CN107577922B - ARM processor-based corn lncRNA screening and classifying method - Google Patents
ARM processor-based corn lncRNA screening and classifying method Download PDFInfo
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Abstract
The invention discloses a corn lncRNA screening and classifying method based on an ARM processor, which comprises the following steps: inputting a full-length cDNA sequence of the corn into a CPLD; screening a cDNA sequence with the length larger than 200, carrying out open reading frame prediction on the cDNA and determining a threshold, determining the cDNA with the length smaller than the threshold, carrying out homologous comparison on the cDNA and known proteins, reserving parts with different sources, carrying out secondary structure prediction on the existing plant lncRNA, extracting secondary structure characteristics, simultaneously carrying out secondary structure prediction on the rest sequences, reserving parts meeting the structural characteristics, comparing the cDNA sequence to be screened with a small RNA sequence, and removing a small RNA precursor; and step three, collecting corn lncRNA sequences with known functions, performing structure prediction, extracting and screening structural features of the functional lncRNA, constructing a structural feature set of the corn lncRNA with the known functions, performing secondary structure extraction on the corn lncRNA screened in the step two, comparing the structural feature set, and confirming and classifying the lncRNA functions.
Description
Technical Field
The invention relates to the field of bioinformatics research, in particular to a corn lncRNA screening and classifying method based on an ARM processor.
Background
The secondary structure of RNA refers to a stem-loop structure formed by folding RNA itself due to complementary base pairing. Due to base complementary pairing, RNA is single-stranded but folds on itself in a partial region to form a double helix structure, which complicates the three-dimensional structure of RNA. In addition to the classic pairing (Waston-Crick pairing, a ≡ U, G ≡ C), there is the exception of G ═ U (wbble baseband oscillation pairing). G ≡ C is the most stable and G ≡ U is the least stable. In the secondary structure, the stem is a double-helical region in which bases are continuously paired and which is composed according to the aforementioned pairing rule of a ≡ U, G ≡ C, G ≡ U. The single-stranded part of the molecule not forming a pair is a loop.
When studying the problem of RNA, we can obtain partial information about RNA through RNA primary sequence, and the study of RNA secondary structure can further explore the biological function. Prediction of RNA secondary structure is a focus problem in the field of bioinformatics at present, and a plurality of algorithms are developed to solve the problem to date. Summarizing the commonly used secondary structure prediction algorithms, there are the following 4 classes: dynamic programming algorithm, comparative sequence analysis method, combined optimization algorithm and heuristic algorithm. At present, the prediction of RNA secondary structure is time-consuming and labor-consuming by using a biological experiment method, but the prediction based on a calculation method is limited to the accuracy of the whole sequence structure, and functional components with conserved local structures are ignored, so that the method is not suitable for the prediction requirement of long sequences. In order to meet the demand of lncRNA secondary structure prediction, a new method for lncRNA segmented secondary structure prediction is provided.
The sequence alignment algorithm mainly comprises three algorithms, namely an enumeration method, a dynamic programming algorithm and a blast algorithm. Enumeration is the most conceivable and understandable method, but this method does not have any optimization, is time-complex, and cannot be calculated within a reasonable time. Therefore, the enumeration method is only theoretically feasible and cannot be practically applied to a longer sequence. The dynamic programming algorithm is an optimized method in solving the decision problem. Dynamic planning is that when aiming at an optimization problem, a plurality of feasible solutions are provided, all the feasible solutions have own values, and the function of dynamic planning is to find the value of the optimal solution. The method has the idea that the big problems are divided, the solutions are merged after being solved respectively, most of non-optimal solutions which do not need to be calculated are removed in the solving process, and much time is saved. Considering an alignment based on a dynamic programming algorithm, the time complexity is proportional to the two sequences, i.e. the amount of computation is equivalent to computing a two-dimensional matrix built up by the length of the two sequences. If the calculation amount of a part of the two-dimensional matrix can be reduced, the calculation speed can be increased, the blast algorithm calculates around the optimal comparison path as much as possible, and a lot of unnecessary operations are reduced.
Disclosure of Invention
The invention designs and develops a corn lncRNA screening and classifying method based on an ARM processor, and aims to solve the problems of reducing the operation time and improving the screening speed and the screening accuracy.
