Package: recan
recanrecan web versionRequirementsIntallationUsage exampleSome notes on usageAutomated testsExample datasetsReferencesrecan citationsrecanrecan[9] is a Python package which allows to construct genetic distance plots to explore and discover recombination events in viral genomes. This method has been previously implemented in desktop software tools: RAT[1], Simplot[2] and RDP4 [8].RequirementsTo userecan, you will need:Python 3BiopythonplotlypandasJupyter notebookrecan web versionrecan django-based web version is currently under developmenthttps://github.com/babinyurii/recan_guitest version available at:http://yuriyb.pythonanywhere.com/IntallationTo install the package viapiprun :$ pip install recanIf you are going to userecanin JupyterLab, followthe insctructions to install the Jupyter Lab Plotly rendererUsage exampleThe package is intended to be used in Jupyter notebook.ImportSimgenclass from the recan package:fromrecan.simgenimportSimgencreate an object of the Simgen class. To initialize the object pass your alignment in fasta format as an argument:sim_obj=Simgen("./datasets/hbv_C_Bj_Ba.fasta")The input data are taken from the article by Sugauchi et al.(2002). This paper describes recombination event observed in hepatitis B virus isolates.The object of the Simgen class has methodget_info()which shows information about the alignment.sim_obj.get_info()index:	sequence id:
0	AB048704.1_genotype_C_
1	AB033555.1_Ba
2	AB010291.1_Bj
alignment length:  3215We have three sequences in our alignment.Simgenclass is based upon theMultipleSequenceAlignmentclass of the Biopython library.  So, we treat our alignment as the array with n_samples and n_features, where 'samples' are sequences themselves, and the features are columns of nucleotides in the alignment. Index corresponds to the sequence. Note, that indices start with 0.After you've created the object you can draw the similarity plot.
Call the methodsimgen()of the Simgen object to draw the plot. Pass the following parameters to the method:window: sliding window size. The number of nucleotides the sliding window will span. It has the value of 500 by default.shift: this is the step our window slides downstream the alignment. It's value is set to 250 by defaultpot_rec: the index of the potential recombinant.  All the other sequences will be plotted as function of distance to that sequence. Use methodget_info()to get the indices, especially if your alignment has many sequences.The isolate of Ba genotype is the recombinant between the virus of C genotype and genotype Bj. Let's plot it. We set genotype Ba as the potential recombinant :sim_obj.simgen(window=200,shift=50,pot_rec=1)Potential recombinant is not shown in the plot, as the distances are calculated relative to it. The higher is the distance function (i.e. the closer to 1), the closer is the sequence to the recombinant and vice versa.We can see typical 'crossover' of the distances which is the indicator of the possible recombination event. The distance of one isolate 'drops down' whereas the distance of the other remains the same of even gets closer to the potential recombinant, this abrupt drop shows that recombination could take place.The picture from the article is shown below. It's just turned upside down relative to our plot, and instead of distance drop we see distance rising. Here Bj 'goes away' from the genotype C, whereas Ba keeps the same distanceBy defaultsimgen()method plots the whole alignment. But after initial exploration, we can take a closer look at a particular region by passing theregionparameter to the simgen method. We can slice the alignment by using this parameter.regionmust be a tuple or a list with two integers: the start and the end position of the alignment slice.region=(start,end)sim_obj.simgen(window=200,shift=50,pot_rec=1,region=(1000,2700))To customize the plot or just to export and store the data, useget_data()method.get_data()returns pandas DataFrame object with sequences as samples, and distances at given points as features.sim_obj.get_data()If optional paremeterdfis set toFalse,get_data()returns a tuple containing list of ticks and a dictionary of lists. Each dictionary key is the sequence id, and lists under the keys contain the corresponding distances.positions,data=sim_obj.get_data(df=False)print(positions)
[1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1650, 1700, 1750, 1800, 1850, 1900, 1950, 2000, 2050, 2100, 2150, 2200, 2250, 2300, 2350, 2400, 2450, 2500, 2550, 2600, 2650, 2700]

print(data)
