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We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two step algorithm: first convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data.We first confirm the presence of overdispersion in several real Hi-C data sets, and we show that the overdispersion arises even in simulated data sets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms: three MDS-based methods (ShRec3D, ChromSDE, and Pastis-MDS) and a statistical methods based on a Poisson model of the data (Pastis-PM). We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions.A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD licenseSupplementary information is available at https://nellev.github.io/pastisnb/

Significance Understanding the molecular response of plants to drought is critical to efforts to improve agricultural yields under increasingly frequent droughts. We grew 2 cultivars of the naturally drought-tolerant food crop sorghum in the field under drought stress. We sequenced the mRNA from weekly samples of these plants, resulting in a molecular profile of drought response over the growing season. We find molecular differences in the 2 cultivars that help explain their differing tolerances to drought and evidence of a disruption in the plant’s symbiosis with arbuscular mycorrhizal fungi. Our findings are of practical importance for agricultural breeding programs, while the resulting data are a resource for the plant and microbial communities for studying the dynamics of drought response., Drought is the most important environmental stress limiting crop yields. The C4 cereal sorghum [Sorghum bicolor (L.) Moench] is a critical food, forage, and emerging bioenergy crop that is notably drought-tolerant. We conducted a large-scale field experiment, imposing preflowering and postflowering drought stress on 2 genotypes of sorghum across a tightly resolved time series, from plant emergence to postanthesis, resulting in a dataset of nearly 400 transcriptomes. We observed a fast and global transcriptomic response in leaf and root tissues with clear temporal patterns, including modulation of well-known drought pathways. We also identified genotypic differences in core photosynthesis and reactive oxygen species scavenging pathways, highlighting possible mechanisms of drought tolerance and of the delayed senescence, characteristic of the stay-green phenotype. Finally, we discovered a large-scale depletion in the expression of genes critical to arbuscular mycorrhizal (AM) symbiosis, with a corresponding drop in AM fungal mass in the plants’ roots.

SummaryGenome wide contact frequencies obtained using Hi-C-like experiments have raised novel challenges in terms of visualization and rationalization of chromosome structuring phenomena. In bacteria, display of Hi-C data should be congruent with the circularity of chromosomes. However, standard representations under the form of square matrices or horizontal bands are not adapted to periodic conditions as those imposed by (most) bacterial chromosomes. Here, we fill this gap and propose a Python library, built upon the widely used Matplotlib library, to display Hi-C data in circular strips, together with the possibility to overlay genomic data. The proposed tools are light and fast, aiming to facilitate the exploration and understanding of bacterial chromosome structuring data. The library further includes the possibility to handle linear chromosomes, providing a fresh way to display and explore eukaryotic data.Availability and implementationThe package runs under Python 3 and is freely available at https://github.com/TrEE-TIMC/circHiC. The documentation can be found at https://tree-timc.github.io/circhic/; images obtained in different organisms are provided in the gallery section and are accompanied with codes.Contactivan.junier@univ-grenoble-alpes.fr, nelle.varoquaux@univ-grenoble-alpes.fr

In proteomic differential analysis, FDR control is often performed through a multiple test correction (i.e., the adjustment of the original p-values). In this protocol, we apply a recent and alternative method, based on so-called knockoff filters. It shares interesting conceptual similarities with the target-decoy competition procedure, classically used in proteomics for FDR control at peptide identification. To provide practitioners with a unified understanding of FDR control in proteomics, we apply the knockoff procedure on real and simulated quantitative datasets. Leveraging these comparisons, we propose to adapt the knockoff procedure to better fit the specificities of quantitive proteomic data (mainly very few samples). Performances of knockoff procedure are compared with those of the classical Benjamini-Hochberg procedure, hereby shedding a new light on the strengths and weaknesses of target-decoy competition.

Just as in eukaryotes, high-throughput chromosome conformation capture (Hi-C) data have revealed nested organizations of bacterial chromosomes into overlapping interaction domains. In this chapter, we present a multiscale analysis framework aiming at capturing and quantifying these properties. These include both standard tools (e.g., contact laws) and novel ones such as an index that allows identifying loci involved in domain formation independently of the structuring scale at play. Our objective is twofold. On the one hand, we aim at providing a full, understandable Python/Jupyter-based code which can be used by both computer scientists and biologists with no advanced computational background. On the other hand, we discuss statistical issues inherent to Hi-C data analysis, focusing more particularly on how to properly assess the statistical significance of results. As a pedagogical example, we analyze data produced in Pseudomonas aeruginosa, a model pathogenetic bacterium. All files (codes and input data) can be found on a GitHub repository. We have also embedded the files into a Binder package so that the full analysis can be run on any machine through Internet.

