Background QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs
Background
QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGsMethods A real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats. Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition. The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number  L  of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals.
Methods
A real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats.Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition.The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number  L  of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals.LResults The algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity  Se  = 99.69 % and specificity  Sp  = 99.65 % for Algorithm 1 and  Se  = 99.74 % and  Sp  = 99.65 % for Algorithm 2.
Results
The algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity  Se  = 99.69 % and specificity  Sp  = 99.65 % for Algorithm 1 and  Se  = 99.74 % and  Sp  = 99.65 % for Algorithm 2.SeSpSeSpConclusion The statistical indices are higher than, or comparable to those, cited in the scientific literature.
Conclusion
The statistical indices are higher than, or comparable to those, cited in the scientific literature.

Background The QRS complexes and ventricular beats in an electrocardiogram represent the depolarization phenomenon of the ventricles and yield useful information about their behavior. Beat detection is a procedure preceding any kind of ECG processing and analysis. For morphological analysis this is the reference for detection of other ECG waves and parameter measurements. Rhythm analysis requires classification of QRS and other ventricular beat complexes as normal and abnormal. Real-time ventricular beat detection is essential for monitoring of patients in critical heart condition. Correct beats recognition is impeded by power-line interference, electromyogram noise and baseline wander often present in the ECG signal. In long-term monitoring electrode impedance can increase considerably, resulting in very low signal-to-noise ratio, which can make detection practically impossible in a single lead. Therefore, usually two or three leads are used for monitoring [ 1 ]. Friesen  et al.  [ 2 ] have presented a comparison of nine QRS detection algorithms, based on: i) amplitude and first derivative, ii) first derivative only, iii) first and second derivative, and iv) digital filtering. Daskalov  et al . [ 3 ] applied these algorithms to selected signals containing records with pronounced baseline drift. The results were unsatisfactory, which was probably due to the use of fixed detection thresholds, whereas adaptive ones would be more appropriate. Poli  et al . [ 4 ] used a generic algorithm for QRS detection. The complexes were emphasized with respect to the rest of the signal by polynomial filters and compared to an adaptive threshold. The authors reported 99.60 % sensitivity (Se) and 99.51 % specificity (Sp) with the MIT-BIH Arrhythmia Database. The method is inapplicable in real-time. Afonso  et al.  [ 5 ] proposed hardware filter banks for ECG signal decomposition, where several parameters were independently computed and combined in a decision rule. The authors reported Se = 99.59 % and Sp = 99.56 % for their real-time, single-channel beat detection algorithm tested with the MIT-BIH Arrhythmia Database. Dotsinsky and Stoyanov [ 6 ] developed a heuristic, pseudo-real-time algorithm for ventricular beat detection for single-channel ECG, based on steep edges and sharp peaks evaluation criteria. They reported Se = 99.04% and Sp = 99.62%, obtained with two channel recordings from AHA and MIT-BIH Arrhythmia Database Moraes  et al . [ 1 ] combined logically two different algorithms working in parallel – the first has been taken from the work of Englese and Zeelenberg [ 7 ] and the other was based on Pan and Tompkins [ 8 ], and Ligtenberg and Kunt [ 9 ]. Moraes  et al . [ 1 ] reported Se = 99.22 % and Sp = 99.73 % after having excluded records of patients with pacemaker. After excluding a few more recordings 108, 200, 201 and 203, containing high amplitude noise (according to the authors), the statistical indices rises to Se = 99.56 % and Sp = 99.82 %. Li  et al . [ 10 ] have used wavelet transforms for detection. They reported 0.15 % false detections out of 46 files from the MIT-BIH Arrhythmia Database, but with exclusion of files 214 and 215. In addition, we found some errors in their Table II. After correction, the reported accuracy slightly decreased. The large variety of QRS detection algorithms, and the continuous efforts for their enhancement, proves that universally acceptable solution has not been found yet. Difficulties arise mainly from the huge diversity of the QRS complex waveforms and the noise and artifacts accompanying the ECG signals.
Background
The QRS complexes and ventricular beats in an electrocardiogram represent the depolarization phenomenon of the ventricles and yield useful information about their behavior. Beat detection is a procedure preceding any kind of ECG processing and analysis. For morphological analysis this is the reference for detection of other ECG waves and parameter measurements. Rhythm analysis requires classification of QRS and other ventricular beat complexes as normal and abnormal. Real-time ventricular beat detection is essential for monitoring of patients in critical heart condition.Correct beats recognition is impeded by power-line interference, electromyogram noise and baseline wander often present in the ECG signal.In long-term monitoring electrode impedance can increase considerably, resulting in very low signal-to-noise ratio, which can make detection practically impossible in a single lead. Therefore, usually two or three leads are used for monitoring [ 1 ].Friesen  et al.  [ 2 ] have presented a comparison of nine QRS detection algorithms, based on: i) amplitude and first derivative, ii) first derivative only, iii) first and second derivative, and iv) digital filtering. Daskalov  et al . [ 3 ] applied these algorithms to selected signals containing records with pronounced baseline drift. The results were unsatisfactory, which was probably due to the use of fixed detection thresholds, whereas adaptive ones would be more appropriate.et al.et alPoli  et al . [ 4 ] used a generic algorithm for QRS detection. The complexes were emphasized with respect to the rest of the signal by polynomial filters and compared to an adaptive threshold. The authors reported 99.60 % sensitivity (Se) and 99.51 % specificity (Sp) with the MIT-BIH Arrhythmia Database. The method is inapplicable in real-time.et alAfonso  et al.  [ 5 ] proposed hardware filter banks for ECG signal decomposition, where several parameters were independently computed and combined in a decision rule. The authors reported Se = 99.59 % and Sp = 99.56 % for their real-time, single-channel beat detection algorithm tested with the MIT-BIH Arrhythmia Database.et al.Dotsinsky and Stoyanov [ 6 ] developed a heuristic, pseudo-real-time algorithm for ventricular beat detection for single-channel ECG, based on steep edges and sharp peaks evaluation criteria. They reported Se = 99.04% and Sp = 99.62%, obtained with two channel recordings from AHA and MIT-BIH Arrhythmia DatabaseMoraes  et al . [ 1 ] combined logically two different algorithms working in parallel – the first has been taken from the work of Englese and Zeelenberg [ 7 ] and the other was based on Pan and Tompkins [ 8 ], and Ligtenberg and Kunt [ 9 ]. Moraes  et al . [ 1 ] reported Se = 99.22 % and Sp = 99.73 % after having excluded records of patients with pacemaker. After excluding a few more recordings 108, 200, 201 and 203, containing high amplitude noise (according to the authors), the statistical indices rises to Se = 99.56 % and Sp = 99.82 %.et alet alLi  et al . [ 10 ] have used wavelet transforms for detection. They reported 0.15 % false detections out of 46 files from the MIT-BIH Arrhythmia Database, but with exclusion of files 214 and 215. In addition, we found some errors in their Table II. After correction, the reported accuracy slightly decreased.et alThe large variety of QRS detection algorithms, and the continuous efforts for their enhancement, proves that universally acceptable solution has not been found yet. Difficulties arise mainly from the huge diversity of the QRS complex waveforms and the noise and artifacts accompanying the ECG signals.ECG databases All 48 ECG recordings of MIT-BIH Arrhythmia database were used, without exception. Each one has a duration of 30 min and includes two leads – the modified limb lead II and one of the modified leads V1, V2, V4 or V5 [ 11 ]. The sampling frequency is 360 Hz with resolution 5 μV/bit. Two cardiologists have annotated all beats. Approximately 70 % of the beats are annotated as  Normal . Four of the records are from patients with pacemakers. The American Heart Association (AHA) database was also considered, during the evaluation of the method, mostly due to the fact that it contains patients with premature ventricular beats of contraction of R-over-T type, some of them very difficult to detect because of their closeness to the previous complex. Statistical indices for this database are not derived, because they can be compared with limited number of articles working with AHA. The database consists of 80 recordings: 2-leads, 250 Hz sampling rate and 5 μV/bit resolution.
ECG databases
All 48 ECG recordings of MIT-BIH Arrhythmia database were used, without exception. Each one has a duration of 30 min and includes two leads – the modified limb lead II and one of the modified leads V1, V2, V4 or V5 [ 11 ]. The sampling frequency is 360 Hz with resolution 5 μV/bit. Two cardiologists have annotated all beats. Approximately 70 % of the beats are annotated as  Normal . Four of the records are from patients with pacemakers.NormalThe American Heart Association (AHA) database was also considered, during the evaluation of the method, mostly due to the fact that it contains patients with premature ventricular beats of contraction of R-over-T type, some of them very difficult to detect because of their closeness to the previous complex. Statistical indices for this database are not derived, because they can be compared with limited number of articles working with AHA. The database consists of 80 recordings: 2-leads, 250 Hz sampling rate and 5 μV/bit resolution.Method The differentiated and summed signals from  L  leads are compared to the absolute value of a threshold  MFR  =  M  +  F  +  R  – a combination of three independent adaptive thresholds, where: •  M  – Steep-slope threshold; •  F  – Integrating threshold for high-frequency signal components; •  R  – Beat expectation threshold. Two algorithms were developed: Algorithm 1  detects at the current beat. Algorithm 2  Pseudo-real-time detection with additional triggering of potentially missed heart beat in the last interval by RR interval analyses. The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number  L  of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals. Preprocessing • Moving averaging filter for power-line interference suppression: averages samples in one period of the power-line interference frequency with a first zero at this frequency. • Moving averaging of samples in 28 ms interval for electromyogram noise suppression – a filter with first zero at about 35 Hz. • Moving averaging of a complex lead (the sintesis is explained in the next section) in 40 ms intervals – a filter with first zero at about 25 Hz. It is suppressing the noise magnified by the differentiation procedure used in the process of the complex lead sintesis. Complex lead The algorithm operates with a complex lead  Y  of several primary leads  L . In cases of 12-standard leads, synthesis of the three quasi-orthogonal Frank leads is recommended first [ 3 , 12 ], thus determining the complex lead as a spatial vector. The complex lead is obtained as: where  Xj(i)  is the amplitude value of the sample  i  in lead  j , and  Y(i)  is the current complex lead. The above formula (except the normalizing coefficient 1/L and the absolute value) was initially adopted from the work of Bakardjian [ 13 ]. Operating with unsigned (absolute) values proved convenient when dealing with QRSs and extrasystoles having different, for example positive (in one lead) and negative (in the other lead) deflections. Adaptive steep-slope threshold – M • Initially  M  = 0.6* max(Y)  is set for the first 5 s of the signal, where at least 2 QRS complexes should occur. A buffer with 5 steep-slope threshold values is preset: MM  = [ M 1 M 2 M 3 M 4 M 5 ], where  M 1  ÷  M 5  are equal to  M . • QRS or beat complex is detected if  Yi  ≥  MFR , • No detection is allowed 200 ms after the current one. In the interval QRS ÷ QRS +200 ms  a new value of  M 5  is calculated: newM 5  = 0.6* max(Yi) The estimated  newM 5  value can become quite high, if steep slope premature ventricular contraction or artifact appeared, and for that reason it is limited to  newM 5  = 1.1*  M 5  if  newM 5  > 1.5*  M 5 . The  MM  buffer is refreshed excluding the oldest component, and including  M 5  =  newM 5 .  