While the functional role of STDP has been investigated extensively, the effect of rhythms on the precise timing of STDP has not been characterized as well. We use a simplified biophysical model of a cortical network that generates pyramidal interneuronal gamma rhythms (PING). Plasticity via STDP is investigated at the excitatory pyramidal cell synapse from a gamma frequency (30–90 Hz) input independent of the network gamma rhythm. The input may represent a corticocortical or an information-specific thalamocortical connection. This synapse is mediated by N-methyl-D-aspartate receptor mediated (NMDAR) currents. For distinct network and input frequencies, the model shows robust frequency regimes of potentiation and depression, providing a mechanism by which responses to certain inputs can potentiate while responses to other inputs depress. For potentiating regimes, the model suggests an optimal amount and duration of plasticity that can occur, which depends on the time course for the decay of the postsynaptic NMDAR current. Prolonging the duration of the input beyond this optimal time results in depression. Inserting pauses in the input can increase the total potentiation. The optimal pause length corresponds to the decay time of the NMDAR current. Thus, STDP in this model provides a mechanism for potentiation and depression depending on input frequency and suggests that the slow NMDAR current decay helps to regulate the optimal amplitude and duration of the plasticity. The optimal pause length is comparable to the time scale of the negative phase of a modulatory theta rhythm, which may pause gamma rhythm spiking. Our pause results may suggest a novel role for this theta rhythm in plasticity. Finally, we discuss our results in the context of auditory thalamocortical plasticity.Spike timing dependent plasticity (STDP) has been observed experimentally  Rhythms are well studied phenomena in many animal species. Brain rhythms in the gamma frequency range (30–90 Hz) are thought to play a role in attention and memory. In this paper, we are interested in how cortical gamma rhythms interact with information specific inputs that also have a significant gamma frequency component. The results from our computational model show that plasticity associated with learning depends on the specific frequencies of the input and cortical gamma rhythms. The results show a mechanism by which both increases and decreases in the strength of the input connection can occur, depending on the specific frequency of the input. A current mediated by NMDA receptors may be responsible for the temporal course of the plasticity seen in these brain regions. We discuss the implications of our results for conditioning paradigms applied to auditory learning. The input is considered as an information encoding input, while the network is considered as a cortical network model in which gamma rhythms are modulated by non-specific drive See . With teWhile N-methyl-D-aspartate receptor mediated (NMDAR) currents are known to be necessary in mediating many synapses affected by STDP Finally, we discuss a possible implication for our model results in the context of gamma rhythms nested in theta rhythms, and we discuss how the gamma frequency input in the model may represent an information specific corticocortical or thalamocortical input to a primary sensory cortical network with an independent gamma frequency.α-amino-3-hydroxyl-5-methyl-4-isoxazole propionic acid receptor mediated (AMPAR) currents. The time constant for the decay of the NMDAR current The model consisted of a conductance-based network model of primary sensory cortex that generated a gamma frequency rhythm, receiving an input of spike times that occurred at an independent gamma frequency . The netThe input consisted of a list of discrete presynaptic spike time events with a period consistent with a gamma frequency (11–24 ms), independent of the network gamma rhythm. These spike time events activated glutamatergic responses in both the E and I cells in the model. Plasticity was modeled at the input E cell synapse by changes in the AMPAR conductance parameter For analysis, a frequency map was used to examine the global behavior of potentiation and depression over a wide range of input and network frequency regimes. In the map, frequency regimes were expressed as a ratio of the input frequency The simplest case of a single gamma frequency input to the cortical network was considered initially. The model showed that broad bands of potentiation and depression existed for different frequency regimes that were periodic in the gamma frequency range . Beta frUnlike the rhythmic gamma frequency inputs, Poisson distributed stochastic inputs did not lead to broad bands of potentiation . HoweverIn potentiating frequency regimes, the rise of potentiation was generally followed by the subsequent decline. Therefore, each frequency regime had a maximal level of potentiation and a finite time over which potentiation lasted. Of the 483 potentiating frequency regimes shown in  . Strong  .In a typical example of potentiation , spike robserved . The modobserved . The sloThough a constant instead of decaying activation of the NMDAR current model did not qualitatively change the frequency map, NMDAR currents in the model were a natural candidate for mediating the transition from potentiation to depression that occurred over time scales around 100 ms. Increases in In the common frequency regimes that showed potentiation for a control  longer  , with a The decay of the NMDAR current can account for the amplitude increase by prolonging E cell spiking activity. When an input spike immediately preceded an E cell spike, the STDP rule increased the AMPAR conductance, and the event-related NMDAR current was initiated. This raised conductance caused the next cycle of the E cell to fire faster, changing the rhythm of the network oscillator. However, as the NMDAR current decayed, the network rhythm was slowly pulled out of a potentiating frequency regime.Due to the prolonged availability of slowly decaying NMDAR currents, a potentiating network rhythm could conceivably be prolonged by allowing for another positive spike timing event to occur. However, once the NMDAR current was extinguished completely, and the spiking rhythm fell out of a potentiating regime, only a strong positive spike timing event could have prolonged or re-initiated the potentiation. The long time course of the model NMDAR current may be an advantage of the system by allowing multiple gamma period spike timing events to occur during the NMDAR current decay. This sets a natural window for greater potentiation to occur, while also limiting the possibility for unlimited spiking pairing to occur after the initial potentiating event.While transient potentiation was observed in nearly all simulations run with the standard no decay .The lack of NMDAR current decay dynamics eliminated the gradual change of the E cell spiking rate that is typically present. Since the rate of E cell spiking did not change dramatically without the NMDAR current decay, positive spike pairs were observed on each cycle of E cell spiking, contributing to a monotonic rise in potentiation.Temporarily suspending presynaptic input prolonged potentiation in the time period after the pause. In all frequency regimes in which potentiation was observed without pauses, greater potentiation was observed after insertion of certain pause conditions, which were identified by start time and duration. Almost any length of pause resulted in post-pause potentiation, as long as spiking resumed before the NMDAR current was completely extinguished (approx. 80–130 ms). The model suggests that there was an optimal pause duration that resulted in maximal post-pause potentiation, which was dependent upon the specifics of the frequency regime.For all frequency regimes of potentiation, several positive spike pairs prolonged the potentiation. However, the increased activation drove the E cell spikes to precede the input spikes, and the potentiation turned to depression, as previously described. Further potentiation was observed  comparedobserved .In simulations with multiple inputs, the maximum contribution from each individual input was scaled inversely to the number of inputs. In this condition, coherence of inputs was necessary for strong potentiation. However, the simulations also showed that when inputs were slightly perturbed, potentiation was greater in some frequency regimes compared to fully coherent cases.For three inputs in a potentiating frequency regime, high coherence inputs . Both the temporal and amplitude asymmetries of the STDP rule were necessary for the frequency map structure seen in of  and  . FurtherAmplitude asymmetry, in which the ratio ntiation . With thIn this paper, for the first time, we investigate the consequences of STDP between two interacting gamma rhythms in a biophysical model. We show that potentiation and depression depend on the frequencies of both the input and the receiving network. The model suggests STDP as a mechanism by which responses to certain inputs can potentiate, while responses to other inputs depress. The bands of potentiation are broad with respect to input frequency and are robust to noise. The time and amplitude of the transient potentiation are primarily explained by phase precession that is dependent on the long time course of the dynamics of the NMDAR current in the model. The NMDAR current has long-lasting effects that persist beyond the initial activation of the receptors in the model, which is relevant to other biophysical models utilizing STDP, since these results suggest that STDP-mediated potentiation is a transient phenomenon when the input is prolonged. Additionally, the model suggests that pauses in the input spiking may increase the efficacy of the potentiation, which may have implications for tone shock presentations in an auditory fear conditioning paradigms. Finally, our network model could represent supragranular layer II/III of sensory cortex, and these results would apply to frequency-specific connections that may represent either a corticocortical input from granular layer IV or a direct thalamocortical input.The time course and amplitude of potentiation for a single frequency regime in the model increased with greater The time scale over which the potentiation switched to depression in most frequency regimes  was compWhile different gamma frequency inputs in the model led to broad bands of potentiation and depression , theta fIn our simulations, a gamma frequency input was necessary for the presence of robust bands of potentiation in the frequency map, suggesting the involvement of a gamma frequency component in the input. Many aspects of our model parallel certain features of the auditory systems of some animals. The cortical network in the model most accurately depicts a supragranular gamma rhythm in primary auditory cortex (A1). However, there is uncertainty regarding the source of the input gamma rhythm. While it is known that there are tone frequency specific lemniscal inputs to A1 from the ventral portion of the medial geniculate complex of the thalamus (MGv) Certain predictions of our model may be testable in the thalamocortical auditory system of rats. In one circuit of thalamocortical auditory plasticity The model results show that STDP may be able to account for this dual plasticity between the cortical gamma rhythm and the input gamma rhythm. The variable input frequency in the model represents the encoded TF and BF frequencies, which result in potentiation and depression, respectively, and is summarized in Within the context of the auditory circuit, the model results suggest that long tone shock pair presentations in a fear conditioning paradigm can result in decreased efficacy of potentiation, especially if the presence of a slower rhythm to effectively pause the gamma rhythm is attenuated Our results suggest that the interaction of different gamma frequency rhythms can lead to potentiation or depression, depending on the frequency ratio. The amplitude and temporal profile of this potentiation is dependent on the NMDAR current dynamics, a result relevant to both models and experimental paradigms.The frequency-specific thalamocortical input is modeled as a list of discrete spike time events with interspike intervals (ISIs) corresponding to rhythms in the gamma frequency range (11–24 ms). Results are reported without noise in the input ISI, unless otherwise noted. In noisy simulations, Gaussian random noise is applied with a mean equal to a predetermined ISI and a standard deviation of Multiple inputs are also considered in certain simulations. Multiple inputs are modeled by distinct spike timing lists corresponding to each input, each with a specified average ISI and noise measure. Coherence The glutamate concentration A-like I to E, AMPAR, NMDAR, leak, and applied currents, respectively. The The single compartment E and I cells are Hodgkin-Huxley type cells that are coupled via synaptic currents and closely follow the formulation used in other work to represent a single auditory frequency channel aptation , as obseThe voltage of the I cell . The differential equations are integrated using MATLAB's built in ode15s stiff solver of variable order and time step.