The technical scheme provided by the invention is as follows:
a corn lncRNA screening and classifying method based on an ARM processor comprises the following steps:
inputting a full-length cDNA sequence of the corn into a CPLD, and storing the cDNA sequence in an SRAM in a coding mode through a coding association table;
screening a cDNA sequence with the length larger than 200, carrying out open reading frame prediction on the cDNA and determining a threshold, determining the cDNA with the length smaller than the threshold, carrying out homologous comparison on the cDNA and known proteins, reserving parts with different sources, carrying out secondary structure prediction on the existing plant lncRNA, extracting secondary structure characteristics, simultaneously carrying out secondary structure prediction on the rest sequences, reserving parts meeting the structural characteristics, comparing the cDNA sequence to be screened with a small RNA sequence, and removing a small RNA precursor;
and step three, collecting corn lncRNA sequences with known functions, performing structure prediction, extracting and screening structural features of the functional lncRNA, constructing a structural feature set of the corn lncRNA with the known functions, performing secondary structure extraction on the corn lncRNA screened in the step two, comparing the structural feature set, and confirming and classifying the lncRNA functions.
Preferably, in the second step, the predicting the reading frame includes the following steps: predicting the open reading frame of the existing lncRNA and coding RNA, distinguishing coding from non-coding according to the length of the open reading frame, setting a reasonable threshold value of the length of the open reading frame and screening out sequences smaller than the threshold value.
Preferably, in the second step, the small RNA includes miRNA, shRNA, siRNA.
Preferably, in the second step, the sequence to be screened and the small RNA sequence are aligned by blast algorithm.
Preferably, in the second step, the prediction of the secondary structure of the lncRNA comprises the following steps:
segmenting lncRNA based on a transcription time sequence, dividing stages formed by structures of all the segments, scoring spiral regions possibly formed, judging the most urgently formed spiral region according to the score difference, obtaining a secondary structure taking the spiral region as a unit through a dynamic programming algorithm, and determining the optimal structure to form the predicted lncRNA secondary structure.
Preferably, the lncRNA segment is 160nt in length, and each segment overlaps with the preceding and succeeding segments by 80 nt.
Preferably, in the third step, the step of classifying the maize lncRNA sequence further comprises the following steps: calculating the normalization of base pairing and the normalization of the number of each base which does not form pairing according to a base pair characteristic algorithm; a root-based secondary structure characteristic algorithm is used for calculating typical characteristics of normalization of the minimum MFE to the full length of the sequence, normalization of hairpin loops in a secondary structure, base match comparison of an average hairpin structure in a stem region, normalization of loop length in the secondary structure and the like; and carrying out quantitative consistency analysis on the extracted typical features.
Preferably, in the third step, the step of classifying the maize lncRNA sequence further comprises the following steps: collecting typical characteristics extracted by a certain function and determining the lncRNA of the corn without the function; training and evaluating the model by using an SVM modeling method; and screening and evaluating the characteristics with ranking in the top 3 as a characteristic set of a certain function.
Compared with the prior art, the invention has the following beneficial effects:
1. high performance, low power consumption, high performance-price ratio and high code density. At present, aiming at embedded application, the ARM processor can also achieve the lowest power consumption on the premise of meeting the performance. The method can give consideration to the aspects of performance, power consumption, code density and price;
2. abundant chips. At present, there are hundreds of processors based on ARM cores. A user can select a proper chip to design an application system according to respective application requirements and consideration of aspects such as performance, functions and the like;
the screening classification of the corn lncRNA is realized by adopting the ARM processor, so that the data can be conveniently processed in batches, the higher processing speed and the higher accuracy rate can be considered, the corn lncRNA can be conveniently solidified into a high-throughput lncRNA data acquisition instrument, the screening classification is convenient, fast and standardized, the screening process is modularized, and the manual intervention can be reduced.
Drawings
FIG. 1 is a flowchart of the method for screening and classifying IncRNA of maize based on ARM processor.
