- Open Access
The prediction of virus mutation using neural networks and rough set techniques
© Salama et al. 2016
- Received: 13 July 2015
- Accepted: 3 May 2016
- Published: 13 May 2016
Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.
- Gene prediction
- Machine learning
The deoxyribonucleic acid (DNA) strands are composed of units of nucleotides. Each nucleotide is composed of a nitrogen-containing nucleobase, which is either guanine (G), adenine (A), thymine (T), or cytosine (C). Most DNA molecules consist of two strands coiled around each other forming a double helix. These DNA strands are used as a template to create the ribonucleic acid (RNA) in a process known as transcription. However, unlike DNA, RNA is often found as a single-strand. One type of RNA is the messenger RNA (mRNA) which carries information from the ribosome, which are where the protein is synthesized. The sequence of mRNA is what specifies the sequence of amino acids the formed protein. DNA and RNA are also the main component of viruses. Some of the viruses are DNA-based, while others are RNA-based such as Newcastle, HIV, and flu . RNA viruses are different than the DNA-based viruses in the sense that they have higher mutation rates, and hence, they have higher adaptive capacity. This mutation causes a continuous evolution that leads to host immunity, and hence, the virus becomes even more virulent . One of the RNA virus mutations is the point mutation which is a small scale mutations that affects the RNA sequence in only one or few nucleotides, such as nucleotide substitution. This substitution refers to the replacement of one nucleobase (i.e., base) either through transition or transversion. This substitution refers to the replacement of one nucleobase (i.e., base) by another either through transition or transversion. Transition is the exchange of two purines (A <−> G) or one pyrimidine (C <−> T), while transversion is the exchange between a purine and pyrimidine [3, 4]. Another type of point mutation is frame-shift which refers to the insertion or deletion of a nucleotide in the RNA sequence.
One of the important focuses in the field of human disease genetics is the prediction of genetic mutation . Having information about the current virus generations and their past evolution could provide a general understanding of the dynamics of virus evolution and the prediction of future viruses and diseases . The evolutionary relationship between species is determined by phylogenetic analysis; additionally, it infers the ancestor sequence of these species. These phylogenetic relationships among RNA sequences can help in predicting which sequence might have an equivalent function . The analysis of the mutation data is very important, and one of the tools used for this purpose is machine learning. Machine learning techniques help predict the effects of non-synonymous single nucleotide polymorphisms on protein stability, function and drug resistance . Some of these techniques that are used in prediction are support vector machine, neural networks and decision trees. These techniques have been utilized to learn the rules describing mutations that affect protein behavior, and use them to infer new relevant mutations that will be resistant to certain drugs . Another use is to predict the potential secondary structure formation based on primary structure sequences [10–12]. A different direction is to predict the discovery of single nucleotide variants in RNA sequence. Another tool in machine learning is Markov chains, which can describe the relative rates of different nucleotide changes in the RNA sequence . These models consider the RNA sequence to be a string of four discrete states, and hence, tracks the nucleotide replacements during the evolution of the sequence. In these models, it was assumed that the different nucleotides in the sequence evolved independently and identically, and justified that using the case of neutral evolution of nucleotides. Following that, several researches developed methods negated that assumption and identified the relevant neighbor-dependent substitution processes .
The prediction of the mutated RNA gives a clear understanding of the mutation process, the future activity of the RNA, and the help direct the development of the drugs that should be designed for it [15, 16]. In this work, we propose a machine learning technique that is based on rough set theory. This technique predicts the potential nucleotide substitutions that may occur in primary RNA sequences. In this technique, a training phase is utilized in which each iteration the input is an RNA sequence of one generation of the virus, while the output is the RNA sequence of the next generation of the virus. Every feature in the input is a nucleotide in the RNA sequence corresponds to a feature in the output. The training of the machine learning technique is fed with aligned RNA sequences of successive generations of the same RNA viruses that exhibited similar environmental conditions. The technique introduced in this paper predicts the last RNA sequence based on the previous RNA generation sequence. Following that, the predicted RNA sequence is compared the actual RNA sequence in order to validate the ability of the machine learning technique to predict the RNA evolution and this prediction accuracy. This comparison results in a percentage which is calculated based on the number of matched nucleotides between both the predicted and actual RNA sequence versus the total number of nucleotides in the sequence. One of the main important methods of this technique is that it extracts the rules governing the past mutations, and hence, is able to infer the possible future mutations. These rules show the effect of a set of nucleotides on the mutation of their neighboring nucleotide. This technique visualizes these rules to allow an integrated analysis of the mutations occurring in successive generations of the RNA virus. Besides using the Rough set theory in our technique, a traditional machine learning technique is utilized which is neural networks in order to clarify the prediction process and validate our results. Finally, we present a way to reform the RNA alignments of a set of successive generations of the same virus. This reformation step is important in order to fit the input requirements for any machine learning technique.
