Investigations of Oligonucleotide Usage Variance Within and Between Prokaryotes

Transcription

1 Investgatons of Olgonucleotde Usage Varance Wthn and Between Prokaryotes Jon Bohln 1, Eysten Skjerve 1, Davd W. Ussery * 1 Norwegan School of Veternary Scence, Oslo, Norway, Center for Bologcal Sequence Analyss, Department of Systems Bology, Techncal Unversty of Denmark, Lyngby, Denmark Abstract Olgonucleotde usage n archaeal and bacteral genomes can be lnked to a number of propertes, ncludng codon usage (trnucleotdes), DNA base-stackng energy (dnucleotdes), and DNA structural conformaton (d- to tetranucleotdes). We wanted to assess the statstcal nformaton potental of dfferent DNA word-szes and explore how olgonucleotde frequences dffer n codng and non-codng regons. In addton, we used olgonucleotde frequences to nvestgate DNA composton and how DNA sequence patterns change wthn and between prokaryotc organsms. Among the results found was that prokaryotc chromosomes can be descrbed by hexanucleotde frequences, suggestng that prokaryotc DNA s predomnantly short range correlated,.e., nformaton n prokaryotc genomes s encoded n short olgonucleotdes. Olgonucleotde usage vared more wthn AT-rch and host-assocated genomes than n GC-rch and free-lvng genomes, and ths varaton was manly located n non-codng regons. Bas (selectonal pressure) n tetranucleotde usage correlated wth GC content, and codng regons were more based than non-codng regons. Non-codng regons were also found to be approxmately 5.5% more AT-rch than codng regons, on average, n the 40 chromosomes examned. Pronounced DNA compostonal dfferences were found both wthn and between AT-rch and GC-rch genomes. GC-rch genomes were more smlar and based n terms of tetranucleotde usage n non-codng regons than AT-rch genomes. The dfferences found between AT-rch and GC-rch genomes may possbly be attrbuted to lfestyle, snce tetranucleotde usage wthn hostassocated bactera was, on average, more dssmlar and less based than free-lvng archaea and bactera. Ctaton: Bohln J, Skjerve E, Ussery DW (008) Investgatons of Olgonucleotde Usage Varance Wthn and Between Prokaryotes. PLoS Comput Bol 4(4): e do: /journal.pcb Edtor: Gary Stormo, Washngton Unversty, Unted States of Amerca Receved October 1, 007; Accepted March 1, 008; Publshed Aprl 18, 008 Copyrght: ß 008 Bohln et al. Ths s an open-access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal author and source are credted. Fundng: Part of ths work was funded by the Dansh Center for Scentfc Computng, DU s funded by grants from the Dansh Research Councl, and JB s funded by the Norwegan School of Veternary Scence. Competng Interests: The authors have declared that no competng nterests exst. * E-mal: dave@cbs.dtu.dk Introducton Prokaryotc DNA can be consdered as a long chan of overlappng olgonucleotdes, and frequences of dfferently szed olgonucleotdes can reveal dfferent propertes and patterns of bacteral and archaeal genomes. On average, roughly 86% of prokaryotc DNA codes for protens [1] and thus a consderable amount of nformaton s held n trnucleotde (codon) frequences. Addtonal nformaton however, can be found by studyng other olgonucleotde szes. Dnucleotde dstrbutons, or nearest neghbor frequences, are used to calculate base-stackng energes [], whle DNA structural propertes can be calculated usng d- to tetranucleotde frequences [ 5]. In addton, the structures of A, B and Z type DNA helces are largely determned by 11-, 10- and 1-mers, respectvely [,6]. Another advantage of consderng genomc DNA as a set of fxed-szed olgonucleotde frequences s that bas and pattern preference,.e. the randomness nherent or lack thereof, n the complete DNA sequence can be detected [3]. Alternatvely, DNA patterns can be nvestgated by examnng occurrences of ndvdual nucleotdes [7,8]. Olgonucleotde frequences are very much nfluenced by codon dstrbutons whch, n turn, are correlated wth GC-content [9]. Snce GC content s correlated wth the envronment of prokaryotes [9,10], so are olgonucleotde dstrbutons [11]. The dstrbutons of olgonucleotde frequences can reveal other propertes as well. GC skews,.e. ncreased cytosne compared wth guanne content on the leadng strand, can be used to determne DNA replcaton