sequence assembly is the first challenge encountered in a typical computational genomics pipeline and it involves the merging and the ordering of shorter sequence fragments ( reads ) with the aim to get as close as possible to the original larger sequence ( genome ) .
advent of next - generation sequencing platforms has allowed the sequencing a huge number of organisms and species at reasonable costs .
however , many issues regarding the computational assembly of large - scale sequencing data have remained unsolved 1 and , actually , the number of draft genomes in databases greatly overtakes the number of completely sequenced ( sometimes referred also as  finished  or  closed  ) ones ( www.genomesonline.org ) .
the output of a de novo assembly is typically a draft genome , consisting of a set of contigs ( i.e. contiguous sequence fragments ) that may be ordered and oriented into scaffold sequences , with gaps between them , representing regions of uncertainty or missing sequence 2 .
alternatively , draft genomes can be represented in the form of ( de bruijn ) graphs , an approach that is currently exploited by a number of assembly algorithms ( reviewed in 3 ) .
the difficulty in obtaining a closed genome may be due to several causes , including the presence of repetitive fragments along the genome and/or the absence of enough reads to produce a reliable assembly .
it is also known that the most rapidly evolving regions are often absent or incorrectly rendered in finished genomes 4 .
furthermore , from a mathematical viewpoint , the de novo genome assembly problem can be proven to be difficult , falling within a class of problems ( np - hard ) for which no efficient computational solution is yet known 5 . in this context , it must be added that some traditional assemblers like newbler ( 454 sequencing , roche diagnostics , indianapolis , in , usa ) usually perform a trimming of the contig edges depending on the quality of the supporting reads .
this conservative procedure , however , may result in a loss of information , discarding many correct bases characterized by a sub - optimal quality . to overcome these limitations ,
we have developed a tool called enly that allows increasing the length of the contigs deriving from de novo assemblies and the closure of part of the gaps commonly present in the draft genome .
enly is sequencing platform - independent since it be used with any kind of sequence type , as long as files are provided in fasta format . accordingly , even hybrid datasets ( obtained with different sequencing technologies ) can be used as input for enly pipeline .
finally , by taking into account scaffold information , enly drastically reduces the probability of generating chimeric assemblies .
the overall procedure requires ( at least ) two multi - fasta files as input , one containing all the contigs deriving from the first assembly and the other all the raw reads resulting from the sequencing run . by applying the strategy described below to each contig in the input file ,
enly tries to increase their length and ( possibly ) to merge them into scaffolds .
more in detail , the pipeline is based on the iteration of multiple cycles ( figure 1 ) and during each of them : 1 ) a fragment of length l1 ( specified by the user ) is detached from the 5'-end of each contig and is used as an input for a blast search 6 against a database embedding all the original reads resulting from the sequencing run ; 2 ) the blast output is parsed to identify reads that can be used to extend the contig ( i.e. those partially aligned at the end of the contig and protruding from its extremity ) . by default identity threshold among the reads and
overlap length between each read and the contig extremity is , instead , specified by the user ( with the -s parameter ) .
this means that , for example setting -s to 70% , only reads aligned for at least 70% of their length and sharing more than 97% of their sequence with the contig extremity will be used to extend contigs .
intuitively , setting this overlapping threshold to higher values will increase accuracy , although reducing the number of closed gaps ( see additional file 1 : supplementary material ) .
the use of higher thresholds ( i.e. higher stringency ) is , therefore , recommended when a reference genome / scaffold is not available for checking possible chimeric joints ( see point 8) .
when a reference scaffold is present , contigs merging will still be reliable even when low overlap thresholds ( e.g. 30% ) are selected , although it must be pointed out that the extended contigs not merged into any ( sub ) scaffold would not be checked for the presence of wrongly incorporated sequence .
3 ) the selected reads and the original contig are assembled together using either phrap 7 or minimo 8 assemblers , possibly resulting in an  enlarged 
4 ) the very same procedure is repeated for the 3'-end of the contig and for all the other contigs of the input file ; 5 ) the extended contigs are used as inputs for a second step , in which a fragment of length l2 ( with l2 < l1 ) is detached from each contig end and used as an input for a further blast against the reads database , assembling the matching reads with the enlarged contig .
this procedure , performed in order to compensate for the presence of reads with a heterogeneous length distribution ( resulting from a typical 454 run 9 ) , is repeated for fragments of decreasing length , until the minimum length ( specified by the user through -m option ) has been reached ; 6 ) if a reference genome ( in fasta format ) has been provided by the user , extended contigs are mapped onto it , taking advantage of an ad hoc modified version of the contiguator tool 10 , allowing launching this tool in an iterative fashion and storing results from each run in a separate folder .
7 ) contigs are mapped by blast one against each other for identifying possible overlaps and gaps closure . no threshold for contigs overlap is set in this stage since scaffold information is used ( see next point ) to discard possible chimeric joints .
8) gap closures are validated against a reference scaffold , provided by the user ( -y option ) or , alternatively , generated by the contiguator tool .
only contigs overlaps that are consistent with scaffold information are merged ( using the  megamerger  tool from the emboss suite ) . in case neither a scaffold nor a reference genome
have not been provided by the user , possible overlaps for each of the enly cycles are stored in a specific output files ( see enly 's manual for details ) that the user can manually inspect for eventual gap closures .