Automatic imitation is the tendency to reproduce observed actions involutarily. Though this topic has been widely treated, at present little is known about the automatic imitation of the kinematic features of an observed movement. The present study was designed to understand if the kinematics of a previously seen stimulus primes the executed action, and if this effect is sensitive to the kinds of stimuli presented. We proposed a simple imitation paradigm in which a dot or a human demonstrator moved in front of the participant who was instructed either to reach the final position of the stimulus or to imitate its motion with his or her right arm. Participants' movements were automatically contaminated by stimulus velocity when it moved according to biological laws, suggesting that automatic imitation was kinematic dependent. Despite that the performance, in term of reproduced velocity, improved in a context of voluntary imitation, subjects did not replicate the observed motions exactly. These effects were not affected by the kind of stimuli used, i.e., motor responses were influenced in the same manner after dot or human observation. These findings support the existence of low-level sensory-motor matching mechanisms that work on movement planning and represent the basis for higher levels of social interaction. Motor imitation, that is, the possibility of interacting physically with others by sharing a behavioural state, represents a powerful biological resource for cognitive development This topic is controversial and became the matter of different theories of imitation that pose either high-level  can tune the imitation process. In particular, the questions we want to answer are the following: 1) is the actual movement production influenced by the vision of a prior biological moving stimulus, leading to a spontaneous contamination? 2) If this is the case, what is the difference between implicit (automatic) and explicit (voluntary) imitation, and how sensitive are they to visual stimuli (abstract vs. human)? 3) Does the biological kinematic tune automatic speed contamination? 4) Are observers able to reproduce exactly the velocity of a visual stimulus when they are explicitly asked to imitate it? To these aims, we compared voluntary imitation performances to the motor features of pointing tasks both executed after the display of moving visual stimuli, differing in velocity , in kinematic  and in shape (dot vs. human demonstrator).This study was divided into three parts hereafter called Preliminary, Movement Observation and Kinematic experiments. A total of nineteen healthy young adults participated in the experiments. All participants were right-handed according to an informal interview, and had normal or corrected-to-normal vision. Written informed consent was obtained from each participant in the study, which was approved by the local ethical committee ASL-3 , Genoa, and was in agreement with the Helsinki Declaration of 1975, as revised in 1983.The preliminary experiment was aimed at measuring participants' natural pointing movements. The kinematic data served as a baseline to be compared with arm kinematics after motion observation. Fourteen healthy participants  took part in this experiment.Apparatus. The experiment was performed in a darkened room. Participants sat on a chair, in front of a large rear projection screen (190×140 cm) at a viewing distance of about 90 cm. A video-projector, with a refresh rate of 75 Hz, placed behind the screen and connected to a PC, back-projected the visual stimuli onto the display screen. A VICON Motion Capture System with seven cameras was used to record movements at a sampling frequency of 100 Hz. One passive infrared reflective marker was applied onto a fingertip of each participant's right hand.Stimuli, tasks, and procedure. Two vertically aligned light blue dots (2 cm in diameter), placed at a distance of 72 cm from each other were displayed for 300 ms on the screen. One of the two dots served to show the starting position of the participant's arm and the other one was the target for the movement. The participants' shoulder level was roughly at the middle of the dots. Participants had to perform upward and downward single arm movements with their right arm extended, at a spontaneous, natural velocity, and without making any final corrections (i.e. one-shot movements). Before each trial, participants were verbally informed of the starting position of the subsequent movement, and they were instructed to point there with their right finger. When both dots disappeared, the subjects moved their arm up to the memorized position of the target dot. Movement accuracy was not emphasized at all. For each direction the pointing arm movement was replicated 10 times in a random order. The beginning of the experiment was preceded by a training phase of 5 movements for each direction.A moving stimulus was used as a template to test the effect of motion perception on subsequent arm movement execution. Participants were the same of the Preliminary experiment.Stimuli, tasks, and procedure. Participants accomplished two types of tasks: implicit (I) and explicit (E). They differed only in the instructions given to the participants: during implicit task they were free to produce movements at a velocity of their choice; whereas during explicit task subjects were requested to imitate the stimulus motion velocity. Each task was divided into two conditions in which either a dot (D) or a human (H) was used as the stimulus.Dot observation. The Apparatus was the same as in the Preliminary experiment. The stimuli sequence is shown in Human observation. The person that acted as the stimulus  was a woman and was the same in all the experiments. She was previously trained to make a one-shot, straight, vertical pointing movement at three different velocities as close as possible to the dot velocities . The demonstrator's mean velocities for each experimental condition are reported in Experimental design. Four within-subjects factors were considered as sources of variability: Task , Stimulus , Direction , and Velocity , resulting in a total of 192 trials (8 replications per condition). After a block of 16 trials, participants took a pause for at least one minute. The experimenter reminded them of the instructions after each pause. The experiment was preceded by a training phase (6 movements: 3 velocities x 2 directions) for each task and stimulus. The implicit task always preceded the explicit one in order to keep the subjects unaware of the latter task during implicit condition. Performing the explicit before the implicit task could lead the subjects to imitate explicitly, regardless of the experimental requirements (to imitate or only to reach the target).A dot moving according to a non-biological kinematic was used to figure out the role of the kinematic features in speed contagion. Five participants  took part in the experiment. No one of them performed Preliminary and Movement Observation experiments.Stimuli, tasks, and procedure. Because our main interest was on the automaticity of movement planning, participants accomplished only the implicit (I) task: thus, they were free to produce movements at a velocity of their choice. Moreover, because of the impossibility to force a human demonstrator to violate the biological law of motion, the dot was the only stimulus used. The apparatus and the stimuli sequence were the same as in the Movements Observation experiment  values in upward and downward pointing movements.Movement Observation experiment. For all trials, V was considered as a main outcome variable. Outlier values (more than twice the standard deviation) were removed from the analysis. In order to determine the role of the observed motions in movement execution, we compared V  with the baseline values  by mean of a paired t-test, with Dunnett correction for multiple comparisons. After that, stimuli (dot and human) V were statistically compared. As reported below, because of the difference between their kinematic features, we analyzed the participants' responses to dot and human stimuli separately, applying two mixed-model analyses of variance on V, one for each stimulus. These allowed for detecting any systematic effects of Task, Direction, and Velocity. This statistical analysis method was chosen for its flexibility to designs that are not perfectly balanced, as in our case. Moreover, it allows for taking into account the intrinsic (and uncontrolled) variability among the participants, which was considered everywhere as a random factor. This information will not be repeated hereafter in the text. Significant interactions between factors were examined with Post-Hoc Newman-Keuls comparisons. A linear regression model illustrated the relationship between stimuli and the participants' V values. The parameters of the linear fits describing the stimuli and the participants' V relationship were mainly used to compare the effects of the two stimuli on the participants' responses: the slopes and the intercepts of the regression lines obtained for each participant were evaluated statistically using three factors  mixed model analysis.Kinematic experiment. A one-way ANOVA was applied to test the effect of the stimulus velocity (factor Velocity). A linear regression model illustrated the relationship between the V values of stimuli and participants. In order to test the effect of the biological and the non-biological kinematics, we compared the slopes here obtained with those calculated in the Movement Observation experiment (factor Kinematic) through a one-way ANOVA.F = 37.89, p<0.01), whereas the mean velocities  were not.Modulations of velocity along the trajectory were in agreement with those already described by Papaxanthis et al. This experiment was aimed at testing to which degree the velocity of an executed arm movement mimicked the velocity of an observed motion. Pointing movements were spontaneous or constrained to imitate previously observed motions . We tested if participant's movements were influenced by the stimuli mean velocity. In order to control against possible contamination, the participants' mean velocity values (V) were examined. In order to determine how much participants modified their natural movement velocity (see Preliminary experiment) after observing a moving stimulus, V, at each level of the Velocity factor, was compared with each participant's baseline velocity using a paired t-test. 2>0.8 in all conditions). Lastly, participants came closer in explicit task to retracing the line representing a perfect imitation of stimuli velocities  = 238.56, p<0.01; intercepts: F = 182.39, p<0.01), whereas the Direction factor did not significantly affect the subjects' performance. Likewise, the Stimulus factor was not statistically significant, suggesting that differences in the qualitative features of the visual stimulation did not evoke different motor responses. Moreover, a statistical comparison of the slopes values between a non-imitative behaviour  and the implicit task gave further proof of the implicit contamination applied by the visual stimuli. A paired t-test was used to compare up and down slopes of the implicit task with the null value of the horizontal line. A significant difference (p<0.01) confirmed the priming role of movement perception in action execution.This experiment was performed to evaluate the changes in participants' performances