start and stop postons n bactera [1]. However, many archaeal and slow replcatng bacteral genomes do not have pronounced GC skews (or AT skews) on leadng and laggng strands, but replcaton start and stop postons can be detected wth ncreased precson by examnng olgonucleotde frequency skews wth progressvely larger olgonucleotde szes [13]. In addton to the propertes descrbed above, transcrpton and regulaton stes are also coded by certan olgonucleotde patterns [14]. Such olgonucleotdes are therefore thought to be severely under- or overrepresented compared wth what s expected from mean genomc GC content or compared to olgonucleotde frequences found n other closely related speces [14]. The examples above llustrate some of the propertes that can be extracted from DNA sequences by examnng olgonucleotde usage varance. Ths motvated us to explore how olgonucleotde dstrbutons change wthn and between prokaryotes n codng and non-codng regons, how based olgonucleotde frequences are, and whether any partcular trends could be detected. In order to do ths a seres of statstcal tests were performed on all sequenced bacteral and archaeal chromosomes (up to September 006). We found that tetranucleotde frequences carred consderable genomc nformaton potental, and were therefore used n PLoS Computatonal Bology 1 Aprl 008 Volume 4 Issue 4 e

2 Author Summary There are potentally many factors responsble for how archaeal and bacteral genomes are composed. Recent advances n DNA sequencng have made t possble to use computatonal and statstcal methods to examne the nterplay between evoluton and genomc composton. We wshed to see whether partcular propertes could be extracted that would provde clues on how prokaryotc DNA s composed. For nstance, we wondered whether or not proten codng regons carred a greater nformaton potental than non-codng regons, f there s a lnk between genome sze and GC content, whether GC content s dfferent n codng and non-codng regons, and possble assocatons between DNA composton and envronment. Our results ndcated that genomc nucleotde frequences are a determnant of many DNA compostonal propertes, but also that other nfluences are at work. For nstance, bactera are known to frequently exchange DNA wth the envronment and other organsms. Acqured DNA can therefore have dfferent compostonal propertes than host DNA, and snce pathogencty and antbotc resstance n bactera s often assocated wth foregn DNA, advancng the knowledge of DNA composton s of great mportance. all statstcal tests based on olgonucleotde usage. The statstcal tests ncluded olgonucleotde usage devaton from mean (OUD, a measure of olgonucleotde frequency varatons n genomes), and olgonucleotde usage varance from expected (OUV, a measure of randomness or bas [3]). These tests were used to examne how tetranucleotde frequences fluctuated wthn chromosomes (OUD), and how tetranucleotde dstrbutons dffered from random tetranucleotde frequences calculated by mean genomc nucleotde frequences (OUV). OUV measures GC content both globally and locally n chromosomes wth a 40 kbp nonoverlappng sldng wndow and was used to calculate expected tetranucleotde frequences as well as GC content n codng and non-codng sequences for each chromosome tested. These tests were performed for all sequenced 40 prokaryotc chromosomes at the tme, and ther correspondng open readng frames. The frst test, however, was concerned wth statstcal nformaton potental n dfferent olgonucleotde szes usng a dfferent approach than [3]. Results/Dscusson Informaton Potental n Olgonucleotdes We measured the statstcal nformaton carred by the dfferently szed olgonucleotdes from d- to octanucleotdes n prokaryotes wth GC contents between 47% and 53%. From Fgure 1, t can be observed that the largest ncrease n nformaton was obtaned by gong from nucleotde frequency approxmaton of dnucleotdes to trnucleotde usage approxmatons based on dnucleotde frequences and GC content (detals can be found n Materals and Methods). A more careful nvestgaton of Fgure 1 revealed that progressvely less nformaton was ganed from usage approxmatons of tetranucleotdes up to heptanucleotdes, and practcally no addtonal nformaton appeared to be present n Fgure 1. Statstcal nformaton potental