CN115641916B - Molecular biology detection method, computer equipment and readable storage medium - Google Patents
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- 238000001514 detection method Methods 0.000 title claims abstract description 45
- 230000003321 amplification Effects 0.000 claims abstract description 53
- 238000003199 nucleic acid amplification method Methods 0.000 claims abstract description 53
- 238000004140 cleaning Methods 0.000 claims abstract description 33
- 239000012472 biological sample Substances 0.000 claims abstract description 25
- 238000005406 washing Methods 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 238000012408 PCR amplification Methods 0.000 claims abstract description 7
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Abstract
The invention discloses a molecular biology detection method, computer equipment and a readable storage medium, which relate to the technical field based on polymerase chain reaction and comprise the following steps: preprocessing, PCR amplification, CT value calculation, detection result determination according to the CT value, and CT value calculation comprises the following sub-steps: calculating a starting period for at least 40 periods of raw amplification data; the original amplification data of each period is the fluorescence value of the biological sample after each amplification and the fluorescence value of water is superimposed; washing all the original amplification data to obtain washing data of each period; the cleaning data of each period are arranged to obtain the arrangement data of each period; and calculating a CT value according to the arrangement data corresponding to the starting point period. The technical scheme provided by the invention reduces the requirement on the sample and ensures the accuracy of the detection result.
Description
Technical Field
The invention relates to the technical field of polymerase chain reaction, in particular to a molecular biology detection method, computer equipment and a readable storage medium.
Background
PCR (Polymerase Chain Reaction ) is a molecular biological technique for amplifying a specific DNA fragment, which uses DNA that is denatured at about 95℃to break down double-stranded DNA into two single-stranded DNA; when the temperature of the DNA decomposed into single strands is reduced to about 60 ℃, the single-stranded DNA is combined with the primer; the DNA combined with the primer is subjected to semi-reserved replication according to the base complementary pairing principle under the action of DNA polymerase, and double DNA can be obtained after replication is completed. The DNA is amplified in large quantity through the circulation of the temperature at 95 ℃ and 60 ℃ for a plurality of times. The PCR technology based on the PCR principle is used as the most important molecular biology detection means at present, and can rapidly identify microorganism types and drug resistance mutation such as bacteria, viruses, fungi and the like, and the analysis sensitivity is far higher than that of other detection means; the method can accurately detect the population quantity of different microorganisms in a short time of thirty-forty minutes, so that the method has important application prospects in almost all life science fields of food detection, clinical examination, disease control, inspection and quarantine, scientific research laboratories, food safety, cosmetic detection, environmental sanitation and the like.
After PCR amplification is completed, CT value is calculated. The CT value represents the number of microorganisms to be detected. In short, in biological detection, the detection instrument needs to amplify genes in a biological sample to a certain extent to detect related microorganisms. The more the number of microorganisms, the higher the fluorescence intensity and the smaller the CT value, which means that the more the number of microorganisms is, the detection can be carried out without amplification for many times. The larger the CT value, the smaller the microorganism number, and the detection can be carried out by a plurality of amplification methods. Thus, the accuracy of the CT value is closely related to the quality of the sample. However, the accuracy of manual sampling cannot be guaranteed, and a plurality of unavoidable objective factors such as loss exist in the transportation process, so that the actual PCR amplification curve is thousands of strangles. Even in the same amplification experiment, the amplification curves of the hole sites are very different. But in general, two categories can be distinguished: curves with significant amplification and curves without significant amplification. If baseline analysis is performed on all samples using the same method, it is difficult to avoid getting in a global embarrassment. On the other hand, for an amplification curve without significant amplification characteristics, when a standard deviation of 10 times is used as a threshold line to determine the standard, if the baseline period is slightly deviated, the curve with amplification head-up is easily eliminated, or the "head-up" which is in the error range is considered as true amplification. Moreover, the existing method cannot eliminate the influence of the baseline, namely fluorescence background intensity, and is difficult to determine the baseline of each unknown sample, so that an accurate CT value cannot be obtained, and even the CT value cannot be obtained.
Disclosure of Invention
In order to solve the problems, the invention provides a molecular biology detection method, which reduces the requirement on samples and ensures the accuracy of detection results.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method of molecular biological detection comprising the steps of: preprocessing a biological sample, performing PCR amplification on the preprocessed biological sample, calculating a CT value, determining a detection result according to the CT value, and calculating the CT value comprises the following sub-steps:
step 1: calculating a starting point period for at least 40 periods of original amplification data, wherein the original amplification data of each period is the fluorescence value of the biological sample after each amplification, and the fluorescence value of water is superimposed;
Step 2: washing all the original amplification data to obtain washing data of each period;
step 3: the cleaning data of each period are arranged to obtain the arrangement data of each period;
step 4: and calculating a CT value according to the arrangement data corresponding to the starting point period.
Optionally, step 1 comprises the following sub-steps:
Step 11: performing linear fitting on original amplification data corresponding to 1 to 15 periods, and calculating first fitting data corresponding to each period after fitting;
Step 12: after subtracting the first fitting data from the original amplification data, taking the minimum value in the range of 16 to N cycles, wherein the cycle corresponding to the minimum value is used as a starting point cycle; n=a-20, where a is the total number of cycles.