after the observation of different stimuli kinematics. We predicted that speed contagion should be more pronounced for biological than for non-biological stimuli motions. This result would explain speed contagion as a phenomenon mediated by automatic imitation process, and not exclusively by visuomotor priming. Since no differences in Movement Observation experiment appeared between the two directions, participants performed only upward movements.F = 8.09, p = 0.01). This indicated that participants' motor performances were affected by motion kinematics.The present study analyzed the effect of moving visual stimuli, differing in shape (dot vs. human demonstrator) and in velocity , on the kinematic features of a pointing task performed after the display of moving stimuli. First and foremost, the results indicate that the participants' pointing velocities varied as a consequence of the stimuli velocities, even when the participants were not explicitly asked to imitate the observed motion. Moreover, dot (an abstract and meaningless stimulus) and demonstrator' motions produced the same effect on subsequent participants' pointing movements. Even though imitative behaviour has been demonstrated in several situations, to the best of our knowledge, no study has described the effects of perception of a moving stimulus on action's kinematics. The following discussion will consider the issues of implicit and explicit imitation and the origin of the visual stimulus (human- or computer- generated) in the imitation process.This study addresses the topic of spontaneous imitation by considering the effect of visuomotor priming optimal module (endogenously imposed) and imitation constraints (exogenously imposed) combine linearly. When the priority is to imitate the displayed movement (explicit condition), the weight of the imitation constraints is maximal. Instead, in implicit condition, the mechanism combining the optimal response with imitation constraints would produce a clockwise rotation of the explicit regression line towards the horizontal line  as observed here.Moving the discussion toward a computational approach, the execution of a natural pointing movement requires a series of processes that seem to be based on the optimality principle Surprisingly, we did not find a better imitation of the human demonstrator as compared to the abstract stimulus. Regarding previous investigations using interference or SRC paradigms, this result was difficult to predict. For instance, it has been proposed that the biological relevance of the visual stimulus is the key feature in eliciting automatic imitation In explicit task, the results show that the observers' slowest and fastest pointing movements were always preceded by slow (S) and fast (F) stimuli, respectively, in agreement with implicit results. This indicates the participants' ability to distinguish stimulus velocities  and their attempt to replicate them (motor ability), though they fail in exact reproduction. The statistical analysis confirmed the expected differences between the tasks. When participants were explicitly instructed to imitate the stimulus velocity, imitation performance improved probably as a result of greater attention directed toward the kinematic features of the stimulus. However, in explicit conditions, the mechanism immediately translating the observed motion into the produced one showed limitations.Biomechanical constraints that reduce velocity during the execution of fast arm movement are one possible explanation for the participants' incapacity to reproduce the observed motion velocity exactly. However, this hypothesis seems unacceptable for several reasons. First, the demonstrator was actually able to produce high pointing-movement velocities. Second, in order to verify the participants' motor capacities to produce rapid movements they were asked to point as fast as possible. The recorded velocity values were all in the range of the velocities performed by the demonstrator or displayed on the screen.Inaccuracy in movement duration estimation may also explain the systematic trend to produce movements slower than the stimulus ones. In the present experiment, subjects were not asked to reproduce movement duration but to imitate the displayed velocity. However, an estimation process of stimulus duration on the basis of its velocity and amplitude cannot be rejected. Several studies have demonstrated that movement duration  is systematically overestimated More plausibly, perceptual inaccuracy of the visual system to measure acceleration can explain the imitation degradation that was found linearly to be velocity-dependent. Additionally, the mechanisms involved in translating the perceived features of a stimulus into a motor command could also degrade a participant's imitation capacity. During an explicit imitation, the motor plan had to produce the finger's specific trajectory and velocity based on the observed trajectory and velocity. Moreover, noise could be generated during the transformation of the visual input into the motor imitation as is the case during action production stricto sensu utilization of the term direct.Hence, even if the present study indicated the motor effect of the observed action and confirmed a strong link between perceptual and action systems, both automatically and voluntarily, a perfect matching between the perceived movement and the observer's motor performance is not possible, even for a simple arm pointing task. This is not in opposition to the direct-matching hypothesis, but it puts in perspective a The linear trend observed both in implicit and in explicit conditions  suggests


The variations within an individual's HLA (Human Leukocyte Antigen) genes have been linked to many immunological events, e.g. susceptibility to disease, response to vaccines, and the success of blood, tissue, and organ transplants. Although the microarray format has the potential to achieve high-resolution typing, this has yet to be attained due to inefficiencies of current probe design strategies.in silico results were obtained for typing of the HLA-B locus.We present a novel three-step approach for the design of high-throughput microarray assays for HLA typing. This approach first selects sequences containing the SNPs present in all alleles of the locus of interest and next calculates the number of base changes necessary to convert a candidate probe sequences to the closest subsequence within the set of sequences that are likely to be present in the sample including the remainder of the human genome in order to identify those candidate probes which are "ultraspecific" for the allele of interest. Due to the high specificity of these sequences, it is possible that preliminary steps such as PCR amplification are no longer necessary. Lastly, the minimum number of these ultraspecific probes is selected such that the highest resolution typing can be achieved for the minimal cost of production. As an example, an array was designed and in silico and preliminary experimental results, we believe that the proposed approach can be readily applied to any highly polymorphic gene system.The assay presented here provides a higher resolution than has previously been developed and includes more alleles than previously considered. Based upon the  The HLA (human leukocyte antigen) system, the group of genes in the human MHC (major histocompatibility complex) located on chromosome VI, encodes the cell-surface antigen-presenting proteins. HLA antigens are the major determinants used by the body's immune system for recognition and differentiation of self from non-self (foreign) substances. This system consists of numerous SNPs (single nucleotide polymorphisms) encoding more than 2,000 known alleles . The allSeveral different techniques have been explored for HLA typing. Historically, serological analysis of allele-specific sera was used to identify the structural differences on the surface of the HLA molecules due to the nucleotide present at the site of the polymorphism. This method, however, is limited by the availability of allele-specific sera and by its inability to discriminate between allelic subtypes within each serologic family . An alteNumerous nucleic acid-based approaches have also been used for genotyping the HLA loci. One such approach, PCR-SSP (Polymerase Chain Reaction – Sequence Specific Primers), employs specific primers targeting a particular polymorphism such that the presence of the polymorphism results in amplification of the product . Becausen-mers: subsequences of length n) are selected which contain the known SNPs present in the allelic sequences of the locus (or loci) of interest. Next, each candidate probes sequence is compared to the collection of sequences which are likely to be present in the sample (the "synthetic background"), including the remainder of the human genome and human-associated microbial genomic sequences. The distance, or the number of base changes that are necessary for a candidate probe sequence to be "converted" to the nearest sequence present in the synthetic background, is calculated for each probe. The candidate probes which are significantly distant from the background are less likely to misprime with, for instance, human genomic DNA. Such candidate probes are "ultraspecific" for identification of the allele even when foreign genomic material is present. It is possible that use of such ultraspecific probe sequences may eliminate the need of PCR amplification of the HLA region. Lastly, the smallest set of ultraspecific probes is selected such that the highest resolution typing can be achieved with a minimal cost in producing the assay. As an example, the proposed approach was applied to the most polymorphic region of the HLA system, the HLA-B locus.Herein, a novel three-step approach for the development of high-throughput microarray assays for HLA typing is presented. First, candidate probe sequences (The proposed method comprises of three steps.n-mers) of length 16 to 22 nucleotides (16 ≤ n ≤ 22) containing each SNP in the allelic sequences producing the set of all candidate probes.1. Selection of candidate probes: Select all short subsequences  and known polymorphisms outside of the HLA-B region. With regards to the ability to detect the set of alleles, all of the alleles contain 16- through 22-mers that are at least 1 change away. Table n-mers must be 2 or more changes away from the background.For each of the 889 alleles within the HLA-B locus, the set of all 16- through 22-mer sequences were identified as candidate probe sequences. Thus, multiple While these ultraspecific probe sequences, when incorporated in a microarray assay, are expected to be able to detect all of the 889 alleles considered, it is not guaranteed that a single assay could distinguish between different alleles. Thus, the set of ultraspecific probe sequences was analyzed using a greedy algorithm (see Methods) in order to generate the minimum set of ultraspecific probes required such that all of the alleles can be detected with the highest typing resolution possible. Based upon the results of our computations, a probe set including 115 ultraspecific probes was identified. Ultraspecific probes included in the set were selected based upon their ability to both detect the 889 alleles as well as their ability to provide resolution for distinguishing between alleles. Each allele is expected to hybridize with