FIG. 2 is a schematic structural diagram of the method for screening and classifying IncRNA of maize based on ARM processor.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1 and 2, in the conventional lncRNA screening and classifying method, high-throughput lncRNA data is collected according to a conventional design rule, and then is subjected to screening and classification, and the like. The lncRNA collection needs to be carried out by a biological experimental instrument, the conventional method is inconvenient to combine with a high-throughput data collection instrument, and the like, and has high labor cost, low speed and low efficiency, so that the lncRNA classification method needs to be cured into the high-throughput data collection instrument urgently to optimize lncRNA screening and classification; therefore, the invention provides a corn lncRNA screening and classifying method based on an ARM processor, and hardware equipment required by the method comprises the processor, a memory and a main board; the invention comprises the following steps:
1. inputting the cDNA sequence into CPLD;
among them, cpld (complex Programmable Logic device) is a complex Programmable Logic device, developed from PAL and GAL devices, relatively large in scale, complex in structure, belonging to the large scale integrated circuit range, and is a digital integrated circuit in which users can construct Logic functions according to their respective needs. The basic design method of the CPLD is realized by generating a corresponding target file by means of an integrated development software platform and using methods such as a schematic diagram, a hardware description language and the like and transmitting codes to a target chip through a download cable (programming in a system). PAL refers to programmable array logic, and is a low-density, one-time programmable logic device introduced first by MMI corporation in the late 70 s. GAL is a general array logic, developed from PAL, because the programming of the device is very convenient due to the adoption of EECMOS process;
2. the CPLD stores the cDNA sequence in the SRAM in a coding form through a coding association table. The lncRNA of the corn is screened together according to sequence characteristics and structural characteristics;
the SRAM is an abbreviation of English Static RAM, and is a memory with a Static access function, and the data stored in the memory can be stored without a refresh circuit. We screened maize lncRNA for both sequence and structural features.
3. Selecting an ARM microprocessor to screen the corn lncRNA:
1) loading the digitized information corresponding to the full-length cDNA sequence of the corn into a screening model;
2) screening cDNA sequences with the length of more than 200;
3) predicting open reading frames of the cDNA; predicting the open reading frame of the existing lncRNA and coding RNA, distinguishing coding from non-coding according to the length of the open reading frame, setting a reasonable threshold value of the length of the open reading frame, and screening out a sequence smaller than the threshold value;
4) carrying out homologous alignment on the cDNA and known proteins, and reserving parts of different homologues;
5) predicting the secondary structure of the existing plant lncRNA, and extracting the secondary structure characteristics;
6) performing secondary structure prediction on the residual sequence, and reserving a part meeting the structural characteristics;
7) storing the collected small RNA sequences such as plant miRNA (micro RNA), shRNA (short hairpin RNA), siRNA (small interfering RNA) and the like in an SRAM (static random access memory), comparing the sequence to be screened with the small RNA sequence by using a blast algorithm, and deleting possible small RNA precursors;
wherein the transcript length: the sequence length 200 is a decisive characteristic for judging whether the RNA is long-chain non-coding RNA; ORF length: the ORF (Open Reading Frame) starts with a start codon, has the potential to encode a protein in DNA, and has a base sequence that terminates without a stop codon. Because the start sites for codon reads and writes are different, there are six possible reads and translations for the sequence (two strands, three for each strand); homology to known proteins: the features of the ORF alone do not necessarily characterize the ability of the protein to encode, but also need to include features of homology to known proteins;
4. selecting an ARM microprocessor to classify the corn lncRNA:
1) collecting a corn lncRNA sequence with known functions, and performing structure prediction;
2) extracting and screening structural features of the functional lncRNA, and outputting a screening result to an LCD (liquid crystal display);
3) and (3) constructing a structural feature set of the lncRNA of the corn with known functions by using SVM modeling, wherein a negative sample is the lncRNA determined not to have the functions. Meanwhile, evaluating and ranking the structural feature set through a cross-validation method, and taking the top three ranked structural feature sets as final feature set elements;
4) extracting a secondary structure of the maize lncRNA with unknown function;
5) comparing the set functional corn lncRNA characteristic set, and judging the matching degree of the set functional corn lncRNA characteristic set, wherein if the matching degree is high, the set functional corn lncRNA characteristic set can be judged to have the function, so that functional characteristic identification is carried out on the unknown functional lncRNA;
6) confirming unknown lncRNA function to realize classification;
wherein, the base pair characteristics are as follows: the method comprises base pairing normalization and normalization calculation of the number of each base which is not paired, can visually determine structural characteristics, and is one of the simplest structural characteristics; secondary structure characteristics: the structural characteristics of basic units such as a stem region, an inner loop, a hairpin loop, a multi-branch loop and the like, and the typical characteristics of normalization of the minimum MFE to the whole sequence length, normalization of the hairpin loop in a secondary structure, base match comparison of an average hairpin structure in the stem region, normalization of the loop length in the secondary structure and the like. By extracting the typical characteristics, the structural difference between the functional lncRNAs can be determined, and the lncRNAs can be better classified functionally.