Optionally, step 2 includes the following sub-steps:
Step 21: performing linear fitting on the original amplified data corresponding to the 1 to starting point period, and calculating second fitted data corresponding to each period after fitting and an average value corresponding to the 1 to starting point period;
Step 22: performing adjacent comparison on the original amplified data of the last five periods, judging whether the original amplified data of the next period is larger than or equal to the original amplified data of the previous period, and if so, enabling the original amplified data of the next period to be equal to the original amplified data of the previous period;
Step 23: subtracting the average value in the step 21 from the original amplification data of the last five periods and the original amplification data of other periods of adjacent comparison to obtain first process data of each period;
Step 24: returning the first process data of 1 to 15 periods to 0, and carrying out first minimum value taking on the first process data of the range from 16 to the last period, and enabling the first process data of the period corresponding to the first minimum value taking from 16 to the first minimum value to be equal to the first minimum value taking from the first time;
Step 25: re-fetching the minimum value of the first process data in the range from the next cycle to the last cycle of the cycle corresponding to the minimum value fetched for the first time, and enabling the first process data of the cycle before the cycle corresponding to the minimum value fetched for the current time to be equal to the minimum value fetched for the current time in the range;
step 26: repeating step 25 until the minimum value reaches the last period;
step 27: and (5) returning the negative number in the data obtained in the steps 21 to 26 to 0, so as to obtain the cleaning data of each period.
Optionally, step 3 comprises the following sub-steps:
Step 31: performing polynomial sixth-order fitting on the cleaning data from the period corresponding to the first positive number to the last period in the cleaning data, calculating third fitting data from the period corresponding to the first positive number to the last period in the cleaning data after fitting, and returning the cleaning data before the period corresponding to the first positive number in the cleaning data to 0 to obtain second process data;
step 32: setting the first process data of the starting period and the period before the starting period as the first process data of the period after the starting period;
step 33: taking the minimum value of the second process data in the range from the starting period to the last period, and setting the second process data in the period corresponding to the minimum value from the starting period to the minimum value as the minimum value;
Step 34: the minimum value corresponds to the second process data from the last cycle to the last cycle, and in the range, the second process data of the cycle before the cycle corresponding to the minimum value is equal to the minimum value;
Step 35: step 34 is repeated until the minimum value is taken to the last cycle, resulting in consolidated data for each cycle.
Optionally, a judging step is further provided between the step 32 and the step 33:
The average deviation of the third fitting data is calculated according to the following formula: d=3σ/Avg 100%, where D is the mean deviation, σ is the standard deviation of all third fit data in the wash data, avg is the mean of all raw amplification data;
If D is less than 25%, the CT value is NoCT, and if D is more than or equal to 25%, the step 33 is continued.
Optionally, step 4 includes the following sub-steps:
Step 41: subtracting the finishing data of the previous period from the finishing data of the next period from the starting period, and calculating a first derivative and a second derivative;
Step 42: obtaining a maximum value: taking the maximum value of the second derivative from the starting period to the last period, and taking the arrangement data of the maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of the two continuous periods before the period corresponding to the maximum value is positive; otherwise, taking a second maximum value of the second derivative from the starting period to the last period, and taking the sorted data of the second maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of two continuous periods before the period corresponding to the second maximum value is positive; and the like until a maximum value is obtained;
Step 43: if the maximum value is not acquired, the CT value is NoCT; if the maximum value is acquired, continuing to acquire the minimum value: traversing the second derivative of the period before the period corresponding to the maximum value, wherein the finishing data corresponding to one period after the period of which the second derivative is less than or equal to 0 is used as the minimum value;
step 44: the CT value is calculated according to the following formula:
wherein CT is the CT value, A is the average value of the maximum value and the minimum value, max is the maximum value, max' is the arrangement data of the period before the period corresponding to the maximum value, min is the minimum value, and N is the serial number of the period corresponding to the maximum value.
The invention has the following beneficial effects:
The technical scheme provided by the invention reduces the requirement on biological samples, thereby releasing the collection pressure faced by staff for collecting the samples. Meanwhile, the sensitivity of the biological sample to loss in the transportation and treatment processes is reduced, and the result distortion caused by the loss of the biological sample is avoided. When the biological sample available for detection cannot meet the requirements of detection in the prior art or the biological sample available for detection is abnormal due to various unavoidable factors, the accuracy of the detection result obtained by adopting the detection method in the prior art cannot be ensured, and even the result cannot be obtained at all. But also adopts samples which cannot meet the detection requirement, and the detection method provided by the invention not only can obtain the detection result, but also can achieve the accuracy of the detection result which is obtained by adopting samples which meet the detection requirement for detection.
Furthermore, the invention provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the method of any of the above when executing the computer program.
Meanwhile, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method of any one of the above.