n dfferently szed olgonucleotdes. Cumulatve nformaton potental s measured n d- to octanucleotde frequences n prokaryotc genomes wth GC content between 47% and 53%. These genomes were selected because of the ncreased senstvty of the Pearson correlaton measure for chromosomes wth smlar AT/GC content. The archaeal and bacteral chromosomes are represented along the horzontal axs, sorted by ncreasng GC content from left to rght, wth correspondng correlaton scores between observed n-mer words and approxmated n-mer words on the vertcal axs. The n-mer words were approxmated by observed (n 1)-mer words and genomc nucleotde frequences. Hgh correlaton scores ndcate ncreased smlarty between observed and approxmated olgonucleotde usage. do: /journal.pcb g001 PLoS Computatonal Bology Aprl 008 Volume 4 Issue 4 e

3 approxmated octanucleotde frequences. Thus, olgonucleotde szes larger than hexanucleotdes possess lttle addtonal nformaton potental, f any, n prokaryotc DNA. GC Content n Codng and Non-Codng Regons GC content was measured n codng and non-codng regons (see Materals and Methods for more detal) and t was found that non-codng regons were, on average, roughly,5.5% more AT rch (,5.3% and,5.5%, for AT and GC rch chromosomes respectvely) than codng regons, wth the assumpton that 14%, on average, of each chromosome was non-codng DNA [1]. Why non-codng regons are,5.5% more AT rch than codng regons may be related to the DNA curvature n promoter regons, and possbly termnaton stes [15], and to lower stackng energes found n AT rch DNA patterns compared wth GC rch []. Increased AT content n non-codng regons suggest less energy s requred to splt the double helx for transcrpton []. GC Content and Genome Sze Although the lnk between GC content and genome sze has been debated [16,17], we obtaned sgnfcant (P,0.001) correlaton wth r = 0.47 (Spearman s rho) between chromosome sze and GC content (see Fgure ). The followng regresson equaton was ftted: Y sze ~ expð0:15 z 1:9X GC Þ wth the assumpton of lnear varance. Y sze gves the sze of the chromosomes (response) n mbp and X GC s global GC content (predctor, P,0.001). Bas n Tetranucleotde Usage We measured how tetranucleotde usage vared n genomes compared wth expected tetranucleotde usage. Ths expected tetranucleotde usage was calculated from mean genomc GC content, and mplctly assumes that each nucleotde n every tetranucleotde, and therefore also the whole chromosome, s ndependent of ts neghbors. In other words, the more smlar observed and expected tetranucleotde frequences are, the more random (.e. less based) are the observed tetranucleotde frequences, and thus the genomc DNA composton. Fgure 3 shows how OUV vared between genomes compared to genomc GC content. Sgnfcant correlaton was found between GC content and OUV values usng the followng regresson equaton: Y OUV ~ exp {11: { 10:1X GC z 1:4XGC, R ~ 0:33, P v 0:001 Y OUV desgnates genomc OUV values (response) whle the predctor, X GC, represents GC content. Our results showed that GC rch archaea and bactera tended to have a less random DNA composton than AT rch. The reason for ths s not known, but t has been argued [3] that thermodynamc propertes of tetranucleotdes may be mportant,.e. base stackng energy and curvature. Tetranucleotde usage varance n codng regons was found to be even more strongly correlated wth global GC content: Y c ~ exp {10:9 { 10:3X OUV GC z 1:7XGC, R ~ 0:41, P v 0:001: Fgure. Prokaryotc genome sze versus GC content. Prokaryotc chromosomes are sorted by ncreasng GC content from left to rght on the horzontal axs. The vertcal axs represents chromosome sze n mbp. do: /journal.pcb g00 PLoS Computatonal Bology 3 Aprl 008 Volume 4 Issue 4 e