M  is calculated as an average value of  MM . •  M  is decreased in an interval 200 to 1200 ms following the last QRS detection at a low slope, reaching 60 % of its refreshed value at 1200 ms. • After 1200 ms  M  remains unchanged. The thresholds definitions are presented in more detail with the help of several examples. Two ECG leads are shown in Fig.  1a . Detected QRSs are marked with 'red O' on Lead 1. The summary lead and the steep-slope threshold are represented in Fig.  1b . Figure 1 Adaptive steep-slope threshold Adaptive integrating threshold –  F The integrating threshold  F  is intended to raise the combined threshold if electromyogram noise is accompanying the ECG, thus protecting the algorithm against 'erroneous beat detection'. Initially  F  is the mean value of the pseudo-spatial velocity  Y  for 350 ms. With every signal sample,  F  is updated adding the maximum of  Y  in the latest 50 ms of the 350 ms interval and subtracting  maxY  in the earliest 50 ms of the interval. F = F + (max(Y in latest 50 ms in the 350 ms interval ) - max(Y in earliest 50 ms in the 350 ms interval ))/150 The way  F  is updated means that not every sample in the interval is integrated, but just the envelope of the pseudo-spatial velocity  Y . The weight coefficient 1/150 is empirically derived. Two ECG leads are shown in Fig.  2a . The pseudo-spatial velocity  Y  and the integrated threshold are presented in Fig.  2b . The correct detection is due to the rise of  F  (hence of  MFR ) with about 0.2 mV. The beat complex is included in the integration process (note the high rise of  F  after any of the complexes), thus making almost impossible a close detection to the previous complex. Figure 2 Adaptive integrating threshold Adaptive beat expectation threshold –  R The beat expectation threshold  R  is intended to deal with heartbeats of normal amplitude followed by a beat with very small amplitude (and respectively with very small slew rate). This can be observed for example in cases of electrode artifacts. Conversely to the integrating threshold protecting against erroneous QRS detection,  R  is protecting against 'QRS misdetection'. A buffer with the 5 last RR intervals is updated at any new QRS detection.  Rm  is the mean value of the buffer. •  R  = 0  V  in the interval from the last detected QRS to 2/3 of the expected  Rm . • In the interval QRS + Rm * 2/3 to QRS + Rm,  R  decreases 1.4 times slower then the decrease of the previously discussed steep slope threshold ( M  in the 200–1200 ms interval). • After QRS + Rm the decrease of  R  is stopped. The time-course of the beat expectation threshold  R  is shown in Fig.  3 . The decrease of  R  (respectively  MFR ) with about 0.2 mV at the fourth QRS allows its detection, despite the lack of complex in Lead 2, which leads to a two-fold decrease of the summary lead amplitude  Y  (Fig.  3b ). Figure 3 Adaptive beat expectation threshold Combined adaptive threshold –  MFR The combined adaptive threshold is a sum of the adaptive steep-slope threshold, adaptive integrating threshold and adaptive beat expectation thresholds. (Fig.  4 ) Figure 4 Combined adaptive threshold MFR  =  M  +  F  +  R Algorithm 2: pseudo-real-time detection with additional triggering of eventually missed heart beat in the last detected RR interval All previous considerations relate to Algorithm 1, which detects a beat at its occurrence. Additional checking for an eventually missed heartbeat is performed by Algorithm 2. Its function is explained by the signal in Fig.  5 . The fourth complex at the 15.2 s in Fig.  5b  should be missed due to the fact that,  MFR  is greater then the summary lead  Y . Figure 5 Pseudo-real-time detection with additional triggering of eventually missed heart beat in the last RR interval. Let's mark the previous RR interval with  t1  and the last – with  t2  (Fig.  5a ). If  t1  is not shortened, which is tested by logic OR of the 2 conditions t1>Rm OR Rm-t1<0.12*Rm AND in the same time  t2  is quite long to fulfill the condition abs(t2-2*Rm)<0.5*Rm, the interval is subjected to check for a missed complex. A test is performed on each of the primary leads where a sharp peak is searched (defined as a product > 4 μV of two signal differences having one central and two lateral points 8 ms apart). If the test is passed, a second one is carried out for the amplitude of the summary lead at that point, which should be bigger then 1/3 of the mean value of the buffer  MM , in order to define this point as a missed QRS complex.
Method
The differentiated and summed signals from  L  leads are compared to the absolute value of a threshold  MFR  =  M  +  F  +  R  – a combination of three independent adaptive thresholds, where:LMFRMFR•  M  – Steep-slope threshold;M•  F  – Integrating threshold for high-frequency signal components;F•  R  – Beat expectation threshold.RTwo algorithms were developed:Algorithm 1  detects at the current beat.Algorithm 1Algorithm 2  Pseudo-real-time detection with additional triggering of potentially missed heart beat in the last interval by RR interval analyses.Algorithm 2The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number  L  of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals.LPreprocessing • Moving averaging filter for power-line interference suppression: averages samples in one period of the power-line interference frequency with a first zero at this frequency. • Moving averaging of samples in 28 ms interval for electromyogram noise suppression – a filter with first zero at about 35 Hz. • Moving averaging of a complex lead (the sintesis is explained in the next section) in 40 ms intervals – a filter with first zero at about 25 Hz. It is suppressing the noise magnified by the differentiation procedure used in the process of the complex lead sintesis.
Preprocessing
• Moving averaging filter for power-line interference suppression: averages samples in one period of the power-line interference frequency with a first zero at this frequency.• Moving averaging of samples in 28 ms interval for electromyogram noise suppression – a filter with first zero at about 35 Hz.• Moving averaging of a complex lead (the sintesis is explained in the next section) in 40 ms intervals – a filter with first zero at about 25 Hz. It is suppressing the noise magnified by the differentiation procedure used in the process of the complex lead sintesis.Complex lead The algorithm operates with a complex lead  Y  of several primary leads  L . In cases of 12-standard leads, synthesis of the three quasi-orthogonal Frank leads is recommended first [ 3 , 12 ], thus determining the complex lead as a spatial vector. The complex lead is obtained as: where  Xj(i)  is the amplitude value of the sample  i  in lead  j , and  Y(i)  is the current complex lead. The above formula (except the normalizing coefficient 1/L and the absolute value) was initially adopted from the work of Bakardjian [ 13 ]. Operating with unsigned (absolute) values proved convenient when dealing with QRSs and extrasystoles having different, for example positive (in one lead) and negative (in the other lead) deflections.
Complex lead
The algorithm operates with a complex lead  Y  of several primary leads  L . In cases of 12-standard leads, synthesis of the three quasi-orthogonal Frank leads is recommended first [ 3 , 12 ], thus determining the complex lead as a spatial vector. The complex lead is obtained as:YLwhere  Xj(i)  is the amplitude value of the sample  i  in lead  j , and  Y(i)  is the current complex lead.Xj(i)ijY(i)The above formula (except the normalizing coefficient 1/L and the absolute value) was initially adopted from the work of Bakardjian [ 13 ]. Operating with unsigned (absolute) values proved convenient when dealing with QRSs and extrasystoles having different, for example positive (in one lead) and negative (in the other lead) deflections.Adaptive steep-slope threshold – M • Initially  M  = 0.6* max(Y)  is set for the first 5 s of the signal, where at least 2 QRS complexes should occur. A buffer with 5 steep-slope threshold values is preset: MM  = [ M 1 M 2 M 3 M 4 M 5 ], where  M 1  ÷  M 5  are equal to  M . • QRS or beat complex is detected if  Yi  ≥  MFR , • No detection is allowed 200 ms after the current one. In the interval QRS ÷ QRS +200 ms  a new value of  M 5  is calculated: newM 5  = 0.6* max(Yi) The estimated  newM 5  value can become quite high, if steep slope premature ventricular contraction or artifact appeared, and for that reason it is limited to  newM 5  = 1.1*  M 5  if  newM 5  > 1.5*  M 5 . The  MM  buffer is refreshed excluding the oldest component, and including  M 5  =  newM 5 .  M  is calculated as an average value of  MM . •  M  is decreased in an interval 200 to 1200 ms following the last QRS detection at a low slope, reaching 60 % of its refreshed value at 1200 ms. • After 1200 ms  M  remains unchanged. The thresholds definitions are presented in more detail with the help of several examples. Two ECG leads are shown in Fig.  1a . Detected QRSs are marked with 'red O' on Lead 1. The summary lead and the steep-slope threshold are represented in Fig.  1b . Figure 1 Adaptive steep-slope threshold
Adaptive steep-slope threshold – M
• Initially  M  = 0.6* max(Y)  is set for the first 5 s of the signal, where at least 2 QRS complexes should occur. A buffer with 5 steep-slope threshold values is preset:Mmax(Y)MM  = [ M 1 M 2 M 3 M 4 M 5 ],MMMMMMMwhere  M 1  ÷  M 5  are equal to  M .MMM• QRS or beat complex is detected if  Yi  ≥  MFR ,YiMFR• No detection is allowed 200 ms after the current one. In the interval QRS ÷ QRS +200 ms  a new value of  M 5  is calculated:msMnewM 5  = 0.6* max(Yi)newMmax(Yi)The estimated  newM 5  value can become quite high, if steep slope premature ventricular contraction or artifact appeared, and for that reason it is limited to  newM 5  = 1.1*  M 5  if  newM 5  > 1.5*  M 5 .newMnewMMnewMMThe  MM  buffer is refreshed excluding the oldest component, and including  M 5  =  newM 5 .  M  is calculated as an average value of  MM .MMMnewMMMM•  M  is decreased in an interval 200 to 1200 ms following the last QRS detection at a low slope, reaching 60 % of its refreshed value at 1200 ms.M• After 1200 ms  M  remains unchanged.MThe thresholds definitions are presented in more detail with the help of several examples. Two ECG leads are shown in Fig.  1a . Detected QRSs are marked with 'red O' on Lead 1. The summary lead and the steep-slope threshold are represented in Fig.  1b .Adaptive integrating threshold –  F The integrating threshold  F  is intended to raise the combined threshold if electromyogram noise is accompanying the ECG, thus protecting the algorithm against 'erroneous beat detection'. Initially  F  is the mean value of the pseudo-spatial velocity  Y  for 350 ms. With every signal sample,  F  is updated adding the maximum of  Y  in the latest 50 ms of the 350 ms interval and subtracting  maxY  in the earliest 50 ms of the interval. F = F + (max(Y in latest 50 ms in the 350 ms interval ) - max(Y in earliest 50 ms in the 350 ms interval ))/150 The way  F  is updated means that not every sample in the interval is integrated, but just the envelope of the pseudo-spatial velocity  Y . The weight coefficient 1/150 is empirically derived. Two ECG leads are shown in Fig.  2a . The pseudo-spatial velocity  Y  and the integrated threshold are presented in Fig.  2b . The correct detection is due to the rise of  F  (hence of  MFR ) with about 0.2 mV. The beat complex is included in the integration process (note the high rise of  F  after any of the complexes), thus making almost impossible a close detection to the previous complex. Figure 2 Adaptive integrating threshold