These features and advantages of the present invention will be disclosed in more detail in the following detailed description and the accompanying drawings. The best mode or means of the present invention will be described in detail with reference to the accompanying drawings, but is not limited to the technical scheme of the present invention. In addition, these features, elements, and components are shown in plural in each of the following and drawings, and are labeled with different symbols or numerals for convenience of description, but each denote a component of the same or similar construction or function.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a graph showing the presence of anomalies in the original amplified data and the presence of anomalies in the original amplified data, but processed by the method of the present example;
FIG. 2 shows the original amplification data curve and the amplification data curve after the treatment according to the method of the present embodiment.
Detailed Description
The technical solutions of the embodiments of the present invention will be explained and illustrated below with reference to the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all embodiments. Based on the examples in the implementation manner, other examples obtained by a person skilled in the art without making creative efforts fall within the protection scope of the present invention.
Reference in the specification to "one embodiment" or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment itself can be included in at least one embodiment of the present patent disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.
Examples:
The embodiment provides a molecular biology detection method, which comprises the following steps:
The biological samples are pre-processed in preparation for PCR amplification. In this step, the pretreatment process and the reagents used for different biological samples are different, and can be flexibly selected by those skilled in the art according to the biological sample to be detected, which is not limited herein. Specifically, in this embodiment, the biological sample, the magnetic beads and the washing liquid sequentially enter the extraction tank to react, and the reaction is performed while heating and ultrasonic mixing are performed on the extraction tank. And then adsorbing the magnetic beads, and discharging the waste liquid out of the extraction tank. Repeating the steps of feeding other washing liquid into the extraction tank, heating, uniformly mixing by ultrasonic waves, adsorbing magnetic beads and discharging waste liquid out of the extraction tank for preset times so as to finish pretreatment of biological samples.
And carrying out PCR amplification on the pretreated biological sample.
CT values are calculated. The smaller the CT value, the smaller the number of cycles, the shorter the time taken to represent the larger the number of microorganisms, and the easier it is to detect. The larger the CT value, the more cycles, the more time it takes to represent the smaller the number of microorganisms, and the more difficult it is to detect. In this embodiment, the calculation of the CT value includes the following steps:
Step 1: the starting period is calculated for at least 40 cycles of raw amplification data. In this example, the cycle, i.e., the number of amplifications, the raw amplification data for each cycle is the fluorescence value of the biological sample after each amplification plus the fluorescence value of water. This embodiment is illustrated by taking 40 cycles as an example.
The calculation steps of the starting point period are as follows:
Step 11: and (3) carrying out linear fitting on original amplification data corresponding to 1 cycle to 15 cycles to obtain a slope and an intercept of a baseline after fitting, wherein the slope is the increase rate of the fluorescence value, and the intercept is the bottom value, namely the fluorescence value of water. And calculating first fitting data corresponding to each period after fitting according to the baseline after fitting. The linear fitting is prior art and will not be described in detail here.
Step 12: after subtracting the first fitting data from the original amplified data, taking a minimum value from 16 cycles to (a-20) cycles, namely, within the range of 40-20=20 cycles, wherein the cycle corresponding to the minimum value is taken as a starting point cycle.
Step 2: all the original amplification data are washed to obtain washing data of each period. This step comprises the following sub-steps:
Step 21: and (3) carrying out linear fitting on the original amplified data corresponding to the 1-cycle to the starting point cycle, and calculating second fitted data corresponding to each cycle after fitting and an average value corresponding to the 1-cycle to the starting point cycle. The linear fitting is prior art and will not be described in detail here.
Step 22: and carrying out adjacent comparison on the original amplified data of the last five periods, namely the original amplified data of 36 to 40 periods, judging whether the original amplified data of the later period is larger than or equal to the original amplified data of the former period, and if so, enabling the original amplified data of the later period to be equal to the original amplified data of the former period.
Step 23: the average value in step 21 is subtracted from the 36-to 40-cycle raw amplification data and the other-cycle raw amplification data, which are adjacently compared, to obtain first process data of each cycle.
Step 24: and returning the first process data from 1 cycle to 15 cycles to 0, and carrying out first minimum value taking on the first process data from 16 cycles to the last cycle, namely 40 cycles, and enabling the first process data from 16 cycles to the cycle corresponding to the first minimum value taking to be equal to the first minimum value taking.
Step 25: and re-fetching the minimum value of the first process data in the range from the next cycle to the last cycle, namely 40 cycles, of the cycle corresponding to the minimum value fetched at the first time, and enabling the first process data of the cycle before the cycle corresponding to the minimum value fetched at the present time to be equal to the minimum value fetched at the present time in the range.
Step 26: step 25 is repeated until the minimum value has been taken for the last cycle.
Step 27: and (5) returning the negative number in the data obtained in the steps 21 to 26 to 0, so as to obtain the cleaning data of each period.
Step 3: and finishing the cleaning data of each period to obtain finishing data of each period. This step comprises the following sub-steps:
Step 31: and performing polynomial sixth-order fitting on the cleaning data from the period corresponding to the first positive number to the last period in the cleaning data. And calculating third fitting data from the period corresponding to the first positive number to the last period in the fitted cleaning data, and returning the cleaning data before the period corresponding to the first positive number in the cleaning data to 0 to obtain second process data. The polynomial sixth-order fitting is the prior art, and is not described in detail herein.
