MARCH 2018 VOL 4 ISSUE 3
“Science is organized knowledge. Wisdom is organized life.� -
Immanuel Kant
Results: India's Top Bioinformaticians award 2018
Simulated sequence alignment software: An alternative to MSA benchmarks
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Contents
March 2018
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Topics Editorial....
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03 Bioinformatics News Results: India's Top Bioinformaticians award 2018 07
04 Sequence Analysis Simulated sequence alignment software: An alternative to MSA benchmarks 08
FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR NABAJIT DAS
REPRINTS AND PERMISSIONS You must have permission before reproducing any material from Bioinformatics Review. Send E-mail requests to info@bioinformaticsreview.com. Please include contact detail in your message. BACK ISSUE Bioinformatics Review back issues can be downloaded in digital format from bioinformaticsreview.com at $5 per issue. Back issue in print format cost $2 for India delivery and $11 for international delivery, subject to availability. Pre-payment is required CONTACT PHONE +91. 991 1942-428 / 852 7572-667 MAIL Editorial: 101 FF Main Road Zakir Nagar, Okhla New Delhi IN 110025 STAFF ADDRESS To contact any of the Bioinformatics Review staff member, simply format the address as firstname@bioinformaticsreview.com PUBLICATION INFORMATION Volume 1, Number 1, Bioinformatics Reviewâ„¢ is published monthly for one year (12 issues) by Social and Educational Welfare Association (SEWA)trust (Registered under Trust Act 1882). Copyright 2015 Sewa Trust. All rights reserved. Bioinformatics Review is a trademark of Idea Quotient Labs and used under license by SEWA trust. Published in India
Bioinformatics- A broad future ahead: Editorial
EDITORIAL
It has been a wonderful time since BiR came into existence. As we enter a new year, BiR tries to look forward towards the development and wonderful achievements and providing the best knowledge regarding bioinformatics. In the past two years, BiR has hit a long road from a few readers to several thousand.
Muniba Faiza
Founding Editor
Every complimentary and appreciation mail we get feels like an achievement for us. Bioinformatics has got a great future ahead of it with a better understanding and precise methodologies for both dry and the wet lab experimentations. In the last two years, BiR has advanced in many aspects. We have come up with an android app which helps our readers to stay connected with the latest updates, our articles have started to appear in Google Scholar, we get a lot of cherishing emails, and collaboration proposals. BiR is trying to broaden the horizons by covering different domains of bioinformatics. Since bioinformatics is multidisciplinary, to date, the team of BiR has tried to go through almost every aspect of it including big data, sequence analysis, structural bioinformatics, data mining, tools, software, biostatistics, and so on. This year BiR is more focused to provide a rich content to our readers and help to understand the concepts of bioinformatics more easily. The team of BiR is trying to reach to the students to encourage them for their career in bioinformatics and to the researchers currently working in the same area. The last internship at BiR was a great success and we got an amazing response from our interns. We are looking forward to presenting our work at school and college level to introduce this to the young minds who are more fascinated by the technology. We have such a long road to drive on which is not possible without the support of our readers, subscribers, and contributors. We are thankful to our readers wholeheartedly for their support and suggestions and wish them a very happy and prosperous new year
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with new hopes and great achievements. We would like to hear your thoughts and feedback about BiR, and what other kinds of articles you would like to read.
EDITORIAL
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BIOINFORMATICS NEWS
Results: India's Top Bioinformaticians award 2018 Image Credit: Stock photos
“India's Top Bioinformaticians award 2018.� ast month we had announced BiR Top 5 Bioinformaticians in India Award. Our announcement was well received with Bioinformatics Review community. We got more nomination entries than expected. After a thorough analysis of all entries, profiles, and names we received, we are glad to present the result this endeavor.
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Click here for the List of Top Bioinformaticians in India
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SEQUENCE ANALYSIS
Simulated sequence alignment software: An alternative to MSA benchmarks Image Credit: Google images
“These sequence simulator software has been applied in different studies on evaluation of MSA programs and found quite helpful in providing sets of reference alignments [15,16]. Apart from the advantages of using the sequence simulators as the reference alignments for comparing the performance and quality of MSA programs, it has some pitfalls also: using the simulation settings more close to an MSA program may provide it an excessive advantage [15].� n our previous article, we discussed different multiple sequence alignment (MSA) benchmarks to compare and assess the available MSA programs. However, since the last decade, several sequence simulation software have been introduced and are gaining more interest. In this article, we will be discussing various sequence simulating software being used as alternatives to MSA benchmarks.
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The basic motivation of using simulated sequence alignments as the reference sets for assessing the quality of MSAs generated by MSA programs is that they help in creating accurate alignments as their evolutionary history is known and can be easily generated by inserting, deleting, or substituting the residues, by changing the sequence length and number of sequences, which is not the case in MSA benchmark alignments. MSA benchmarks are semi-automatedly generated and generate a specific set of reference alignments such as
different sets of BAliBase [1-3]. One of the benchmark databases has been generated using a sequence simulator, i.e., ROSE [4]. There is various sequence simulating software available which have been used in assessing the performance of different MSA programs. ROSE software can be used for DNA, RNA, and protein sequences incorporating indels in accordance with the evolutionary distance guided by an evolutionary tree [4]. SIMPROT is one of the most widely used Bioinformatics Review | 8
sequence simulators, which can be applied to protein sequences only [5]. Indel-Seq-Gen 2.1.03 creates highly divergent DNA sequences and protein families and incorporates various indel models [6]. MySSP also incorporates different models of evolution such as Jukes-Cantor [7], Hasegawa-Kishino-Yano [8], and Kimura-two parameter [9]. Another software DAWG is used to simulate evolution by incorporating the general time reversible model, gamma, and invariant rate heterogeneity [10]. Recently, some other software has been introduced such as NetRecodon [11], PhyloSim [12], ProteinEvolver [13], and ∏-BUSS [14].