This paper presents a proof of concept by applying this technique on a set of successive generations of the Newcastle Disease Virus (NDV) from two different countries, Korea and China [17, 18]. In these sets, the percentage of nucleotides that exhibits variation from a generation to another is 57–65 % for the two used sets of sequences. The proposed techniques percentage accuracy in the prediction of the varied nucleotides in the tested sequence in the last generation are 68–76 %. Although these results are not statistically significant at this point, it still however proves the applicability of the proposed technique. It is worth noting that the learning and accuracy of prediction of this technique increases as the number of instances in the data set increases. The rest of the paper is organized as follows. Section 2 presents the related work in applying machine learning techniques in genetic problems, as well as the proposed technique to solve these problems. The experimental work and discussion appear in Sections 3 and 4, and finally, the conclusion is presented in Section 5.
2.1 Related work
Predicting techniques have been utilized in the field of genetics for many years, and have been geared towards different directions. One of these directions is the detection of the resistance of the virus to drugs after its mutation , in which, machine learning techniques are focused on learning the rules characterizing the aligned sequences that are resistant to drugs. The rules will be later on used to detect the drug resistance gene sequences amongst a set of testing sequences. The training phase of these techniques works by having each protein sequence represented as a feature vector and fed as the input to this technique . An example of these machine learning techniques are support vector machine (SVM) and neural networks, which can be trained on data sets of drug-resistant and non-resistant DNA sequences of virus population. In the training phase, these techniques learn the rules of classifying new generations of the virus a being drug-resistant or not . However, these techniques are black box techniques, that cannot be used to infer any information about the rules used in this classification. Another disadvantage is that these techniques utilize 20 bit binary numbers instead of characters as a representation for 20 different amino acids condons. This increases the size of the input to the used algorithm, which in return increases the complexity of the classification process. However, the authors in  introduced a technique that uses characters instead of number as the input, and they utilize a decision tree in order to provide direct evidence on the drug resistance genes through a set of rules. Their technique trains and tests its efficiency on 471 isolates against 14 antiviral drugs, creating a decision tree for each drug. The input of this decision tree is the isolates sequence in which, each position is one of 20 naturally occurring amino acids represented in characters. The results of this technique concludes whether the virus is a drug-resgstenc virus or not. An example of this generated decision tree is: if the codon at position 184 is for methionine (M) and the codon at position 75 for alanine (A), glutamic acid (E) or threonine (T), then the virus carrying this gene is resistant to (3TC) antivirus drug. Another example is the detection of whether a point mutation in a cell is transmitted to the offspring or not, i.e., gremlin mutation vs. somatic mutation .
Another research direction is the prediction of the secondary structure of the RNA/DNA of the organism in the generation post-mutation. The ribonucleic acid (RNA) molecule consists of a sequence of ribo-nucleotides that determines the amino acids’ sequence in the protein. The primary structure of the RNA molecule is the linear form of the nucleotides’ sequence. The nucleotides can be paired based on specific rules that is: adenine (A) pairs with uracil (U) and cytosine (C) with guanine (G). Base pairs can occur within single-stranded nucleic acids. The RNA sequence is folded into secondary structure in which a pair of basis is bonded together. This structure contains a set of canonical base pairs, whose variation is considered as a form of mutation that can be predicted. Several researches have been focused on automating the RNA sequence folding  and predicting the secondary structure form . The probability of the generation of any secondary structure is inversely proportional to the energy of this structure. This energy is modeled based on extensive thermodynamic measurements . Applications like “RNAMute” analyzes the effects of point mutations on RNAs secondary structure based on thermodynamic parameters .
