Journal of Bioinformatics and Computational Biology Vol. 12, No. 3 (2014) 1401001 (4 pages) # .c Imperial College Press DOI: 10.1142/S021972001401001X
JBCB, the ¯rst decade
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
Published 12 June 2014
We are pleasantly surprised by the submission of an unsolicited article by Eisenhaber and Sherman.5 We are reminded that a decade has passed since the publication of the ¯rst issue of JBCB in April 2003. We are grateful for the informative statistics gathered and presented in the paper. Inspired by the paper, we did a little study of our own and are pleased to supply below some further statistics about papers published in JBCB in the 2003–2013 period: .
The total number of citations is 9,331 (Google Scholar, 5 May 2014). There is 1 paper with > 700 citations, 5 with > 200 citations, 5 with > 100 citations, and 225 papers with > 10 citations. . Excluding special issues, the acceptance rates have been just below 30%, though some years' dipped below 20%. For example, it was 19% for regular submissions in 2011 and 14% in 2013. . Excluding special issues, approximately 50% of regular submissions received their ¯nal decision within 1 month, and most received an initial decision within 3 months. For accepted papers, the time from submission to publication has been mostly within 1–1.5 years. Eisenhaber and Sherman5 have highlighted a list of 10 most highly cited JBCB papers. Naturally, these are the older papers in the 2003–2005 period. Looking over the papers published in JBCB, we are glad that we have covered many interesting topics and a remarkable range of approaches throughout the past decade. And there are in°uential papers on such topics and approaches every year. We mention below a small sample of papers, one for each year from 2003 to 2013: .
Xu et al.13 presented RAPTOR, which pioneered a novel linear programming approach to protein structure threading. This approach complements the traditional dynamic programming approach, as it is able to globally optimize scoring functions that use pairwise contact potential. RAPTOR was the top performer at the CASP5 evaluation and remained in the top tier in subsequent CASP evaluations, until it was replaced by its successor software RaptorX.
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JBCB, the ¯rst decade .
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J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
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Li et al.9 introduced Patternhunter II, which extended the spaced seed idea to multiple spaced seeds. This well-known method achieves Smith–Waterman level of sensitivity at BLASTN speed. The idea is now adopted in practically every e±cient homology search engine. Ding and Peng4 presented a maximum-relevance feature-selection framework that provides a more balanced coverage of the characteristics of phenotypes, leading to signi¯cantly improved class predictions from microarray data. The paper is remarkable in its sustained in°uence not only on microarray data analysis but also in a wide range of areas such as surface topography and FTIR spectroscopy. Danilova et al.3 presented the RNAKinetics server, which models RNA secondary structure by kinetic analysis of possible folding transitions at di®erent time points. The server is still fully functional today. It is outstanding in that it outputs a collection of RNA structures along with kinetic details of their folding pathways, which are not revealed by earlier RNA folding servers. Worth et al.12 covered the important topic of predicting the functional e®ect of non-synonymous single-nucleotide polymorphisms (nsSNPs). The paper presented techniques for fast sequence-structure homology recognition and for performing comparative modeling, to assess the likely impact of amino acid substitutions on structure and interactions. Chua et al.2 is one of the most heavily cited works on protein complex prediction from protein–protein interaction networks. It was one of the earliest to articulate an explicit network cleansing strategy to deal with the noisy protein–protein interaction networks. It ¯rst evaluates the reliability of each direct and indirect interaction based on a topological weight, and then it removes unreliable interactions and adds reliable indirection interactions as direct edges. Both existing clustering algorithms and a quasi-clique-merging algorithm introduced in the paper can then use this modi¯ed network to achieve better sensitivity and precision in protein complex prediction. Langmead and Jha8 presented an exact algorithm for ¯nding control policies for Boolean networks that reproduces the qualitative behavior of gene regulatory networks. The method is based on model checking. It is very fast; e.g. it identi¯es a good model of D. melanogaster embryogenesis in 5.3 s, from a space containing 6:9 1010 possible models. Miwa et al.10 applied natural language processing techniques to extract complex relationships, such as binding and regulation, between multiple proteins and genes from biomedical literature. Their event extraction system is based on learning a feature-rich classi¯er. It was one of the ¯rst systems able to process free text and identify and extract complex biological events therein, and had the best reported performance on the BioNLP'09 shared task challenge when it was published. Wang and Jiang11 worked on inferring haplotypes from genotype data in the presence of genotyping errors, mutations, and missing alleles. They formulated this as a combinatorial optimization problem and heuristically solved it by integer