Adaptive integrating threshold –  F
FThe integrating threshold  F  is intended to raise the combined threshold if electromyogram noise is accompanying the ECG, thus protecting the algorithm against 'erroneous beat detection'.FInitially  F  is the mean value of the pseudo-spatial velocity  Y  for 350 ms.FYWith every signal sample,  F  is updated adding the maximum of  Y  in the latest 50 ms of the 350 ms interval and subtracting  maxY  in the earliest 50 ms of the interval.FYmaxYF = F + (max(Y in latest 50 ms in the 350 ms interval ) - max(Y in earliest 50 ms in the 350 ms interval ))/150The way  F  is updated means that not every sample in the interval is integrated, but just the envelope of the pseudo-spatial velocity  Y . The weight coefficient 1/150 is empirically derived.FYTwo ECG leads are shown in Fig.  2a . The pseudo-spatial velocity  Y  and the integrated threshold are presented in Fig.  2b . The correct detection is due to the rise of  F  (hence of  MFR ) with about 0.2 mV. The beat complex is included in the integration process (note the high rise of  F  after any of the complexes), thus making almost impossible a close detection to the previous complex.YFMFRFAdaptive beat expectation threshold –  R The beat expectation threshold  R  is intended to deal with heartbeats of normal amplitude followed by a beat with very small amplitude (and respectively with very small slew rate). This can be observed for example in cases of electrode artifacts. Conversely to the integrating threshold protecting against erroneous QRS detection,  R  is protecting against 'QRS misdetection'. A buffer with the 5 last RR intervals is updated at any new QRS detection.  Rm  is the mean value of the buffer. •  R  = 0  V  in the interval from the last detected QRS to 2/3 of the expected  Rm . • In the interval QRS + Rm * 2/3 to QRS + Rm,  R  decreases 1.4 times slower then the decrease of the previously discussed steep slope threshold ( M  in the 200–1200 ms interval). • After QRS + Rm the decrease of  R  is stopped. The time-course of the beat expectation threshold  R  is shown in Fig.  3 . The decrease of  R  (respectively  MFR ) with about 0.2 mV at the fourth QRS allows its detection, despite the lack of complex in Lead 2, which leads to a two-fold decrease of the summary lead amplitude  Y  (Fig.  3b ). Figure 3 Adaptive beat expectation threshold
Adaptive beat expectation threshold –  R
RThe beat expectation threshold  R  is intended to deal with heartbeats of normal amplitude followed by a beat with very small amplitude (and respectively with very small slew rate). This can be observed for example in cases of electrode artifacts. Conversely to the integrating threshold protecting against erroneous QRS detection,  R  is protecting against 'QRS misdetection'.RRA buffer with the 5 last RR intervals is updated at any new QRS detection.  Rm  is the mean value of the buffer.Rm•  R  = 0  V  in the interval from the last detected QRS to 2/3 of the expected  Rm .RVRm• In the interval QRS + Rm * 2/3 to QRS + Rm,  R  decreases 1.4 times slower then the decrease of the previously discussed steep slope threshold ( M  in the 200–1200 ms interval).RM• After QRS + Rm the decrease of  R  is stopped.RThe time-course of the beat expectation threshold  R  is shown in Fig.  3 . The decrease of  R  (respectively  MFR ) with about 0.2 mV at the fourth QRS allows its detection, despite the lack of complex in Lead 2, which leads to a two-fold decrease of the summary lead amplitude  Y  (Fig.  3b ).RRMFRYCombined adaptive threshold –  MFR The combined adaptive threshold is a sum of the adaptive steep-slope threshold, adaptive integrating threshold and adaptive beat expectation thresholds. (Fig.  4 ) Figure 4 Combined adaptive threshold MFR  =  M  +  F  +  R
Combined adaptive threshold –  MFR
MFRThe combined adaptive threshold is a sum of the adaptive steep-slope threshold, adaptive integrating threshold and adaptive beat expectation thresholds. (Fig.  4 )MFR  =  M  +  F  +  RMFRMFRAlgorithm 2: pseudo-real-time detection with additional triggering of eventually missed heart beat in the last detected RR interval All previous considerations relate to Algorithm 1, which detects a beat at its occurrence. Additional checking for an eventually missed heartbeat is performed by Algorithm 2. Its function is explained by the signal in Fig.  5 . The fourth complex at the 15.2 s in Fig.  5b  should be missed due to the fact that,  MFR  is greater then the summary lead  Y . Figure 5 Pseudo-real-time detection with additional triggering of eventually missed heart beat in the last RR interval. Let's mark the previous RR interval with  t1  and the last – with  t2  (Fig.  5a ). If  t1  is not shortened, which is tested by logic OR of the 2 conditions t1>Rm OR Rm-t1<0.12*Rm AND in the same time  t2  is quite long to fulfill the condition abs(t2-2*Rm)<0.5*Rm, the interval is subjected to check for a missed complex. A test is performed on each of the primary leads where a sharp peak is searched (defined as a product > 4 μV of two signal differences having one central and two lateral points 8 ms apart). If the test is passed, a second one is carried out for the amplitude of the summary lead at that point, which should be bigger then 1/3 of the mean value of the buffer  MM , in order to define this point as a missed QRS complex.
Algorithm 2: pseudo-real-time detection with additional triggering of eventually missed heart beat in the last detected RR interval
All previous considerations relate to Algorithm 1, which detects a beat at its occurrence. Additional checking for an eventually missed heartbeat is performed by Algorithm 2. Its function is explained by the signal in Fig.  5 . The fourth complex at the 15.2 s in Fig.  5b  should be missed due to the fact that,  MFR  is greater then the summary lead  Y .MFRYLet's mark the previous RR interval with  t1  and the last – with  t2  (Fig.  5a ).t1t2If  t1  is not shortened, which is tested by logic OR of the 2 conditions t1>Rm OR Rm-t1<0.12*Rm AND in the same time  t2  is quite long to fulfill the condition abs(t2-2*Rm)<0.5*Rm, the interval is subjected to check for a missed complex.t1t2A test is performed on each of the primary leads where a sharp peak is searched (defined as a product > 4 μV of two signal differences having one central and two lateral points 8 ms apart). If the test is passed, a second one is carried out for the amplitude of the summary lead at that point, which should be bigger then 1/3 of the mean value of the buffer  MM , in order to define this point as a missed QRS complex.MMResults and discussion Normally the statistical indices Se and Sp are derived from the following parameters: correctly detected beats TP (true positive), falsely detected beats FP (false positive) and undetected beats FN (false negative). In addition, we used two parameters, adopted from Dotsinsky and Stoyanov [ 6 ], as described below. SP – shifted positive error was introduced in order to explain cases like the one shown in Fig.  6 . Here the algorithm made a false positive error before the 3rd QRS and missed the following QRS. Formally, this is a false positive error, immediately followed by a false negative. However, if the time interval between these two detections is reasonably short, for example ≤ 200 ms, we accepted this as one error only, labeled as  Shifted False Positive Error  (SP). Figure 6 Shifted positive error at the P wave Another example of SP error as a result of artifacts just before the normal complexes is shown in Fig.  7 . Figure 7 Shifted positive errors, false positive + false negative twins SN – shifted negative error was assumed by the same principle as SP, but in the opposite sense. It also included twin FN+FP error occurring within 200 ms. The first incoming FP or FN error of the shifted is defining it as SP or SN. The logic of using shifted errors (instead of FP and FN or FN and FP in cases when they appear within 200 ms of each other) is that thus the total number of beats in a record retains its value. Otherwise it would change depending on the type and number of errors and thus impede correct computation of Se and Sp. The sensitivity Se is calculated by summing FN   SN, while the specificity Sp – by summing FP+SP. The method was developed in Matlab. All 48 recordings from the MIT-BIH Arrhythmia database, without any exception, were used for testing the two algorithms. The processed files containing detection marks were automatically compared with the original MIT-BIH annotated beats by specially designed software. It shows all cases where the annotation and detection marks differ by more than 60 ms. These cases were examined by an independent expert, thus excluding possible author's influence. The results for the two algorithms are presented in Table  1 . Table 1 Statistical results for the two algorithms File Annotated beats Algorithm 1 Algorithm 2 TP FN FP SN SP TP FN FP SN SP 100 2273 2273 0 0 0 0 2273 0 0 0 0 101 1863 1862 1 4 0 0 1862 1 4 0 0 102 2187 2187 0 0 0 0 2187 0 0 0 0 103 2084 2062 2 54 11 9 2065 0 58 12 7 104 2212 2211 1 0 0 0 2211 1 0 0 0 105 2567 2543 2 35 8 14 2544 2 36 8 13 106 2027 2017 1 1 0 9 2018 0 1 0 9 107 2137 2135 2 0 0 0 2137 0 0 0 0 108 1763 1664 2 40 3 94 1674 1 42 3 85 109 2532 2521 11 1 0 0 2527 5 0 0 0 111 2124 2124 0 0 0 0 2124 0 0 0 0 112 2539 2539 0 0 0 0 2539 0 0 0 0 113 1797 1797 0 0 0 0 1797 0 0 0 0 114 1879 1879 0 0 0 0 1879 0 0 0 0 115 1953 1951 0 4 1 1 1952 0 4 0 1 116 2412 2389 22 2 0 1 2392 19 2 0 1 117 1535 1535 0 0 0 0 1535 0 0 0 0 118 2275 2275 0 0 0 0 2275 0 0 0 0 119 1987 1987 0 0 0 0 1987 0 0 0 0 121 1863 1863 0 0 0 0 1863 0 0 0 0 122 2476 2476 0 0 0 0 2476 0 0 0 0 123 1518 1516 2 0 0 0 1516 2 0 0 0 124 1619 1617 2 0 0 0 1619 0 0 0 0 200 2601 2549 9 39 18 25 2552 6 41 20 23 201 1963 1902 60 0 0 1 1902 60 0 0 1 202 2136 2130 6 0 0 0 2130 6 0 0 0 203 2978 2901 71 13 3 3 2911 62 27 3 2 205 2656 2652 4 0 0 0 2652 4 0 0 0 207 1862 1860 2 0 0 0 1862 0 1 0 0 208 2954 2937 14 7 2 1 2939 11 7 2 2 209 3004 3004 0 1 0 0 3004 0 1 0 0 210 2647 2591 56 1 0 0 2603 44 1 0 0 212 2748 2748 0 0 0 0 2748 0 0 0 0 213 3551 3548 3 0 0 0 3550 1 0 0 0 214 2260 2258 1 1 1 0 2256 4 1 0 0 215 3362 3362 0 0 0 0 3362 0 0 0 0 217 2208 2204 3 0 0 1 2205 2 0 0 1 219 2154 2153 1 0 0 0 2153 1 0 0 0 220 2048 2048 0 0 0 0 2048 0 0 0 0 221 2427 2426 1 0 0 0 2426 1 0 0 0 222 2483 2480 2 0 0 1 2482 0 0 0 1 223 2595 2585 10 0 0 0 2590 5 0 0 0 228 2053 2053 0 0 0 0 2053 0 1 0 0 230 2256 2256 0 0 0 0 2256 0 0 0 0 231 1886 1886 0 0 0 0 1886 0 0 0 0 232 1767 1766 0 12 0 1 1766 0 12 0 1 233 3076 3073 3 0 0 0 3074 2 0 0 0 234 2753 2753 0 0 0 0 2753 0 0 0 0 Sum 110050 109548 294 215 47 161 109615 240 239 48 147 Of all 110050 annotated beats ('unknown' or 'questionable' were excluded from the study), true detected are 109548 for Algorithm 1 and 109616 for Algorithm 2. The statistical indices are: Algorithm 1 : Se = 99.69 %, Sp = 99.66 %; Algorithm 2 : Se = 99.74 %, Sp = 99.65 %. The standard way of Se and Sp calculation, considering the joint SP and SN errors as two separate errors gives the following results: Algorithm 1 : Se = 99.54 %, Sp = 99.61 %; Algorithm 2 : Se = 99.60 %, Sp = 99.60 %. Algorithm 2 improved the sensitivity by 0.05 % (0.06 % for the standard evaluation) as a result of decreased number of undetected beats. This result can be observed for example in recordings 109,203, 210 and 223, where the additionally detected beats are respectively 6, 9, 12 and 5. The performance of both algorithms was especially tested with the file A5001 from the AHA containing R-over-T premature ventricular complexes, very close to the previous normal QRS complex (Fig.  5a ). An improvement of 74 undetected by Algorithm 1 R-on-T complexes was observed. The detection of such premature ventricular complexes occurring at the time of ventricular repolarization was considered important, having in mind possible risk of ventricular fibrillation triggering by R-on-T events.
