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Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). other GIs. published recently [13]. Thus, there is there Thus, [13]. recently published of prediction specific the for developed completely [16]. overtime content (e.g., information signature genomic G+C the genomic only using GIs homogeneous predict the that tools software more Therefore, in a genes have the to of region whole content the G+C taking the region, drive also may codons AT-rich or GC-rich INDeGenIUS, IslandViewer, PAIDB, PIPS and RPGFinder) and PIPS PAIDB, IslandViewer, INDeGenIUS, GIST, GIPSy, GI-POP, GIHunter, (e.g., result better a achieve to features additional other and analyses, genomics comparative the signature, genomic using of importance the highlighting neeIS IlnVee, n PIB Tbe ) Hwvr te is sfwr to t be to tool software first the However, 1). (Table PAIDB and IslandViewer, InDeGenIUS, I wt hmgnos eoi sgaue eg, GD Ilne ad Islandpath) and Islander EGID, (e.g., signature genomic homogeneous with GIs identifying in helpful be may factors specific and sequences insertion genes, mobility tRNAs, the query or reference genome caused by unresolved gaps [13]. Therefore, the prediction of prediction the Therefore, [13]. gaps unresolved by caused genome reference or query the may analyses the one of the acceptor genome, increasing the efficiency. Also, the preference for preference the Also, efficiency. translation the increasing genome, to acceptor the gene of one the the of usage codon the adapt that mutations select the ultimately fitness, will pressure bacterial selective the to relevant are that genes present sequence GIs because genome However, whole 1). the (Table to compared anomalies usage codon and/or G+C with genomic regions of identification like prediction, the for features GI shared commonly the on focus -SVM, IGIPT, PAI-IDA and SIGI-HMM) and PAI-IDA IGIPT, GI-SVM, features the software tool uses to predict GIs, the more efficient it is in tackling the problem, the tackling in is it efficient more the GIs, predict to uses tool software the features closely-related a more the in Indeed, [17]. reported previously region as features, important most the the of one is of organism absence the showing comparison genomic the However, GIs. The specific prediction of these subclasses of GIs of subclasses these these of prediction specific of The GIs. coordinates genomic return not does PubMed in SIs and MIs, RIs, for A search PAIs. quick than others GIs of sub-classes the about information little was there recently, Until EGID, (e.g., GIPSy, GIST,IslandViewerandPIPS)(Table1). features GI all of analysis comprehensive a with user the providing of goal the achieve to tools software different combine which tools, software ensemble of appearing the lmn ad o Tret ltom, epciey my ae o o-yoios substitutions non-synonimous to by take may insertion/deletions respectively, and platforms, Torrent substitutions Ion nucleotide and Ilumina of frequency high the noticeable, Also good quality sequence. a achieve to genomes, needed quality complete base the of in result assembly would Torrent the Ion and in Illumina whereas helpful be would MinIon and PacBio technologies this, sequencing circumvent long-read the scenario, this To In [14]. platforms Torrent Ion sequences. or Illumina with along genome complete using MinIon or PacBio using approaches, sequencing performed combined of advantage take may researchers only be should GIs recently lo h gn cmoiin 1] Tu, hg gnm cvrg culd ih manual a with coupled coverage genome high a desirable. also is tools software visualization mapping Thus, genome using sequence the of curation [15]. composition and content gene G+C the the usage, also codon the impact will which genes, of pseudogeneization and problem of GI identification from the genome sequence. The existent software tools mainly tools software existent The sequence. genome the from identification GI Nowadays, of the problem tackle to [10]. developed been consuming have tools software money some technologies, sequencing and generation time is strategy this The first identification of a PAI was achieved using molecular biology approaches; however, approaches; biology molecular using achieved was PAI a of identification first The 3.2 manual curationofthewholegenomeannotationtoavoidpoorqualityannotation. perform to recommendable also is it Thus, correlations. biological find to analyses prediction post the in also, and, GIs of prediction the in important also is composition gene the Finally, Journal of IntegrativeBioinformatics,13(1):301,2016 Journal doi:10.2390/biecoll-jib-2016-301 ! Software tools Tbe 1) (Table' take to false-positive or false negative results, due to the absence of regions in regions of absence the to due results, negative false or false-positive to take Atraiey te s o ohr I etrs lk te rsne f flanking of presence the like features, GI other of use the Alternatively, . still a huge urge for the widespread of information on information of widespread the for urge huge a fail in predicting GIs that were not acquired not were that GIs predicting in fail may all 4 classes of GIs, individually, was only was individually, GIs, of classes 4 was partially addressed in the software the in addressed partially (Table'1) with . This scenario explains scenario This . h avn o next- of advent the http://journal.imbio.de/