{'AB048704.1_genotype_C_': [0.88, 0.935, 0.925, 0.955, 0.955, 0.965, 0.95, 0.935, 0.94, 0.92, 0.9299999999999999, 0.945, 0.925, 0.945, 0.96, 0.95, 0.975, 0.9733333333333334, 0.96, 0.96], 'AB010291.1_Bj': [0.98, 0.975, 0.97, 0.97, 0.965, 0.95, 0.91, 0.88, 0.85, 0.83, 0.825, 0.865, 0.885, 0.9299999999999999, 0.98, 0.97, 0.98, 0.9733333333333334, 0.96, 0.96]}Once you've returned the data, you can easily customize the plot by using your favourite plotting library:dist_data=sim_obj.get_data()importmatplotlib.pyplotaspltimportseabornassnssns.set()fig_dist1=plt.figure(figsize=(20,8))plt.plot(df.loc["AB048704.1_genotype_C_",:],lw=7,alpha=0.7,label="AB048704.1_genotype_C_")plt.plot(df.loc["AB010291.1_Bj",:],lw=7,alpha=0.7,label="AB010291.1_Bj")plt.ylim(0.75,1.05)plt.title("similarity distance plot",fontsize=25)plt.ylabel("distance relative to Ba",fontsize=20)plt.xlabel("nucleotide position",fontsize=20)plt.xticks(fontsize=15)plt.yticks(fontsize=15)plt.axvline(1750,alpha=0.5,color="red",lw=3,linestyle="dashed",label="putative recombination break points")plt.axvline(2250,alpha=0.5,color="red",lw=3,linestyle="dashed")plt.legend(prop={"size":20})plt.show()simgen()method has optional parameterdistwhich denoted method used to calculate pairwise distance. By default its value is set topdist, sosimgen()calculates simple pairwise distance.Parameters for distance calculation methods:pdist: pairwise distance (default)jcd: Jukes-Cantor distancek2p:  Kimura 2-parameter distancetd: Tamura distancesim_obj.simgen(window=200,shift=50,pot_rec=1,region=(1000,2700),dist='k2p')to save the distance data in csv format use the methodsave_data():sim_obj.save_data(out_name="hbv_distance_data")If there are about 20 or 30 sequences in the input file and their names are long, legend element may hide the plot. So, to be able to analyze many sequences at once, it's better to use short consice sequence names instead of long ones. Like this:To illustrate how typical breakpoints may look like, here are shown some examples of  previously described recombinations in the genomes of different viruses. The fasta alignments used are available atdatasets folder.Putative recombinations in the of 145000 bp genome of lumpy skin disease virus [4]:Recombination in HIV genome [5]:HCV intergenotype recombinant 2k/1b [6]:Norovirus recombinant isolate [7]:Some notes on usagethe optimal window size is about 200-250 bp, the optimal window shift is typicall about 50-150 bpnow distance calculation skips degenerate nucleotides and gaps and they do not influence the distance valuesAutomated testsTo verify the installation, go to therecan/test/folder and run:$ pytest test.pyExample datasetsTo download the datasets use the following link:https://drive.google.com/drive/folders/1v2lg5yUDFw_fgSiulsA1uFeuzoGz0RjH?usp=sharingReferencesRecombination Analysis Tool (RAT): a program for the high-throughput detection of recombination. Bioinformatics, Volume 21, Issue 3,
1 February 2005, Pages 278–281,https://doi.org/10.1093/bioinformatics/bth500https://sray.med.som.jhmi.edu/SCRoftware/simplot/Hepatitis B Virus of Genotype B with or without Recombination with Genotype C over the Precore Region plus the Core Gene. Fuminaka Sugauchi et al. JOURNAL OF VIROLOGY, June 2002, p. 5985–5992. 10.1128/JVI.76.12.5985-5992.2002https://jvi.asm.org/content/76/12/5985Sprygin A, Babin Y, Pestova Y, Kononova S, Wallace DB, Van Schalkwyk A, et al. (2018) Analysis and insights into recombination signals in lumpy skin disease virus recovered in the field. PLoS ONE 13(12): e0207480.https://doi.org/10.1371/journal.pone.0207480Liitsola, K., Holm K., Bobkov, A., Pokrovsky, V., Smolskaya,T., Leinikki,P., Osmanov,S. and Salminen,M. (2000) An AB recombinant and its parental HIV type 1 strains in the area of the former Soviet Union: low requirements for sequence identity in recombination. UNAIDS Virus Isolation Network. AIDS Res. Hum. Retroviruses, 16, 1047–1053.Smith, D. B., Bukh, J., Kuiken, C., Muerhoff, A. S., Rice, C. M., Stapleton, J. T., & Simmonds, P. (2014). Expanded classification of hepatitis C virus into 7 genotypes and 67 subtypes: Updated criteria and genotype assignment web resource. Hepatology, 59(1), 318–327.https://doi.org/10.1002/hep.26744Jiang,X., Espul,C., Zhong,W.M., Cuello,H. and Matson,D.O. (1999) Characterization of a novel human calicivirus that may be a naturally occurring recombinant. Arch. Virol., 144, 2377–2387.Martin, D. P., Murrell, B., Golden, M., Khoosal, A., & Muhire, B. (2015). RDP4: Detection and analysis of recombination patterns in virus genomes. Virus Evolution, 1(1), 1–5.https://doi.org/10.1093/ve/vev003Babin, Y., (2020). Recan: Python tool for analysis of recombination events in viral genomes. Journal of Open Source Software, 5(49), 2014.https://doi.org/10.21105/joss.02014recan citationsZimerman RA, Ferrareze PAG, Cadegiani FA, Wambier CG, Fonseca DdN, de Souza AR, Goren A, Rotta LN, Ren Z and Thompson CE (2022) Comparative Genomics and
Characterization of SARS-CoV-2 P.1 (Gamma) Variant of Concern From Amazonas, Brazil. Front. Med. 9:806611. doi: 10.3389/fmed.2022.806611In book: Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 2. Chapter: Python Data Analysis Techniques in Administrative Information Integration Management System April 2023 DOI: 10.1007/978-981-99-1157-8_35