Step 32: setting the first process data of the starting period and the period before the starting period as the first process data of the period after the starting period.
Before step 33, a determination step is further performed, where the determination step is as follows:
The average deviation of the third fitting data is calculated according to the following formula: d=3σ/Avg, where D is the average deviation, σ is the standard deviation of all third fitting data in the cleaning data, and the standard deviation calculation method is a conventional standard deviation calculation method, which is not described herein. Avg is the average of all raw amplification data;
If D is less than 25%, the CT value is NoCT, and if D is more than or equal to 25%, the step 33 is continued.
Step 33: taking the minimum value in the second process data from the starting period to the last period, namely within the range of 40 periods, and setting the second process data from the starting period to the period corresponding to the minimum value as the minimum value.
Step 34: the minimum value corresponds to the second process data from the last cycle to the last cycle, namely 40 cycles, and the second process data of the cycle before the cycle corresponding to the minimum value is equal to the minimum value in the range.
Step 35: step 34 is repeated until the minimum value is taken to the last cycle, resulting in consolidated data for each cycle.
Step 4: and calculating a CT value according to the arrangement data corresponding to the starting point period. This step comprises the following sub-steps:
Step 41: from the start period, the sorted data of the previous period is subtracted from the sorted data of the next period, and the first derivative and the second derivative are calculated.
Step 42: obtaining a maximum value: taking the maximum value of the second derivative from the starting period to the last period, and taking the arrangement data of the maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of the two continuous periods before the period corresponding to the maximum value is positive; otherwise, taking a second maximum value of the second derivative from the starting period to the last period, and taking the sorted data of the second maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of two continuous periods before the period corresponding to the second maximum value is positive; and so on until a maximum is obtained.
Step 43: if the maximum value is not acquired, the CT value is NoCT; if the maximum value is acquired, continuing to acquire the minimum value: traversing the second derivative of the period before the period corresponding to the maximum value, and taking the sorted data corresponding to one period after the period of which the second derivative is less than or equal to 0 as the minimum value.
Step 44: the CT value is calculated according to the following formula:
wherein CT is the CT value, A is the average value of the maximum value and the minimum value, max is the maximum value, max' is the arrangement data of the period before the period corresponding to the maximum value, min is the minimum value, and N is the serial number of the period corresponding to the maximum value.
After the CT value is calculated, the detection result is determined according to the CT value. If the CT value is smaller than the preset value, the detected microorganism number reaches a certain index, and if the CT value is larger than the preset value, the detected microorganism number does not reach a certain index. The specific index and the preset value of the CT value are selected by those skilled in the art according to the microorganism to be detected, and are not limited herein.
The biological detection method provided by the embodiment reduces the requirement on biological samples, thereby releasing the collection pressure faced by staff to collect the samples. Meanwhile, the sensitivity of the biological sample to loss in the transportation and treatment processes is reduced, and the result distortion caused by the loss of the biological sample is avoided. When the biological sample available for detection cannot meet the requirements of detection in the prior art or the biological sample available for detection is abnormal due to various unavoidable factors, the accuracy of the detection result obtained by adopting the detection method in the prior art cannot be ensured, and even the result cannot be obtained at all. However, the sample which cannot meet the detection requirement is also adopted, and the detection method provided by the embodiment can not only obtain the detection result, but also can also reach the accuracy of the detection result, as shown in fig. 1, by adopting the sample which meets the detection requirement for detection.