Arbuscular mycorrhizal fungi (AMF), the mutualistic symbionts with most crops, constitute a research system of human-associated fungi whose relative simplicity and synchrony are conducive to experimental ecology. However, little is known about the shifts in adaptive strategies of sorghum associated AMFs where strong AMF succession replaces initially ruderal species with competitive ones and where the strongest plant response to drought is to manage these AMF. First, we hypothesize that, when irrigation is stopped to mimic drought, competitive AMF species should be replaced by AMF species tolerant to drought stress. We then, for the first time, correlate AMF abundance and host plant transcription to test two novel hypotheses about the mechanisms behind the shift from ruderal to competitive AMF. Surprisingly, despite imposing drought stress, we found no stress tolerant AMF. Remarkably, we found strong and differential correlation between the successional shift from ruderal to competitive AMF and sorghum genes whose products (i) produce and release strigolactone signals, (ii) perceive mycorrhizal-lipochitinoligosaccharide (Myc-LCO) signals, (iii) provide plant lipid and sugar to AMF and, (iv) import minerals and water provided by AMF. These novel insights into host gene expression and succession of AMF show adaptive strategies evolved by AMF and their hosts and provide a rationale for selecting AMF to reduce inputs and maximize yield in commercial agriculture. Future research opportunities include testing the specifics and generality of our hypotheses by employing genetically modified host plants, and exploring additional genes underlying the adaptive strategies in natural succession.

Recent studies have demonstrated that drought leads to dramatic, highly conserved shifts in the root microbiome. At present, the molecular mechanisms underlying these responses remain largely uncharacterized. Here we employ genome-resolved metagenomics and comparative genomics to demonstrate that carbohydrate and secondary metabolite transport functionalities are overrepresented within drought-enriched taxa. These data also reveal that bacterial iron transport and metabolism functionality is highly correlated with drought enrichment. Using time-series root RNA-Seq data, we demonstrate that iron homeostasis within the root is impacted by drought stress, and that loss of a plant phytosiderophore iron transporter impacts microbial community composition, leading to significant increases in the drought-enriched lineage, Actinobacteria. Finally, we show that exogenous application of iron disrupts the drought-induced enrichment of Actinobacteria, as well as their improvement in host phenotype during drought stress. Collectively, our findings implicate iron metabolism in the root microbiome’s response to drought and may inform efforts to improve plant drought tolerance to increase food security., Advances in omics provide a tool to understand mechanisms for plant–microbial interactions under stress. Here the authors apply genome-resolved metagenomics to investigate sorghum and its microbiome responses to drought, identifying an unexpected role of iron metabolism.

Bacteria of the genus Streptomyces are prolific producers of specialized metabolites, including antibiotics. The linear chromosome includes a central region harboring core genes, as well as extremities enriched in specialized metabolite biosynthetic gene clusters. Here, we show that chromosome structure in Streptomyces ambofaciens correlates with genetic compartmentalization during exponential phase. Conserved, large and highly transcribed genes form boundaries that segment the central part of the chromosome into domains, whereas the terminal ends tend to be transcriptionally quiescent compartments with different structural features. The onset of metabolic differentiation is accompanied by a rearrangement of chromosome architecture, from a rather ‘open’ to a ‘closed’ conformation, in which highly expressed specialized metabolite biosynthetic genes form new boundaries. Thus, our results indicate that the linear chromosome of S. ambofaciens is partitioned into structurally distinct entities, suggesting a link between chromosome folding, gene expression and genome evolution., Streptomyces bacteria have a linear chromosome, with core genes located in the central region and gene clusters for specialized metabolite biosynthesis found in the ‘arms’. Here, Lioy et al. show that such chromosome structure correlates with genetic compartmentalization, and the onset of metabolic differentiation is accompanied by a rearrangement of chromosome architecture.