1401001-2
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
JBCB, the ¯rst decade
linear programming. Their approach can identify and correct genotyping errors that cannot be detected by simply checking the Mendelian law of inheritance. . Hashmi et al.7 described a novel approach to protein docking. The paper articulated two important ideas — geometric hashing for more e±cient search and focusing on evolutionary-conserved interfaces for narrowing the large search space of con¯gurations — that would bene¯t di®erent search procedures. . Garcia-Martin et al.6 addressed a key step in RNA design: Given a target RNA secondary structure, produce a RNA sequence that folds into that structure. This RNA inverse folding problem was solved here using a constraint programming approach. The software, RNAiFold, supports a wide range of design constraints and de¯nes the current state of art in this ¯eld. As we enter the second decade of the post-human genome era and the second decade of JBCB, advances in genome sequencing, microscopy, high-throughput analytical techniques for DNA, RNA, and proteins, and a host of other new experimental technologies have continued unabated. This °ow of information enables scientists to model and understand biological systems and human diseases in novel ways. For example, the rapid growth of experimental information brought \big data" to the biological and clinical sciences, and enabled advances in mining biomedical literature and patient data. Similarly, modeling has already yielded insight into the spread of infection disease. We expect a greater emphasis on panomics analysis and causal modeling of molecular and clinical data. We also expect a greater emphasis on showing that computational methods actually provide new biological insights, including notably, in systems biology and for medical interventions and human wellbeing, as well as continuing advances in traditional but ongoing challenges, such as predicting the functional attributes of macromolecules. Last, but not least, we take this opportunity to thank all our authors for contributing such a wide range of ideas, which have helped advance our ¯eld and to keeping it vibrant. We thank also our editorial board members and reviewers for generously contributing their time to ensure the high quality of JBCB. John Wooley Ming Li Limsoon Wong (Managing Editors) References 1. Alvarez MA, Yan C, A graph-based semantic similarity measure for the Gene Ontology, J. Bioinform. Comput. Biol. 9(6):681–695, 2011. 2. Chua HN, Ning K, Sung WK, Leong HW, Wong L, Using indirect protein–protein interactions for protein complex prediction, J. Bioinform. Comput. Biol. 6(3):435–466, 2008.
1401001-3
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
JBCB, the ¯rst decade
3. Danilova LV, Pervouchine DD, Favorov AV, Mironov AA, RNAKinetics: A web server that models secondary structure kinetics of an elongating RNA, J. Bioinform. Comput. Biol. 4(2):589–596, 2006. 4. Ding C, Peng H, Minimum redundancy feature selection from microarray gene expression data, J. Bioinform. Comput. Biol. 3(2):185–205, 2005. 5. Eisenhaber F, Sherman W, 10 years for the Journal of Bioinformatics and Computational Biology (2003–2013) a retrospective, J. Bioinform. Comput. Biol. 12(3):1471001, 2014. 6. Garcia-Martin JA, Clote P, Dotu I, RNAiFold: A constraint programming algorithm for RNA inverse folding and molecular design, J. Bioinform. Comput. Biol. 11(2):1350001, 2013. 7. Hashmi I, Akbal-Delibas B, Haspel N, Shehu A, Guiding protein docking with geometric and evolutionary information, J. Bioinform. Comput. Biol. 10(3):1242008, 2012. 8. Langmead CJ, Jha SK, Symbolic approaches for ¯nding control strategies in Boolean networks, J. Bioinform. Comput. Biol. 7(2):323–338, 2009. 9. Li M, Ma B, Kisman D, Tromp J, Patternhunter II: Highly sensitive and fast homology search, J. Bioinform. Comput. Biol. 2(3):417–439, 2004. 10. Miwa M, Saetre R, Kim JD, Tsujii J, Event extraction with complex event classi¯cation using rich features, J. Bioinform. Comput. Biol. 8(1):131–146, 2010. 11. Wang WB, Jiang T, Inferring haplotypes from genotypes on a pedigree with mutations, genotyping errors, and missing alleles, J. Bioinform. Comput. Biol. 9(2):339–365, 2011. 12. Worth CL, Bickerton GRJ, Schreyer A, Forman JR, Cheng TMK, Lee S, Gong S, Burke DF, Blundell TL, A structural bioinformatics approach to the analysis of nonsynonymous single nucleotide polymorphisms (nsSNPs) and their relation to disease, J. Bioinform. Comput. Biol. 5(6):1297–1318, 2007. 13. Xu J, Li M, Kim D, Xu Y, RAPTOR: Optimal protein threading by linear programming, J. Bioinform. Comput. Biol. 1(1):95–117, 2003.