between two to 19 different probes in this set. While all ultraspecific probes are able to distinguish one to many different targets, not all members of the set provide the ability to indentify a particular allele (distinguishability). This set will produce distinctive hybridization patterns for 634 out of the 889 alleles such that 72.33% of the alleles can not only be detected but also definitively typed; the remaining 255 alleles, while detectable by this probe set, will not hybridize to any unique or unique combination of ultraspecific probe sequences.As shown in Figure In silico assays for the 115 ultraspecific probe set and each of the alleles were conducted. As one would expect, it is the groups of highly similar alleles that the ultraspecific probe set is unable to distinguish between. In order to visualize the degree of similarity between the alleles based upon their expected hybridization patterns, we calculated the distance (D) between any two patterns as:n1 and n2 are the numbers of probes present in each of alleles being compared and n12 is number of probes present in both alleles simultaneously. By computing the distances between each pair of 889 patterns (the distance matrix) with PHYLIP's neighbor program [where  program , we were program  based onin silico experiments as well as preliminary experimental results , we believe that the proposed approach can be readily applied to the entire HLA system as well as other highly polymorphic gene systems. We are in the process of developing an "HLA-chip" to enable population-scale HLA typing in a portable, field-ready environment.The microarray of 115 probes represents what many researchers can produce in-house at low cost. While unable to uniquely identify every allele, the probe set presented here provides a higher resolution than has previously been developed and includes more alleles than previously considered. For the 255 alleles which cannot be distinguished, the nucleotide(s) present at the polymorphic positions defining the allele are included in candidate probe sequences that did not meet the ultraspecificity criteria. Incorporating probe sequences expected to hybridize within these polymorphic sites have a greater likelihood of interacting with regions outside of the HLA-B exons. As our goal here is to develop an assay such that amplification of the HLA loci exon prior to assaying is not necessary, such nonspecific probe sequences are excluded from the set. In the event that the assay generates a hybridization pattern common amongst several known alleles, further high resolution typing may be required. Furthermore, this assay lends itself towards exploratory typing. In the event that a novel hybridization pattern is observed, subsequent sequencing can be performed to determine the sequence of the new allele; when a known allele is encountered, however, the cost of this additional sequencing can be avoided. Based upon the results of The catalogued set of resequenced alleles for class I HLA-B was retrieved from the IMGT/HLA Sequence Database (release 2.17 04/2007) [. For simplicity, all n-mers containing unknown/unidentified characters were excluded from the calculations. The original and complementary strands were considered for both the HLA-B locus and the synthetic background collection.Sequences containing these SNPs from the allele exons were screened against the synthetic background of the set of genomic sequences likely to appear in the sample. This synthetic background includes the human genome (Build 36.1) with the exception of the HLA-B locus. The synthetic background was created by referencing the annotation (GenBank summary file) of the human genome build and removing the HLA-B locus' nucleotide sequence from the complete sequence of chromosome 6 n <18, it was discovered that only when n ≥ 16 that the human genome does not include a sequence within two changes of any selected n-mer [n greater than 22 are excluded from the results presented here.All of the 16- through 22-mer sequences containing the SNPs present in each of the 889 allele sequences were identified. Based upon the results of calculations performed for the complete human genomic sequence for ed n-mer . Thus, cn-mer contained within the sequence of interest to any n-mer present in the background collection. If any one combination is present in the background, for instance given two base changes, the n-mer is said to be two changes away from the nearest background sequence or "two away". By calculating the number of changes away each of the candidate probe sequences identified in the first step is from the background, it is possible to identify those candidate probes which are sufficiently "distant" from the background and thus have a reduced probability of hybridizing with non-target genomic material present in the sample.Using specialized algorithms and data structures , we rigoTo serotype HLA alleles, an assay must be able to not only detect the presence of the allele but also reliably distinguish between allelic variations. The issue of detection necessitates one to identify the set of ultraspecific candidate probes which are expected to hybridize with the particular allele. The ability to distinguish between alleles can be rather straightforward if each allele contains at least one candidate probe which is only expected to hybridize with that particular allele. Due to the high sequence similarity between the HLA-B alleles, however, the vast majority of the 889 alleles do not contain a unique candidate probe. The microarray platform provides an alternative means for approaching this challenge – rather than type a particular allele by a single unique probe, an allele can be identified by a unique set of probes such that an allele can be distinguished from another allele by its pattern of hybridization. A naïve solution is to incorporate all of the ultraspecific candidate probes. Due to the fact that the cost of microarray assays is directly proportionate to the number of probes included, it is ideal to design the assay with the minimal number of probes necessary in order to minimize cost and increase the ease in analyzing the assay results.The problem of identifying the minimum number of probes to detect and distinguish a set of targets is an instance of the classical set cover problem, a well studied computational problem which is known to be NP Complete. Several algorithms have been designed to approximate the minimum probe set -29. HereOnce a probe is selected, it is included within the probe set and all targets that are covered by this probe and are distinguishable from all other targets are removed from consideration when assessing a future candidate probe's coverage of targets. Likewise, one can stipulate that each target is expected to hybridize with at least x different probes, in order to improve the reliability of the assay, such that when a probe is selected only targets meeting this threshold are removed from further consideration. This process of probe assessment and selection is repeated until either (a) all of the targets are expected to be detected by the probe set and distinguishable from either other based upon the subset of probes expected to hybridize with the individual target or (b) there are no more probes which can improve the detection and distinguishability of the set of targets. This approach was implemented a set of C++ data structures and efficient algorithms to select feasible sets among a large set of probes in a matter of seconds to minutes.CF participated in the code design and implementation, performing the computations, analysis of the results and drafting of the manuscript. CP participated in the design of the study, code development, analysis of the results and drafting of the manuscript. MZ participated in the design of the study and code development. RE, RM, MH, and KJ participated in the design of the study, experimental design critique, preliminary experimental design, and revision of the manuscript. YF conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript.Additional table 1. Complete listing of the number of candidate probes for each of the individual 889 alleles and distance values.Click here for fileAdditional table 2. The probe set including 115 ultraspecific probes for HLA-B typing.Click here for fileAdditionalfile1.txt. NEWICK file for the tree based upon the hybridization patterns for the undistinguishable alleles.Click here for file


Burden of disease estimates, which combine mortality and morbidity into a single measure, are used increasingly for priority setting in disease control, prevention and surveillance. However, because there is no clear exclusion criterion for highly prevalent minimal disease in burden of disease studies its application may be restricted. The aim of this study was to apply a newly developed relevance criterion based on preferences of a population panel, and to compare burden of disease estimates of five foodborne pathogens calculated with and without application of this criterion.Preferences for twenty health states associated with foodborne disease were obtained from a population panel (n = 107) with the Visual Analogue Scale and the Time Trade-off (TTO) technique. The TTO preferences were used to derive the relevance criterion: if at least 50% of a panel of judges is willing to trade-off time in order to be restored to full health the health state is regarded as relevant, i.e. TTO median is greater than 0. Subsequently, the burden of disease of each of the five foodborne pathogens was calculated both with and without the relevance criterion.The panel ranked the health states consistently. Of the twenty health states, three did not meet the preference-based relevance criterion. Application of the relevance criterion reduced the burden of disease estimate of all five foodborne pathogens. The reduction was especially significant for norovirus and rotavirus, decreasing with 94% and 78% respectively.Individual preferences elicited with the TTO from a population panel can be used to empirically derive a relevance criterion for burden of disease estimates. Application of this preference-based relevance criterion results in considerable changes in ranking of foodborne pathogens. Since the application of the concept in 1993, the Disability Adjusted Life Year (DALY) is used increasingly for priority setting in disease control, prevention and surveillance . In ordeAn essential component of YLD computation is the disability weight. A disability weight is a scaling factor assigned to living with disability that ranges from 0  to 1  . This vaTo arrive at YLDs, the disability weights have to be combined with incidence and duration data. In some burden of disease studies hospital admissions and Emergency Department treatments are used as data source in this regard ,6. OtherMinimal disease consists of temporary health states of short duration that have an anticipated and observed minor impact on individual health-related quality of life. However, due to high prevalence of most minimal disease, collectively they may account for a large number of YLDs in the aggregate, and may therefore get policy priority above severe, but less frequently occurring diseases. As a result, the application of burden of disease estimates in prioritization discussion may be obfuscated. This can be overcome by including a criterion for relevant disease. A prerequisite of this relevance criterion