International audience; Just as in eukaryotes, high-throughput chromosome conformation capture (Hi-C) data have revealed nested organizations of bacterial chromosomes into overlapping interaction domains. In this chapter, we present a multiscale analysis framework aiming at capturing and quantifying these properties. These include both standard tools (e.g. contact laws) and novel ones such as an index that allows identifying loci involved in domain formation independently of the structuring scale at play. Our objective is twofold. On the one hand, we aim at providing a full, understandable Python/Jupyter-based code which can be used by both computer scientists as well as biologists with no advanced computational background. On the other hand, we discuss statistical issues inherent to Hi-C data analysis, focusing more particularly on how to properly assess the statistical significance of results. As a pedagogical example, we analyze data produced in Pseudomonas aeruginosa, a model pathogenetic bacterium. All files (codes and input data) can be found on a github repository. We have also embedded the files into a Binder package so that the full analysis can be run on any machine through internet.

The development of new ways to probe samples for the three-dimensional (3D) structure of DNA paves the way for in depth and systematic analyses of the genome architecture. 3C-like methods coupled with high-throughput sequencing can now assess physical interactions between pairs of loci in a genome-wide fashion, thus enabling the creation of genome-by-genome contact maps. The spreading of such protocols creates many new opportunities for methodological development: how can we infer 3D models from these contact maps? Can such models help us gain insights into biological processes? Several recent studies applied such protocols to P. falciparum (the deadliest of the five human malaria parasites), assessing its genome organization at different moments of its life cycle. With its small genomic size, fairly simple (yet changing) genomic organization during its lifecyle and strong correlation between chromatin folding and gene expression, this parasite is the ideal case study for applying and developing methods to infer 3D models and use them for downstream analysis. Here, I review a set of methods used to build and analyse three-dimensional models from contact maps data with a special highlight on P. falciparum’s genome organization.

International audience

There are many recommendations of "best practices" for those doing data science, data-intensive research, and research in general. These documents usually present a particular vision of how people should work with data and computing, recommending specific tools, activities, mechanisms, and sensibilities. However, implementation of best (or better) practices in any setting is often met with resistance from individuals and groups, who perceive some drawbacks to the proposed changes to everyday practice. We offer some definitions of resistance, identify the sources of researchers' hesitancy to adopt new ways of working, and describe some of the ways resistance is manifested in data science teams. We then offer strategies for overcoming resistance based on our group members' experiences working alongside resistors or resisting change themselves. Our discussion concluded with many remaining questions left to tackle, some of which are listed at the end of this piece.

The development of malaria parasites throughout their various life cycle stages is coordinated by changes in gene expression. We previously showed that the three-dimensional organization of the Plasmodium falciparum genome is strongly associated with gene expression during its replication cycle inside red blood cells. Here, we analyze genome organization in the P. falciparum and P. vivax transmission stages. Major changes occur in the localization and interactions of genes involved in pathogenesis and immune evasion, host cell invasion, sexual differentiation, and master regulation of gene expression. Furthermore, we observe reorganization of subtelomeric heterochromatin around genes involved in host cell remodeling. Depletion of heterochromatin protein 1 (PfHP1) resulted in loss of interactions between virulence genes, confirming that PfHP1 is essential for maintenance of the repressive center. Our results suggest that the three-dimensional genome structure of human malaria parasites is strongly connected with transcriptional activity of specific gene families throughout the life cycle., The development of malaria parasites is controlled by coordinated changes in gene expression. Here, the authors show that the three-dimensional genome structure of human malaria parasites is strongly connected with transcriptional activity of specific gene families throughout the life cycles of Plasmodium falciparum and Plasmodium vivax parasites.

The development of malaria parasites throughout their various life cycle stages is controlled by coordinated changes in gene expression. We previously showed that the three-dimensional organization of theP. falciparumgenome is strongly associated with gene expression during its replication cycle inside red blood cells. Here, we analyzed genome organization in theP. falciparumandP. vivaxtransmission stages. Major changes occurred in the localization and interactions of genes involved in pathogenesis and immune evasion, erythrocyte and liver cell invasion, sexual differentiation and master regulation of gene expression. In addition, we observed reorganization of subtelomeric heterochromatin around genes involved in host cell remodeling. Depletion of heterochromatin protein 1 (PfHP1) resulted in loss of interactions between virulence genes, confirming that PfHP1 is essential for maintenance of the repressive center. Overall, our results suggest that the three-dimensional genome structure is strongly connected with transcriptional activity of specific gene families throughout the life cycle of human malaria parasites.

Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) "libraries" -- curated collections of reusable code that programmers import to perform a specific task. Software documentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them. Yet documentation for open source software libraries is widely considered low-quality. This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethnographic fieldwork and qualitative interviews. We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer-based projects. There are many different kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles. The work behind documentation is similarly multifaceted, including writing, reviewing, maintaining, and organizing documentation. Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers. Finally, most of our interviewees do not report high levels of intrinsic enjoyment for doing documentation work (compared to writing code). Their motivation is affected by personal and project-specific factors, such as the perceived level of credit for doing documentation work versus more "technical" tasks like adding new features or fixing bugs. In studying documentation work for data analytics OSS libraries, we gain a new window into the changing practices of data-intensive research, as well as help practitioners better understand how to support this often invisible and infrastructural work in their projects.

Background Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other. Results In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations. We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes. Conclusions Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs. Electronic supplementary material The online version of this article (10.1186/s12859-018-2256-5) contains supplementary material, which is available to authorized users.

Centromeres are essential for proper chromosome segregation. Despite extensive research, centromere locations in yeast genomes remain difficult to infer, and in most species they are still unknown. Recently, the chromatin conformation capture assay, Hi-C, has been re-purposed for diverse applications, including de novo genome assembly, deconvolution of metagenomic samples and inference of centromere locations. We describe a method, Centurion, that jointly infers the locations of all centromeres in a single genome from Hi-C data by exploiting the centromeres’ tendency to cluster in three-dimensional space. We first demonstrate the accuracy of Centurion in identifying known centromere locations from high coverage Hi-C data of budding yeast and a human malaria parasite. We then use Centurion to infer centromere locations in 14 yeast species. Across all microbes that we consider, Centurion predicts 89% of centromeres within 5 kb of their known locations. We also demonstrate the robustness of the approach in datasets with low sequencing depth. Finally, we predict centromere coordinates for six yeast species that currently lack centromere annotations. These results show that Centurion can be used for centromere identification for diverse species of yeast and possibly other microorganisms.

Plasmodium falciparum is the most deadly human malarial parasite, responsible for an estimated 207 million cases of disease and 627,000 deaths in 2012. Recent studies reveal that the parasite actively regulates a large fraction of its genes throughout its replicative cycle inside human red blood cells and that epigenetics plays an important role in this precise gene regulation. Here, we discuss recent advances in our understanding of three aspects of epigenetic regulation in P. falciparum: changes in histone modifications, nucleosome occupancy and the three-dimensional genome structure. We compare these three aspects of the P. falciparum epigenome to those of other eukaryotes, and show that large-scale compartmentalization is particularly important in determining histone decomposition and gene regulation in P. falciparum. We conclude by presenting a gene regulation model for P. falciparum that combines the described epigenetic factors, and by discussing the implications of this model for the future of malaria research.

HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0831-x) contains supplementary material, which is available to authorized users.

The development of the human malaria parasite Plasmodium falciparum is controlled by coordinated changes in gene expression throughout its complex life cycle, but the corresponding regulatory mechanisms are incompletely understood. To study the relationship between genome architecture and gene regulation in Plasmodium, we assayed the genome architecture of P. falciparum at three time points during its erythrocytic (asexual) cycle. Using chromosome conformation capture coupled with next-generation sequencing technology (Hi-C), we obtained high-resolution chromosomal contact maps, which we then used to construct a consensus three-dimensional genome structure for each time point. We observed strong clustering of centromeres, telomeres, ribosomal DNA, and virulence genes, resulting in a complex architecture that cannot be explained by a simple volume exclusion model. Internal virulence gene clusters exhibit domain-like structures in contact maps, suggesting that they play an important role in the genome architecture. Midway during the erythrocytic cycle, at the highly transcriptionally active trophozoite stage, the genome adopts a more open chromatin structure with increased chromosomal intermingling. In addition, we observed reduced expression of genes located in spatial proximity to the repressive subtelomeric center, and colocalization of distinct groups of parasite-specific genes with coordinated expression profiles. Overall, our results are indicative of a strong association between the P. falciparum spatial genome organization and gene expression. Understanding the molecular processes involved in genome conformation dynamics could contribute to the discovery of novel antimalarial strategies.