1401001-4
JBCB, the ¯rst decade
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
Published 12 June 2014
We are pleasantly surprised by the submission of an unsolicited article by Eisenhaber and Sherman.5 We are reminded that a decade has passed since the publication of the ¯rst issue of JBCB in April 2003. We are grateful for the informative statistics gathered and presented in the paper. Inspired by the paper, we did a little study of our own and are pleased to supply below some further statistics about papers published in JBCB in the 2003–2013 period: .
The total number of citations is 9,331 (Google Scholar, 5 May 2014). There is 1 paper with > 700 citations, 5 with > 200 citations, 5 with > 100 citations, and 225 papers with > 10 citations. . Excluding special issues, the acceptance rates have been just below 30%, though some years' dipped below 20%. For example, it was 19% for regular submissions in 2011 and 14% in 2013. . Excluding special issues, approximately 50% of regular submissions received their ¯nal decision within 1 month, and most received an initial decision within 3 months. For accepted papers, the time from submission to publication has been mostly within 1–1.5 years. Eisenhaber and Sherman5 have highlighted a list of 10 most highly cited JBCB papers. Naturally, these are the older papers in the 2003–2005 period. Looking over the papers published in JBCB, we are glad that we have covered many interesting topics and a remarkable range of approaches throughout the past decade. And there are in°uential papers on such topics and approaches every year. We mention below a small sample of papers, one for each year from 2003 to 2013: .
Xu et al.13 presented RAPTOR, which pioneered a novel linear programming approach to protein structure threading. This approach complements the traditional dynamic programming approach, as it is able to globally optimize scoring functions that use pairwise contact potential. RAPTOR was the top performer at the CASP5 evaluation and remained in the top tier in subsequent CASP evaluations, until it was replaced by its successor software RaptorX.
1401001-1
JBCB, the ¯rst decade .
.
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
.
.
.
.
.
.
Li et al.9 introduced Patternhunter II, which extended the spaced seed idea to multiple spaced seeds. This well-known method achieves Smith–Waterman level of sensitivity at BLASTN speed. The idea is now adopted in practically every e±cient homology search engine. Ding and Peng4 presented a maximum-relevance feature-selection framework that provides a more balanced coverage of the characteristics of phenotypes, leading to signi¯cantly improved class predictions from microarray data. The paper is remarkable in its sustained in°uence not only on microarray data analysis but also in a wide range of areas such as surface topography and FTIR spectroscopy. Danilova et al.3 presented the RNAKinetics server, which models RNA secondary structure by kinetic analysis of possible folding transitions at di®erent time points. The server is still fully functional today. It is outstanding in that it outputs a collection of RNA structures along with kinetic details of their folding pathways, which are not revealed by earlier RNA folding servers. Worth et al.12 covered the important topic of predicting the functional e®ect of non-synonymous single-nucleotide polymorphisms (nsSNPs). The paper presented techniques for fast sequence-structure homology recognition and for performing comparative modeling, to assess the likely impact of amino acid substitutions on structure and interactions. Chua et al.2 is one of the most heavily cited works on protein complex prediction from protein–protein interaction networks. It was one of the earliest to articulate an explicit network cleansing strategy to deal with the noisy protein–protein interaction networks. It ¯rst evaluates the reliability of each direct and indirect interaction based on a topological weight, and then it removes unreliable interactions and adds reliable indirection interactions as direct edges. Both existing clustering algorithms and a quasi-clique-merging algorithm introduced in the paper can then use this modi¯ed network to achieve better sensitivity and precision in protein complex prediction. Langmead and Jha8 presented an exact algorithm for ¯nding control policies for Boolean networks that reproduces the qualitative behavior of gene regulatory networks. The method is based on model checking. It is very fast; e.g. it identi¯es a good model of D. melanogaster embryogenesis in 5.3 s, from a space containing 6:9 1010 possible models. Miwa et al.10 applied natural language processing techniques to extract complex relationships, such as binding and regulation, between multiple proteins and genes from biomedical literature. Their event extraction system is based on learning a feature-rich classi¯er. It was one of the ¯rst systems able to process free text and identify and extract complex biological events therein, and had the best reported performance on the BioNLP'09 shared task challenge when it was published. Wang and Jiang11 worked on inferring haplotypes from genotype data in the presence of genotyping errors, mutations, and missing alleles. They formulated this as a combinatorial optimization problem and heuristically solved it by integer