Results and discussion
Normally the statistical indices Se and Sp are derived from the following parameters: correctly detected beats TP (true positive), falsely detected beats FP (false positive) and undetected beats FN (false negative). In addition, we used two parameters, adopted from Dotsinsky and Stoyanov [ 6 ], as described below.SP – shifted positive error was introduced in order to explain cases like the one shown in Fig.  6 . Here the algorithm made a false positive error before the 3rd QRS and missed the following QRS. Formally, this is a false positive error, immediately followed by a false negative. However, if the time interval between these two detections is reasonably short, for example ≤ 200 ms, we accepted this as one error only, labeled as  Shifted False Positive Error  (SP).Shifted False Positive ErrorAnother example of SP error as a result of artifacts just before the normal complexes is shown in Fig.  7 .SN – shifted negative error was assumed by the same principle as SP, but in the opposite sense. It also included twin FN+FP error occurring within 200 ms. The first incoming FP or FN error of the shifted is defining it as SP or SN.The logic of using shifted errors (instead of FP and FN or FN and FP in cases when they appear within 200 ms of each other) is that thus the total number of beats in a record retains its value. Otherwise it would change depending on the type and number of errors and thus impede correct computation of Se and Sp.The sensitivity Se is calculated by summing FN   SN, while the specificity Sp – by summing FP+SP.The method was developed in Matlab. All 48 recordings from the MIT-BIH Arrhythmia database, without any exception, were used for testing the two algorithms.The processed files containing detection marks were automatically compared with the original MIT-BIH annotated beats by specially designed software. It shows all cases where the annotation and detection marks differ by more than 60 ms. These cases were examined by an independent expert, thus excluding possible author's influence.The results for the two algorithms are presented in Table  1 .Statistical results for the two algorithmsOf all 110050 annotated beats ('unknown' or 'questionable' were excluded from the study), true detected are 109548 for Algorithm 1 and 109616 for Algorithm 2. The statistical indices are:Algorithm 1 : Se = 99.69 %, Sp = 99.66 %;Algorithm 1Algorithm 2 : Se = 99.74 %, Sp = 99.65 %.Algorithm 2The standard way of Se and Sp calculation,considering the joint SP and SN errors as two separate errors gives the following results:Algorithm 1 : Se = 99.54 %, Sp = 99.61 %;Algorithm 1Algorithm 2 : Se = 99.60 %, Sp = 99.60 %.Algorithm 2Algorithm 2 improved the sensitivity by 0.05 % (0.06 % for the standard evaluation) as a result of decreased number of undetected beats. This result can be observed for example in recordings 109,203, 210 and 223, where the additionally detected beats are respectively 6, 9, 12 and 5. The performance of both algorithms was especially tested with the file A5001 from the AHA containing R-over-T premature ventricular complexes, very close to the previous normal QRS complex (Fig.  5a ). An improvement of 74 undetected by Algorithm 1 R-on-T complexes was observed. The detection of such premature ventricular complexes occurring at the time of ventricular repolarization was considered important, having in mind possible risk of ventricular fibrillation triggering by R-on-T events.Conclusions The proposed algorithms for real-time and pseudo-real-time implementation are adaptive, independent of thresholds and constants values. They are self-synchronized to the QRS steep slope and the heart rhythm, regardless of the resolution and sampling frequency used. Due to the integration threshold, the algorithms are practically insensitive to electromyogram and similar high-frequency noise. The algorithms can operate with one, two or more leads, using a combined lead signal derived from the sum of absolute values of the differentiated lead signals. The statistical indices are higher than, or comparable to those, cited in the scientific literature.
Conclusions
The proposed algorithms for real-time and pseudo-real-time implementation are adaptive, independent of thresholds and constants values. They are self-synchronized to the QRS steep slope and the heart rhythm, regardless of the resolution and sampling frequency used. Due to the integration threshold, the algorithms are practically insensitive to electromyogram and similar high-frequency noise.The algorithms can operate with one, two or more leads, using a combined lead signal derived from the sum of absolute values of the differentiated lead signals.The statistical indices are higher than, or comparable to those, cited in the scientific literature.


Background Mitochondria contain small genomes that are physically separate from those of nuclei. Their comparison serves as a model system for understanding the processes of genome evolution. Although hundreds of these genome sequences have been reported, the taxonomic sampling is highly biased toward vertebrates and arthropods, with many whole phyla remaining unstudied. This is the first description of a complete mitochondrial genome sequence of a representative of the phylum Echiura, that of the fat innkeeper worm,  Urechis caupo .
Background
Mitochondria contain small genomes that are physically separate from those of nuclei. Their comparison serves as a model system for understanding the processes of genome evolution. Although hundreds of these genome sequences have been reported, the taxonomic sampling is highly biased toward vertebrates and arthropods, with many whole phyla remaining unstudied. This is the first description of a complete mitochondrial genome sequence of a representative of the phylum Echiura, that of the fat innkeeper worm,  Urechis caupo .Urechis caupoResults This mtDNA is 15,113 nts in length and 62% A+T. It contains the 37 genes that are typical for animal mtDNAs in an arrangement somewhat similar to that of annelid worms. All genes are encoded by the same DNA strand which is rich in A and C relative to the opposite strand. Codons ending with the dinucleotide GG are more frequent than would be expected from apparent mutational biases. The largest non-coding region is only 282 nts long, is 71% A+T, and has potential for secondary structures.
Results
This mtDNA is 15,113 nts in length and 62% A+T. It contains the 37 genes that are typical for animal mtDNAs in an arrangement somewhat similar to that of annelid worms. All genes are encoded by the same DNA strand which is rich in A and C relative to the opposite strand. Codons ending with the dinucleotide GG are more frequent than would be expected from apparent mutational biases. The largest non-coding region is only 282 nts long, is 71% A+T, and has potential for secondary structures.Conclusions Urechis caupo  mtDNA shares many features with those of the few studied annelids, including the common usage of ATG start codons, unusual among animal mtDNAs, as well as gene arrangements, tRNA structures, and codon usage biases.
Conclusions
Urechis caupo  mtDNA shares many features with those of the few studied annelids, including the common usage of ATG start codons, unusual among animal mtDNAs, as well as gene arrangements, tRNA structures, and codon usage biases.Urechis caupo

Background Mitochondrial genomes are physically separate from the nuclear genome. For animals, they are typically circular with a compact arrangement of an identical set of 37 genes [ 1 ]. For some animals, all genes are on the same strand, whereas for others they are divided between the two. The arrangement of these genes can remain stable for long periods of time; for example, human [ 2 ] and shark [ 3 ] mtDNAs have the same gene arrangement, and do those of fruit fly [ 4 ] and shrimp [ 5 ]. However, in other lineages, rearrangements have occurred much more rapidly. Many of the same processes that occur in large and complex nuclear genomes also take place in these diminutive genomes, so comparisons among mtDNAs can address general questions of genome evolution, but in a model system that is much more tractable for a large number of taxa. Toward this end, this article describes the complete mtDNA sequence of the fat innkeeper worm,  Urechis caupo , the first example from the phylum Echiura. Echiurans comprise about 150 species and are commonly called spoon worms because of the shape of their extensible proboscis. Unlike annelids, they have no overt segmentation, but they develop via trochophore larvae, very similar to those of annelids.  U. caupo  is a pink, sausage shaped worm that lives in U-shaped burrows in the mud or sand of the intertidal or subtidal zones. Unlike other echiurans, it feeds on plankton by filtering using an elaborate mucus net.
Background
Mitochondrial genomes are physically separate from the nuclear genome. For animals, they are typically circular with a compact arrangement of an identical set of 37 genes [ 1 ]. For some animals, all genes are on the same strand, whereas for others they are divided between the two. The arrangement of these genes can remain stable for long periods of time; for example, human [ 2 ] and shark [ 3 ] mtDNAs have the same gene arrangement, and do those of fruit fly [ 4 ] and shrimp [ 5 ]. However, in other lineages, rearrangements have occurred much more rapidly. Many of the same processes that occur in large and complex nuclear genomes also take place in these diminutive genomes, so comparisons among mtDNAs can address general questions of genome evolution, but in a model system that is much more tractable for a large number of taxa.Toward this end, this article describes the complete mtDNA sequence of the fat innkeeper worm,  Urechis caupo , the first example from the phylum Echiura. Echiurans comprise about 150 species and are commonly called spoon worms because of the shape of their extensible proboscis. Unlike annelids, they have no overt segmentation, but they develop via trochophore larvae, very similar to those of annelids.  U. caupo  is a pink, sausage shaped worm that lives in U-shaped burrows in the mud or sand of the intertidal or subtidal zones. Unlike other echiurans, it feeds on plankton by filtering using an elaborate mucus net.Urechis caupoU. caupoResults and discussion Gene content and organization The mtDNA of  Urechis caupo  is 15,113 nts in length (GenBank accession number  AY619711 ) and contains the same 37 genes found for nearly all animal mtDNAs [see ref. [ 1 ]]. All genes are transcribed from the same strand (Fig.  1 ), as is the case for the two studied annelid mtDNAs, the polychaete  Platynereis dumerilii  [ 6 ] and the oligochaete  Lumbricus terrestris  [ 7 ] and for several other animal mtDNAs. The arrangement of the genes is substantially similar to those of the two annelids, and shares short regions of similarity with several other mtDNAs, as can be seen in Table  1 . Figure 1 Mitochondrial gene map of the echiuran  Urechis caupo.  