The third research direction is the prediction of single nucleotide variants (SNV) at each locus, i.e., nucleotide location. The SNV existence are identified from the results of the Next Generation Sequencing (NGS) methods . NSG is capable of typing SNP, which is the mutation that produces a single base pair change in the DNA sequence. For example, the two sequenced DNA fragments from different generations are AAGCCTA and AAGCTTA. Noticed that the fifth single nucleotide C in the first fragment varied to a T in the second fragment. This genotype variation change represents the mutation in the child genomes of the next generation. The steps of allocating the SNVs start with collecting a set of aligned sequences from NGS readings analyzed against a reference sequence. At each position i in the genome data, the number of reads a i that match the reference genome and the number of reads b i that do not match the reference genome are counted. The total number of reads, depth, is given by N i =a i + b i . A naïve approach to detect the SNV locations is to find the location i ∈ [1, T] whose fraction f i =a i / N i is less than a certain threshold .
These three research direction focus on predicting the activity of the newly evolved viruses. A different direction is the prediction of the rates of variation of the nucleotides in the RNA sequences of successive generations of the virus . In this direction, models are introduced to analyze the historical patterns and variation mechanism of the virus and detect the rates of variation of each point substitution of its RNA sequence. These models are characterized as either neighbor-dependent or non-dependent variation.
The contribution in this paper focuses on the prediction of the RNA sequence of newly evolved virus by detecting the possible point mutations. The nucleotide substitutions of the coding regions in this Newcastle virus occurred frequently . These substitutions and dependency between nucleotides is captured to form a set of rules that describes the evolution of the RNA virus.
2.2 The proposed algorithm and discussion
The focus of this paper is to analyze the changes and mutations that occur after each generation of the virus. This analysis is done through monitoring these changes and detecting their patterns. The mutation of the RNA viruses are characterized by high drug-resistance, however, these mutations can be predicted by applying machine learning techniques in order to extract their rules and patterns. The proposed algorithm here is applied on a data set of aligned RNA sequences observed over a period of time. This data was collected and presented in previous research [17, 18], in which all animal procedures performed were reviewed, approved, and supervised by the Institutional Animal Care and Use Committee of Konkuk University. This space of potential virus mutations provides a proper data set required for the computational methods used in the algorithm. The preprocessing is started by the alignment of RNA sequence for the purpose of predicting the evolution of the virus based on the gathered sequences. The steps performed are the mining and learning the rules of the virus mutation, in order to predict the mutations to help creating new drugs. The input data set is a set of aligned DNA sequences. The RNA isolates are ordered from old to new according to the time of getting the virus and isolating the RNA. The machine learning techniques used in this step are neural networks, and a proposed technique that is based on the rough set theory. An important step in the proposed technique is building the decision matrix required for rule extraction, which is based on the idea that if the value of the attribute has no effect on describing the category of the object, then this attribute can be excluded from the rule set . An important clarification is that every nucleotide in both the neural networks and proposed technique act as the target class label required in the classification. These target nucleotides will have one of four different genotype values that will be predicted by the classifier.
The preprocessing step includes the feature selection which is important in decreasing the processing cost by the removal unnecessary nucleotides. These nucleotides are those that do not change during the different generations of the virus, hence, they do not add any information in the classification/prediction step. This is due to the fact that not changing across RNA generations imply that they have no effect on the mutated nucleotides. Hence, their existence will have no effect, and will moreover deteriorate the net accuracy prediction result. Although this step is important as a preprocessing step in the neural network methods, it can be avoided in the proposed rough set based technique since the technique removes the non-required nucleotides internally.
2.2.1 Neural network technique
The disadvantage in using a neural network technique or support vector machine is the scaling of each nucleotide in the DNA sequence to four input states. This scaling process increase the computational complexity of the technique. On the other hand, the limited number of input instances could negatively affect the accuracy of the prediction process. Finally, the extraction of the rules in this technique is not possible, and hence, the prediction process is not feasible.
2.2.2 Rough set gene evolution (RSGE) proposed technique
This paper proposed a new algorithm for solving the evolution prediction problem that is based on the input time series data set. Each RNA sequence is mutated, i.e., evolution passage, to the next RNA sequence in the data set, as these sequences are sorted from old to recent dates. For each iteration in this learning phase of this algorithm, the input is an RNA sequence and the output is the next RNA sequence in the data set. The learned output is not the classification of the RNA virus; however, it is the RNA virus after mutation. Because, techniques like support vector machine, neural networks, and Bayesian belief networks have to deal with data in a numerical form, the proposed technique deals with alphabetical and numerical data in the same fashion.