Taking 40 cycles as an example, the actual calculation process deduction is performed as follows:
The original amplification data were:
| Cycle number | Raw amplification data | Cycle number | Raw amplification data |
| 1 | 3207 | 21 | 3425 |
| 2 | 3218 | 22 | 3496 |
| 3 | 3229 | 23 | 3623 |
| 4 | 3240 | 24 | 3807 |
| 5 | 3251 | 25 | 3968 |
| 6 | 3261 | 26 | 4099 |
| 7 | 3272 | 27 | 4209 |
| 8 | 3282 | 28 | 4305 |
| 9 | 3291 | 29 | 4395 |
| 10 | 3299 | 30 | 4444 |
| 11 | 3306 | 31 | 4505 |
| 12 | 3314 | 32 | 4564 |
| 13 | 3319 | 33 | 4600 |
| 14 | 3325 | 34 | 4640 |
| 15 | 3331 | 35 | 4685 |
| 16 | 3335 | 36 | 4726 |
| 17 | 3340 | 37 | 4765 |
| 18 | 3348 | 38 | 4787 |
| 19 | 3359 | 39 | 4816 |
| 20 | 3384 | 40 | 4834 |
Step 1: the starting period was calculated for the 40 periods of raw amplification data in the above table, and the calculation steps were as follows:
Step 11: performing linear fitting on original amplification data corresponding to 1 cycle to 15 cycles, and obtaining a slope k1=9 and an intercept b1=3204 after fitting; and calculating first fitting data corresponding to each period after fitting according to the fitted baselines, wherein the first fitting data are as shown in the following table:
| Cycle number | First fitting data | Cycle number | First fitting data |
| 1 | 3213 | 21 | 3393 |
| 2 | 3222 | 22 | 3402 |
| 3 | 3231 | 23 | 3411 |
| 4 | 3240 | 24 | 3420 |
| 5 | 3249 | 25 | 3429 |
| 6 | 3258 | 26 | 3438 |
| 7 | 3267 | 27 | 3447 |
| 8 | 3276 | 28 | 3456 |
| 9 | 3285 | 29 | 3465 |
| 10 | 3294 | 30 | 3474 |
| 11 | 3303 | 31 | 3483 |
| 12 | 3312 | 32 | 3492 |
| 13 | 3321 | 33 | 3501 |
| 14 | 3330 | 34 | 3510 |
| 15 | 3339 | 35 | 3519 |
| 16 | 3348 | 36 | 3528 |
| 17 | 3357 | 37 | 3537 |
| 18 | 3366 | 38 | 3546 |
| 19 | 3375 | 39 | 3555 |
| 20 | 3384 | 40 | 3564 |
Step 12: the data obtained by subtracting the first fitting data from the original amplification data are shown in the following table:
| Cycle number | Data | Cycle number | Data |
| 1 | -6.43 | 21 | 31.85 |
| 2 | -4.42 | 22 | 93.87 |
| 3 | -2.40 | 23 | 211.88 |
| 4 | -0.39 | 24 | 386.90 |
| 5 | 1.62 | 25 | 538.91 |
| 6 | 2.64 | 26 | 660.92 |
| 7 | 4.65 | 27 | 761.94 |
| 8 | 5.67 | 28 | 848.95 |
| 9 | 5.68 | 29 | 929.97 |
| 10 | 4.70 | 30 | 969.98 |
| 11 | 2.71 | 31 | 1022.00 |
| 12 | 1.72 | 32 | 1072.01 |
| 13 | -2.26 | 33 | 1099.02 |
| 14 | -5.25 | 34 | 1130.04 |
| 15 | -8.23 | 35 | 1166.05 |
| 16 | -13.22 | 36 | 1198.07 |
| 17 | -17.20 | 37 | 1228.08 |
| 18 | -18.19 | 38 | 1241.10 |
| 19 | -16.18 | 39 | 1261.11 |
| 20 | -0.16 | 40 | 1270.12 |
Taking the minimum value in the range from 16 cycles to 20 cycles, wherein the minimum value is-18, and the cycle corresponding to the minimum value, namely, the 18 cycles, is taken as the starting point cycle.
Step 2: all the original amplification data are washed to obtain washing data of each period. This step comprises the following sub-steps:
step 21: performing linear fitting on original amplification data corresponding to 1 period to a starting point period, and obtaining a slope k2=8.2 and an intercept b2=3209 after fitting; calculating an average value of 3287 corresponding to 1 cycle to 18 cycles according to the fitted baseline, wherein second fitting data corresponding to each cycle after fitting is shown in the following table:
| Cycle number | Second fitting data | Cycle number | Second fitting data |
| 1 | 3217.20 | 21 | 3381.20 |
| 2 | 3225.40 | 22 | 3389.40 |
| 3 | 3233.60 | 23 | 3397.60 |
| 4 | 3241.80 | 24 | 3405.80 |
| 5 | 3250.00 | 25 | 3414.00 |
| 6 | 3258.20 | 26 | 3422.20 |
| 7 | 3266.40 | 27 | 3430.40 |
| 8 | 3274.60 | 28 | 3438.60 |
| 9 | 3282.80 | 29 | 3446.80 |
| 10 | 3291.00 | 30 | 3455.00 |
| 11 | 3299.20 | 31 | 3463.20 |
| 12 | 3307.40 | 32 | 3471.40 |
| 13 | 3315.60 | 33 | 3479.60 |
| 14 | 3323.80 | 34 | 3487.80 |
| 15 | 3332.00 | 35 | 3496.00 |
| 16 | 3340.20 | 36 | 3504.20 |
| 17 | 3348.40 | 37 | 3512.40 |
| 18 | 3356.60 | 38 | 3520.60 |
| 19 | 3364.80 | 39 | 3528.80 |
| 20 | 3373.00 | 40 | 3537.00 |
Step 22: and performing adjacent comparison on the original amplified data of 36 cycles to 40 cycles, judging whether the original amplified data of the next cycle is greater than or equal to the original amplified data of the previous cycle, if so, enabling the original amplified data of the next cycle to be equal to the original amplified data of the previous cycle, wherein the obtained data are shown in the following table:
Step 23: the data in the above table is averaged in step 21 to obtain the first process data for each cycle as shown in the following table:
| Cycle number | First process data | Cycle number | First process data |
| 1 | -80 | 21 | 138 |
| 2 | -69 | 22 | 209 |
| 3 | -58 | 23 | 336 |
| 4 | -47 | 24 | 520 |
| 5 | -36 | 25 | 681 |
| 6 | -26 | 26 | 812 |
| 7 | -15 | 27 | 922 |
| 8 | -5 | 28 | 1018 |
| 9 | 4 | 29 | 1108 |
| 10 | 12 | 30 | 1157 |
| 11 | 19 | 31 | 1218 |
| 12 | 27 | 32 | 1277 |
| 13 | 32 | 33 | 1313 |
| 14 | 38 | 34 | 1353 |
| 15 | 44 | 35 | 1398 |
| 16 | 48 | 36 | 1439 |
| 17 | 53 | 37 | 1478 |
| 18 | 61 | 38 | 1500 |
| 19 | 72 | 39 | 1529 |
| 20 | 97 | 40 | 1547 |
Step 24: returning the first process data from 1 cycle to 15 cycles to 0, and carrying out first minimum value taking on the first process data from 16 cycles to 40 cycles, wherein the first process data from 16 cycles to the first minimum value taking corresponds to the first process data from the first cycle is equal to the first minimum value taking;
Step 25: and re-fetching the minimum value of the first process data in the range from the next cycle to 40 cycles corresponding to the minimum value fetched for the first time, wherein the first process data in the cycle before the cycle corresponding to the minimum value fetched for the current time is enabled to be equal to the minimum value fetched for the current time in the range.