4 Fgure 3. Tetranucleotde usage varance measures of 40 archaeal and bacteral chromosomes. Prokaryotc chromosomes are sorted by ncreasng GC content from left to rght (vertcal axs), wth red and blue regresson lnes representng OUV values (horzontal axs) for chromosomes and codng regons, respectvely. Larger values mply more bas, or stronger selectonal pressure, n genomc tetranucleotde usage. The surroundng dotted lnes ndcate 99% predcton ntervals. do: /journal.pcb g003 Y C OUV desgnates OUV values n codng regons (response), whle X GC s global GC content (predctor). Thus, bas n tetranucleotde usage n non-codng regons was less affected by global GC content than codng regons. Prelmnary tests on a set of sequenced genomes nvolvng randomzaton by ncreasng AT/GC content smlarly to noncodng % sze produced sgnfcantly larger dfferences n tetranucleotde usage bas than what was observed for non-codng regons. Ths ndcates that non-codng regons do carry nformaton and are exposed to selectonal pressures and bas although consderably less than codng regons. Ths can be seen from Fgure 3 where bas n tetranucleotde usage n codng regons ncreased more wth GC content than tetranucleotde usage n noncodng regons. It should be noted that the above analyss s based on average values from concatenated DNA sequences, and nothng s stated about how OUV values vary wthn chromosomes and codng regons. Prelmnary tests ndcate that OUV values vary consderably wthn archaeal and bacteral chromosomes. Tetranucleotde Usage Varaton Wthn Genomes OUD gves a measure of how homogeneous or heterogeneous genomes are n terms of DNA composton. The OUD (but not OUV) measure can also detect to what extent tetranucleotde patterns are dstrbuted throughout the genome. Low OUD values thus ndcate ncreased smlarty and not ncreased randomness. In contrast to OUV, the OUD measure s calculated as the average varance of olgonucleotde occurrences wthn the chromosome based on olgonucleotde frequences from a nonoverlappng 40 kbp sldng wndow compared wth mean genomc olgonucleotde frequences. From Fgure 4 t can be observed that OUD scores were lower n codng regons (blue lne) than n chromosomes contanng both codng and non-codng regons (red lne). Genomc OUD scores were progressvely decreasng wth ncreasng GC content ndcatng that tetranucleotde patterns n non-codng regons become progressvely more smlar wth growng GC content. The followng regresson equatons were obtaned: Y OUD ~ exp {11:3 { 1:X GC z 11:1XGC, R ~ 0:8, P v 0:001 YOUD C ~ exp {13:7 { 6:1X GC z 6:6XGC, R ~ 0:11, P v 0:001: The response-functons Y OUD and Y C OUD represent OUD scores n genomes and codng regons, respectvely, whle the predctor X GC s GC content. Dfference n tetranucleotde usage wthn genomes was supported by a rato test where observed tetranucleotde usage varance wthn genomes was dvded by expected tetranucleotde varance approxmated by nucleotde frequences. From Fgure 5 t can be observed that consderably less varaton was detected n the codng regons (blue lne) compared wth chromosomes PLoS Computatonal Bology 4 Aprl 008 Volume 4 Issue 4 e

5 Fgure 4. Average olgonucleotde usage devance wthn prokaryotc chromosomes (OUD). Each archaeal and bacteral chromosome s sorted on the horzontal axs wth ncreasng GC content from left to rght, wth correspondng OUD values on the vertcal axs. The red and blue lnes represent OUD scores for whole chromosomes and codng regons, respectvely. Smaller OUD values mean more homogeneous chromosomes n terms of tetranucleotde usage. The surroundng dotted lnes ndcate 99% predcton ntervals. do: /journal.pcb g004 contanng both codng and non-codng regons (red lne) wth the followng regresson equatons: Y O E ~ expð0:09 z 1:44X GC Þ, R ~ 0:38, P v 0:001 YO C E ~ exp ð 0:83 z 0:55X GCÞ, R ~ 0:07, P v 0:001 Y O_E and Y C O_E (response) are the ratos of observed dvded by expected OUD values for genomes and codng regons respectvely, whle X GC represents mean global GC content (predctor). Ths varance s, however, connected to larger fluctuatons n GC content wthn genomes n AT rch prokaryotes. See the below secton on varance n GC content wthn genomes for more detal. No correlaton was found between OUD values and genome sze. Varaton of GC Content Wthn Genomes Predcted tetranucleotde usage based on genomc nucleotde frequences was used to estmate varance n GC content wthn genomes (Fgure 6). Snce ntrnsc tetranucleotde usage varance predctons were only based on nucleotde frequences, these values were drectly assocated wth fluctuatons n local GC content obtaned by comparng 40 kbp sldng wndows wth global (mean) GC content. We therefore wanted to nvestgate f fluctuatons of ntrnsc GC content showed any relaton to global GC content, and whether there was a dfference between codng and non-codng sectons. Usng regresson analyss, sgnfcant correlaton was found between global GC content and expected tetranucleotde usage varance, wth the followng equaton: Y E OUV ~ exp {10:7 { 16:7X GC z 14:3XGC, R ~ 0:35, P v 0:001 Y E_OUV (response) represents expected OUV usage and X GC GC content (predctor). Ths result showed that there was sgnfcant correlaton between global GC content and how GC content vared locally wthn genomes. The obtaned correlaton was also hgher than what was observed for the OUD measure (R = 0.8), whch means that varance n tetranucleotde frequences wthn genomes s less senstve to mean genomc GC content than varance n nucleotde frequences. Restrctng the test to codng sectons, correlaton was found between global GC content and expected tetranucleotde usage varance, usng the followng equaton: YE C OUV ~ exp {13:6 { 10:6X GC z 10:6XGC, R ~ 0:10, P v 0:001 Y C E_OUV represents expected OUV values n concatenated codng regons (response), and the predctor, X GC, global GC content. Thus, only weak correlaton was found between fluctuatons n ntrnsc nucleotde frequences and global GC content n codng regons. Snce roughly 86% of prokaryotc DNA codes for PLoS Computatonal Bology 5 Aprl 008 Volume 4 Issue 4 e

6 Fgure 5. Rato of observed dvded by expected OUD values. The vertcal axs shows the rato of observed dvded by expected OUD values for each chromosome sorted wth respect to genomc GC content from left to rght on the horzontal axs. The rato test measures how observed olgonucleotde usage vares wthn chromosomes (red lne) and codng regons (blue lne) compared wth expected based on GC content. Rsng rato values above 1 (vertcal axs) means ncreased observed varance compared wth expected. The dotted lnes represent 99% predcton ntervals. do: /journal.pcb g005 protens, on average, [1] the above result suggest that the GC content of non-codng regons wthn prokaryotes vary consderably, and that the rate of ths varaton wthn genomes s negatvely correlated wth mean genomc GC content. Hence, our results show that, on average, the more GC rch a genome s, the less varaton of nucleotde frequences are found n the noncodng regons, whle the opposte s true for AT rch genomes,.e. the more AT rch an archaeon or bacterum s the more vared are the nucleotde frequences of the non-codng sectons. Evolutonary Implcatons Although a lnk was establshed between genomc GC content and OUV, (R = 0.33, see above secton) AT rch bactera wth hgh OUV values and GC rch bactera wth low OUV values were also observed. Hgh and low scores mean here beng ranked among the 100 hghest or lowest OUV scorng chromosomes of the 40 tested, respectvely. In general, a larger number of AT rch bactera were found wth hgh varance scores than GC rch bactera wth low OUV scores. Closely related bactera were also found at the opposte ends of the OUV score lst. For nstance, we found that Canddatus Blochmanna flordanus obtaned an OUV value fve tmes that of Buchnera aphdcola subsp. SG (ranked #8 and #356, respectvely). Addtonal detals can be found n Dataset S1. Both speces are thought to have undergone genome reducton as they adopt a symbotc lfestyle and have small genome sze of comparable GC content [18]. Snce low OUV scores mply hgh genomc mutaton rates, dfferent mechansms may be responsble for the dfferent tetranucleotde dstrbutons n these genomes, wth Bl. flordanus havng a more mutated genome than B. aphdcola. Thus, dfferent evolutonary mechansms may stll result n smlar lfestyles. Both Bl. flordanus and B. aphdcola obtaned smlarly hgh OUD scores (respectvely ranked #387 and #346), whch means that they both rank among the prokaryotes wth the most heterogeneous chromosomes. Host assocated (ncludng pathogenc) bactera, n general, had the most heterogeneous genomes, whle free lvng bactera were the most homogeneous (see Dataset S1). It should also be added that no correlaton was found between OUV values and genome sze, thereby removng any lnk between random genome composton and genome sze. Summary Consderng prokaryotc genomes from an olgonucleotde perspectve, there seemed to be lttle ncrease n nformaton potental n olgonucleotde szes larger than hexanucleotdes. Comparng observed to expected tetranucleotde usage (OUV), we found that codng regons are, n general, more based than non-codng regons, and more homogeneous accordng to the OUD test. GC rch genomes were also found to have more based tetranucleotde frequences than AT rch genomes. Although AT content ncreased n non-codng regons n both AT rch and GC rch genomes, t dd not appear to be a consequence of tetranucleotde preference. OUD was found to decrease wth ncreasng mean genomc GC content, ndcatng that GC rch genomes have a more homogeneous DNA composton, especally n non-codng regons. Ths result was addtonally supported by a PLoS Computatonal Bology 6 Aprl 008 Volume 4 Issue 4 e

7 Fgure 6. Varaton of GC content wthn genomes. The vertcal axs shows the varance of nucleotde frequences wthn chromosomes (red lne) and codng regons (blue lne) compared wth correspondng mean genomc GC content on the horzontal axs. Lower average nucleotde varance scores (vertcal axs) means more smlar dstrbutons of GC content wthn chromosomes (and vce versa). do: /journal.pcb g006 rato test based on observed and expected OUD scores ndcatng that codng regons vared smlarly for AT and GC rch genomes alke, whle non-codng regons vared more wthn AT rch genomes. No correlaton was found between OUV and OUD scores, mplyng that bas n tetranucleotde usage s not connected to ntra-chromosomal homogenety n prokaryotes. Materals and Methods All sequenced archaea and bactera avalable up to September 006, (40 chromosomes and correspondng open readng frames, from 366 genomes n total) were downloaded from NCBI Genbank [19]. The dfferent statstcal tests were carred out wth computer programs made accordng to the procedures descrbed below. The free statstcal package R [0] was used for vsualzaton of results, regresson analyss and curve-fttng. All analyses of DNA sequences were carred out n the 59R39 drecton. Analyss of genomc codng regons was performed by concatenatng every open readng frame for each chromosome nto one large DNA sequence. Notaton and Formulas For notaton we let F z (.) desgnate frequency wth respect to a DNA sequence z of an ndependent varable consstng of a nucleotde (AT-content, F z (w), w s any nucleotde or F z (A), F z (G), F z (C) and F z (T)) or an olgonucleotde (F z (w 1 w w n ), where w 1 w w n are nucleotdes makng up a DNA word consstng of n nucleotdes. Correlaton between frequences of DNA words was performed wth the standard Pearson correlaton formula: P N ðx { x Þðy { y Þ sffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff P N P x j { x N ðy k { y Þ j x = F x(w 1 w w n ) represents DNA word frequency for word and x s the average frequency for all possble combnatons of DNA words consstng of n nucleotdes (see below). The sums are taken for all possble olgonucleotde combnatons,.e. N = 4 n Average values are found usng the equaton: x ~ 1 N Methods The statstcal nformaton potental of the dfferent olgonucleotde szes was estmated by comparng frequency functons F x (w 1 w n ) to F x (w 1 w n 1 )F x (w n ) usng Formula 1 for genomes wth AT content between 47% and 53%. Genomes wth AT content n that range were chosen due to the ncreased senstvty of the Pearson correlaton method for chromosomes wth smlar AT/GC content. By leavng one nucleotde free the cumulatve k x ð1þ ðþ PLoS Computatonal Bology 7 Aprl 008 Volume 4 Issue 4 e

8 nformaton n the dfferent olgonucleotde szes,.e. combned nformaton of all smaller olgonucleotdes, could be measured. GC-content n non-codng regons was calculated by fndng GC-content n open readng frames and whole chromosomes wth the followng formula: Fx nc ðwþ ~ F x wc ðwþ { cfx cðwþ nc where c = codng fracton measured by the sum of open readng frame szes dvded by correspondng chromosome sze. Noncodng fracton s then nc = 1 c. The superscrpts c, nc, wc desgnate GC content n codng, noncodng and whole chromosomes, respectvely. GLM based regresson analyss was used to examne the relatonshp between global GC content (predctor) and genome sze (response). Because of the hghly non-lnear nature of the data, Spearman s rank based correlaton test was addtonally used between genome sze and global GC content. OUV was calculated for each genome wth the followng formula: ð3þ ðx { Ex ð ÞÞ ð4þ where E s expected frequency for DNA word x based on nucleotde frequences: Ex ð Þ ~ F x w 1 ð ÞF x ðw Þ...F x ðw n Þ: ð5þ The expected value s thus calculated based on the assumpton that each olgonucleotde frequency conssts of ndvdual nucleotde frequences ndependent of each other. Snce Formula 5 bases calculatons of the expected value only on genomc nucleotde frequences t can be consdered as a measure of how random the genomc composton of the organsm s. Low OUV scores can therefore be nterpreted to mean that a hgh degree of non-drected mutatons has taken place wthn the genome, or, alternatvely, that the ndvdual nucleotdes n each tetranucleotde are more loosely ted to each other. Hgh OUV scores may also be taken to mean that a larger degree of bas, or order, s present n the genomc olgonucleotde usage. An addtonal pont of vew s that OUV s a measure of nformaton potental, where low OUV values mean that the DNA sequence n queston has lower statstcal potental of carryng nformaton (and vce versa). OUV was calculated for both whole chromosomes and codng regons. Regresson analyss was then carred out between OUV values (response), both codng and whole chromosomes, and global GC content (predctor). The resultng regresson equatons, wth correspondng coeffcent of determnaton, here denoted by R, can be found n the Results/Dscusson secton together wth the correspondng P values. The OUD test gves an average estmate of how olgonucleotde frequences vary wthn prokaryotc chromosomes. Varance s calculated between the olgonucleotde frequences calculated from a 40 kbp non-overlappng sldng wndow and mean genomc olgonucleotde frequences wth the followng formula: 1 X M M j : z j { m x ð6þ Summatons are taken over all possble olgonucleotde combnatons 1##N, and all non-overlappng sldng wndows 1#j#M n DNA sequence x. z j = F zj(w 1 w w n ) represents the n-word frequences for sldng wndow j, whle m x = F x (w 1 w n ) s the mean frequency of word w 1 w n n DNA sequence x. Regresson analyss was performed for both whole chromosomes and codng regons wth OUD as response and global GC content as predctor. The resultng regresson equatons wth correspondng coeffcent of determnaton, R, and P-values can be found n the Results/Dscusson secton. The rato of observed dvded by expected OUD values was addtonally used to test whether any fluctuatons n tetranucleotde frequences could be detected n codng and non-codng regons. To calculate ths, the OUD values obtaned for each chromosome wth Formula 6 was dvded by the followng equaton: 1 X M M j whch resulted n the formula: X M j, E z j { Ex ð Þ ð7þ z j { m x 1 A : ð8þ E z j { Ex ð Þ In ths case, the varance was calculated, for each chromosome, between expected olgonucleotde frequences from a 40 kbp sldng wndow and expected olgonucleotde frequences based on Formula 5. Varaton of nucleotde frequences wthn genomes was calculated for each chromosome smlarly to the OUD test, but expected olgonucleotde frequences based on Formula 5 were used nstead of observed. Ths s the same as calculatng the varance between local and global GC content (.e. GC content) and Formula 7 was used for ths as well. Regresson analyss was performed on values obtaned wth Formula 7 and global GC content for both codng and whole chromosomes. The resultng equatons, wth the correspondng coeffcents of determnaton, R, and sgnfcance, can be found n the Results/Dscusson secton. Supportng Informaton Dataset S1 Mcrosoft Excel fle consstng of the data used to generate the results n the manuscrpt. Each column s labeled accordng to the abbrevatons used n the text and addtonally explaned on a separate sheet. Found at: do: /journal.pcb s001 (0.17 MB DOC) Acknowledgments Specal thanks to Smon Hardy and the revewers for constructve comments and for crtcally readng through the manuscrpt. Also, thanks to Stg Larsen for help wth the statstcal analyss and to Sten Marvold for the graphcal mage. Author Contrbutons Conceved and desgned the experments: JB. Performed the experments: JB. Analyzed the data: JB ES DU. Contrbuted reagents/materals/ analyss tools: JB. Wrote the paper: JB ES DU. Revsed and drafted the manuscrpt: DU. PLoS Computatonal Bology 8 Aprl 008 Volume 4 Issue 4 e

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