The purpose of this technique is to infer the rules that governs certain value [A,C,G o r T] for each nucleotide. At this point, the training applied will consider each nucleotide as a target class of four values. The input that leads to one of the values of the target class is the sequence before the sequence containing the current value of the target class. The machine learning algorithm will learn what input will produce which output accordingly. The rules learned from the used machine learning algorithm will be used to predict the generated nucleotide corresponding to the input. The rule will be in the form of short sequences of nucleotides genotype and location that govern the mutation of a nucleotide from certain genotype to another. The number of iterations will be the number of nucleotides in the RNA sequence. After each iteration, the required rule for the nucleotide x corresponding to the current iteration will be extracted.
The analysis of RNA mutations requires the gathering and preparing a set of aligned RNA sequences that go through different mutations over a long period of time. A set of time-series successive isolates of the Newcastle virus RNA are collected from two different countries, China and South Korea [17, 18]. The GenBank accession numbers of NDVs isolates recovered from live chicken markets in Korea in year 2000 are AY630409.1-AY630436.1, and from healthy domestic ducks on farms are EU547752.1-EU547764.1. The total number of isolates for this data set is 22, each isolate is of 200 nucleotide. While the accession numbers of sequenced isolates extracted from chicken in China in years from 2011 to 2012 are KJ184574-KJ184600 . The total number of isolates for this data set is 45, each isolate is of 240 nucleotide. Each set of isolates is listed and sorted according to the date of extraction for time series analysis of the evolution of the NDV virus. The experimental is applied only a partial F gene sequence of NDV from the GenBank record. The virus RNA sequences were monitored and collected retrospectively at regular intervals from similar animal type. The intervals between successive RNA sequences in the Chinese dataset is short relative to the Korean data set. The difference between both types of intervals does not affect neither the analysis process nor the accuracy of the prediction. The extracted data examined for the Korean and Chinese datasets are represented by two different regions of viral genome with the different lengths (200 and 240). It is important to clarify that the two input data sets can not be merged because each input set of segments are aligned separately. So it is impossible here to apply prediction performance of the proposed models on the Chinese dataset using the Korean dataset for training.
The Chinese data set shows 33 % of the total number of nucleotides have been mutated, and this percentage in the Korean is 43 %. The rate of genome mutations over the time period applied shows variable number mutations.
3.1 Neural network (NN) results
3.2 Rough set gene evolution (RSGE) results
The validation of the proposed rough set gene evolution (RSGE) is achieved through testing the total number of nucleotides. For the Korean input data set where the total number of nucleotides is 200, the number of correct nucleotide matches is 148, which is considered 74 % classification accuracy percentage. Also, only four nucleotides have shown incorrect matches, which corresponds to 2 % out of all nucleotides. 48 nucleotides have shown no matching results, which indicates that none of the four rules of each of these nucleotides is applied. These results show the predicted and actual target RNA sequence of nucleotides.
Actual : ATGGGTTCCAAATCTTCTACCAG
Predicted : ATGGGNNCCANANCTTCTACCAN
Actual : ATGGGCTCCAAACCTTCTACCAG
Predicted : TGGGCTCCAAACNTTCTACCNG
For Nucleotide P131:
If Genotype is A then P47=C, P57=A
If Genotype is G then P23=A
For Nucleotide P101:
If Genotype is C then P6=T, P118=C
If Genotype is T then P6=G, P118=A
AB genotype rules for the Chinese data set
4.1 NN vs. RSGE
The contribution of machine learning techniques in the RNA mutation was limited to the prediction of the activity of the virus of resulted RNA. This work paves the way to a new horizon where the prediction of the mutations, such as virus evolution, is possible. It can assist the designing of new drugs for possible drug-resistant strains of the virus before a possible outbreak. Also it can help in devising diagnostics for the early detection of cancer and possibly for the early start-of-treatment. This work studies the correlation between the nucleotides in RNA including the effect of each nucleotide in chaining the genotypes of other nucleotides. These rules of these correlations are explored and visualized for the prediction of the mutations that may appear in the following generations. The prediction rules are extracted by a proposed technique based on RSGE, and is trained by two data sets extracted from two different countries. This work proves the existence of a correlation between the mutation of nucleotides, and successfully predicts the nucleotides in the next generation in the testing parts of two used data sets with a success rate of 75 %. On the other hand, the proposed rough set (RSGE) based technique shows a better prediction result than the neural networks technique, and moreover, it extract the rules used in the prediction.