All genes are transcribed from the same DNA strand. Scaling is only approximate. Genes are designated by standard nomenclature except for tRNAs, which are identified only by the one-letter code for the corresponding amino acid, with the two serine and two leucine tRNAs differentiated by numeral as identified in Fig. 3. "nc" indicates the largest non-coding regions; it may be that transcription initiates here, but this is not known. Table 1 Mitochondrial gene arrangement identities found in pairwise comparisons between  Urechis caupo  and various animals. Full taxon names are given here for the annelids  Lumbricus terrestris  and  Platynereis dumerilii , the mollusks  Katharina tunicata ,  Loligo bleekeri ,  Cepaea nemoralis , and  Mytilus edulis , the brachiopods  Terebratulina retusa  and  Terebratalia transversa , the platyhelminths  Fasciola hepatica ,  Taenia crassiceps ,  Echinococcus multilocularis , and  Hymenolepis diminuta , the arthropods  Drosophila yakuba ,  Anopheles gambiae ,  Artemia franciscana ,  Daphnia pulex ,  Apis mellifera ,  Locusta migratoria ,  Ixodes hexagonus ,  Rhiphicephalus sanguineus ,  Limulus polyphemus  and  Lithobius forficatus , the nematodes  Trichinella spirallis ,  Onchocerca volvulus ,  Meloidogyne javanica ,  Ascaris suum , and  Caenorhabditis elegans , the echinoderms  Arabacia lixula ,  Asterina pectinifera ,  Paracentrotus lividus ,  Strongylocentrotus purpuratus , and  Florometra serratissima , the hemichordate  Balanoglossus carnosus , and the chordate  Branchiostoma floridae  along with the gene order most typical for vertebrates. Complete citations can be found in Boore (1999) or updated by following the "Evolutionary Genomics" link at  . Contiguous gene arrangements are separated by a comma; a slash indicates a gap containing one or more unrelated genes. L. terrestris  and  P. dumerilii cox3 ,  trnQ ,  nad6 ,  cob ,  trnW ,  atp6 ,  trnR ,  trnH ,  nad5 ,  trnF / trnL2 ,  nad1 ,  trnI ,  trnK ,  nad3 / trnT ,  nad4L ,  nad4 L. terrestris  but not  P. dumerilii trnL1 ,  trnA ,  trnS2 ,  trnL2 / trnD ,  atp8 K. tunicata trnL2 ,  nad1 / nad4L ,  nad4 / trnH ,  nad5 ,  trnF L. bleekeri nad6 ,  cob / nad4L ,  nad4 / nad5 ,  trnF / trnD ,  atp8 C. nemoralis trnL1 ,  trnA M. edulis trnL2 ,  nad1 / trnT ,  nad4L T. retusa trnL2 ,  nad1 / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnL1 ,  trnA / trnD ,  atp8 / cox1 ,  cox2 T. transversa trnP ,  trnD F. hepatica ,  T. crassiceps ,  E. multilocularis trnI ,  trnK ,  nad3 / nad4L ,  nad4 / trnY ,  trnL1 / trnS2 ,  trnL2 H. diminuta trnI ,  trnK ,  nad3 / nad4L ,  nad4 / trnY ,  trnL1 D. yakuba ,  A. gambiae ,  A. franciscana ,  D. pulex nad6 ,  cob / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnD ,  atp8 A. mellifera ,  L. migratoria nad6 ,  cob / nad4L ,  nad4 / trnH ,  nad5 ,  trnF I. hexagonus ,  R. sanguineus ,  L. polyphemus ,  L. forficatus nad6 ,  cob / trnL2 ,  nad1 / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnD ,  atp8 / cox1 ,  cox2 T. spirallis nad6 ,  cob / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnR ,  trnH / trnL1 ,  trnA / trnD ,  atp8 / cox1 ,  cox2 O. volvulus trnP ,  trnD M. javanica trnN ,  trnG A. suum NONE C. elegans NONE A. lixula ,  A. pectinifera ,  P. lividus  and  S. purpuratus trnL2 ,  nad1 ,  trnI F. serratissima nad1 ,  trnI / nad2 ,  trnY B. carnosus trnL2 ,  nad1 / nad4L ,  nad4 B. floridae  and the typical vertebrate arrangement trnL2 ,  nad1 ,  trnI / nad4L ,  nad4 Base composition and codon usage The  U. caupo  mtDNA is 62% A+T, about the same as for annelid mtDNAs (64% and 62% for  P. dumerilii  and  L. terrestris , respectively). As is typical, all homodinucleotides and homotrinucleotides are greatly over represented relative to a random distribution and CG is the least frequent dinucleotide, both in absolute number and in the ratio of observed to expected. The gene-coding strand has a strong skew against G vs. C but about equal amounts of A vs. T; G-skew ([G-C]/[G+C]) is – 0.24 and T-skew ([T-A]/[T+A]) is – 0.016 [ 8 ]. These values show no striking variation across the length of the mtDNA. Codon usage (Table  2 ) reflects these values, with those ending in A or T being most frequent. In all cases except for two, where they are synonymous, NNC codons are in greater abundance than NNG codons, as is consistent with the coding strand being rich in C relative to G. The two exceptions are CGG and GGG codons, which are each in greater abundance than their respective synonyms, CGC and GGC. This invites the speculation that there is something favored about the GG dinucleotide created when G appears in the second codon position. However, this is not consistently seen, since in the remaining case, AGC codons outnumber AGG codons two-to-one. This effect has been shown to be very strong for codon usage pattern of the mtDNA of the brachiopod  Terebratalia transversa  [ 9 ]. Table 2 Codon usage in the 13 protein-encoding genes of the  Urechis caupo  mitochondrial genome. The total number of codons is 3722. The anticodon of the corresponding tRNA gene is shown in parentheses below each amino acid designation. Stop codons are not included in this analysis. Amino acid Codon N % Amino acid Codon N % Phe (F) TTT 161 4.3% Ser (S2) TCT 108 2.9% (GAA) TTC 115 3.1% (TGA) TCC 65 1.7% Leu (L2) TTA 146 3.9% TCA 74 2.0% (TAA) TTG 10 0.3% TCG 3 0.1% Tyr (Y) TAT 42 1.1% Cys (C) TGT 18 0.5% (GTA) TAC 65 1.7% (GCA) TGC 11 0.3% TER TAA --- --- Trp (W) TGA 77 2.1% TAG --- --- (TCA) TGG 21 0.6% Leu (L1) CTT 105 2.8% Pro (P) CCT 72 1.9% (TAG) CTC 62 1.7% (TGG) CCC 44 1.2% CTA 224 6.0% CCA 80 2.1% CTG 38 1.0% CCG 6 0.2% His (H) CAT 33 0.9% Arg (R) CGT 6 0.2% (GTG) CAC 55 1.5% (TCG) CGC 5 0.1% Gln (Q) CAA 84 2.3% CGA 46 1.2% (TTG) CAG 12 0.3% CGG 9 0.2% Ile (I) ATT 200 5.4% Thr (T) ACT 76 2.0% (GAT) ATC 100 2.7% (TGT) ACC 91 2.4% Met (M) ATA 171 4.6% ACA 93 2.5% (CAT) ATG 52 1.4% ACG 5 0.1% Asn (N) AAT 61 1.6% Ser (S1) AGT 7 0.2% (GTT) AAC 65 1.7% (TCT) AGC 16 0.4% Lys (K) AAA 78 2.1% AGA 62 1.7% (TTT) AAG 14 0.4% AGG 8 0.2% Val (V) GTT 49 1.3% Ala (A) GCT 75 2.0% (TAC) GTC 28 0.8% (TGC) GCC 75 2.0% GTA 99 2.7% GCA 122 3.3% GTG 18 0.5% GCG 14 0.4% Asp (D) GAT 23 0.6% Gly (G) GGT 14 0.4% (GTC) GAC 37 1.0% (TCC) GGC 27 0.7% Glu (E) GAA 73 2.0% GGA 127 3.4% (TTC) GAG 10 0.3% GGG 36 1.0% Gene initiation and termination Mitochondrial genes commonly use several alternatives to ATG as start codons. However, 11 of the 13 proteins coding genes of  U. caupo  mtDNA use ATG. The only exceptions are  cox1 , which uses GTG and  nad3  which uses ATC. In the case of  cox1 , there is an in frame stop only three codons upstream and neither of the intervening codons is ATG. Also, this inference of starting on GTG specifies a set of amino acids well matched to those at the beginning of other Cox1 proteins. The situation for  nad3  is nearly identical, with an in frame stop only four codons upstream and no intervening ATG codons. However, downstream of the inferred start are several ATA codons that can not be ruled out as alternatives. The commonality of using ATG as a start codon has also been noted for mitochondrial genes of four annelids,  Platynereis dumerilii  [ 6 ],  Lumbricus terrestris  [ 7 ],  Helobdella robusta  and  Galathealinum brachiosum  (previously considered to be of the phylum Pogonophora) [ 10 ] and a sipunculid,  Phascolopsis gouldii  [ 11 ]. A complete stop codon without overlap of the downstream gene is found for all except  cox2 ,  nad1 ,  nad2 ,  cob , and  nad5  (Fig.  2 ). In each of these cases, it appears that an abbreviated stop codon is generated by cleavage of a downstream tRNA from the polycistronic transcript, which is then completed to a TAA stop codon by polyadenylation. However, in two of these cases ( nad2  and  cob ), a complete stop codon could be formed by including only the next two nucleotides, and two other cases ( nad1  and  cox2 ), there is an in frame stop codon just one or two codons downstream, respectively. It is not clear how gene overlaps could be resolved from a polycistronic transcript (assuming that the genes of this mtDNA are expressed in this way), but the presence of these stop codons seems beyond coincidence. It could be that they serve as a "back up" in case translation should begin in the absence of transcript cleavage. Figure 2 A greatly abbreviated schematic of the sequence of  Urechis caupo  mtDNA.  In the interest of brevity, the middle portion of each large gene is omitted and replaced by a numeral indicating the number of nucleotides removed. Since all mitochondrial proteins are thought to initiate with formyl-methionine, an M is placed in parentheses at the first codon position of  cox1  (GTG) and  nad3  (ATC) to indicate nonconformity to the genetic code. Asterisks indicate inferred stop codons whether complete or abbreviated and plus symbols mark nucleotides that would form the first in frame, complete stop codon if genes instead overlap. Transfer RNAs Twenty-two regions can be folded into the typical cloverleaf structures of the expected set of tRNAs (Fig.  3 ). There are several mismatched nucleotide pairs within stems; nearly all of these are flanked by multiple G-C pairs, suggesting that they may provide compensatory stability for these arms. T precedes the anticodon and a purine follows it for all tRNAs. The two serine tRNAs lack potential for folding a DHU arm, as has been found for a number of other animal mtDNAs. There is an alternative folding possible for tRNA(S2) with a six-member anticodon stem and only one nucleotide separating the acceptor and DHU stems; this unusual folding has been found for the homologous genes of some mammals. tRNA(R) also does not have potential for a normally paired DHU arm, although there are three potential nucleotide pairs if two (rather than one) nucleotides were between the DHU and anticodon stems. However, this potential pairing could, alternatively, be a coincidence, with the DHU arm having no paired stem for this tRNA. Those with paired DHU arms have stems of three to five nucleotide pairs and loops of three to eight nts. All tRNAs have potential for stems of three to six nucleotide pairs for their TΨC arms with loops of three to seven nts. One of the tRNAs for serine has the anticodon TCT; although this is often found, the alternative of GCT is otherwise common. Figure 3 The 22 inferred tRNA genes folded into the typical cloverleafstructures.  Nomenclature for tRNA substructures is indicated on tRNA(V). Ribosomal RNAs As has been the case for all studied animal mtDNAs to date, two rRNA genes are identified, one for each of the small and large mitochondrial ribosomal subunits. Determining the precise ends of the rRNA transcript requires experimentation, but if it's assumed that they extend to the boundaries of the adjacent genes, then  rrnS  is 903 nucleotides and  rrnL  is 1266 nucleotides in length. These genes are arranged sequentially, but without an intervening tRNA gene as is otherwise commonly found. Non-coding regions The largest non-coding region is only 282 nts long. The region is 71% A+T and contains one palindrome of an 11 nt sequence (TCAAAAGGGGT/ACCCCTTTTGA, with a slash indicating the center), but otherwise no large repeat elements. Obviously, this has potential for forming a stem-loop structure, and it may be significant that a short sequence a few nucleotides upstream, TCAAAA, has the potential for competing with this to form a short hairpin with the TTTTGA at the end of the palindrome. There has been previous speculation that regions with potential for competing, mutually exclusive hairpins may play a role in regulating transcription and/or replication [e.g. ref. [ 7 ]]. There are four other potential hairpins in this region with stems of 5–6 bp and loops of 5–17 nt. All four nucleotides occur in homopolymers with much greater frequency than expected by chance, often in runs of four or five. The second largest non-coding region is 43 nt between  trnS1  and  cox3 . This has no repeat elements and the base composition is unremarkable. What role, if any, these sequences have in the regulation of transcription and/or replication awaits further study. Aside from these 282 and 43 nt regions, there are only 36 total intergenic nucleotides scattered among 14 regions. In seven cases these are 2–6 nts long (CCAAA, AT, TCCC, TAAA, CATAAA, AT, and ACACCT). For the other seven cases, genes are separated by a single nucleotide, and in six of these, that nucleotide is a C. (The remaining case is a T.) The prevalence of C is consistent with the measured G-skew between the strands, although it is possible that this otherwise indicates some function of these nucleotides.