As shown in fig. 1 and fig. 2, the invention provides a corn lncRNA screening and classifying method based on an ARM processor, which comprises the following steps:
step one, digging out secondary structure characteristics of long-chain non-coding RNA in corn, inputting a corn full-length cDNA sequence into a CPLD, and searching for possible long-chain non-coding RNA by using sequence characteristics and structural characteristics of the long-chain non-coding RNA as an identification standard;
step two, storing the cDNA sequence in an SRAM in a form of a coding sequence, and then screening and classifying lncRNA by an ARM processor;
step three, carrying out first screening according to the sequence length to obtain a cDNA sequence with the length more than 200;
step four, performing ORF size statistical analysis on the existing long-chain non-coding RNA, setting a reasonable threshold, and performing secondary screening according to the characteristic to obtain a sequence smaller than the threshold;
step five, performing homologous comparison on the data identified in the previous step and known proteins, and performing third screening to obtain non-homologous sequences;
step six, storing the collected small RNA sequences in an SRAM, establishing a local database required for comparison by using a blast algorithm, taking long-chain non-coding RNA to be screened as the database, taking the small RNA as a comparison sequence, and removing possible small RNA precursors;
seventhly, performing structural analysis on the known functional corn lncRNA, establishing a functional structural feature set by utilizing svm modeling, and performing feature extraction on the screened unknown functional lncRNA;
and step eight, comparing the known feature set, finally judging the lncRNA with high matching degree to have the function, and finally outputting the result to an LCD.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (5)
1. A corn lncRNA screening and classifying method based on an ARM processor is characterized by comprising the following steps:
inputting a full-length cDNA sequence of the corn into a CPLD, and storing the cDNA sequence in an SRAM in a coding mode through a coding association table;
screening a cDNA sequence with the length larger than 200, carrying out open reading frame prediction on the cDNA and determining a threshold, determining the cDNA with the length smaller than the threshold, carrying out homologous comparison on the cDNA and known proteins, reserving parts with different sources, carrying out secondary structure prediction on the existing plant lncRNA, extracting secondary structure characteristics, simultaneously carrying out secondary structure prediction on the rest sequences, reserving parts meeting the structural characteristics, comparing the cDNA sequence to be screened with a small RNA sequence, and removing a small RNA precursor;
step three, collecting corn lncRNA sequences with known functions, performing structure prediction, extracting and screening structural features of the functional lncRNA, constructing a structural feature set of the corn lncRNA with the known functions, performing secondary structure extraction on the corn lncRNA screened in the step two, comparing the structural feature set, and confirming and classifying the functions of the lncRNA;
in the second step, the prediction of the secondary structure of lncRNA comprises the following steps:
segmenting lncRNA based on a transcription time sequence, forming stage division of each segment of structure, scoring the formed spiral region, judging the most urgently formed spiral region according to the score difference, obtaining a secondary structure taking the spiral region as a unit through a dynamic programming algorithm, and determining the optimal structure to form a predicted lncRNA secondary structure;
in the third step, classifying the maize lncRNA sequence further comprises the steps of: collecting typical characteristics extracted by a certain function and determining the lncRNA of the corn without the function; training and evaluating the model by using an SVM modeling method; and screening and evaluating the characteristics with ranking in the top 3 as a characteristic set of a certain function.
2. The ARM processor-based corn lncRNA screening and classifying method of claim 1, wherein in the second step, the open reading frame prediction comprises the following steps: predicting the open reading frame of the existing lncRNA and coding RNA, distinguishing coding from non-coding according to the length of the open reading frame, setting a reasonable threshold value of the length of the open reading frame and screening out sequences smaller than the threshold value.
3. The ARM processor-based maize lncRNA screening and classifying method of claim 1 or 2, wherein in the second step, the small RNA comprises miRNA, shRNA and siRNA.
4. The ARM processor-based corn lncRNA screening and classifying method as claimed in claim 3, wherein in the second step, the sequence to be screened and the small RNA sequence are aligned through a blast algorithm.
5. The ARM processor-based maize lncRNA screening and sorting method of claim 4, wherein the lncRNA segments are 160nt in length, and each segment overlaps with the preceding and succeeding segments by 80 nt.
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