We thank all the members of the group of scientific research in Egypt, Cairo university for their continuous support and encouragement to accomplish this work. Besides, we thank the faculty of computer science in the British university in Egypt for providing us with all the required support. A special thanks for Mr. Saleh Esmate for helping us in gathering the data set of this work.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- SF Elena, R Sanjuán, Adaptive value of high mutation rates of RNA viruses: separating causes from consequences. J. Virol. 79(18), 11555–11558 (2005).View ArticleGoogle Scholar
- B Wilson, NR Garud, AF Feder, ZJ Assaf, PS Pennings, The population genetics of drug resistance evolution in natural 2 populations of viral, bacterial, and eukaryotic pathogens. Mol. Ecol. 25:, 42–66 (2016).View ArticleGoogle Scholar
- T Baranovich, S Wong, J Armstrong, H Marjuki, R Webby, R Webster, E Govorkova, T-705 (Favipiravir) induces lethal mutagenesis in influenza A H1N1 Viruses In Vitro. J. Virol. 87(7), 3741–3751 (2013).View ArticleGoogle Scholar
- L Loewe, Genetic mutation. Nat. Educ. 1(1), 113 (2008).Google Scholar
- BE Stranger, ET Dermitzakis, From DNA to RNA to disease and back: the ‘central dogma’ of regulatory disease variation. Hum. Genomics. 2(6), 383–390 (2006).View ArticleGoogle Scholar
- J Shendure, H Ji, Next-generation DNA sequencing. Nat. Biotechnol. 26:, 1135–1145 (2008).View ArticleGoogle Scholar
- J Xu, HC Guo, YQ Wei, L Shu, J Wang, JS Li, SZ Cao, SQ Sun, Phylogenetic analysis of canine parvovirus isolates from Sichuan and Gansu provinces of China in 2011. Transbound. Emerg. Dis. 62:, 91–95 (2015).View ArticleGoogle Scholar
- E Capriotti, P Fariselli, I Rossi, R Casadio, A three-state prediction of single pointmutations on protein stability changes. BMC Bioinformatics. 9(2), S6 (2008).View ArticleGoogle Scholar
- E Cilia, S Teso, S Ammendola, T Lenaerts, A Passerini, Predicting virus mutations through statistical relational learning. BMC Bioinformatics. 15:, 309 (2014). doi:http://dx.doi.org/10.1186/1471-2105-15-309.View ArticleGoogle Scholar
- M Lotfi, Zare-Mirakabad F, Montaseri S, RNA secondary structure prediction based on SHAPE data in helix regions. J. Theor. Biol. 380:, 178–182 (2015).MathSciNetView ArticleGoogle Scholar
- TH Chang, LC Wu, YT Chen, HD Huang, BJ Liu, KF Cheng, JT Horng, Characterization and prediction of mRNA polyadenylation sites in human genes. Med. Biol. Eng. Comput. 49(4), 463–72 (2011).View ArticleGoogle Scholar
- M Kusy, B Obrzut, J Kluska, Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients. Med. Biol. Eng. Comput. 51(12), 1357–1365 (2013).View ArticleGoogle Scholar
- A Hobolth, A Markov chain Monte Carlo expectation maximization algorithm for statistical analysis of DNA sequence evolution with neighbor-dependent substitution rates. J. Comput. Graph. Stat. 17(1), 138–162 (2008).MathSciNetView ArticleGoogle Scholar
- PF Arndt, T Hwa, Identification and measurement of neighbor-dependent nucleotide substitution processes. Binformatics. 21(10), 2322–2328 (2005).View ArticleGoogle Scholar
- NM Ferguson, RM Anderson, Predicting evolutionary change in the influenza A virus. Nat. Med. 8:, 562–563 (2002).View ArticleGoogle Scholar
- DJ Smith, AS Lapedes, JC de Jong, TM Bestebroer, GF Rimmelzwaan, Mapping the antigenic and genetic evolution of influenza virus. Science. 305(5682), 371–376 (2004).View ArticleGoogle Scholar
- K-S Choi, E-K Lee, W-J Jeon, J-H Kwon, J-H Lee, H-W Sung, Molecular epidemiologic investigation of lentogenic Newcastle disease virus from domestic birds at live bird markets in Korea. Avian Dis. 56(1), 218–223 (2012).View ArticleGoogle Scholar
- Z-M Qin, L-T Tan, H-Y Xu, B-C Ma, Y-L Wang, X-Y Yuan, W-J Liu, Pathotypical characterization and molecular epidemiology of Newcastle disease virus isolates from different hosts in China from 1996 to 2005. J. Clin. Microbiol. 46(4), 601–611 (2008).View ArticleGoogle Scholar
- Y Choi, GE Sims, S Murphy, JR Miller, AP Chan, Predicting the functional effect of amino acid substitutions and indels. PLoS One. 7(10), e46688 (2012). doi:http://dx.doi.org/10.1371/journal.pone.0046688.