Results and discussion
Gene content and organization The mtDNA of  Urechis caupo  is 15,113 nts in length (GenBank accession number  AY619711 ) and contains the same 37 genes found for nearly all animal mtDNAs [see ref. [ 1 ]]. All genes are transcribed from the same strand (Fig.  1 ), as is the case for the two studied annelid mtDNAs, the polychaete  Platynereis dumerilii  [ 6 ] and the oligochaete  Lumbricus terrestris  [ 7 ] and for several other animal mtDNAs. The arrangement of the genes is substantially similar to those of the two annelids, and shares short regions of similarity with several other mtDNAs, as can be seen in Table  1 . Figure 1 Mitochondrial gene map of the echiuran  Urechis caupo.  All genes are transcribed from the same DNA strand. Scaling is only approximate. Genes are designated by standard nomenclature except for tRNAs, which are identified only by the one-letter code for the corresponding amino acid, with the two serine and two leucine tRNAs differentiated by numeral as identified in Fig. 3. "nc" indicates the largest non-coding regions; it may be that transcription initiates here, but this is not known. Table 1 Mitochondrial gene arrangement identities found in pairwise comparisons between  Urechis caupo  and various animals. Full taxon names are given here for the annelids  Lumbricus terrestris  and  Platynereis dumerilii , the mollusks  Katharina tunicata ,  Loligo bleekeri ,  Cepaea nemoralis , and  Mytilus edulis , the brachiopods  Terebratulina retusa  and  Terebratalia transversa , the platyhelminths  Fasciola hepatica ,  Taenia crassiceps ,  Echinococcus multilocularis , and  Hymenolepis diminuta , the arthropods  Drosophila yakuba ,  Anopheles gambiae ,  Artemia franciscana ,  Daphnia pulex ,  Apis mellifera ,  Locusta migratoria ,  Ixodes hexagonus ,  Rhiphicephalus sanguineus ,  Limulus polyphemus  and  Lithobius forficatus , the nematodes  Trichinella spirallis ,  Onchocerca volvulus ,  Meloidogyne javanica ,  Ascaris suum , and  Caenorhabditis elegans , the echinoderms  Arabacia lixula ,  Asterina pectinifera ,  Paracentrotus lividus ,  Strongylocentrotus purpuratus , and  Florometra serratissima , the hemichordate  Balanoglossus carnosus , and the chordate  Branchiostoma floridae  along with the gene order most typical for vertebrates. Complete citations can be found in Boore (1999) or updated by following the "Evolutionary Genomics" link at  . Contiguous gene arrangements are separated by a comma; a slash indicates a gap containing one or more unrelated genes. L. terrestris  and  P. dumerilii cox3 ,  trnQ ,  nad6 ,  cob ,  trnW ,  atp6 ,  trnR ,  trnH ,  nad5 ,  trnF / trnL2 ,  nad1 ,  trnI ,  trnK ,  nad3 / trnT ,  nad4L ,  nad4 L. terrestris  but not  P. dumerilii trnL1 ,  trnA ,  trnS2 ,  trnL2 / trnD ,  atp8 K. tunicata trnL2 ,  nad1 / nad4L ,  nad4 / trnH ,  nad5 ,  trnF L. bleekeri nad6 ,  cob / nad4L ,  nad4 / nad5 ,  trnF / trnD ,  atp8 C. nemoralis trnL1 ,  trnA M. edulis trnL2 ,  nad1 / trnT ,  nad4L T. retusa trnL2 ,  nad1 / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnL1 ,  trnA / trnD ,  atp8 / cox1 ,  cox2 T. transversa trnP ,  trnD F. hepatica ,  T. crassiceps ,  E. multilocularis trnI ,  trnK ,  nad3 / nad4L ,  nad4 / trnY ,  trnL1 / trnS2 ,  trnL2 H. diminuta trnI ,  trnK ,  nad3 / nad4L ,  nad4 / trnY ,  trnL1 D. yakuba ,  A. gambiae ,  A. franciscana ,  D. pulex nad6 ,  cob / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnD ,  atp8 A. mellifera ,  L. migratoria nad6 ,  cob / nad4L ,  nad4 / trnH ,  nad5 ,  trnF I. hexagonus ,  R. sanguineus ,  L. polyphemus ,  L. forficatus nad6 ,  cob / trnL2 ,  nad1 / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnD ,  atp8 / cox1 ,  cox2 T. spirallis nad6 ,  cob / nad4L ,  nad4 / trnH ,  nad5 ,  trnF / trnR ,  trnH / trnL1 ,  trnA / trnD ,  atp8 / cox1 ,  cox2 O. volvulus trnP ,  trnD M. javanica trnN ,  trnG A. suum NONE C. elegans NONE A. lixula ,  A. pectinifera ,  P. lividus  and  S. purpuratus trnL2 ,  nad1 ,  trnI F. serratissima nad1 ,  trnI / nad2 ,  trnY B. carnosus trnL2 ,  nad1 / nad4L ,  nad4 B. floridae  and the typical vertebrate arrangement trnL2 ,  nad1 ,  trnI / nad4L ,  nad4
Gene content and organization
The mtDNA of  Urechis caupo  is 15,113 nts in length (GenBank accession number  AY619711 ) and contains the same 37 genes found for nearly all animal mtDNAs [see ref. [ 1 ]]. All genes are transcribed from the same strand (Fig.  1 ), as is the case for the two studied annelid mtDNAs, the polychaete  Platynereis dumerilii  [ 6 ] and the oligochaete  Lumbricus terrestris  [ 7 ] and for several other animal mtDNAs. The arrangement of the genes is substantially similar to those of the two annelids, and shares short regions of similarity with several other mtDNAs, as can be seen in Table  1 .Urechis caupoAY619711Platynereis dumeriliiLumbricus terrestrisMitochondrial gene arrangement identities found in pairwise comparisons between  Urechis caupo  and various animals. Full taxon names are given here for the annelids  Lumbricus terrestris  and  Platynereis dumerilii , the mollusks  Katharina tunicata ,  Loligo bleekeri ,  Cepaea nemoralis , and  Mytilus edulis , the brachiopods  Terebratulina retusa  and  Terebratalia transversa , the platyhelminths  Fasciola hepatica ,  Taenia crassiceps ,  Echinococcus multilocularis , and  Hymenolepis diminuta , the arthropods  Drosophila yakuba ,  Anopheles gambiae ,  Artemia franciscana ,  Daphnia pulex ,  Apis mellifera ,  Locusta migratoria ,  Ixodes hexagonus ,  Rhiphicephalus sanguineus ,  Limulus polyphemus  and  Lithobius forficatus , the nematodes  Trichinella spirallis ,  Onchocerca volvulus ,  Meloidogyne javanica ,  Ascaris suum , and  Caenorhabditis elegans , the echinoderms  Arabacia lixula ,  Asterina pectinifera ,  Paracentrotus lividus ,  Strongylocentrotus purpuratus , and  Florometra serratissima , the hemichordate  Balanoglossus carnosus , and the chordate  Branchiostoma floridae  along with the gene order most typical for vertebrates. Complete citations can be found in Boore (1999) or updated by following the "Evolutionary Genomics" link at  . Contiguous gene arrangements are separated by a comma; a slash indicates a gap containing one or more unrelated genes.Urechis caupoLumbricus terrestrisPlatynereis dumeriliiKatharina tunicataLoligo bleekeriCepaea nemoralisMytilus edulisTerebratulina retusaTerebratalia transversaFasciola hepaticaTaenia crassicepsEchinococcus multilocularisHymenolepis diminutaDrosophila yakubaAnopheles gambiaeArtemia franciscanaDaphnia pulexApis melliferaLocusta migratoriaIxodes hexagonusRhiphicephalus sanguineusLimulus polyphemusLithobius forficatusTrichinella spirallisOnchocerca volvulusMeloidogyne javanicaAscaris suumCaenorhabditis elegansArabacia lixulaAsterina pectiniferaParacentrotus lividusStrongylocentrotus purpuratusFlorometra serratissimaBalanoglossus carnosusBranchiostoma floridaeL. terrestrisP. dumeriliicox3trnQnad6cobtrnWatp6trnRtrnHnad5trnFtrnL2nad1trnItrnKnad3trnTnad4Lnad4L. terrestrisP. dumeriliitrnL1trnAtrnS2trnL2trnDatp8K. tunicatatrnL2nad1nad4Lnad4trnHnad5trnFL. bleekerinad6cobnad4Lnad4nad5trnFtrnDatp8C. nemoralistrnL1trnAM. edulistrnL2nad1trnTnad4LT. retusatrnL2nad1nad4Lnad4trnHnad5trnFtrnL1trnAtrnDatp8cox1cox2T. transversatrnPtrnDF. hepaticaT. crassicepsE. multilocularistrnItrnKnad3nad4Lnad4trnYtrnL1trnS2trnL2H. diminutatrnItrnKnad3nad4Lnad4trnYtrnL1D. yakubaA. gambiaeA. franciscanaD. pulexnad6cobnad4Lnad4trnHnad5trnFtrnDatp8A. melliferaL. migratorianad6cobnad4Lnad4trnHnad5trnFI. hexagonusR. sanguineusL. polyphemusL. forficatusnad6cobtrnL2nad1nad4Lnad4trnHnad5trnFtrnDatp8cox1cox2T. spirallisnad6cobnad4Lnad4trnHnad5trnFtrnRtrnHtrnL1trnAtrnDatp8cox1cox2O. volvulustrnPtrnDM. javanicatrnNtrnGA. suumC. elegansA. lixulaA. pectiniferaP. lividusS. purpuratustrnL2nad1trnIF. serratissimanad1trnInad2trnYB. carnosustrnL2nad1nad4Lnad4B. floridaetrnL2nad1trnInad4Lnad4Base composition and codon usage The  U. caupo  mtDNA is 62% A+T, about the same as for annelid mtDNAs (64% and 62% for  P. dumerilii  and  L. terrestris , respectively). As is typical, all homodinucleotides and homotrinucleotides are greatly over represented relative to a random distribution and CG is the least frequent dinucleotide, both in absolute number and in the ratio of observed to expected. The gene-coding strand has a strong skew against G vs. C but about equal amounts of A vs. T; G-skew ([G-C]/[G+C]) is – 0.24 and T-skew ([T-A]/[T+A]) is – 0.016 [ 8 ]. These values show no striking variation across the length of the mtDNA. Codon usage (Table  2 ) reflects these values, with those ending in A or T being most frequent. In all cases except for two, where they are synonymous, NNC codons are in greater abundance than NNG codons, as is consistent with the coding strand being rich in C relative to G. The two exceptions are CGG and GGG codons, which are each in greater abundance than their respective synonyms, CGC and GGC. This invites the speculation that there is something favored about the GG dinucleotide created when G appears in the second codon position. However, this is not consistently seen, since in the remaining case, AGC codons outnumber AGG codons two-to-one. This effect has been shown to be very strong for codon usage pattern of the mtDNA of the brachiopod  Terebratalia transversa  [ 9 ]. Table 2 Codon usage in the 13 protein-encoding genes of the  Urechis caupo  mitochondrial genome. The total number of codons is 3722. The anticodon of the corresponding tRNA gene is shown in parentheses below each amino acid designation. Stop codons are not included in this analysis. Amino acid Codon N % Amino acid Codon N % Phe (F) TTT 161 4.3% Ser (S2) TCT 108 2.9% (GAA) TTC 115 3.1% (TGA) TCC 65 1.7% Leu (L2) TTA 146 3.9% TCA 74 2.0% (TAA) TTG 10 0.3% TCG 3 0.1% Tyr (Y) TAT 42 1.1% Cys (C) TGT 18 0.5% (GTA) TAC 65 1.7% (GCA) TGC 11 0.3% TER TAA --- --- Trp (W) TGA 77 2.1% TAG --- --- (TCA) TGG 21 0.6% Leu (L1) CTT 105 2.8% Pro (P) CCT 72 1.9% (TAG) CTC 62 1.7% (TGG) CCC 44 1.2% CTA 224 6.0% CCA 80 2.1% CTG 38 1.0% CCG 6 0.2% His (H) CAT 33 0.9% Arg (R) CGT 6 0.2% (GTG) CAC 55 1.5% (TCG) CGC 5 0.1% Gln (Q) CAA 84 2.3% CGA 46 1.2% (TTG) CAG 12 0.3% CGG 9 0.2% Ile (I) ATT 200 5.4% Thr (T) ACT 76 2.0% (GAT) ATC 100 2.7% (TGT) ACC 91 2.4% Met (M) ATA 171 4.6% ACA 93 2.5% (CAT) ATG 52 1.4% ACG 5 0.1% Asn (N) AAT 61 1.6% Ser (S1) AGT 7 0.2% (GTT) AAC 65 1.7% (TCT) AGC 16 0.4% Lys (K) AAA 78 2.1% AGA 62 1.7% (TTT) AAG 14 0.4% AGG 8 0.2% Val (V) GTT 49 1.3% Ala (A) GCT 75 2.0% (TAC) GTC 28 0.8% (TGC) GCC 75 2.0% GTA 99 2.7% GCA 122 3.3% GTG 18 0.5% GCG 14 0.4% Asp (D) GAT 23 0.6% Gly (G) GGT 14 0.4% (GTC) GAC 37 1.0% (TCC) GGC 27 0.7% Glu (E) GAA 73 2.0% GGA 127 3.4% (TTC) GAG 10 0.3% GGG 36 1.0%