is that it is able to distinguish 'experienced' minimal disease from relevant disease unambiguously, yet it should also allow relevant mild disease to be included in burden of disease estimates in order to avoid incomplete estimates of diseases characterized by heterogeneous levels of severity.Other than on health care use, the criterion to distinguish minimal diseases from relevant diseases may be based on health outcome; anatomical characteristics of the disease, absenteeism , or on societal preferences that are derived to assess the disability weights necessary for the YLD calculation. To obtain the latter, preferences from a panel of judges elicited with dedicated preference measurement methods commonly are used ,10. A prIn the area of foodborne disease, often high incidences of infectious disease are observed with most cases leading to full recovery within only a few days. Comprehensive studies on foodborne disease are vulnerable to inadvertently putting too much emphasis on such minimal diseases.The aim of our study in this context was 1) to derive a relevance criterion for foodborne disease based on the preferences of a population panel, and 2) to compare YLD estimations of five common foodborne pathogens calculated with and without the application of the preference-based relevance criterion.Campylobacter spp., Salmonella spp., and Shiga-toxin producing Escherichia coli O157 (STEC O157). We carefully defined the diseases caused by these foodborne pathogens, collected empirical evidence on the associated functional consequences over time and subdivided the diseases into disease severity grades that were presumed to be homogeneous regarding disability, treatment and prognosis. This resulted in 20 health states.Five enteric pathogens were selected, namely norovirus and rotavirus, thermophilic The functional consequences of each of the 20 health states were presented on a vignette. A vignette is a preformatted A4 size sheet that provided the disease label, clinical description and a generic description. For the generic description, we used an extended version of the EQ-5D classification system -14. ThisAdditionally, the vignette provided a visual representation of the body sites affected by the disease, and described the duration of the disease over one year time. The duration of the condition was presented as an annual profile, which describes the course of the health state – the disability profile – over one year, allowing assessment of diseases with a rapid course ,17. CondThe Visual Analogue Scale (VAS) and the TTO were used to elicit preferences for the 20 health states . The VASAll health states were valued independently according to both methods.The panel participants were randomly selected from a sample of 560 lay people who applied to participate in the Mild Diseases and Ailments Study, conducted in 2003 . For theValuation data were collected through a two-step procedure. Firstly, the participants attended a three hour panel session, during which they valued 10 vignettes with the VAS and the TTO. The second part of the data-collection consisted of an unsupported postal questionnaire which the participants received at home one week after attending the panel meeting. Apart from the 20 health states related to foodborne disease, the participants valued 24 other health states  totalling the number of questionnaire vignettes to 34. Because of the high total number of vignettes, we developed two versions of the questionnaire. Each version of the questionnaire asked the participant to value 17 vignettes with the VAS and the TTO. The order of the vignettes was randomized and the questionnaire version was randomly assigned to the participants. The participants received 20 euros for attending the panel session and 30 euros for filling out the follow up questionnaire.Firstly, we standardized VAS and TTO weights for each individual response using the following formulas:To establish whether the disease stages were ranked in consistent order, the ranking of a vignette according to the VAS and TTO weights was compared using Spearman's and Pearson's rank correlation coefficients. We performed regression analysis to determine whether socio-demographic variables age, sex, education level and disease experience had independent significant effects on the VAS and TTO weights. To establish the inter-rater reliability, which measures group homogeneity, we defined each of the eight panel meetings as a group and calculated the intra-class correlation (ICC) of these groups.In order to calculate the YLD, national incidence data on foodborne disease (year 2004) was combined with the disability weight derived from the panel study. YLD was calculated both with and without application of the relevance criterion. The relevance criterion implies that if the median TTO = 0, the disease stage is regarded as not relevant and therefore not included in the YLD calculation.To calculate YLL we used mortality data from Statistics Netherlands. The resulting YLL were combined with YLD in order to calculate the number of DALYs lost due to the five foodborne pathogens, all following standard procedures (no age weighting). For a more detailed description of the DALY calculation, see Kemmeren et al.The panel meeting was attended by 107 participants. Each of the 107 participants responded to the questionnaire. On average it took the participants 1 hour and 43 minutes to fill out the questionnaire. The average age of the participants was 51 years and 62% was female. Statistics of the participants are presented in Table Table Table Table Campylobacter spp. (1137 DALYs). Least DALYs were lost due to Salmonella spp. (747 DALYs) and STEC O157 (120 DALYs).The burden of disease was calculated for all five foodborne pathogens  Salmonella spp., and 5) STEC O157. Conversely, when the preference-based relevance criterion was applied the ranking was: 1) thermophilic Campylobacter spp., 2) Salmonella spp. 3) rotavirus, 4) norovirus, and 5) STEC O157.As a result of the reductions in part of the burden of disease estimates, the ranking of the foodborne pathogens changed. Without a relevance criterion, the ranking according to descending burden of disease was: 1) norovirus, 2) rotavirus, 3) thermophilic The results showed that for three health states associated with foodborne disease less than 50% of the panel members were willing to trade-off any time. Therefore these health states did not meet the relevance criterion proposed by Bonsel et al. . ApplicaThe burden of disease would decrease even more when disability weights based on median rather than mean TTO values were used to calculate YLD. The benefit of using median TTO values is that the majority rules principle is applied to all health states and not only minimal disease.In our study, the three health states that did not meet the preference-based relevance criterion of median TTO > 0 all had length-of-illness of one week or less and this short duration may have resulted in a majority of participants not willing to trade-off any time. Nonetheless, the results of two similar health state valuation studies showed that several health states that lasted over one week did not meet the preference-based relevance criterion either ,20. Of tIn the Netherlands, intestinal infectious disease ranks among the least burdening conditions and the results of this study might give the impression that application of the relevance criterion will substantially diminish it even further compared to other diseases . HoweverPreviously, relevance criteria based on anatomy and health care use have been suggested . A problThe aforementioned problems are overcome by the preference-based relevance criterion. This method to empirically derive a relevance criterion is simple and transparent, and the resulting relevance criterion can be applied to each condition. Moreover, the relevance criterion is based on individual preferences derived from a population panel, which concurs with the societal perspective of the burden of disease concept . FurtherA limitation of the preference-based criterion is that for any new health state preferences have to be obtained in order to derive the preference-based criterion. On the other hand, the results of the current study point out that preferences obtained from new panel studies collate with preferences derived previously with the same protocol.Furthermore, it should be noted that the TTO preferences from this study are derived with the annual profile approach in stead of the standard QALY/DALY approach used in the original DALY-approach of the Global Burden of Disease study . Unlike To alleviate this inability to assess preference weights for health states with complex patterns of duration and severity, the annual profile approach was developed . The mosThis alleged lack of discrimination between low severity conditions is not endorsed by the results of the current study, which show that the population panel in the Netherlands assigned values to health states that including the low severity conditions increase by level of severity of the health state. Additionally, they appeared to be capable to discriminate minimal from relevant disease and the preference-based relevance criterion appeared to be stable over time. Moreover, Janssen et al. (2008) showed that the criterion is similar for lay people, medical advisors, as well as general practitioners .Conversely to the agreement between panels with different perspectives, it is not yet clear whether the preference-based relevance criterion is similar for different countries. Diseases that did not meet the relevance criterion in this study could be regarded as relevant by people who do not have good access to good sanitary facilities and health care. Previous studies did find that ranking of health states is similar across countries, yet the assigned values differed significantly ,25,26. SSecondly, it remains to be investigated whether the approach used to derive the preferences affects the preference-based relevance criterion. In the current study, the annual profile approach was used. The alternative standard QALY/DALY approach, unlike the annual profile approach, presents diseases with a so-called period profile. Using the standard QALY/DALY approach to derive preferences may cause a shift in the derived preferences and consequently affect the preference-based relevance criterion.We conclude that individual preferences derived with the TTO method from a population panel can be used to empirically derive a relevance criterion for burden of disease estimates, and that application of this preference-based relevance criterion results in considerable changes in ranking of foodborne pathogens.Escherichia coli O157; TTO: Time Trade-off; VAS: Visual Analogue Scale; YLD: Years Lived with Disability; YLL: Years of Life Lost.DALY: Disability Adjusted Life Year; HUS: Hemolytic Uremic Syndrome; STEC O157: Shiga-toxin producing The authors declare that they have no competing interests.JH constructed the disease vignettes, performed the panel study, executed the statistical analysis and drafted the manuscript. AH supervised the construction of the vignettes, collected population data necessary for the burden of disease calculation and assisted with the drafting the manuscript. MJ participated in the design and execution of the panel study. GB supervised and participated in the design of the panel study and drafting of the manuscript.


Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications.The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments.In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data. An important application is the detection of transcription factor binding sites to characterize the regulatory networks controlling, for instance, various cellular processes or physiological states. The high-throughput ChIP techniques are based on identifying on a global scale the sequences and genomic locations of the immunoprecipitated DNA fragments that are bound by the transcription factor of interest. The most common approach until now has been to hybridize the DNA fragments to a tiling microarray (ChIP-chip) [Chromatin immunoprecipitation (ChIP) enables the identification of IP-chip) . HoweverIP-chip) ,3.ChIP-seq involves the sequencing of the ChIP-enriched DNA fragments for ~30 bp from their ends. These short sequence reads (tags) are then aligned to a reference genome and binding sites are identified on the basis of their significant accumulation at particular genomic loci using various peak detection algorithms . In addiTogether with the development of the ChIP-seq technology, a burst of methods has been introduced for analysing the resulting datasets. In general, these binding site detection algorithms can be divided into window-based approaches, which first define the start and end of a candidate region and then count the number of reads within it (e.g. CisGenome ), or oveThe ChIP-seq experiments, in turn, can be divided into those involving negative control samples and those that contain only ChIP samples. The main difference in analysing these datasets is that the former allow the statistical significance of the peaks to be estimated on the basis of an empirical background. Accordingly, some of the current algorithms are applicable only when a control sample is available (e.g. QuEST ), while While several recent publications have introduced new algorithms for ChIP-seq data analysis and the authors have shown improved performance over the previous approaches in selected datasets, an independent comparison of their relative performance using common datasets is still lacking. At the moment, there is only limited information on the use of the different approaches in practical applications; what are their relative merits and limitations; or are there actually large differences between the methods.The present study compares systematically the performance of the different peak detection algorithms in predicting transcription factor binding sites in ChIP-seq data. In particular, we compare nine publicly available algorithms that represent the current state-of-the-art of the field: PeakFinder , GeneTraA short summary of the algorithms being compared in the present study is given below. For more details, the reader is referred to the original publications or the software websites Table . When apPeakFinder identifies candidate binding sites as aggregations of k or more ChIP reads not separated by more than n bp (default 75) and with an additional requirement that at least 5 of the reads are overlapping. If a control sample is available, then it is further required that there is at least an m-fold enrichment (default 5-fold) of reads in the ChIP sample over the control within the same boundaries, and the data are recommended to be normalized into numbers on a per-million reads basis. Following the default settings, we used k = 20 when a ChIP sample was analysed without a control, and k = 8 when a control sample was utilized. PeakFinder does not provide any estimates for false discovery rate (FDR).GeneTrack applies a Gaussian smoothing procedure to represent the read densities along the chromosomes. More specifically, a normal distribution with a specified standard deviation (default 20) is utilized. Peaks are identified by finding the local maxima in the smoothed data. The results are reported for both strands separately as well as for their composition . An optional exclusion zone parameter allows to determine a distance, within which only a single peak is identified. Since no guidance is provided on how to optimally utilize the strand information nor how to define the exclusion zone parameter in the context of transcription factor binding, we focused here on the composite-strand detections with the exclusion zone set to 0. To reduce the large number of identifications made due to these choices , we only considered peaks having a score over 7, which is a compromise between the default overlap and count requirements of PeakFinder. GeneTrack does not provide any options to estimate the FDR levels of the detections.FindPeaks extends each aligned read directionally to an estimated length of the DNA fragment. The candidate binding sites are then identified on the basis of overlapping fragments (peak height) along the genome. The FDR is estimated using a Monte Carlo simulation, in which random locations are repeatedly generated for the reads and the number of peaks at a given height threshold in the randomized data is divided by the number of peaks observed at the same threshold in the real experimental data. As suggested in the FindPeaks manual, the reads were filtered to remove duplicate hits, the fragment length distributions were estimated by assuming a triangle-based distribution with a median value of 200, while enabling directional peak detection, peak trimming and subpeak detection modules. Peaks at FDR < 0.05 were identified.SISSRs (Site Identification from Short Sequence Reads) also starts by extending the reads to the estimated length of the DNA fragment, similarly as FindPeaks. It then scans the genome using a window of width w (default 20) and, for each window, it subtracts the number of antisense reads from the number of sense reads . Binding sites are identified as transition points of this count from positive to negative, provided that the number of directional reads on each side of the inferred binding site is at least n (default 2). A Poisson background or a negative control sample, if available, is used to estimate the FDR, which is determined as the ratio of the number of peaks indicated by the background model to the number of peaks observed in the real data. The default FDR threshold of 0.001 was applied.QuEST (Quantitative Enrichment of Sequence Tags) shares with GeneTrack the idea of applying a Gaussian kernel separately to both strands (default bandwidth 30). The major difference is that QuEST estimates a peak shift between the strands before aggregating the results. Moreover, while GeneTrack does not provide any practical guidance on how to choose a suitable threshold for a peak, QuEST determines peak calls based on the properties of separate control data. In particular, the ratio of the detected peak to the background is required to exceed a specified threshold value, called the rescue ratio (default 10). If the negative control has enough reads (at least approximately twice as many as the ChIP sample), then also FDR estimation is provided, in which the control sample is divided into two parts to mimic a random two-sample comparison and FDR is estimated as the ratio between the number of peaks detected in this random comparison and the number of peaks detected in the actual comparison in the real data with the same parameters. QuEST does not support analyses without controls.MACS  is another algorithm besides QuEST that takes advantage of the observed bimodal enrichment patterns of binding sites by empirically modelling the shift size. Unlike QuEST, however, it is applicable also without negative controls. To capture local biases in the genome, a dynamic Poisson distribution is used to model a local background. Candidate peaks with p-values below a user-defined threshold (default p < 10-5) are identified. If negative control data are available, then also FDR is estimated. More specifically, the same parameters are used to determine the number of ChIP peaks over the control sample and the number of control peaks over the ChIP sample, and the FDR is defined by dividing the latter by the former. In the present study, the default p-value threshold was applied.CisGenome scans the genome with a sliding window (default width 100) and identifies regions with enriched read counts. The FDRs are estimated assuming that the background read occurrence follows a negative binomial distribution, which was suggested to provide a better fit to the real data than the  Poisson distribution [ribution . The FDRPeakSeq first constructs a similar read density map as FindPeaks by extending the reads directionally to the average length of the DNA fragments and determining their overlap along the genome. Similarly as in MACS, a candidate set of peak regions is then identified in a ChIP-sample assuming a local Poisson background, taking into account also the variability in genomic mappability. Finally, the significance of enrichment of these candidates is determined relative to a negative control sample using a binomial distribution, similarly as in CisGenome, assuming that under the null hypothesis the reads should occur with equal likelihood from the ChIP and control sample. To control FDR, the obtained p-values are adjusted using the procedure of Benjamini and Hochberg [Hochberg . The defHpeak identifies peaks using a two-state hidden Markov model (HMM), whose states correspond to the binding sites and the background. The emission probabilities are described by two different Poisson distributions. The significance of enrichment of the peaks is adjusted using the Bonferroni correction for multiple testing. The default significance threshold of 0.001 was applied.To compare the peak detection methods across diverse studies, four different datasets were considered, which measure the binding of three human transcription factors, STAT6, NRSF (two datasets) and FoxA1 Table . The STAet al., unpublished).Our in-house data measures the binding of STAT6  transcription factor in human T helper (Th) cells. One of the samples is from naïve Th precursor (Thp) cells, whereas the other two samples are activated and induced by IL4 to polarize towards the Th2 subtype (measurements at 1 h and 4 h after polarization). Further details about the data and the biological conclusions will be presented elsewhere (Elo Two of the public datasets measure the binding of the transcription factor NRSF (neuron-restrictive silencer factor) in Jurkat T cells. The first NRSF dataset is from the study of  and was The STAT6 read data were aligned to the human reference genome (NCBI v36) using the short oligonucleotide alignment program SOAP . The pubThe binding site detections with each algorithm were made using the individual ChIP samples alone  as well as with the corresponding negative controls . Thus, in total, fourteen peak lists were generated for each ChIP sample. In the following, we refer to the results involving the control data by adding an additional C to the name of the method . In the STAT6 data, the Thp sample was used in place of a negative control, although it may contain also some true biological signal. This choice was motivated by the assumption that the relevant binding events do not yet occur in the Thp samples .n regions. These were then compared to the original two lists to determine the number of regions identified in both lists n1. Finally, the reproducibility was defined as n1/n, which obtains the value 1 if all the peaks are overlapping and the value 0 if none of the peaks overlaps.First, we evaluated the reproducibility of the methods across biological replicates in the NRSF datasets. This gives indications of the robustness of the methods, as the biologically relevant binding sites are expected to be detected in each replicate. The reproducibility between two peak lists was defined in terms of their overlap, following the approach of  to deal z-scores were considered, which measure the overrepresentation of a motif against an equally sized sample of the genomic background. With GeneTrack and QuEST, which reported only a single coordinate for each peak, 200 bp sequences from around the peak calls were utilized, similarly as in [Second, the detected peaks were evaluated in terms of their overlap with high-scoring sequence motifs. It is known that transcription factors exhibit binding sequence specificities, and hence, the genomic coordinate of a peak should be marked by a canonical binding site motif . Since sly as in . These sFinally, the performance of the peak detection algorithms was evaluated with respect to regions that were confirmed in independent qPCR experiments to be bound by the particular transcription factor (true positives) as well as regions that did not show binding (true negatives) in these experiments. For NRSF, we used the 83 true positives and 30 true negatives given in . For FoxWe first investigated the characteristics of the detected binding sites in terms of an example region in the STAT6 data Figure . AlthougTo get a more general picture of the binding sites identified by the different algorithms, we calculated the average peak width for each NRSF, FoxA1 and STAT6 sample listed in Table The number of detected binding sites varied greatly depending on the algorithm  suggested that there were more binding sites at 1 h than at 4 h, whereas another half  identified more binding sites at 4 h (data not shown). The extreme cases were CisGenome/PeakSeq and SISSRs/HpeakC, whose results varied from an ~8-fold decrease to a ~2-fold increase in the numbers of detections from 1 h to 4 h. This example demonstrates how the differences in the peak identification algorithms may lead to a notably variable overall picture of the dataset under analysis, emphasizing the importance of careful assessment of the resulting peak lists. A plausible technical explanation for the differences in the numbers of peak detections in different samples with different read distributions lies in the preferences of the algorithms. For instance, CisGenome and PeakSeq rely strongly on the peak heights, whereas SISSRs and Hpeak consider more the local statistical enrichment patterns. The biological significance of the identified STAT6 binding sites and further analysis of the binding kinetics remains to be addressed in further studies.In addition to the number of detected peaks, another practical question is their physical location in the genome. Accordingly, we divided the detections into groups on the basis of their location within 10 kb upstream/downstream of a transcription start/end site, within a gene (intragenic), or over 10 kb from a gene (intergenic) using the CisGenome software . Again, The two NRSF datasets with biological replicate samples provided an opportunity to assess the performance of the algorithms internally by investigating their ability to reproduce the detections robustly across the replicates. When all the peaks were considered, the best reproducibility was observed with PeakFinderC and QuEST Figure . With eaWhen considering only the top 1000 or top 2000 peaks with each algorithm, all the algorithms showed high reproducibility and the overall differences between the methods were typically negligible. Only SISSRsC showed somewhat lower reproducibility values than the other algorithms. The lower reproducibility of MACS with top 1000 peaks disappeared when the top 2000 detections were considered.A comparison of the detected peaks with high-scoring sequence motifs confirmed that all the algorithms identified binding sites at a highly significant overlap with the corresponding sequence motif Figure . When thz-score. In principle, a narrower peak width can indicate a better resolution and can be beneficial, for instance, for the discovery of de novo binding site motifs [When focusing only on the top 1000 candidates Figure , in whice motifs . The topTo complement the motif overlap evaluations, qPCR-validated regions were used to assess the sensitivity and specificity of the methods Figure . In the The qPCR validations enabled also to determine an empirical FDR for the detections. In the NRSF data, the empirical FDR was below 0.05 with all the algorithms. In the FoxA1 data, no negatives were detected with any of the algorithms, giving an empirical FDR estimate of 0. In the STAT6 data, the estimated FDR remained typically below 0.2. Notably, however, these values were often markedly higher than those suggested by the background models of the algorithms, supporting the earlier observation that especially the Poisson-based randomization model can severely underestimate the FDRs .The present study evaluated from a practical point of view the performance of the currently available open source software for detecting transcription factor binding sites in ChIP-seq data. A main observation was that the choice of the algorithm may considerably affect the overall conclusions made from the data Figure . Moreovep-values, provided by the peak detection software.In most of the currently published ChIP-seq studies, the choice of the peak detection algorithm lacks detailed motivation or description  , a ChIP-If the binding motif of the transcription factor of interest is known, then it can provide useful information about the relative performance of the different approaches Figure . HoweverFrom the point of view of an ordinary user, a major complication of the peak detection software is the typically large number of adjustable parameters. While the default parameters are a natural choice and were applied also by us, they may not be optimal for the particular data under analysis. On the other hand, if an algorithm lacks the possibility to easily adjust the parameters properly, it can be regarded as a weakness of the method. Other critical issues in ChIP-seq data analysis are the memory requirements for the computer and the diversity of the current data formats. The required input formats of the peak detection software as well as their output peak lists are far from being standardized, neither are the output formats produced by the different read alignment software. Further technical challenges include, for instance, the quality of the aligned reads and the required depth of sequencing . Also thDespite the challenges, the next-generation DNA sequencing has a great potential to accelerate biological and biomedical research by enabling a comprehensive analysis of genomes, transcriptomes and interactomes to be performed routinely without having the resources of large genomic centres . While sWith the growing importance of the technology, rigorous computational approaches to transform the large datasets into biological knowledge are required to truly leverage the potential of these data. Rather than introducing a number of closely related algorithms, it is critical to objectively evaluate their performance to provide practical guidance to the researchers analysing their data and to the developers of the algorithms to evaluate their new ideas. An important future direction is also to effectively integrate ChIP-seq data with other types of datasets, such as those generated in siRNA interference experiments, to improve the detection of target genes.in vivo protein-DNA interactions on a genome-wide scale. With the increasing popularity of the ChIP-seq technology, a number of algorithms have been presented to analyse the resulting datasets. However, only little is known about their relative merits and limitations in practical applications. Rather than introducing a range of closely related procedures, it is therefore crucial to objectively assess their performance in practice. In the present study, we compare systematically the current state-of-the-art of detecting transcription factor binding sites from ChIP-seq data under a variety of practical research settings, including reproducibility across biological replicates as well as comparisons to high-scoring binding motifs or independent experimental validations. Our results suggest that the optimal choice of the algorithm depends heavily on the dataset under analysis. In addition to providing practical guidance to the users of the ChIP-seq technology, our systematic evaluation framework is also a useful reference to the developers of improved algorithms.ChIP-seq combines chromatin immunoprecipitation with next-generation sequencing to identify TDL performed the computational testing. SR carried out the STAT6 ChIP-seq and ChIP-qPCR experiments and participated in the interpretation of their biological results. ST participated in the interpretation of the biological results. RL conceived, designed and supervised the STAT6 experiments, introduced the biological problem and participated in the interpretation of the biological results. TA participated in the design and evaluation of the computational study and in drafting the manuscript. LLE conceived, designed and supervised the computational study, participated in the computational tests and their evaluation, and drafted the manuscript. All the authors read and approved the final manuscript.The qPCR-validated regions in STAT6 study. A table of the regions selected for qPCR validation in the STAT6 study. The presence of the STAT6 binding motif (V$STAT6.01) was determined with the Genomatix MatInspector tool using Matrix Library 8.1 and optimized matrix similarity (default settings).Click here for file