- D Wang, B Larder, Enhanced prediction of lopinavir resistance from genotype by use of artificial neural networks. J. Infect. Dis. 188(11), 653–660 (2003).View ArticleGoogle Scholar
- ZW Cao, LY Han, CJ Zheng, ZL Ji, X Chen, HH Lin, YZ Chen, Computer prediction of drug resistance mutations in proteins. Drug Discov. Today. 10(7), 521–529 (2005).View ArticleGoogle Scholar
- N Beerenwinkel, B Schmidt, H Walter, R Kaiser, T Lengauer, D Hoffmann, K Korn, J Selbig, Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype. Proc. Natl. Acad. Sci. USA. 99(12), 8271–8276 (1999).View ArticleGoogle Scholar
- J Ding, A Bashashati, A Roth, A Oloumi, K Tse, T Zeng, G Haffari, M Hirst, MA Marra, A Condon, S Aparicio, SP Shah, Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data. Bioinformatics. 28(2), 167–75 (2012).View ArticleGoogle Scholar
- D Lai, JR Proctor, IM Meyer, On the importance of cotranscriptional RNA structure formation. RNA. 19(11), 1461–1473 (2013).View ArticleGoogle Scholar
- DH Mathews, WN Moss, DH Turner, Folding and finding RNA secondary structure. Cold Spring Harb Perspect Biol. 2(12), a003665 (2010). doi:http://dx.doi.org/10.1101/cshperspect.a003665.View ArticleGoogle Scholar
- IL Hofacker, M Fekete, PF Stadler, Secondary structure prediction for aligned RNA sequences. J. Mol. Biol. 319(5), 1059–1066 (2002).View ArticleGoogle Scholar
- D Barash, A Churkin, Mutational analysis in RNAs: comparing programs for RNA deleterious mutation prediction. Brief Bioinform.12(2), 104–114 (2011).View ArticleGoogle Scholar
- R Morin, et al, Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques. 45(1), 81–94 (2008). doi:http://dx.doi.org/10.2144/000112900.View ArticleGoogle Scholar
- R Goya, MG Sun, RD Morin, G Leung, G Ha, KC Wiegand, J Senz, A Crisan, MA Marra, M Hirst, D Huntsman, KP Murphy, S Aparicio, SP Shah, SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics. 26(6), 730–736 (2010).View ArticleGoogle Scholar
- H Li, J Ruan, R Durbin, Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res.18:, 1851–1858 (2008). doi:http://dx.doi.org/10.1101/gr.078212.108.View ArticleGoogle Scholar
- J Berard, L Guéguen, Accurate estimation of substitution rates with neighbor-dependent models in a phylogenetic context. J. Systmatic Biol. 61(3), 510–21 (2012).View ArticleGoogle Scholar
- GM Ke, KP Chuang, CD Chang, MY Lin, HJ Liu, Analysis of sequence and haemagglutinin activity of the HN glycoprotein of New-castle disease virus. Avian Pathol.39(3), 235–244 (2010). doi:http://dx.doi.org/10.1080/03079451003789331.View ArticleGoogle Scholar
- M Bal, Rough sets theory as symbolic data mining method: an application on complete decision table. Inform. Sci. Lett. 2(1), 35–47 (2013).View ArticleGoogle Scholar
- J-Y Wang, W-H Liu, J-J Ren, P Tang, N Wu, H-J Liu, Complete genome sequence of a newly emerging Newcastle disease virus. Genome Announc. 1(3), 196–13 (2013).Google Scholar