Base composition and codon usage
The  U. caupo  mtDNA is 62% A+T, about the same as for annelid mtDNAs (64% and 62% for  P. dumerilii  and  L. terrestris , respectively). As is typical, all homodinucleotides and homotrinucleotides are greatly over represented relative to a random distribution and CG is the least frequent dinucleotide, both in absolute number and in the ratio of observed to expected. The gene-coding strand has a strong skew against G vs. C but about equal amounts of A vs. T; G-skew ([G-C]/[G+C]) is – 0.24 and T-skew ([T-A]/[T+A]) is – 0.016 [ 8 ]. These values show no striking variation across the length of the mtDNA. Codon usage (Table  2 ) reflects these values, with those ending in A or T being most frequent. In all cases except for two, where they are synonymous, NNC codons are in greater abundance than NNG codons, as is consistent with the coding strand being rich in C relative to G. The two exceptions are CGG and GGG codons, which are each in greater abundance than their respective synonyms, CGC and GGC. This invites the speculation that there is something favored about the GG dinucleotide created when G appears in the second codon position. However, this is not consistently seen, since in the remaining case, AGC codons outnumber AGG codons two-to-one. This effect has been shown to be very strong for codon usage pattern of the mtDNA of the brachiopod  Terebratalia transversa  [ 9 ].U. caupoP. dumeriliiL. terrestrisTerebratalia transversaCodon usage in the 13 protein-encoding genes of the  Urechis caupo  mitochondrial genome. The total number of codons is 3722. The anticodon of the corresponding tRNA gene is shown in parentheses below each amino acid designation. Stop codons are not included in this analysis.Urechis caupoGene initiation and termination Mitochondrial genes commonly use several alternatives to ATG as start codons. However, 11 of the 13 proteins coding genes of  U. caupo  mtDNA use ATG. The only exceptions are  cox1 , which uses GTG and  nad3  which uses ATC. In the case of  cox1 , there is an in frame stop only three codons upstream and neither of the intervening codons is ATG. Also, this inference of starting on GTG specifies a set of amino acids well matched to those at the beginning of other Cox1 proteins. The situation for  nad3  is nearly identical, with an in frame stop only four codons upstream and no intervening ATG codons. However, downstream of the inferred start are several ATA codons that can not be ruled out as alternatives. The commonality of using ATG as a start codon has also been noted for mitochondrial genes of four annelids,  Platynereis dumerilii  [ 6 ],  Lumbricus terrestris  [ 7 ],  Helobdella robusta  and  Galathealinum brachiosum  (previously considered to be of the phylum Pogonophora) [ 10 ] and a sipunculid,  Phascolopsis gouldii  [ 11 ]. A complete stop codon without overlap of the downstream gene is found for all except  cox2 ,  nad1 ,  nad2 ,  cob , and  nad5  (Fig.  2 ). In each of these cases, it appears that an abbreviated stop codon is generated by cleavage of a downstream tRNA from the polycistronic transcript, which is then completed to a TAA stop codon by polyadenylation. However, in two of these cases ( nad2  and  cob ), a complete stop codon could be formed by including only the next two nucleotides, and two other cases ( nad1  and  cox2 ), there is an in frame stop codon just one or two codons downstream, respectively. It is not clear how gene overlaps could be resolved from a polycistronic transcript (assuming that the genes of this mtDNA are expressed in this way), but the presence of these stop codons seems beyond coincidence. It could be that they serve as a "back up" in case translation should begin in the absence of transcript cleavage. Figure 2 A greatly abbreviated schematic of the sequence of  Urechis caupo  mtDNA.  In the interest of brevity, the middle portion of each large gene is omitted and replaced by a numeral indicating the number of nucleotides removed. Since all mitochondrial proteins are thought to initiate with formyl-methionine, an M is placed in parentheses at the first codon position of  cox1  (GTG) and  nad3  (ATC) to indicate nonconformity to the genetic code. Asterisks indicate inferred stop codons whether complete or abbreviated and plus symbols mark nucleotides that would form the first in frame, complete stop codon if genes instead overlap.
Gene initiation and termination
Mitochondrial genes commonly use several alternatives to ATG as start codons. However, 11 of the 13 proteins coding genes of  U. caupo  mtDNA use ATG. The only exceptions are  cox1 , which uses GTG and  nad3  which uses ATC. In the case of  cox1 , there is an in frame stop only three codons upstream and neither of the intervening codons is ATG. Also, this inference of starting on GTG specifies a set of amino acids well matched to those at the beginning of other Cox1 proteins. The situation for  nad3  is nearly identical, with an in frame stop only four codons upstream and no intervening ATG codons. However, downstream of the inferred start are several ATA codons that can not be ruled out as alternatives. The commonality of using ATG as a start codon has also been noted for mitochondrial genes of four annelids,  Platynereis dumerilii  [ 6 ],  Lumbricus terrestris  [ 7 ],  Helobdella robusta  and  Galathealinum brachiosum  (previously considered to be of the phylum Pogonophora) [ 10 ] and a sipunculid,  Phascolopsis gouldii  [ 11 ].U. caupocox1nad3cox1nad3Platynereis dumeriliiLumbricus terrestrisHelobdella robustaGalathealinum brachiosumPhascolopsis gouldiiA complete stop codon without overlap of the downstream gene is found for all except  cox2 ,  nad1 ,  nad2 ,  cob , and  nad5  (Fig.  2 ). In each of these cases, it appears that an abbreviated stop codon is generated by cleavage of a downstream tRNA from the polycistronic transcript, which is then completed to a TAA stop codon by polyadenylation. However, in two of these cases ( nad2  and  cob ), a complete stop codon could be formed by including only the next two nucleotides, and two other cases ( nad1  and  cox2 ), there is an in frame stop codon just one or two codons downstream, respectively. It is not clear how gene overlaps could be resolved from a polycistronic transcript (assuming that the genes of this mtDNA are expressed in this way), but the presence of these stop codons seems beyond coincidence. It could be that they serve as a "back up" in case translation should begin in the absence of transcript cleavage.cox2nad1nad2cobnad5nad2cobnad1cox2Transfer RNAs Twenty-two regions can be folded into the typical cloverleaf structures of the expected set of tRNAs (Fig.  3 ). There are several mismatched nucleotide pairs within stems; nearly all of these are flanked by multiple G-C pairs, suggesting that they may provide compensatory stability for these arms. T precedes the anticodon and a purine follows it for all tRNAs. The two serine tRNAs lack potential for folding a DHU arm, as has been found for a number of other animal mtDNAs. There is an alternative folding possible for tRNA(S2) with a six-member anticodon stem and only one nucleotide separating the acceptor and DHU stems; this unusual folding has been found for the homologous genes of some mammals. tRNA(R) also does not have potential for a normally paired DHU arm, although there are three potential nucleotide pairs if two (rather than one) nucleotides were between the DHU and anticodon stems. However, this potential pairing could, alternatively, be a coincidence, with the DHU arm having no paired stem for this tRNA. Those with paired DHU arms have stems of three to five nucleotide pairs and loops of three to eight nts. All tRNAs have potential for stems of three to six nucleotide pairs for their TΨC arms with loops of three to seven nts. One of the tRNAs for serine has the anticodon TCT; although this is often found, the alternative of GCT is otherwise common. Figure 3 The 22 inferred tRNA genes folded into the typical cloverleafstructures.  Nomenclature for tRNA substructures is indicated on tRNA(V).
Transfer RNAs
Twenty-two regions can be folded into the typical cloverleaf structures of the expected set of tRNAs (Fig.  3 ). There are several mismatched nucleotide pairs within stems; nearly all of these are flanked by multiple G-C pairs, suggesting that they may provide compensatory stability for these arms. T precedes the anticodon and a purine follows it for all tRNAs. The two serine tRNAs lack potential for folding a DHU arm, as has been found for a number of other animal mtDNAs. There is an alternative folding possible for tRNA(S2) with a six-member anticodon stem and only one nucleotide separating the acceptor and DHU stems; this unusual folding has been found for the homologous genes of some mammals. tRNA(R) also does not have potential for a normally paired DHU arm, although there are three potential nucleotide pairs if two (rather than one) nucleotides were between the DHU and anticodon stems. However, this potential pairing could, alternatively, be a coincidence, with the DHU arm having no paired stem for this tRNA. Those with paired DHU arms have stems of three to five nucleotide pairs and loops of three to eight nts. All tRNAs have potential for stems of three to six nucleotide pairs for their TΨC arms with loops of three to seven nts. One of the tRNAs for serine has the anticodon TCT; although this is often found, the alternative of GCT is otherwise common.Ribosomal RNAs As has been the case for all studied animal mtDNAs to date, two rRNA genes are identified, one for each of the small and large mitochondrial ribosomal subunits. Determining the precise ends of the rRNA transcript requires experimentation, but if it's assumed that they extend to the boundaries of the adjacent genes, then  rrnS  is 903 nucleotides and  rrnL  is 1266 nucleotides in length. These genes are arranged sequentially, but without an intervening tRNA gene as is otherwise commonly found.