Clinical evidence continues to expand and is increasingly difficult to overview. We aimed at conceptualizing a visual assessment tool, i.e., a matrix for overviewing studies and their data in order to assess the clinical evidence at a glance.A four-step matrix was constructed using the three dimensions of systematic error, random error, and design error. Matrix step I ranks the identified studies according to the dimensions of systematic errors and random errors. Matrix step II orders the studies according to the design errors. Matrix step III assesses the three dimensions of errors in studies. Matrix step IV assesses the size and direction of the intervention effect.The application of this four-step matrix is illustrated with two examples: peri-operative beta-blockade initialized in relation to surgery versus placebo for major non-cardiac surgery, and antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation. When clinical evidence is deemed both internally and externally valid, the size of the intervention effect is to be assessed.The error matrix provides an overview of the validity of the available evidence at a glance, and may assist in deciding which interventions to use in clinical practice. Evidence-based medicine (EBM) was first introduced in 1992 ,4. ThankEBM usually follows a four-phase process starting from a clinical question proceeding to the implementation of new evidence Figure  3]. Pha. Pha3]. In daily clinical practice, the question of whether sufficient evidence is available to recommend the implementation of a specific intervention as a treatment arises frequently . DependiSince results may be contradictory and studies may differ in more than one aspect, to draw a clear, practical conclusion from the publications may be problematic .We aimed at conceptualizing a visual assessment tool, i.e., a matrix for overviewing studies and their data in order to assess the clinical evidence. The matrix is constructed from the three dimensions of errors: systematic error ('bias'), random error ('play of chance'), and design error ('wrong design to answer the question posed' or 'wrong context'). The application of this matrix will be illustrated by two examples: peri-operative beta-blockade initialized in relation to surgery versus placebo for major non-cardiac surgery, and antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation.When evaluating a clinical study, one should always try to assess its risk of systematic error ,4,9-16. For randomized trials, there is empirical evidence that at least six components are associated with systematic error: generation of the allocation sequence , allocatThe systematic error dimension can be measured by an ordinal variable expressed in the levels of evidence Table .The risk of random error is the risk of drawing a false conclusion based on sparse data. There are two types of false conclusions: a false rejection of the null hypothesis  or a false acceptance of the null hypothesis . When data are sparse, then the so called 'intervention effect', whether beneficial or harmful, may in fact be caused by randomly skewed variation in prognostic factors between the intervention groups due to sampling error.p1 = a/n1 and p2 = c/n2  are considered in an individual study or a trial i, then the relative risk (RRi) is defined by:The question, however, is how we quantify and compare the risk of random error between different studies with varying numbers of participants. A p-value reflects the risk that the difference in outcome between two interventions has arisen by chance, given the data and the null hypothesis are true. Since random low (and random high) p-values occur, especially during accumulation of data and sequential testing, the p-value does not sufficiently reflect the true risk of random error. Therefore, the p-values of intervention effect estimates certainly are not suitable for comparison of the risk of random error between different studies ,34-37. WThe SE of the log risk ratio for an individual study is calculated by the following formula:ORpeto,i) for an individual study or trial i is defined by:The Peto odds ratio  the natural logarithm of the RRMH has the standard error given by:In a meta-analysis results of studies or trials are meta-analysed into one intervention effect estimate. For the Mantel-Haenszel pooled risk ratio  the natural logarithm of the ORpeto has the standard error given by:For the pooled Peto OR . The design (or context) of any piece of research determines its external validity or generalisability Table  4]. The. The4]. Outcome measures can be divided into three categories according to the GRADE classifications Figure  13]. Pr. Pr13]. GRADE has schematically ordered outcomes according to patients' perspective on a categorical scale from 1 to 9, with the most critical outcome, mortality, being graded 9 . DependiEventually, the design error dimension can be expressed by the priority of outcome measures as an ordinal variable according to GRADE .A four-step matrix can be constructed, building upon the three dimensions: systematic error, random error, and design error. Matrix step I ranks the identified studies according to the dimensions of systematic errors and random errors. Matrix step II orders the studies according to the design errors. Matrix step III assesses the three dimensions of errors in studies. Here, a 'Manhattan-like' error matrix is constructed where the best evidence is represented by the largest skyscrapers located on the 'upper-west side'. Matrix step IV assesses the size and direction of the intervention effect, e.g., by calculating the number-needed-to-treat to obtain benefit or to harm one patient.RR, ORpeto, or any other association metric), and the types of outcomes.The principle of the matrix approach can be used in different situations. The overall effort in research should be to minimize all three risks of errors before the size and the direction of the intervention effect can be assessed reliably. The 'algorithm' of the matrix approach is generally applicable to all kinds of interventions, although details may differ according to the specific clinical question. Or, the character of the three dimensions remains the same, while according to the specific question details may differ, like: the preferred hierarchy for levels of evidence, the chosen formula for standard error  1.03 to 5.59) -67 were In step I, we assessed the risk of systematic error and the risk of random error for the chosen outcome of each study Figure , Table 4From Figure The aim of our matrix is to facilitate the overview of evidence in clinical intervention research. The matrix can serve as a tool to provide visual assessment of reliability of observations with respect to systematic error, random error , and design error .The matrix should not replace the thorough process of systematically reviewing evidence and profound evaluations of data, but could be integrated within these research activities as a tool for overviewing the results. Also, this matrix is not an absolute measure of the risks of errors. The position of studies in relation to each other is relative rather than absolute.There is a lack of awareness of the importance of the 'play of chance' for the reliability of study findings. Ordering the standard errors of the studies might be a tool for ranking studies according to the level of random error. We have used natural logarithm (ln) transformations for calculating standard errors, although the logarithm with the base 10 may be used without producing different conclusions.As an alternative, the Bayes factor can be considered ,68. The or simple approximations can be very difficult or even impossible to implement for the clinician, since a search for the maximum of the multidimensional posterior may be required for each association . This alThe aim of minimising error risks according to the three dimensions actually combines the methodological efforts of falsifying any alternative hypothesis in the evaluation of an intervention. Thereby, the matrix concept visualises how far the scientific process has evolved to fulfil Poppers falsification criterion stating that researchers should primarily engage trying to falsify any relevant alternative hypothesis and not only the null hypothesis . The minThe conclusion based on an assessment of the evidence using the matrix approach may be implemented into clinical practice or serve as an incentive for new research. The matrix facilitates the identification of lacunae in our knowledge and is likely to benefit the process of developing evidence-based guidelines.One has to be aware of the multiple forms of bias, potentially present in evidence below level 1  may be found favouring one intervention, while large differences in secondary outcome measures  may favour the comparator. Eventually, one may prefer the larger advantages in secondary outcomes to the smaller disadvantages in a primary outcome measure.Another limitation in the outcome measure dimension is that often outcome measures are correlated and mostly this correlation is ignored. For example when mortality is an outcome measure and complications is another, which again counts deaths as complications, then there is a correlation between the two outcome measures. Authors usually carry out multiple univariate analyses ignoring correlations between outcome measures.Step IV of the matrix includes the assessment of the size of the intervention effect, e.g., expressed in numbers-needed-to-treat to obtain benefit or to harm one patient with the intervention. This step is the last one since it is irrelevant to consider effect sizes and their directions if a study does not appear to be internally and externally valid.Another aspect to consider is heterogeneity ,77. StatAssessment of risks of systematic error, random error, and design error are essential factors in evaluating evidence and drawing conclusions. We used the standard error in our matrix to rank studies according to their risk of random error. The risks of these error types were incorporated into a three dimensional matrix to create a schematic overview of the internal and external validity of the evidence, seen at a glance.The authors declare that they have no competing interests.FK, JW, CG, and CL contributed to the development of the ideas in the manuscript. FK and JW performed the analyses, created the figures, and drafted the original text. CG and CL commented and contributed to the discussion. All authors read and approved the final manuscript.There was no funding.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/10/90/prepubTable S1. Word DOC table showing the sustained scientific process.Click here for file