Indicates that single (not comma separated) fasta/fastq files contain paired reads [flag] Assembly options: --vector_percent arg (=0.05) Percentage of reads containing 19-mer for the 19-mer to be considered a vector (1. disables) [float (0,1]] Before doing any assembly SAUTE removes vectors from the reads. It finds and clips read after all 19-mers found in a relatively high number of reads. --min_count (=2) Minimal count for kmers retained in graph [integer] This option keeps noise kmers out of the de Bruijn graphs used for assembling. By default, a kmer has to be seen in at least 2 reads to be included. It is allowed to use --min_count 1, and it could help in assembling low coverage spots. The drawback of this setting is that all read errors satisfying --fraction criterion will be recognized as valid SNPs. To alleviate this, the program will remove all sections supported by only 1 read if they were not necessary for a connection. --fraction (=0.05) Maximum noise to signal ratio acceptable for extension [float [0,1)] All fork branches with relative abundance less than this are ignored. --kmer Primary kmer length for assembly (default automatic) [integer] --secondary_kmer Shorter kmer length for low coverage spots (default automatic) [integer] For assembling SAUTE uses two de Bruijn graphs with different kmer lengths. It uses the longer kmer most of the time and switches to the shorter kmer to assemble the low coverage spots. Both kmers must be odd numbers. In case of protein references, both kmers must be divisible by 3. By default, the program will use the closest valid values not exceeding half and a fifth of the read length. --secondary_kmer_threshold (=1) Coverage threshold for using shorter kmer [integer] This parameter defines what is considered to be low coverage for the secondary kmer use. With default, these are spots with no possible extension or extension supported by only 1 read (could happen only with --min-count 1). Specifying a higher number may help with detecting some low coverage forks. With more work shifted toward the shorter kmer, there is a higher chance that the program will enter a highly repetitive area and will create a very complex graph. --word Word size for seeds [integer <= 16] To start assembling, SAUTE scans the main de Bruijn graph and finds seed kmers with high similarity to the reference. These kmers are used as starting points for the assembly. To trigger a kmer comparison to the reference, one of its ends has to have an exact match of --word length. For a protein reference, the word must be divisible by 3. Defaults are 8 for nucleotide references and 12 for protein references. Latter translates to 4 aa match. --kmer_complexity (=2000) Hard mask reference areas with high number of variant seed possibilities (0 disables masking) [integer] Repetitive regions could result in very complex output graph and excessive calculation time. All reference areas for which SAUTE found in each position more different seeds than this parameter are internally hard masked and will not be assembled. --max_fork_density (=0.1) Maximal fork density averaged over --buf_length before abandoning assembling (0 disables) [float] --buf_length (=200) Buffer length for fork density [integer] If assembling enters an unmasked repetitive area, the number of forks and paths to follow may become very high. SAUTE calculates the average fork density for the last --buf_length long portion of the assembly. If the fork density is above the --max_fork_density threshold, assembling for the current seed is stopped and the program moves to the next seed. --target_coverage (=0.5) Keep a path in output if it has alignment to the reference that is at least target_coverage*(reference length) long [float (0,1]] --min_hit_len If a path is shorter than target_coverage*(reference length), use this length threshold for keeping paths (optional) [integer] --extend_ends Unambiguously extend graph ends using de-novo assembly [flag] --protect_reference_ends Near the reference ends, don't check if reads support some minimal extension of the fork's branches [flag] Each fork is analysed using the algorithm described in the SKESA paper, and less supported branches are deleted. There is one check which could be detrimental to assembling RNAseq -- each valid branch has to be extendable by ~100bp. If this is close to a transcript 5' or 3' end, extension is expected to fail. This option disables the extension check at the reference ends. --keep_subgraphs Don't remove redundant subgraphs [flag] If the reference file contains similar references, SAUTE may create multiple graphs which are either identical to or are sub-graphs of some other graphs. By default, the program will remove redundant graphs and sub-graphs. This option disables sub-graph removal. --use_ambiguous_na Use ambiguous nucleotide codes for SNPs [flag] If the option is used, SAUTE will replace SNPs with ambiguous nucleotide codes. This will simplify the graph and will reduce the number of sequence variants. The counts for individual SNPs will be lost. --gap_open Penalty for gap opening [integer] (default of 5 for nucleotide references and 11 for protein references) --gap_extend Penalty for gap extension [integer] (default of 3 for nucleotide references and 1 for protein references) --drop_off (=300) Maximal decrease of score before abandoning assembly path [integer] SAUTE uses a Smith-Waterman type algorithm for aligning references to the de Bruijn graph. For nucleotide references, the usual match bonus and mismatch penalty are used. They could be changed from the input (see below Options specific for nucleotide targets). For protein references BLOSUM62 substitution matrix is used. In both cases, penalties for gaps are specified using --gap_open and --gap_extend parameters. The --drop_off parameter determines how far from the reference the assembly may go before the extension is stopped, and the assembly is trimmed to the aligned part. Larger gap open penalty is used for opening frameshifts (see below). Graph cleaning options: --no_filter_by_reads Don't use full length reads for variants filtering [flag] --no_filter_by_pairs Don't use mate pairs for variants filtering [flag] After assembling, SAUTE will use full length reads and read pairs to remove chimeric connections from the graph. Above options will partially or completely disable these steps. --max_path (=1000) Maximal number of path extensions allowed for a single filtering check [integer] For each fork in graph, SAUTE expands all sequences starting at this fork using either the read length or the insert size. If the number of expanded sequences exceed this parameter value, the extension length is reduced. --not_aligned_len (=10) Not aligned read length for break count [integer] --not_aligned_count (=3) Number of not aligned reads to make a break [integer] --aligned_count (=2) Number of aligned reads to confirm a connection [integer] For each expanded sequence, SAUTE counts how many reads or pairs confirm this path and how many contradict it. Based on these counts, the path is either kept or discarded. --remove_homopolymer_indels Remove homopolymer indels [flag] --homopolymer_len (=4) Minimal length of homopolymer [integer] --homopolymer_ratio (=0.33) Coverage ratio threshold for removing homopolymer indels [float] ``` See the github repo link below for more information. software ref: research ref: research ref: