Best Practices

When you start running IAMBEE what you should know:

  • IAMBEE exploits parallel evolution to distinguish drivers from passengers.
  • It searches on an interaction network for sets of connected genes that are frequently mutated across a set of independently evolved populations.
  • These connected gene sets are proxies of adaptive pathways.

Whether the algorithm will be able to identify these adaptive pathways and will be able to successfully distinguish the drivers from the passengers depends on:

  1. The size of the network used as prior scaffold. If the network prior is large and contains many edges the chance is higher that genes with spurious mutations will start connecting to true sub-networks (referred to as clusters of mutations by the reviewer). Start IAMBEE with a rather curated network (less than 20000 edges for yeast or bacteria).
  2. The assumption of the network approach is that hitchhiking or other spurious mutations will be less connected to driver mutations than driver mutations would be connected to each other in the network. The network connectivity is a way to distinguish drivers from passengers. However, if the ratio of passengers to drivers is high (in an unfiltered input list), the chance that passenger mutations (spurious mutations) will connect spuriously to driver clusters is higher. That is why it is important to use, in case many mutations occur per population the additional information on the impact score of the mutations and on the frequency increase of the mutations during a selective sweep. This additional information performs an implicit weighting of the mutations when searching for paths that connect mutated genes and helps avoiding spurious paths.

Here we provide two case studies that show step by step how to run IAMBEE and how to interpret its results.

Case study 1: Analysis of data from an evolution experiment in E. coli

Below IAMBEE is run with the default settings using the E.coli case study using information on the functional impact scores, frequency increase of the mutation during a selective sweep and compensation for mutator phenotypes. In the runs below only genes with non-synonymous mutations were considered.

The dataset consists of 16 independent evolved populations. The interaction network of E. coli is the network used in the study of Swings et al which can be downloaded from the tab "Download Networks". The network contains high confident edges only (about 16000 edges). A curated network helps keeping the computational times reasonable and avoids prioritizing spurious genes (passengers that get connected by chance to the true adaptive genes).


Also provide the alternative gene names if you have them available. It facilitates the interpretation of the output (in the network visualization below the common gene names will be used).

The experiment contains populations with a mutator phenotype i.e. some populations have an aberrantly high mutation frequency. Because these populations carry so many mutations, they are less informative in pinpointing the correct driver mutations. So we want the contribution of the mutations occurring in such frequently mutated populations to have a lower impact on the analysis and on hence on the distinction between drivers and passengers. To down weight the impact of mutated genes occurring in mutator populations we switch on the slide "correction for mutation rate". We also use information on the functional impact scores, the frequency increase of the mutation during a selective sweep.

In the form upload the network file and the mutation file and the Gene Names file. If you want to close your browser after submitting the job do not forget to provide your email address. Results will be sent to you as soon as the run finishes (runs will automatically aborted if no results have been obtained after 48 h).

When the browser is open during the run, you can follow the progress. Possible errors in input files will also appear.

After the run is finished results can be:

  • Downloaded
  • Visualized in the browser
  • Emailed

In the network plot, the width of the edges represents the largest edge cost at which the edge was detected. The higher the edge cost, the more stringent the search and the more pronounced the signal was in the data that gave rise to the edges. Along the same lines you can download from the results folder the ‘rankedMutations.txt’ (in the folder opt/resulting_networks). Here all prioritized nodes are shown together with a rank. The higher the rank the more stringent the edge cost was at which this node was retrieved as member of the resulting sub-network (see list below).

How to interpret the results?

Start from an edge with large edge size or a node that is prioritized with a high rank. In the results above, a nice example of a prioritized path is tamB–tamA. TamB is an inner membrane protein that forms a complex (the translocation and assembly module or TAM) with the outer membrane protein, TamA. Almost all populations carry a mutation in the TamA-B system, being either through mutations in TamA or TamB.

TamA and B tend to be mutated in a mutually exclusive way which is to be expected if they together form the same system (colored triangles around the gene indicate the populations in which the gene was mutated): a mutation in TamA or TamB is sufficient to disrupt the entire system. Because of their connectivity in the network the TamA-B system is detected as a recurrently mutated sub-network. The same is observed for the other systems like e.g. the FabB, FabG and FabA system or for the mutS, mutL, mutH system.

The same visualization is also stored in the results folder (/opt/resulting_networks/d3js_visualization) . If you want to view the visualization html file in this folder do not forget to extract the folder prior to opening the visualization file.

Effect of the correction for mutator strains

Now we repeat the same run, but without using correction for mutator strains (still using the information on the functional impact and frequency increase).

The table above shows how some populations clearly exhibit a mutator phenotype

Some of the populations show clearly more mutations than others (HT1, HT9, HT11). Using the formula described in Swings et al and on the help-file of the website the impact on the search of mutations originating from the mutator populations will be relatively down weighted. The net effect of the correction is that mutated genes originating from a highly mutated population will receive a relatively lower relevance score and hence will less affect the outcome of the optimization.

The impact of the down weighting is illustrated by comparing the list of prioritized genes obtained from the run without correction for mutator strains with the genes obtained from the run in which the correction was applied. These lists can be obtained from the downloaded results folder.

The gene list obtained without the correction is longer and contains several additional genes not present in the list in which the correction was applied. These additional genes were mainly mutated in populations with an elevated mutation rate (HT1, HT9, HT11) and most likely constitute spurious genes (genes carrying passenger mutations that were linked spuriously through the network to true adaptive genes). Correction for elevated mutation rate avoids prioritizing these genes.

Click here to display the gene list.
Gene ID Gene Name Condition/Population Rank with Correction Rank without Correction
b0954 fabA HT7, HT10, HT13, HT14, HT15, HT16 1 1
b1093 fabG HT1 1 1
b3306 rpsH HT5, HT10, HT10 1 1
b3342 rpsL HT1, HT5, HT6, HT7, HT8, HT8, HT9, HT11, HT12, HT13, HT15 1 1
b3783 rho HT1, HT11, HT12, HT14, HT15, HT16 2 1
b3982 nusG HT9, HT10, HT11 2 1
b0463 acrA HT12, HT14, HT15, HT16 3 3
b2470 acrD HT1 3 3
b3169 nusA HT1, HT2, HT8, HT13 3 4
b3749 rbsA HT10 3 5
b3753 rbsR HT7, HT12, HT13 3 6
b3987 rpoB HT6, HT6, HT12, HT13 3 2
b4220 tamA HT4, HT9, HT11, HT13 3 4
b4221 tamB HT7, HT12, HT15 3 4
b0169 rpsB HT3 4 2
b0462 acrB HT1, HT7, HT9, HT9, HT9, HT11 4 3
b2114 metG HT9, HT9 4 3
b3229 sspA HT1, HT6, HT10, HT10, HT10, HT11, HT14, HT16 4 3
b3829 metE HT1, HT9, HT11 4 4
b0779 uvrB HT6, HT11 5 5
b1125 potB HT7, HT12 5 5
b1126 potA HT11, HT13 5 5
b1808 yoaA HT6, HT9, HT9, HT14, HT16 5 4
b2733 mutS HT10, HT13, HT15, HT15 5 5
b2831 mutH HT12 5 5
b3649 rpoZ HT6, HT7 5 4
b3750 rbsC HT4, HT8, HT12 5 5
b4019 metH HT11, HT11 5 3
b4058 uvrA HT9, HT11 5 6
b4170 mutL HT11 5 5
b0014 dnaK HT1, HT1, HT5, HT9, HT13 6 6
b0151 fhuC HT3 6 6
b0429 cyoD HT13, HT15 6 6
b0894 dmsA HT4, HT6, HT9 6 6
b1024 pgaA HT11, HT13 6 6
b1224 narG HT11, HT12, HT15 6 5
b1225 narH HT1, HT11 6 6
b1302 puuE HT1, HT4, HT9, HT15 6 6
b1468 narZ HT6, HT7, HT9 6 6
b1475 fdnH HT6, HT6, HT9 6 6
b1608 rstA HT1, HT7, HT9, HT11, HT13, HT16, HT16 6 6
b2167 fruA HT4, HT9 6 6
b2323 fabB HT1, HT11, HT12 6 6
b2662 gabT HT4, HT9, HT13 6 6
b2699 recA HT1, HT9, HT14, HT16 6 6
b2780 pyrG HT6 6
b2935 tktA HT13, HT15 6
b3006 exbB HT4, HT9 6 6
b3164 pnp HT9, HT11 6 5
b3296 rpsD HT4, HT9, HT14, HT16 6 5
b3365 nirB HT6, HT12 6 5
b3404 envZ HT5, HT6, HT9, HT11 6 6
b3564 xylB HT9, HT9, HT15, HT15, HT15 6
b3639 dfp HT4, HT9, HT10, HT13 6 4
b3700 recF HT9, HT9, HT11 6 6
b3770 ilvE HT6 6
b3974 coaA HT6, HT10 6 4
b3988 rpoC HT1, HT1, HT10, HT15, HT15 6 6
b0098 secA HT1, HT6
b0143 pcnB HT3, HT3, HT12
b0150 fhuA HT6, HT11
b0394 mak HT13
b0884 infA HT7
b0978 appC HT9
b1084 rne HT4, HT9, HT12
b1465 narV HT6
b2763 cysI HT8, HT8
b3059 ygiH HT5
b3517 gadA HT9, HT9
b3705 yidC HT4, HT9, HT9
b3813 uvrD HT1, HT9, HT13
b3878 yihQ HT5, HT6, HT9, HT12
b3925 glpX HT6
b4041 plsB HT7, HT7, HT9, HT11, HT11

We now repeat the run omitting the information from the frequency increase during the selective sweep.

Results show that the nodes and paths that were detected with a high confidence (at high cost) are the same as in the other runs. So the most pronounced signals are still always recovered irrespective of the parameter setting. This indicates that the algorithm’s performance is robust against potential noise in the data. However the current network contains more mutated genes that could be connected to the network. Most of these genes are detected at a low cost parameter only, indicating their signal is not extremely pronounced (meaning that they are mutated in a few populations only and not well connected to genes mutated in other populations). These are genes that are mutated in the populations but of which the frequency did not necessarily increase during the sweep. So despite being present in the populations and being connected in the network, they are not necessarily related to the adaptive phenotype.

We now repeat the run omitting the information from the functional impact of the mutations.

Doing this tremendously increase the search space as our input file contains for some populations a high number of mutations. Without weighting these mutations the algorithm has many different options to explore. The run took > 4 h so we closed the browser and waited for the email. From the results it appears that again the most prominent signals are present in the results (tamA-B, Fab, uvr-mut, acr are ranked highest) but not so much prioritized as previously. Several additional small connected components can be detected that might consist of passenger mutations that hitchhiked during the sweep but of which the likely functional impact is low (more spurious connections and prioritizations, ranks of most prioritizations are low).

Click here to display the gene list.
Gene ID Gene Name Rank Population/Condition
b3296 rpsD 1 HT4, HT9, HT14, HT16
b3342 rpsL 1 HT1, HT5, HT6, HT7, HT8, HT8, HT9, HT11, HT12, HT13, HT15
b3783 rho 1 HT1, HT11, HT12, HT14, HT15, HT16
b3982 nusG 1 HT9, HT10, HT11
b0954 fabA 2 HT7, HT10, HT13, HT14, HT15, HT16
b1093 fabG 2 HT1
b4220 tamA 3 HT4, HT9, HT11, HT13
b4221 tamB 3 HT7, HT12, HT15
b0014 dnaK 4 HT1, HT1, HT5, HT9, HT13
b0462 acrB 4 HT1, HT7, HT9, HT9, HT9, HT11
b0463 acrA 4 HT12, HT14, HT15, HT16
b1084 rne 4 HT4, HT9, HT12
b1808 yoaA 4 HT6, HT9, HT9, HT14, HT16
b2470 acrD 4 HT1
b3169 nusA 4 HT1, HT2, HT8, HT13
b3649 rpoZ 4 HT6, HT7
b3987 rpoB 4 HT6, HT6, HT12, HT13
b1468 narZ 5 HT6, HT7, HT9
b1469 narU 5 HT1, HT4, HT15, HT15
b2733 mutS 5 HT10, HT13, HT15, HT15
b3749 rbsA 5 HT10
b3753 rbsR 5 HT7, HT12, HT13
b3813 uvrD 5 HT1, HT9, HT13
b0054 lptD 6 HT1, HT9, HT10, HT11
b0394 mak 6 HT13
b0473 htpG 6 HT9
b0677 nagA 6 HT1
b0679 nagE 6 HT7, HT9, HT12
b0779 uvrB 6 HT6, HT11
b0937 ssuE 6 HT1, HT2
b1225 narH 6 HT1, HT11
b1241 adhE 6 HT5, HT9, HT11
b1302 puuE 6 HT1, HT4, HT9, HT15
b1525 sad 6 HT1
b1608 rstA 6 HT1, HT7, HT9, HT11, HT13, HT16, HT16
b1757 ynjE 6 HT1, HT2, HT3
b2114 metG 6 HT9, HT9
b2526 hscA 6 HT1, HT3, HT10
b2527 hscB 6 HT15
b2662 gabT 6 HT4, HT9, HT13
b2763 cysI 6 HT8, HT8
b2831 mutH 6 HT12
b2989 yghU 6 HT1, HT11
b3229 sspA 6 HT1, HT6, HT10, HT10, HT10, HT11, HT14, HT16
b3231 rplM 6 HT7
b3306 rpsH 6 HT5, HT10, HT10
b3450 ugpC 6 HT13
b3750 rbsC 6 HT4, HT8, HT12
b3878 yihQ 6 HT5, HT6, HT9, HT12
b3988 rpoC 6 HT1, HT1, HT10, HT15, HT15
b4261 lptF 6 HT11
b0169 rpsB 7 HT3
b1224 narG 7 HT11, HT12, HT15
b3168 infB 7 HT3, HT5, HT12, HT13, HT13, HT13, HT13, HT13, HT13, HT13, HT13, HT13, HT13
b3201 lptB 7 HT11, HT13
b1184 umuC 8 HT13, HT13
b2699 recA 8 HT1, HT9, HT14, HT16
b3639 dfp 8 HT4, HT9, HT10, HT13
b3751 rbsB 8 HT9, HT11
b3974 coaA 8 HT6, HT10
b4058 uvrA 8 HT9, HT11
b4143 groL 8 HT11
b1296 ycjJ 9 HT3, HT6
b1764 selD 9 HT9, HT12
b2220 atoC 9 HT11, HT15
b2297 pta 9 HT9, HT15
b2890 lysS 9 HT7
b3591 selA 9 HT1, HT11
b3706 mnmE 9 HT6, HT9
b3741 mnmG 9 HT8, HT8, HT9, HT9
b3746 ravA 9 HT6, HT7
b3846 fadB 9 HT6
b4131 cadA 9 HT3
b1385 feaB 10 HT9, HT11
b1386 tynA 10 HT9, HT9, HT10, HT13
b2301 yfcF 10 HT5, HT9
b3114 tdcE 10 HT1, HT9, HT11, HT13
b3189 murA 10 HT1, HT9, HT10
b3722 bglF 10 HT13
b3972 murB 10 HT12
b0055 djlA 11 HT9
b0143 pcnB 11 HT3, HT3, HT12
b0418 pgpA 11 HT8, HT13
b0431 cyoB 11 HT1
b0512 allB 11 HT1, HT9
b0516 allC 11 HT4, HT9, HT9
b0933 ssuB 11 HT9
b0936 ssuA 11 HT7, HT13
b1278 pgpB 11 HT4, HT9, HT9
b1494 pqqL 11 HT5, HT5, HT13
b1912 pgsA 11 HT8, HT9, HT11
b2441 eutB 11 HT9
b2451 eutA 11 HT3, HT11
b3017 ftsP 11 HT5, HT15
b3164 pnp 11 HT9, HT11
b3566 xylF 11 HT5, HT6
b3567 xylG 11 HT11
b3839 tatC 11 HT13
b0884 infA 12 HT7
b0694 kdpE 13 HT8
b0695 kdpD 13 HT1, HT13
b0789 clsB 13 HT4, HT5
b1125 potB 13 HT7, HT12
b1126 potA 13 HT11, HT13
b1174 minE 13 HT15
b1913 uvrC 13 HT11
b2194 ccmH 13 HT9
b2196 ccmF 13 HT9, HT13, HT15
b3725 pstB 13 HT5, HT9
b3727 pstC 13 HT4, HT9
b0593 entC 14 HT4, HT6
b1040 csgD 14 HT6
b1244 oppB 14 HT11
b1329 mppA 14 HT5, HT9
b1603 pntA 14 HT6, HT9
b1638 pdxH 14 HT6
b1747 astA 14 HT1
b2264 menD 14 HT11
b2418 pdxK 14 HT11, HT15
b2615 nadK 14 HT9, HT11
b2938 speA 14 HT3, HT11
b3447 ggt 14 HT7, HT9
b3564 xylB 14 HT9, HT9, HT15, HT15, HT15
b3583 sgbE 14 HT7, HT9
b3786 rffE 14 HT7, HT9
b3787 rffD 14 HT6, HT9

Yeast Case Study

The data were obtained from the study of Jerison et al. 2017 contained S. cerevisiae populations that were evolved for 500 generations in optimal temperature. 273 different founder strains were used (different genomic context). The founders selected for this sequencing were chosen to ensure approximately equal representation of each parental KRE33 allele, but were otherwise random. For each founder 4 parallel populations were obtained that were evolved under ordinary temperature (OT). Per population the variants with an allele frequency >60% were selected, 68% were at > 90% frequency. Most mutations in the given list were nonsynonymous. The list of de novo mutations in each of the populations evolved under OT conditions was obtained from the authors of the paper and can be downloaded here.

The figure below gives an overview of the different genes containing de novo mutations as were detected in the original paper.

Fig. taken from “Genetic variation in adaptability and pleiotropy in budding yeast. Jerison ER, Kryazhimskiy S, Mitchell JK, Bloom JS, Kruglyak L, Desai MM. Elife. 2017 Aug 17;6. pii: e27167. doi: 10.7554/eLife.27167.”

The list below recapitulates the genes described in the paper, (*) indicates that the genes were detected with IAMBEE-run1; (not in strict network) indicates the genes were not in the network used in run and hence could not be retrieved.

  • YNL132W,KRE33 *
  • YGR145W,ENP2 (*, not in strict network)
  • YDR299W,BFR2 (*, not in strict network)
  • YPL217C,BMS1 (*)
  • YBL004W,UTP20 (*)
  • YBL072C,RPS8A (*)
  • YPL090C,RPS6A (*)
  • YGR218W, CRM1
  • YMR128W,ECM16 (*)
  • YBR140C,IRA1 (*, not in strict network)
  • YOL081W,IRA2 (*, not in strict network)
  • YOR360C,PDE2 (*, not in strict network)
  • YDL042C,SIR2
  • YLR442C,SIR3 (*, not in strict network)
  • YDR227W,SIR4 (*, not in strict network)

Run1: Strict network (edge probability cutoff of 0.95)

In this setting we run IAMBEE with a S. cerevisiae STRING derived network (stringency of the edges larger >0.95). The network contains about 16000 edges. We used as input all variants excluding the synonymous variants and the variants that were in the input file not mapped on genes (intergenic etc). A default parameter sweep was performed (note that in the default setting the parameters of the cost used during the sweep are determined automatically and are dataset specific. The parameter values used are available in the results folder. Only compensation for the mutator phenotype was used as the functional information and the increase in frequency during the sweep were not available (make sure these sliders are switched off).

This run took about 30 minutes.

The result can be visualized in the browser. However to assess the relevance of the results, download the results folder and unzip the folder. Go to the folder "\opt\resulting_networks\d3js_visualization"

You can open the file ‘highestScoringSubnetwork.html’ and visualize the network in the browser. The edge width is here replaced by the opacity of the edges. Edges which are more dark in color represent the more significant signals in the data. A print screen of this network is given below.

From the folder \opt\resulting_networks\ open the file rankedMutations.txt. You will see the set of genes that were prioritized together with their rank (based on the sweep on the cost parameter). A lower rank means that the gene was already prioritized at a higher cost value and hence represents a more significant signal in the data.

To interpret the visualization results it is important to focus on the genes indicated in this file. These are the genes that carry the actual mutations. In the network visualization many more genes are indicated some of which are ‘connector genes’. Genes that are used to find paths that connected the mutated genes. For each mutated gene the visualization provides in the circle around the gene a colored section which indicates the population in which the gene was mutated (see below as indicated in the figure). Genes with a colored section are the ones that are mutated and can be retrieved in the text file.

Example of the text file with prioritized genes for run1.

  • YPL217C    1
  • YGL078C    1
  • (*) YNL132W    1
  • YPL090C    2
  • YEL037C    3
  • YER025W    3
  • YBL072C    3
  • YER162C    3
  • YNL308C    3
  • YMR128W    3
  • YKR095W    3
  • YMR047C    3
  • YBL079W    3
  • YDR390C    3
  • YNL182C    3
  • YER102W    3
  • YJL039C    3
  • YGL150C    4
  • YER127W    4
  • YDL132W    4
  • YJL081C    4
  • YDR096W    5
  • YGR184C    5
  • YPR019W    5
  • YNL262W    5
  • YDR238C    6
  • YOL138C    6
  • YOR022C    6
  • YMR218C    6

To focus on genes or sub-network, start with the highest ranked genes from the list is possibility. Usually these genes are also the most frequently mutated (indicating that the optimization does its job correctly). For instance, YNL132W (KRE33). The figure shows the location of KRE33 and its neighboring mutated genes (indicated figure with red and blue box respectively). These are the genes prioritized because they are part of a recurrently mutated sub-network (despite the fact that most of them are only mutated once or twice over all populations. The network in its entirety is frequently mutated.

  1. YNL132W (KRE33 - frequently mutated, KRE33 was present in the background genome of certain founders of the population): Protein required for biogenesis of the small ribosomal subunit; heterozygous mutant shows haploinsufficiency in K1 killer toxin resistance; essential gene; NAT10, the human homolog, implicated in several types of cancer and premature aging
  2. YGL078C (DBP33 - mutated once): RNA-Dependent ATPase, member of DExD/H-box family; involved in cleavage of site A3 within the ITS1 spacer during rRNA processing; not essential for growth, but deletion causes severe slow-growth phenotype
  3. YNL308C (KRI1 - mutated once): Essential nucleolar protein required for 40S-ribosome biogenesis; associate with snR30; physically and functionally interacts with Krr1p-kri1
  4. YNL182C (IPI1 - mutated once): Component of the Rix1 complex and pre-replicative complexes (pre-RCs). It is required for processing of ITS2 sequences from 35S pre-rRNA, component of the pre-60S ribosomal particle with the dynein-related AAA-type ATPase Mdn1p; required for pre-RC formation and maintenance during DNA replication licensing; highly conserved protein which contains several WD40 motifs; IPI3 is an essential gene; other members include Rix1p, Ipi1p, and Ipi3p, IPI3

Another frequently mutated gene is YPL217C (BMS1). This gene together with its neighbor are also located on the network (red boxed). These genes together with its mutated neighbors from the prioritized list forms a second recurrently mutated subcluster.

  1. YPL217C (BMS1 - mutated frequently): GTPase required for ribosomal subunit synthesis and rRNA processing; required for synthesis of 40S ribosomal subunits and for processing the 35S pre-rRNA at sites A0, A1, and A2; interacts with Rcl1p, which stimulates its GTPase and U3 snoRNA binding activities; has similarity to Tsr1p
  2. YBL004W (UTP20 - mutated twice): Component of the small-subunit (SSU) processome; SSU processome is involved in the biogenesis of the 18S rRNA
  3. YLR409C (UTP21) (mutated once): Subunit of U3-containing 90S pre-ribosome and SSU processome complexes; involved in production of 18S rRNA and assembly of small ribosomal subunit; synthetic defect with STI1-Hsp90 cochaperone; human homolog linked to glaucoma; Small Subunit processome is also known as SSU processome.
  4. YPL090C (RPS6A - mutated twice): Protein component of the small (40S) ribosomal subunit; homologous to mammalian ribosomal protein S6, no bacterial homolog; phosphorylated on S233 by Ypk3p in a TORC1-dependent manner, and on S232 in a TORC1/2-dependent manner by Ypk1/2/3p; RPS6A has a paralog, RPS6B.
  5. YBL072C (RPS8A - mutated once): Protein component of the small (40S) ribosomal subunit; homologous to mammalian ribosomal protein S8, no bacterial homolog; RPS8A has a paralog, RPS8B, that arose from the whole genome duplication
  6. YMR128W (ECM16- mutated once): Essential DEAH-box ATP-dependent RNA helicase specific to U3 snoRNP; predominantly nucleolar in distribution; required for 18S rRNA synthesis.

The sub-networks indicated with the blue and red boxes are also the sub-networks that receive the highest edge width (in the online visualization) or lowest opacity in the html offline visualization.

Some of the infrequently mutated genes (those that received e.g. rank 6), that appear to have been mutated only once in the population and that are not located in a cluster of frequently mutated genes are likely passengers. At the least stringent cost, passengers can get connected spuriously.

*e.g. YMR047C (NUP116 - mutated once): nup116FG-nucleoporin component of central core of the nuclear pore complex; contributes directly to nucleocytoplasmic transport and maintenance of the nuclear pore complex (NPC) permeability barrier; forms a stable association with Nup82p, Gle2p and two other FG-nucleoporins (Nsp1p and Nup159p); NUP116 has a paralog, NUP100, that arose from the whole genome duplication
The complete results for this run can be downloaded clicking here.

The example shows that IAMBEE finds as a recurrently mutated sub-network a set of genes that is involved in 90s preribosomal pathway which is in line with the results described in the original paper. Note that some of the genes described in the paper of Jerison et al could not be recovered in this run, mainly because they were not connected in the network. Therefore it is important to ensure that the ‘mutated genes’ are connected in the network, otherwise one might not be able to recover them.