Ribosomal RNAs
As has been the case for all studied animal mtDNAs to date, two rRNA genes are identified, one for each of the small and large mitochondrial ribosomal subunits. Determining the precise ends of the rRNA transcript requires experimentation, but if it's assumed that they extend to the boundaries of the adjacent genes, then  rrnS  is 903 nucleotides and  rrnL  is 1266 nucleotides in length. These genes are arranged sequentially, but without an intervening tRNA gene as is otherwise commonly found.rrnSrrnLNon-coding regions The largest non-coding region is only 282 nts long. The region is 71% A+T and contains one palindrome of an 11 nt sequence (TCAAAAGGGGT/ACCCCTTTTGA, with a slash indicating the center), but otherwise no large repeat elements. Obviously, this has potential for forming a stem-loop structure, and it may be significant that a short sequence a few nucleotides upstream, TCAAAA, has the potential for competing with this to form a short hairpin with the TTTTGA at the end of the palindrome. There has been previous speculation that regions with potential for competing, mutually exclusive hairpins may play a role in regulating transcription and/or replication [e.g. ref. [ 7 ]]. There are four other potential hairpins in this region with stems of 5–6 bp and loops of 5–17 nt. All four nucleotides occur in homopolymers with much greater frequency than expected by chance, often in runs of four or five. The second largest non-coding region is 43 nt between  trnS1  and  cox3 . This has no repeat elements and the base composition is unremarkable. What role, if any, these sequences have in the regulation of transcription and/or replication awaits further study. Aside from these 282 and 43 nt regions, there are only 36 total intergenic nucleotides scattered among 14 regions. In seven cases these are 2–6 nts long (CCAAA, AT, TCCC, TAAA, CATAAA, AT, and ACACCT). For the other seven cases, genes are separated by a single nucleotide, and in six of these, that nucleotide is a C. (The remaining case is a T.) The prevalence of C is consistent with the measured G-skew between the strands, although it is possible that this otherwise indicates some function of these nucleotides.
Non-coding regions
The largest non-coding region is only 282 nts long. The region is 71% A+T and contains one palindrome of an 11 nt sequence (TCAAAAGGGGT/ACCCCTTTTGA, with a slash indicating the center), but otherwise no large repeat elements. Obviously, this has potential for forming a stem-loop structure, and it may be significant that a short sequence a few nucleotides upstream, TCAAAA, has the potential for competing with this to form a short hairpin with the TTTTGA at the end of the palindrome. There has been previous speculation that regions with potential for competing, mutually exclusive hairpins may play a role in regulating transcription and/or replication [e.g. ref. [ 7 ]]. There are four other potential hairpins in this region with stems of 5–6 bp and loops of 5–17 nt. All four nucleotides occur in homopolymers with much greater frequency than expected by chance, often in runs of four or five. The second largest non-coding region is 43 nt between  trnS1  and  cox3 . This has no repeat elements and the base composition is unremarkable. What role, if any, these sequences have in the regulation of transcription and/or replication awaits further study.trnS1cox3Aside from these 282 and 43 nt regions, there are only 36 total intergenic nucleotides scattered among 14 regions. In seven cases these are 2–6 nts long (CCAAA, AT, TCCC, TAAA, CATAAA, AT, and ACACCT). For the other seven cases, genes are separated by a single nucleotide, and in six of these, that nucleotide is a C. (The remaining case is a T.) The prevalence of C is consistent with the measured G-skew between the strands, although it is possible that this otherwise indicates some function of these nucleotides.Conclusions This is the first description of a complete mitochondrial genome sequence of a representative of the phylum Echiura. The genome contains the same 37 genes most commonly found in animal mtDNAs. Many features are most similar to those found for annelid mtDNAs, including A+T content, use of protein initiation codons, size and potential secondary structures of the largest non-coding region, and the relative arrangement of many genes. As in annelids examined to date, all genes are found on the same DNA strand. As noted for brachiopod mtDNA, there is a preference for G nucleotides to appear in tandem, without obvious explanation. Further description and comparison of complete mtDNA sequences will continue to produce a picture of genome evolution, particularly once sampling includes representatives of each animal phylum.
Conclusions
This is the first description of a complete mitochondrial genome sequence of a representative of the phylum Echiura. The genome contains the same 37 genes most commonly found in animal mtDNAs. Many features are most similar to those found for annelid mtDNAs, including A+T content, use of protein initiation codons, size and potential secondary structures of the largest non-coding region, and the relative arrangement of many genes. As in annelids examined to date, all genes are found on the same DNA strand. As noted for brachiopod mtDNA, there is a preference for G nucleotides to appear in tandem, without obvious explanation. Further description and comparison of complete mtDNA sequences will continue to produce a picture of genome evolution, particularly once sampling includes representatives of each animal phylum.Methods Molecular techniques A preparation of  Urechis caupo  total DNA was the kind gift of Eric Rosenthal. The entire mtDNA sequence was obtained using techniques detailed in [ 9 ]. Briefly, small fragments (450–710 nt) were amplified from  cox1 ,  cob , and  rrnS  using primer pairs HCO 2198/LCO 1490 [ 12 ], CytbF/CytbR [ 10 ], and 16SARL/16SBRH [ 13 ], respectively. The sequences of these fragments were determined using dye-terminator chemistry (PE Biosystems) on an ABI 377 automated DNA sequencer. Primers were then designed facing "out" from these fragments to amplify the intervening regions (~2.9 to ~8 Kb) using long-PCR protocols with rTth-XL polymerase (PE Biosystems) as in [ 9 ]. Sequences were determined from the ends of these long-PCR fragments, then internally by "primer walking". To ensure quality, all sequences were determined on both strands and base calls for all chromatograms were verified by eye. Gene annotation Genes encoding rRNAs and proteins were identified by matching nucleotide or inferred amino acid sequences to those of  Lumbricus terrestris  mtDNA [ 7 ]. Since it is not possible to precisely determine the ends of rRNA genes by sequence data alone, they were assumed to extend to the boundaries of flanking genes. Each protein gene start was inferred as the eligible initiation codon nearest to the beginning of its alignment with homologous genes that does not cause overlap with the preceding gene. In five cases, an abbreviated stop codon was inferred where cleavage of a downstream tRNA from the transcript would leave a partial codon of T or TA, such that subsequent mRNA polyadenylation could generate a TAA stop codon. In each case an extension of this gene to the first in frame stop codon would cause overlap with the downstream tRNA. Genes for tRNAs were identified generically by their ability to fold into a cloverleaf structure and specifically by anticodon sequence.
Methods
Molecular techniques A preparation of  Urechis caupo  total DNA was the kind gift of Eric Rosenthal. The entire mtDNA sequence was obtained using techniques detailed in [ 9 ]. Briefly, small fragments (450–710 nt) were amplified from  cox1 ,  cob , and  rrnS  using primer pairs HCO 2198/LCO 1490 [ 12 ], CytbF/CytbR [ 10 ], and 16SARL/16SBRH [ 13 ], respectively. The sequences of these fragments were determined using dye-terminator chemistry (PE Biosystems) on an ABI 377 automated DNA sequencer. Primers were then designed facing "out" from these fragments to amplify the intervening regions (~2.9 to ~8 Kb) using long-PCR protocols with rTth-XL polymerase (PE Biosystems) as in [ 9 ]. Sequences were determined from the ends of these long-PCR fragments, then internally by "primer walking". To ensure quality, all sequences were determined on both strands and base calls for all chromatograms were verified by eye.
Molecular techniques
A preparation of  Urechis caupo  total DNA was the kind gift of Eric Rosenthal. The entire mtDNA sequence was obtained using techniques detailed in [ 9 ]. Briefly, small fragments (450–710 nt) were amplified from  cox1 ,  cob , and  rrnS  using primer pairs HCO 2198/LCO 1490 [ 12 ], CytbF/CytbR [ 10 ], and 16SARL/16SBRH [ 13 ], respectively. The sequences of these fragments were determined using dye-terminator chemistry (PE Biosystems) on an ABI 377 automated DNA sequencer. Primers were then designed facing "out" from these fragments to amplify the intervening regions (~2.9 to ~8 Kb) using long-PCR protocols with rTth-XL polymerase (PE Biosystems) as in [ 9 ]. Sequences were determined from the ends of these long-PCR fragments, then internally by "primer walking". To ensure quality, all sequences were determined on both strands and base calls for all chromatograms were verified by eye.Urechis caupocox1cobrrnSGene annotation Genes encoding rRNAs and proteins were identified by matching nucleotide or inferred amino acid sequences to those of  Lumbricus terrestris  mtDNA [ 7 ]. Since it is not possible to precisely determine the ends of rRNA genes by sequence data alone, they were assumed to extend to the boundaries of flanking genes. Each protein gene start was inferred as the eligible initiation codon nearest to the beginning of its alignment with homologous genes that does not cause overlap with the preceding gene. In five cases, an abbreviated stop codon was inferred where cleavage of a downstream tRNA from the transcript would leave a partial codon of T or TA, such that subsequent mRNA polyadenylation could generate a TAA stop codon. In each case an extension of this gene to the first in frame stop codon would cause overlap with the downstream tRNA. Genes for tRNAs were identified generically by their ability to fold into a cloverleaf structure and specifically by anticodon sequence.
Gene annotation
Genes encoding rRNAs and proteins were identified by matching nucleotide or inferred amino acid sequences to those of  Lumbricus terrestris  mtDNA [ 7 ]. Since it is not possible to precisely determine the ends of rRNA genes by sequence data alone, they were assumed to extend to the boundaries of flanking genes. Each protein gene start was inferred as the eligible initiation codon nearest to the beginning of its alignment with homologous genes that does not cause overlap with the preceding gene. In five cases, an abbreviated stop codon was inferred where cleavage of a downstream tRNA from the transcript would leave a partial codon of T or TA, such that subsequent mRNA polyadenylation could generate a TAA stop codon. In each case an extension of this gene to the first in frame stop codon would cause overlap with the downstream tRNA. Genes for tRNAs were identified generically by their ability to fold into a cloverleaf structure and specifically by anticodon sequence.Lumbricus terrestrisAbbreviations cox1 ,  cox2 ,  cox3 , cytochrome oxidase subunit I, II, and III protein genes;  cob , cytochrome b gene;  atp6 ,  atp8 , ATP synthase subunit 6 and 8 genes;  nad1 ,  nad2 ,  nad3 ,  nad4 ,  nad4L ,  nad5 ,  nad6 , NADH dehydrogenase subunit 1–6, 4L genes;  trnA ,  trnC ,  trnD ,  trnE ,  trnF ,  trnG ,  trnH ,  trnI ,  trnK ,  trnL1 ,  trnL2 ,  trnM ,  trnN ,  trnP ,  trnQ ,  trnR ,  trnS1 ,  trnS2 ,  trnT ,  trnV ,  trnW ,  trnY , transfer RNA genes designated by the one-letter code for the specified amino acid, with numerals differentiating cases where there are two tRNAs for the same amino acid.
Abbreviations
cox1 ,  cox2 ,  cox3 , cytochrome oxidase subunit I, II, and III protein genes;  cob , cytochrome b gene;  atp6 ,  atp8 , ATP synthase subunit 6 and 8 genes;  nad1 ,  nad2 ,  nad3 ,  nad4 ,  nad4L ,  nad5 ,  nad6 , NADH dehydrogenase subunit 1–6, 4L genes;  trnA ,  trnC ,  trnD ,  trnE ,  trnF ,  trnG ,  trnH ,  trnI ,  trnK ,  trnL1 ,  trnL2 ,  trnM ,  trnN ,  trnP ,  trnQ ,  trnR ,  trnS1 ,  trnS2 ,  trnT ,  trnV ,  trnW ,  trnY , transfer RNA genes designated by the one-letter code for the specified amino acid, with numerals differentiating cases where there are two tRNAs for the same amino acid.cox1cox2cox3cobatp6atp8nad1nad2nad3nad4nad4Lnad5nad6trnAtrnCtrnDtrnEtrnFtrnGtrnHtrnItrnKtrnL1trnL2trnMtrnNtrnPtrnQtrnRtrnS1trnS2trnTtrnVtrnWtrnY