These sequence simulator software has been applied in different studies on evaluation of MSA programs and found quite helpful in providing sets of reference alignments [15,16]. Apart from the advantages of using the sequence simulators as the reference alignments for comparing the performance and quality of MSA programs, it has some pitfalls also: using the simulation settings more close to an MSA program may provide it an excessive advantage [15]. Another drawback is that the simulated sequences cannot explain the evolutionary aspects because of the dependency of all observations
https://books.google.com/books?hl=e n&lr=&id=FDHLBAAAQBAJ&oi=fnd&pg =PA21&dq=Evolution+of+protein+mol ecules+jukes+cantor&ots=blcsZIY2gB& sig=TuCtkRMRPIk0aXXOkOkGAvegaM 0#v=onepage&q=Evolution of protein molecules jukes cantor&f=false
obtained from the true alignments on assumptions of the model used to reconstruct the simulated alignments. References 1.
2.
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4.
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Thompson, J., Plewniak, F., & Poch, O. (1999). BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics, 15(1), 87–88. https://doi.org/10.1093/bioinformatic s/15.1.87 Bahr, A., Thompson, J. D., Thierry, J.-C., & Poch, O. (2001). BAliBASE (Benchmark Alignment dataBASE): enhancements for repeats, transmembrane sequences and circular permutations. Nucleic Acids Research, 29(1), 323–326. https://doi.org/10.1093/nar/29.1.323 Thompson, J. D., Koehl, P., Ripp, R., & Poch, O. (2005). BAliBASE 3.0: Latest developments of the multiple sequence alignment benchmark. Proteins: Structure, Function, and Bioinformatics, 61(1), 127–136. https://doi.org/10.1002/prot.20527 Stoye, J., Evers, D., & Meyer, F. (1998). Rose: generating sequence families. Bioinformatics, 14(2), 157–163. https://doi.org/10.1093/bioinformatic s/14.2.157 Pang, A., Smith, A. D., Nuin, P. A., & Tillier, E. R. (2005). SIMPROT: using an empirically determined indel distribution in simulations of protein evolution. BMC bioinformatics, 6(1), 236.
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Strope, C. L., Abel, K., Scott, S. D., & Moriyama, E. N. (2009). Biological sequence simulation for testing complex evolutionary hypotheses: indel-Seq-Gen version 2.0. Molecular biology and evolution, 26(11), 25812593.
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Jukes, T. H., & Cantor, C. R. (1969). Evolution of Protein Molecules. Mammalian Protein Metabolism, 3, 21–132. Retrieved from
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Hasegawa, M., Kishino, H., & Yano, T. aki. (1985). Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution, 22(2), 160–174. https://doi.org/10.1007/BF02101694
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Kimura, M. (1980). A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution, 16(2), 111–120. https://doi.org/10.1007/BF01731581
10. Cartwright, R. A. (2005). DNA assembly with gaps (Dawg): simulating sequence evolution. Bioinformatics, 21(Suppl 3), iii31-iii38. https://doi.org/10.1093/bioinformatic s/bti1200 11. Arenas, M. (2012). Simulation of molecular data under diverse evolutionary scenarios. PLoS Computational Biology, 8(5). https://doi.org/10.1371/journal.pcbi.1 002495 12. Sipos, B., Massingham, T., Jordan, G., & Goldman, N. (2011). PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment. BMC Bioinformatics, 12(1), 104+. https://doi.org/10.1186/1471-210512-104 13. Arenas, M., Dos Santos, H. G., Posada, D., & Bastolla, U. (2013). Protein evolution along phylogenetic histories under structurally constrained substitution models. Bioinformatics, 29(23), 3020–3028. https://doi.org/10.1093/bioinformatic s/btt530 14. Bielejec, F., Lemey, P., Carvalho, L., Baele, G., Rambaut, A., & Suchard, M. A. (2014). πBUSS: a parallel BEAST/BEAGLE utility for sequence
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simulation under complex evolutionary scenarios. BMC Bioinformatics, 15(1), 133. https://doi.org/10.1186/1471-210515-133 15. Nuin, P. A., Wang, Z., & Tillier, E. R. (2006). The accuracy of several multiple sequence alignment programs for proteins. BMC bioinformatics, 7(1), 471. 16. Pervez, M. T., Babar, M. E., Nadeem, A., Aslam, M., Awan, A. R., Aslam, N., ... & Shoaib, M. (2014). Evaluating the accuracy and efficiency of multiple sequence alignment methods. Evolutionary Bioinformatics, 10, EBO-S19199. 17. Iantorno, S., Gori, K., Goldman, N., Gil, M., & Dessimoz, C. (2014). Who watches the watchmen? An appraisal of benchmarks for multiple sequence alignment. In Multiple Sequence Alignment Methods (pp. 59-73). Humana Press, Totowa, NJ.
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