Step 26: step 25 is repeated until the minimum value has been taken for the last cycle.
Step 27: and (3) returning the negative numbers in the data obtained in the steps 21 to 26 to 0 to obtain cleaning data of each period, wherein the cleaning data are shown in the following table:
| Cycle number | Cleaning data | Cycle number | Cleaning data |
| 1 | 0 | 21 | 137.89 |
| 2 | 0 | 22 | 208.89 |
| 3 | 0 | 23 | 335.89 |
| 4 | 0 | 24 | 519.89 |
| 5 | 0 | 25 | 680.89 |
| 6 | 0 | 26 | 811.89 |
| 7 | 0 | 27 | 921.89 |
| 8 | 0 | 28 | 1017.89 |
| 9 | 0 | 29 | 1107.89 |
| 10 | 0 | 30 | 1156.89 |
| 11 | 0 | 31 | 1217.89 |
| 12 | 0 | 32 | 1276.89 |
| 13 | 0 | 33 | 1312.89 |
| 14 | 0 | 34 | 1352.89 |
| 15 | 0 | 35 | 1397.89 |
| 16 | 47.89 | 36 | 1438.89 |
| 17 | 52.89 | 37 | 1477.89 |
| 18 | 60.89 | 38 | 1499.89 |
| 19 | 71.89 | 39 | 1528.89 |
| 20 | 96.89 | 40 | 1546.89 |
Step 3: and finishing the cleaning data of each period to obtain finishing data of each period. This step comprises the following sub-steps:
Step 31: and performing polynomial sixth-order fitting on the cleaning data from the period corresponding to the first positive number to the last period in the cleaning data, and calculating coefficients of a polynomial for the sixth-order fitting as follows:
| Coefficient of 6 th order term | -0.000246439 |
| Coefficient of 5 th order term | 0.014766496 |
| Coefficient of 4 th order term | -0.269072287 |
| Coefficient of 3 rd order term | 0.42091583 |
| Coefficient of 2 degree term | 29.69072506 |
| Coefficient of 1 st order term | -118.3740706 |
| Constant term | 167.1582024 |
And calculating third fitting data from the period corresponding to the first positive number to the last period in the fitted cleaning data, wherein the third fitting data is shown in the following table:
| Cycle number | Third fitting data | Cycle number | Third fitting data |
| 1 | - | 21 | 62.24 |
| 2 | - | 22 | 165.65 |
| 3 | - | 23 | 292.67 |
| 4 | - | 24 | 432.26 |
| 5 | - | 25 | 574.39 |
| 6 | - | 26 | 710.54 |
| 7 | - | 27 | 834.25 |
| 8 | - | 28 | 941.31 |
| 9 | - | 29 | 1029.87 |
| 10 | - | 30 | 1100.35 |
| 11 | - | 31 | 1155.18 |
| 12 | - | 32 | 1198.34 |
| 13 | - | 33 | 1234.75 |
| 14 | - | 34 | 1269.44 |
| 15 | - | 35 | 1306.60 |
| 16 | - | 36 | 1348.44 |
| 17 | - | 37 | 1393.81 |
| 18 | - | 38 | 1436.71 |
| 19 | - | 39 | 1464.62 |
| 20 | - | 40 | 1456.62 |
Meanwhile, the cleaning data before the period corresponding to the first positive number in the cleaning data is reset to 0, so that second process data is obtained, and the second process data is shown in the following table:
| Cycle number | Second process data | Cycle number | Second process data |
| 1 | 0 | 21 | 62.24 |
| 2 | 0 | 22 | 165.65 |
| 3 | 0 | 23 | 292.67 |
| 4 | 0 | 24 | 432.26 |
| 5 | 0 | 25 | 574.39 |
| 6 | 0 | 26 | 710.54 |
| 7 | 0 | 27 | 834.25 |
| 8 | 0 | 28 | 941.31 |
| 9 | 0 | 29 | 1029.87 |
| 10 | 0 | 30 | 1100.35 |
| 11 | 0 | 31 | 1155.18 |
| 12 | 0 | 32 | 1198.34 |
| 13 | 0 | 33 | 1234.75 |
| 14 | 0 | 34 | 1269.44 |
| 15 | 0 | 35 | 1306.60 |
| 16 | 0 | 36 | 1348.44 |
| 17 | 0 | 37 | 1393.81 |
| 18 | 0 | 38 | 1436.71 |
| 19 | 0 | 39 | 1464.62 |
| 20 | 0 | 40 | 1456.62 |
Step 32: setting the first process data of the starting period and the period before the starting period as the first process data of the period after the starting period.
