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Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”. Los Grupos de Estudiantes Regionales (RSG) del Consejo de Estudiantes de la Sociedad Internacional de Biología Computacional (ISCB-SC) han sido fundamentales para conectar a los biólogos computacionales a nivel mundial y crear más conciencia sobre la educación bioinformática. Este artículo destaca las iniciativas llevadas a cabo por los RSG tanto a nivel nacional como internacional para fortalecer el presente y el futuro de la comunidad bioinformática. Además, discutimos las direcciones futuras que tomará la organización y los desafíos para avanzar más en la misión principal de ISCB-SC: “Nutrir a la nueva generación de biólogos computacionales”. Iowa State University, Estados Unidos European Molecular Biology Laboratory, Alemania Uppsala University, Suecia University of Padova, Italia Institute of Tropical Medicine, Bélgica University of Antwerp, Bélgica University of KwaZulu Natal, Sudáfrica Universidade Federal de Minas Gerais, Brazil Universitat Autònoma de Barcelona, España European Bioinformatics Institute, Reino Unido Universidad de Antioquía, Colombia Universidad CES, Colombia University of South Carolina, Estados Unidos Mohammed V University in Rabat, Marruecos University of Cape Town, Sudáfrica Jawaharlal Nehru University, India Universidad Andres, Chile Universidad de Talca, Chile Université libre de Bruxelles-Vrije Universiteit Brusse, Bélgica Technical University of Denmark, Dinamarca University of Copenhagen, Dinamarca University of Luxembourg, Luxemburgo University of Lausanne, Suiza APIN Public Health Initiatives, Nigeria Institute of Ecology, Argentina Lund University, Suecia Universidad Nacional, Costa Rica Max Planck Institute for Biophysical Chemistry, Alemania Rhodes University, Sudáfrica Dokuz Eylül University, Turquía Hacettepe University, Turquía University Medical Center Rostock, Alemania University Heidelberg, Alemania La Jolla Institute for Immunology, Estados Unidos Katholieke Universiteit Leuven, Belgica Roche, Alemania Carnegie Mellon University, Estados Unidos University of South Wales, Reino Unido Canterbury Christ Church University, Reino Unido Escuela de Ciencias Biológicas

Additional file 7: Figure S4. Pol II, H3K36me2 and H3K36me3 densities at 60 h post-PH for the Cebpa gene. (A) Genomic view of the Cebpa gene. Densities of the central 50 bp of paired-end reads for Pol II (pink), H3K36me3 (dark blue) and H3K36me2 (light blue) ChIP fragments are shown for 60 h post-PH. Similarly, input fragments at 60 h post-PH are shown.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This “spike” chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes.

Additional file 5: Figure S2. (A) Distribution of silhouette scores as a result of the PAM clustering of the varying gene expression Set 3 into 2–13 groups (k). The clustering into 7 groups was retained for our analyses in the main text (Fig. 2). In this Set 3.1 to 3.7 clustering, set 3.7 contained a large proportion of cell-cycle genes. The set in the k = 2–6 and 8–12 PAM clusterings most like Set 3.7 is labeled “3.7-like” in each case. For each clustering, the number of genes (nj) per group (j) is indicated to the right together with the average silhouette score (aveiϵCj Si). To the left of each clustering, the number of Set 3.7 genes from k = 7 in each group is indicated. Across clusterings, the highest average silhouette score is found for the most 3.7-like sets of genes. (B) Summary of the results of the functional enrichment analysis on the RNA-seq Set 1, Set 2 and Set 3 results. The GO terms displaying an enrichment p value lower than 10E − 10 were kept for analysis with the REVIGO tool. REVIGO aggregates synonymous GO terms and displays the aggregated terms as circles where the distance among circles indicates their similarity within the GO structure and their color indicates the associated p value, with blue signifying the lowest p values. Selected GO terms with highest p values are shown with the circle aggregates. Below, the highlighted GO terms have been listed with their associated p values with a log10 scale. (C) Gene-expression patterns post-PH in the KEGG cell-cycle pathway. The gene nodes in the KEGG cell-cycle pathway were colored using the “pathview” R package. Set 1 genes are colored gray, Set 2 genes are colored yellow, and Set 3 genes are displayed as a heat map that shows the relative transcript abundance between 0 h and 4 weeks post-PH from Fig. 2a. For the twenty-seven KEGG cell-cycle pathway nodes shared by multiple genes only the pattern for a representative gene (identified in Additional file 2: Figure S2d). (D) Selection of the representative gene within shared KEGG cell-cycle nodes. The names of the shared KEGG cell-cycle nodes are indicated with the node-associated genes indicated with their respective RNA-Seq set. Within nodes, genes with differential expression post-PH were favored. Otherwise genes with the highest gene expression within a node were selected. The final selections are highlighted in green.

Additional file 5: Figure S2. (A) Distribution of silhouette scores as a result of the PAM clustering of the varying gene expression Set 3 into 2–13 groups (k). The clustering into 7 groups was retained for our analyses in the main text (Fig. 2). In this Set 3.1 to 3.7 clustering, set 3.7 contained a large proportion of cell-cycle genes. The set in the k = 2–6 and 8–12 PAM clusterings most like Set 3.7 is labeled “3.7-like” in each case. For each clustering, the number of genes (nj) per group (j) is indicated to the right together with the average silhouette score (aveiϵCj Si). To the left of each clustering, the number of Set 3.7 genes from k = 7 in each group is indicated. Across clusterings, the highest average silhouette score is found for the most 3.7-like sets of genes. (B) Summary of the results of the functional enrichment analysis on the RNA-seq Set 1, Set 2 and Set 3 results. The GO terms displaying an enrichment p value lower than 10E − 10 were kept for analysis with the REVIGO tool. REVIGO aggregates synonymous GO terms and displays the aggregated terms as circles where the distance among circles indicates their similarity within the GO structure and their color indicates the associated p value, with blue signifying the lowest p values. Selected GO terms with highest p values are shown with the circle aggregates. Below, the highlighted GO terms have been listed with their associated p values with a log10 scale. (C) Gene-expression patterns post-PH in the KEGG cell-cycle pathway. The gene nodes in the KEGG cell-cycle pathway were colored using the “pathview” R package. Set 1 genes are colored gray, Set 2 genes are colored yellow, and Set 3 genes are displayed as a heat map that shows the relative transcript abundance between 0 h and 4 weeks post-PH from Fig. 2a. For the twenty-seven KEGG cell-cycle pathway nodes shared by multiple genes only the pattern for a representative gene (identified in Additional file 2: Figure S2d). (D) Selection of the representative gene within shared KEGG cell-cycle nodes. The names of the shared KEGG cell-cycle nodes are indicated with the node-associated genes indicated with their respective RNA-Seq set. Within nodes, genes with differential expression post-PH were favored. Otherwise genes with the highest gene expression within a node were selected. The final selections are highlighted in green.

Mouse liver regeneration after partial hepatectomy involves cells in the remaining tissue synchronously entering the cell division cycle. We have used this system and H3K4me3, Pol II and Pol III profiling to characterize adaptations in Pol III transcription. Our results broadly define a class of genes close to H3K4me3 and Pol II peaks, whose Pol III occupancy is high and stable, and another class, distant from Pol II peaks, whose Pol III occupancy strongly increases after partial hepatectomy. Pol III regulation in the liver thus entails both highly expressed housekeeping genes and genes whose expression can adapt to increased demand.