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Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). DB,#database;#SW,#software frequency;#ON,#oligonucleotide;#CU,#codon#usage Zisland$Explorer$ IslandViewer$3 Journal of IntegrativeBioinformatics,13(1):301,2016 Journal doi:10.2390/biecoll-jib-2016-301 INDeGenIUS PAIDB$v2.0: Al RGPFinder Islandpath IslandPick GC SIGI HGTector GIHunter GEMINI Islander PAI ien GI GI Pre_GI MSGIP PAIDB GIPSy IGIPT EGID GIST PIPS T A A A Profile ool SVM Hunter $ POP A A HMM IDA $ $ $ $ $ $ $ *$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ database S DB+ oftware /$ tool SW SW SW SW SW SW SW SW SW SW SW SW SW SW SW DB DB DB DB DB ES# ES# ES# ES# ES# # # # # # # # # # # # # # # # # # # # # # tool;# $ $ ES GC Genomic ,#ensemble GC+ +DI+TRI+ON+CU GC GC+ON+CU GC+DI GC+DI+CU# G GC+DI+CU# GC+ON# C+DI+CU# GC GC+CU# GC+CU# GC+CU# GC+CU# GC+DI DI+ +CU+ON GC GC ON GC ON CU# +CU $ &# &# &# &# signature ON+CU# +# # # # # # CU# # # software#that#combines#different#software # #* Gemini#uses# # # Table 1: $ # tRNA$genes GI prediction tools and their methodologies +# +# +# +# +# +# +# +# +# +# +# +# a#genome# &# &# &# &# &# &# &# &# &# &# &# &# &# $ Mobility s egmentation#and#clustering# +# +# +# +# +# +# +# +# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# $ genes$ Comparative$genomics # tools ;# GC, clustering$ # G+C#content;#DI,#dinucleotide#frequency;#TRI,#trinucleotide# +# +# +# +# +# +# +# +# +# +# +# +# +# +# +# +# +# &# &# &# &# &# &# &# &# approach# . . /$ sequences Insertion +# +# +# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# $ $ VF+RF+MF+SF VF+RF+MF+SF Specific$ f VF VF actors$ VF VF http://journal.imbio.de/ # +RF # +RF &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &# &#

Copyright 2016 The Author(s). Published by Journal# of Integrative# Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). # # [25,26] rences Ref [41] [40] [39] [38] [17] [37] [36] [35] [34] [33] [32] [31] [30] [29] [28] [27] [24] [13] [23] [22] [21] [20] [19] [18] eA # # # # # # # # # # # # # # # # # # # # # # # # $ # 4 in bacterial species, for every new field created in comparative genomics there is a hidden a is there genomics potential forthecreation ofnewGIcomparisonanalyses. comparative in created field new every for species, bacterial in could analyses GI Future to [47]. allied dataset pan-genomics this in for strategies account normally GIs in genes hence, and, environments new to adaptation differential for responsible normally are singletons the and genome shared the in commonly The (singletons). genes The development. strain drug and vaccine for single important are genome core a the in genes shared to unique and, genome”); (“shared all not but strains, more or 2 between shared genome); (core strains all between prediction shared commonly orthology are: the of use identifies approach the makes Then, dataset. the in genomes all normally from genes all between approach The analyses. complete the in genes of the set non-redundant from the define species to used also of is pan-genome set term The a [46]. genus same or species same the from strains of set a between differences and pan-genomics. is nowadays analyses GI from advantage taking constantly is that area Another available metagenomicsdata. obnd taey a b hlfl n rcn te rgn f e coa cmlxs in complexes, clonal new variability genomic the of of much for account this GIs because origin emerging Finally, [48,49]. for strains Overall, pathogenic methods the diagnostic organism. new of tracing creation donor the in in the also and helpful analyses, of epidemiological be traits may specific to strategy correlated combined and traits be new may to bacteria which of hosts, adaptability the influencing genes of blocks of acquirement the reveal may analyses pan-genomics with allied GIs the of origin the of identification The the GIsfromgenesyntenyconservationbetweendistantly related species. After, mosaicism. of origin the of of identification the include may step final the degree Then, strains. the throughout GIs their measure to those on approaches phylogenomics-based GIs using performed be may analyses epidemiological identified the comparing and strains of prediction [45]. the for bioavailability also and origin putative their tRNA predicting to key the be to could organisms other in genes due usage, orthologous with GI codon the inside genes syntenic same of comparison phylogenetic the Alternatively, the have may organisms related distantly two Besides, [16]. organisms other of signature genomic the with them comparing by origin their predict to possible always not is it time, with signature genomic their adapt GIs Because [38,44]. GIs the of origin the of prediction the is addressing needs that task one Also, GIs usingdifferentfeatures[42,43]. The area was created by Tettelin by created was area The approaches may be helpful in detecting all the possible scenarios during the classification of classification the during scenarios possible the all detecting in helpful be may approaches tutr my ae o as-eaie eut ee i esml mtooois ht uses that methodologies ensemble in learning even machine of implementation the Hence, results features. all cover to tools software false-negative different to take may structure obnto o dfeet etrs 1,1. o isac, G my ae GC content G+C a have harbor may only may one GI another while tRNAs, a by a flanked be present instance, and genes not transposase For harbors may deviation, or [17,41]. may features GI each different and the nature of in i.e., combination regions mosaic region, are GIs genomic [42]. a on so in and features all genes transposase deviation, of usage codon variation, content G+C with genes of concentration concentration the on based classification GI for approaches learning machine of use the involve may area the in improvements Future 4

Journal of IntegrativeBioinformatics,13(1):301,2016 Journal doi:10.2390/biecoll-jib-2016-301 ! a Future improvements large number of virulence factors and present codon usage deviation. This mosaic This deviation. usage codon present and factors virulence of number large aa ol i bceil ouain fo te oprsn f GIs of comparison the from populations bacterial in pools data MGE (2005) and consists in the identification of similarities of identification the in consists and (2005) al. et aim firstly dniyn Gs n all in GIs identifying at http://journal.imbio.de/ genes which with 5

Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). [14]! [13]! [12]! [11]! [10]! [9]! [8]! [7]! [6]! [5]! [4]! [3]! [2]! [1]! References from support financial for grateful CAPES andCNPq.ThisworkwasfundedbyCAPES,CNPq,Fapemig. are Oliveira Castro de Letícia and Jaiswal Kumar Arun 5

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