Before step 33, a determination step is further performed, where the determination step is as follows:
The average deviation of the third fitting data is calculated according to the following formula: d=3σ/Avg 100%, where D is the mean deviation, σ is the standard deviation of all third fit data in the wash data, avg is the mean of all raw amplification data; the average deviation is found to be 46%, greater than 25%, so step 33 is continued:
step 33: taking the minimum value 0 in the second process data in the range from the start period 18 period to the 40 period, and setting the second process data in the range from the start period to the minimum value is completed immediately.
Step 34: and the second process data from 19 cycles to 40 cycles is minimized, and in the range, the second process data from the cycle before the cycle corresponding to the minimum value is equal to the minimum value.
Step 35: step 34 is repeated until the minimum value is taken to the last cycle, and the finishing data of each cycle are obtained, as shown in the following table:
| Cycle number | Sorting data | Cycle number | Sorting data |
| 1 | 0 | 21 | 62.24 |
| 2 | 0 | 22 | 165.65 |
| 3 | 0 | 23 | 292.67 |
| 4 | 0 | 24 | 432.26 |
| 5 | 0 | 25 | 574.39 |
| 6 | 0 | 26 | 710.54 |
| 7 | 0 | 27 | 834.25 |
| 8 | 0 | 28 | 941.31 |
| 9 | 0 | 29 | 1029.87 |
| 10 | 0 | 30 | 1100.35 |
| 11 | 0 | 31 | 1155.18 |
| 12 | 0 | 32 | 1198.34 |
| 13 | 0 | 33 | 1234.75 |
| 14 | 0 | 34 | 1269.44 |
| 15 | 0 | 35 | 1306.60 |
| 16 | 0 | 36 | 1348.44 |
| 17 | 0 | 37 | 1393.81 |
| 18 | 0 | 38 | 1436.71 |
| 19 | 0 | 39 | 1456.62 |
| 20 | 0 | 40 | 1456.62 |
Step 4: and calculating a CT value according to the arrangement data corresponding to the starting point period. This step comprises the following sub-steps:
Step 41: from the start period, the sorted data of the previous period is subtracted from the sorted data of the next period, and the first derivative and the second derivative are calculated.
Step 42: obtaining a maximum value: taking the maximum value of the second derivative from the starting period to the last period, and taking the arrangement data of the maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of the two continuous periods before the period corresponding to the maximum value is positive; otherwise, taking a second maximum value of the second derivative from the starting period to the last period, and taking the sorted data of the second maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of two continuous periods before the period corresponding to the second maximum value is positive; and so on until a maximum value is obtained, and in this embodiment, the obtained maximum value is 292.67, and the corresponding period is 23.
Step 43: if the maximum value is not acquired, the CT value is NoCT; if the maximum value is acquired, continuing to acquire the minimum value: traversing the second derivative of the period before the period corresponding to the maximum value, taking the sorted data corresponding to one period after the period of which the second derivative is less than or equal to 0 as the minimum value, and specifically in the embodiment, the obtained minimum value is 62.24, and the corresponding period is 21.
Step 44: the CT value is calculated according to the following formula:
Wherein, CT is CT value, A is the average value of maximum and minimum, max is the maximum, max' is the arrangement data of the previous cycle of the corresponding cycle of maximum, min is the minimum, N is the serial number of the corresponding cycle of maximum, calculate and get the average value to 177.46, CT value is 21.09. Meanwhile, the amplification curve of PCR is shown in FIG. 2.
Meanwhile, the embodiment also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method in any embodiment. Those skilled in the art will appreciate that implementing all or part of the processes in the methods of the embodiments described above may be accomplished by computer programs to instruct related hardware. Accordingly, a computer program may be stored in a non-volatile computer readable storage medium, which when executed, performs the method of any of the above embodiments. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The above is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it should be understood by those skilled in the art that the present invention includes but is not limited to the accompanying drawings and the description of the above specific embodiment. Any modifications which do not depart from the functional and structural principles of the present invention are intended to be included within the scope of the appended claims.