1401001-2
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
JBCB, the ¯rst decade
linear programming. Their approach can identify and correct genotyping errors that cannot be detected by simply checking the Mendelian law of inheritance. . Hashmi et al.7 described a novel approach to protein docking. The paper articulated two important ideas — geometric hashing for more e±cient search and focusing on evolutionary-conserved interfaces for narrowing the large search space of con¯gurations — that would bene¯t di®erent search procedures. . Garcia-Martin et al.6 addressed a key step in RNA design: Given a target RNA secondary structure, produce a RNA sequence that folds into that structure. This RNA inverse folding problem was solved here using a constraint programming approach. The software, RNAiFold, supports a wide range of design constraints and de¯nes the current state of art in this ¯eld. As we enter the second decade of the post-human genome era and the second decade of JBCB, advances in genome sequencing, microscopy, high-throughput analytical techniques for DNA, RNA, and proteins, and a host of other new experimental technologies have continued unabated. This °ow of information enables scientists to model and understand biological systems and human diseases in novel ways. For example, the rapid growth of experimental information brought \big data" to the biological and clinical sciences, and enabled advances in mining biomedical literature and patient data. Similarly, modeling has already yielded insight into the spread of infection disease. We expect a greater emphasis on panomics analysis and causal modeling of molecular and clinical data. We also expect a greater emphasis on showing that computational methods actually provide new biological insights, including notably, in systems biology and for medical interventions and human wellbeing, as well as continuing advances in traditional but ongoing challenges, such as predicting the functional attributes of macromolecules. Last, but not least, we take this opportunity to thank all our authors for contributing such a wide range of ideas, which have helped advance our ¯eld and to keeping it vibrant. We thank also our editorial board members and reviewers for generously contributing their time to ensure the high quality of JBCB. John Wooley Ming Li Limsoon Wong (Managing Editors) References 1. Alvarez MA, Yan C, A graph-based semantic similarity measure for the Gene Ontology, J. Bioinform. Comput. Biol. 9(6):681–695, 2011. 2. Chua HN, Ning K, Sung WK, Leong HW, Wong L, Using indirect protein–protein interactions for protein complex prediction, J. Bioinform. Comput. Biol. 6(3):435–466, 2008.
1401001-3
J. Bioinform. Comput. Biol. 2014.12. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/03/15. For personal use only.
JBCB, the ¯rst decade
3. Danilova LV, Pervouchine DD, Favorov AV, Mironov AA, RNAKinetics: A web server that models secondary structure kinetics of an elongating RNA, J. Bioinform. Comput. Biol. 4(2):589–596, 2006. 4. Ding C, Peng H, Minimum redundancy feature selection from microarray gene expression data, J. Bioinform. Comput. Biol. 3(2):185–205, 2005. 5. Eisenhaber F, Sherman W, 10 years for the Journal of Bioinformatics and Computational Biology (2003–2013) a retrospective, J. Bioinform. Comput. Biol. 12(3):1471001, 2014. 6. Garcia-Martin JA, Clote P, Dotu I, RNAiFold: A constraint programming algorithm for RNA inverse folding and molecular design, J. Bioinform. Comput. Biol. 11(2):1350001, 2013. 7. Hashmi I, Akbal-Delibas B, Haspel N, Shehu A, Guiding protein docking with geometric and evolutionary information, J. Bioinform. Comput. Biol. 10(3):1242008, 2012. 8. Langmead CJ, Jha SK, Symbolic approaches for ¯nding control strategies in Boolean networks, J. Bioinform. Comput. Biol. 7(2):323–338, 2009. 9. Li M, Ma B, Kisman D, Tromp J, Patternhunter II: Highly sensitive and fast homology search, J. Bioinform. Comput. Biol. 2(3):417–439, 2004. 10. Miwa M, Saetre R, Kim JD, Tsujii J, Event extraction with complex event classi¯cation using rich features, J. Bioinform. Comput. Biol. 8(1):131–146, 2010. 11. Wang WB, Jiang T, Inferring haplotypes from genotypes on a pedigree with mutations, genotyping errors, and missing alleles, J. Bioinform. Comput. Biol. 9(2):339–365, 2011. 12. Worth CL, Bickerton GRJ, Schreyer A, Forman JR, Cheng TMK, Lee S, Gong S, Burke DF, Blundell TL, A structural bioinformatics approach to the analysis of nonsynonymous single nucleotide polymorphisms (nsSNPs) and their relation to disease, J. Bioinform. Comput. Biol. 5(6):1297–1318, 2007. 13. Xu J, Li M, Kim D, Xu Y, RAPTOR: Optimal protein threading by linear programming, J. Bioinform. Comput. Biol. 1(1):95–117, 2003.
1401001-4