9 ) these contigs , together with those that have not been enlarged / merged are then used as input for the following cycle of the pipeline .
the first cycle of the enly pipeline is completed when all the contigs have been processed , mapped and saved to a new multi - fasta file .
the procedure is then repeated ( re - starting from point 1 ) for a user - specified number of cycles or , alternatively , until no more bases have been added to the contigs during the last cycle .
output files from the mapping procedure ( contiguator ) are saved in separate folders ( one per cycle ) , ready for being loaded by the artemis comparison tool 11 , in order to visually inspect contigs alignment against the reference genome .
reads used for extending contigs may derive from any of the currently available sequencing technologies ( illumina , 454 , iontorrent , etc . ) , as long as they are provided in the form of fasta files .
moreover , reads obtained by different sequencing technologies may be pooled in a single file to be used as input for enly pipeline . intuitively , since enly uses the sequence of the reads partially protruding from each contig , the probability of extending its length increases with the average length of the reads dataset .
it should be noticed that , besides 454 pyrosequencing , other sequencing platforms are now starting to generate reads of length comparable to the ones of the datasets used in this work ( e.g. illumina miseq , iontorrent ) , thus paving to the use of this alternative datasets for gap - filling through enly 's approach . to evaluate the reliability of the pipeline
, we tested enly on three different 454 reads datasets retrieved from either the ncbi short read archive database ( sra , http://www.ncbi.nlm.nih.gov/sra ) , namely escherichia coli ko11 ( srs084754 ) and staphylococcus aureus 649 ( srs114535 ) , or from a previous sequencing run on streptococcus pneumoniae ap200 12 .
ap200 12 , 13 ) or the genome of a phylogenetically close bacterium ( s. aureus col 14 ) were available , allowing the validation of the results obtained with enly .
each reads dataset was first assembled with newbler v. 2.6 , using default parameters , resulting in 719 , 254 and 124 contigs for e. coli ko11 , s. aureus 649 and s. pneumoniae ap200 , respectively .
contigs obtained from de novo assembly were , then , used , together with the corresponding reads , as input for the enly pipeline .
parameter values ( and an evaluation of their effects on the genome assembly ) used during these tests are reported in additional file 1 : supplementary figure 1 , together with introduced mismatch rates for different reads alignment thresholds ( -s option , additional file 1 : supplementary figure 2 and 3 ) . in all cases , enly was able to improve the de novo assembly ( table 1 ) . in detail , the number of closed gaps ranged from 16% to 19.1% of all the gaps present in each input draft genome .
most of the closed gaps resulted from merging two contigs although , in some cases ( e.g. e. coli ko11 drafts assembly ) , up to 5 different contigs were merged in a single ( sub ) scaffold .
importantly , after running enly on the de novo assemblies , an increase in the n50 value for each of the genome was observed , accounting for the overall improvement of the input draft assemblies .
interestingly , enly showed to perform reasonably good also when non-454 reads were used as input for the pipeline .
results obtained with reads obtained from iontorrent , miseq and pacbio sequencing runs are reported in additional file 1 : supplementary table 2 and 3 . the computational time required by enly correlates with the amount of contigs
/ reads embedded in the input files and with the number of cycles required by the user . on a machine with eight 3.1 ghz processors , the computational time required for the tests performed in this work ( and according to the parameters specified in additional file 1 : supplementary table 1 ) ranged from 2 up to 5 hours depending on the size of the reads datasets and parameters used ( see additional file 1 : supplementary figure 4 ) .
recently , two scaffolding approaches apparently similar to the one presented here have been developed .
both of them take advantage of the additional information that is present on a typical illumina paired - end sequencing run 15 , 16 .
currently , enly does not use paired - end information , being able to process single end sequencing reads ( as long as they are provided in fasta format ) for contigs extension ; moreover , by implementing the possibility to guide contigs extension / merging through the use of scaffold information ( independently generated by the user or obtained through the implemented contiguator tool ) , our pipeline reduces the probability of chimeric scaffolds . in conclusion , enly is a simple , cross - platform and parallelizable tool allowing the improvement of draft genomes resulting from de novo - assembled high - throughput sequencing reads .
it is based on the iterative mapping of reads at contigs ends and it is also able to use ( and generate ) scaffold information to guide contigs merging , reducing the probability of chimeric scaffolding .
testing it on a set of de novo draft genomes led to the closure of up to 20% of the gaps originally present , thus resulting particularly helpful during genome finishing procedures .
 the reconstruction of the complete genome sequence of an organism is an important point for comparative , functional and evolutionary genomics . nevertheless , overcoming the problems encountered while completing the sequence of an entire genome can still be demanding in terms of time and resources . 
 we have developed enly , a simple tool based on the iterative mapping of sequence reads at contig edges , capable to extend the genomic contigs deriving from high - throughput sequencing , especially those deriving by newbler - like assemblies . 
 testing it on a set of de novo draft genomes led to the closure of up to 20% of the gaps originally present . 
 enly is cross - platform and most of the steps of its pipeline are parallelizable , making easy and fast to improve a draft genome resulting from a de novo assembly . 
Introduction
Results and Discussion
Supplementary Material