Claims (5)
1. A method of molecular biological detection comprising the steps of: the method is characterized by comprising the following steps of preprocessing a biological sample, performing PCR amplification on the preprocessed biological sample, calculating a CT value, and determining a detection result according to the CT value, wherein the calculation of the CT value comprises the following sub-steps:
step 1: calculating a starting point period for at least 40 periods of original amplification data, wherein the original amplification data of each period is the fluorescence value of the biological sample after each amplification, and the fluorescence value of water is superimposed;
Step 2: washing all the original amplification data to obtain washing data of each period;
step 3: the cleaning data of each period are arranged to obtain the arrangement data of each period;
step 4: calculating a CT value according to the arrangement data corresponding to the starting point period;
wherein, step 1 comprises the following sub-steps:
Step 11: performing linear fitting on original amplification data corresponding to 1 to 15 periods, and calculating first fitting data corresponding to each period after fitting;
Step 12: after subtracting the first fitting data from the original amplification data, taking the minimum value in the range of 16 to N cycles, wherein the cycle corresponding to the minimum value is used as a starting point cycle; n=a-20, where a is the total number of cycles;
wherein, step 2 comprises the following sub-steps:
Step 21: performing linear fitting on the original amplified data corresponding to the 1 to starting point period, and calculating second fitted data corresponding to each period after fitting and an average value corresponding to the 1 to starting point period;
Step 22: performing adjacent comparison on the original amplified data of the last five periods, judging whether the original amplified data of the next period is larger than or equal to the original amplified data of the previous period, and if so, enabling the original amplified data of the next period to be equal to the original amplified data of the previous period;
Step 23: subtracting the average value in the step 21 from the original amplification data of the last five periods and the original amplification data of other periods of adjacent comparison to obtain first process data of each period;
Step 24: returning the first process data of 1 to 15 periods to 0, and carrying out first minimum value taking on the first process data of the range from 16 to the last period, and enabling the first process data of the period corresponding to the first minimum value taking from 16 to the first minimum value to be equal to the first minimum value taking from the first time;
Step 25: re-fetching the minimum value of the first process data in the range from the next cycle to the last cycle of the cycle corresponding to the minimum value fetched for the first time, and enabling the first process data of the cycle before the cycle corresponding to the minimum value fetched for the current time to be equal to the minimum value fetched for the current time in the range;
step 26: repeating step 25 until the minimum value reaches the last period;
Step 27: returning the negative numbers in the data obtained in the steps 21 to 26 to 0 to obtain cleaning data of each period;
wherein, step 3 comprises the following sub-steps:
Step 31: performing polynomial sixth-order fitting on the cleaning data from the period corresponding to the first positive number to the last period in the cleaning data, calculating third fitting data from the period corresponding to the first positive number to the last period in the cleaning data after fitting, and returning the cleaning data before the period corresponding to the first positive number in the cleaning data to 0 to obtain second process data;
step 32: setting the first process data of the starting period and the period before the starting period as the first process data of the period after the starting period;
step 33: taking the minimum value of the second process data in the range from the starting period to the last period, and setting the second process data in the period corresponding to the minimum value from the starting period to the minimum value as the minimum value;
Step 34: the minimum value corresponds to the second process data from the last cycle to the last cycle, and in the range, the second process data of the cycle before the cycle corresponding to the minimum value is equal to the minimum value;
Step 35: step 34 is repeated until the minimum value is taken to the last cycle, resulting in consolidated data for each cycle.
2. The method according to claim 1, wherein the step of determining is further performed between the step 32 and the step 33:
The average deviation of the third fitting data is calculated according to the following formula: d=3σ/Avg 100%, where D is the mean deviation, σ is the standard deviation of all third fit data in the wash data, avg is the mean of all raw amplification data;
If D is less than 25%, the CT value is NoCT, and if D is more than or equal to 25%, the step 33 is continued.
3. The method of claim 1, wherein step 4 comprises the sub-steps of:
Step 41: subtracting the finishing data of the previous period from the finishing data of the next period from the starting period, and calculating a first derivative and a second derivative;
Step 42: obtaining a maximum value: taking the maximum value of the second derivative from the starting period to the last period, and taking the arrangement data of the maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of the two continuous periods before the period corresponding to the maximum value is positive; otherwise, taking a second maximum value of the second derivative from the starting period to the last period, and taking the sorted data of the second maximum value of the second derivative corresponding to the period as the maximum value if the second derivative of two continuous periods before the period corresponding to the second maximum value is positive; and the like until a maximum value is obtained;
Step 43: if the maximum value is not acquired, the CT value is NoCT; if the maximum value is acquired, continuing to acquire the minimum value: traversing the second derivative of the period before the period corresponding to the maximum value, wherein the finishing data corresponding to one period after the period of which the second derivative is less than or equal to 0 is used as the minimum value;
step 44: the CT value is calculated according to the following formula:
wherein CT is the CT value, A is the average value of the maximum value and the minimum value, max is the maximum value, max' is the arrangement data of the period before the period corresponding to the maximum value, min is the minimum value, and N is the serial number of the period corresponding to the maximum value.
4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 3 when executing the computer program.
5. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any one of claims 1 to 3.
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