MOTIVATION: Wikipedia is a vital open educational resource in computational biology; however, a significant knowledge gap exists between English and non-English Wikipedias. Reducing this knowledge gap via intensive editing events, or "editathons," would be beneficial in reducing language barriers that disadvantage learners whose native language is not English. Results: We present a framework to guide educators in organizing editathons for learners to improve and create relevant Wikipedia articles. As a case study, we present the results of an editathon held at the 2024 ISCB Latin America conference, in which ten new articles were created for the Spanish-language edition of Wikipedia. We also present a web tool, "compbio-on-wiki," which identifies relevant English Wikipedia articles missing in other languages. We demonstrate the value of editathons to expand the accessibility and visibility of computational biology content in multiple languages. AVAILABILITY AND IMPLEMENTATION: Source code for the compbio-on-wiki Toolforge site is available at: https://github.com/lubianat/compbio-on-wiki.

MOTIVATION: Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large 'knowledge gap' between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology. RESULTS: Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages. AVAILABILITY AND IMPLEMENTATION: Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish.

Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case approximation ratio or runtime bound. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that a data-driven approach to parameter tuning can lead to significant improvements in performance. This approach uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set. We provide techniques for deriving generalization guarantees that bound the difference between the algorithm’s average performance over the training set and its expected performance on the unknown distribution. Our results apply no matter how the parameters are tuned, be it via an automated or manual approach. The challenge is that for many types of algorithms, performance is a volatile function of the parameters: slightly perturbing the parameters can cause a large change in behavior. Prior research [e.g., 12 , 16 , 20 , 62 ] has proved generalization bounds by employing case-by-case analyses of greedy algorithms, clustering algorithms, integer programming algorithms, and selling mechanisms. We streamline these analyses with a general theorem that applies whenever an algorithm’s performance is a piecewise-constant, piecewise-linear, or—more generally— piecewise-structured function of its parameters. Our results, which are tight up to logarithmic factors in the worst case, also imply novel bounds for configuring dynamic programming algorithms from computational biology.

Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case guarantee. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that data-driven algorithm design can lead to significant improvements in performance. This approach uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set.

MOTIVATION: Sequence alignment is a central operation in bioinformatics pipeline and, despite many improvements, remains a computationally challenging problem. Locality-sensitive hashing (LSH) is one method used to estimate the likelihood of two sequences to have a proper alignment. Using an LSH, it is possible to separate, with high probability and relatively low computation, the pairs of sequences that do not have high-quality alignment from those that may. Therefore, an LSH reduces the overall computational requirement while not introducing many false negatives (i.e. omitting to report a valid alignment). However, current LSH methods treat sequences as a bag of k-mers and do not take into account the relative ordering of k-mers in sequences. In addition, due to the lack of a practical LSH method for edit distance, in practice, LSH methods for Jaccard similarity or Hamming similarity are used as a proxy. RESULTS: We present an LSH method, called Order Min Hash (OMH), for the edit distance. This method is a refinement of the minHash LSH used to approximate the Jaccard similarity, in that OMH is sensitive not only to the k-mer contents of the sequences but also to the relative order of the k-mers in the sequences. We present theoretical guarantees of the OMH as a gapped LSH. AVAILABILITY AND IMPLEMENTATION: The code to generate the results is available at http://github.com/Kingsford-Group/omhismb2019. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density and thereby making existing and future bioinformatics tools even more efficient. Results: From the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare k-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the three type of schemes.

This article describes the motivation, origin and evolution of the student symposia series organised by the ISCB Student Council. The meeting series started thirteen years ago in Madrid and has spread to four continents. The article concludes with the highlights of the most recent edition of annual Student Council Symposium held in conjunction with the 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology, in Prague, in July 2017.

Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one's field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one's research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.

While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for the alignment scoring function (such as the choice of gap penalties and substitution scores), most users rely on the single default parameter setting provided by the aligner. A different parameter setting, however, might yield a much higher-quality alignment for the specific set of input sequences. The problem of picking a good choice of parameter values for specific input sequences is called parameter advising. A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that provides an estimate of the accuracy of the alignment computed by the aligner using a parameter choice. The parameter advisor picks the parameter choice from the set whose resulting alignment has highest estimated accuracy. In this paper, we consider for the first time the problem of learning the optimal set of parameter choices for a parameter advisor that uses a given accuracy estimator. The optimal set is one that maximizes the expected true accuracy of the resulting parameter advisor, averaged over a collection of training data. While we prove that learning an optimal set for an advisor is NP-complete, we show there is a natural approximation algorithm for this problem, and prove a tight bound on its approximation ratio. Experiments with an implementation of this approximation algorithm on biological benchmarks, using various accuracy estimators from the literature, show it finds sets for advisors that are surprisingly close to optimal. Furthermore, the resulting parameter advisors are significantly more accurate in practice than simply aligning with a single default parameter choice.

Table of contents A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for its alignment scoring function (i.e. choice of gap penalties and substitution scores), most users rely on the single default parameter setting. A different parameter setting, however, might yield a much higher-quality alignment for a specific set of input sequences. The problem of picking a good choice of parameter values for a given set of input sequences is called parameter advising. A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that estimates the accuracy of a computed alignment; the parameter advisor then picks the parameter choice from the set whose resulting alignment has highest estimated accuracy. Our estimator Facet ( F eature-based Ac curacy E s t imator) is a linear combination of real-valued feature functions of an alignment. We assume the feature functions are given as well as the universe of parameter choices from which the advisor's set is drawn. For this scenario we define the problem of learning an optimal advisor by finding the best possible parameter set for a collection of training data of reference alignments. Learning optimal advisor sets is NP-complete [ 1 ]. For the advisor sets problem, we develop a greedy ℓ k -approximation algorithm that finds near optimal sets of size at most k given an optimal solution of size ℓ

This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) conference in Boston, USA, on July 11th, 2014.

Structure-based RNA multiple alignment is particularly challenging because covarying mutations make sequence information alone insufficient. Existing tools for RNA multiple alignment first generate pairwise RNA structure alignments and then build the multiple alignment using only sequence information. Here we present PMFastR, an algorithm which iteratively uses a sequence-structure alignment procedure to build a structure-based RNA multiple alignment from one sequence with known structure and a database of sequences from the same family. PMFastR also has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. The algorithm also provides a method to utilize a multicore environment. We present results on benchmark data sets from BRAliBase, which shows PMFastR performs comparably to other state-of-the-art programs. Finally, we regenerate 607 Rfam seed alignments and show that our automated process creates multiple alignments similar to the manually curated Rfam seed alignments. Thus, the techniques presented in this paper allow for the generation of multiple alignments using sequence-structure guidance, while limiting memory consumption. As a result, multiple alignments of long RNA sequences, such as 16S and 23S rRNAs, can easily be generated locally on a personal computer. The software and supplementary data are available at http://genome.ucf.edu/PMFastR.