Recent technological advances allow the measurement, in a single Hi-C experiment, of the frequencies of physical contacts among pairs of genomic loci at a genome-wide scale. The next challenge is to infer, from the resulting DNA-DNA contact maps, accurate three dimensional models of how chromosomes fold and fit into the nucleus. Many existing inference methods rely upon \emph{multidimensional scaling} (MDS), in which the pairwise distances of the inferred model are optimized to resemble pairwise distances derived directly from the contact counts. These approaches, however, often optimize a heuristic objective function and require strong assumptions about the biophysics of DNA to transform interaction frequencies to spatial distance, thereby leading to incorrect structure reconstruction. We propose a novel approach to infer a consensus three-dimensional structure of a genome from Hi-C data. The method incorporates a statistical model of the contact counts, assuming that the counts between two loci follow a Poisson distribution whose intensity decreases with the physical distances between the loci. The method can automatically adjust the transfer function relating the spatial distance to the Poisson intensity and infer a genome structure that best explains the observed data. We compare two variants of our Poisson method, with or without optimization of the transfer function, to four different MDS-based algorithms---two metric MDS methods using different stress functions, a nonmetric version of MDS, and ChromSDE, a recently described, advanced MDS method---on a wide range of simulated datasets. We demonstrate that the Poisson models reconstruct better structures than all MDS-based methods, particularly at low coverage and high resolution, and we highlight the importance of optimizing the transfer function. On publicly available Hi-C data from mouse embryonic stem cells, we show that the Poisson methods lead to more reproducible structures than MDS-based methods when we use data generated using different restriction enzymes, and when we reconstruct structures at different resolutions.

A critical component of the learning process lies in the feedback that students receive on their work that validates their progress, identifies flaws in their thinking, and identifies skills that still need to be learned. Many higher-education institutions have developed an active pedagogy that gives students opportunities for different forms of assessment and feedback. This means that students have numerous lab exercises, assignments, and projects. Both instructors and students thus require effective tools to efficiently manage the submission, assessment, and individualized feedback of students’ work. The open-source web application MarkUs aims at meeting these needs: it facilitates the submission and assessment of students’ work. Students directly submit their work using MarkUs, rather than printing it, or sending it by email. The instructors or teaching assistants use MarkUs’s interface to view the students’ work, annotate it, and fill in a marking rubric. Students use the same interface to read the annotations and learn from the assessment. Managing the students’ submissions and the instructors assessments within a single online system, has led to several positive pedagogical outcomes: the number of late submissions has decreased, the assessment time has been drastically reduced, students can access their results and read the instructor’s feedback immediately after the grading process is completed. Using MarkUs has also significantly reduced the time that instructors spend collecting assignments, creating the marking schemes, passing them on to graders, handling special cases, and returning work to the students. In this paper, we introduce MarkUs’ features, and illustrate their benefits for higher education through our own teaching experiences and that of our colleagues. We also describe an important benefit of the fact that the tool itself is open-source. MarkUs has been developed entirely by students giving them a valuable learning opportunity as they work on a large software system that real users depend on. Virtuous circles indeed arise, with former users of MarkUs becoming developers and then supervisors of further development. We will conclude by drawing perspectives about forthcoming features and use, both technically and pedagogically.

International audience

Background Several recently developed experimental methods, each an extension of the chromatin conformation capture (3C) assay, have enabled the genome-wide profiling of chromatin contacts between pairs of genomic loci in 3D. Especially in complex eukaryotes, data generated by these methods, coupled with other genome-wide datasets, demonstrated that non-random chromatin folding correlates strongly with cellular processes such as gene expression and DNA replication. Results We describe a genome architecture assay, tethered multiple 3C (TM3C), that maps genome-wide chromatin contacts via a simple protocol of restriction enzyme digestion and religation of fragments upon agarose gel beads followed by paired-end sequencing. In addition to identifying contacts between pairs of loci, TM3C enables identification of contacts among more than two loci simultaneously. We use TM3C to assay the genome architectures of two human cell lines: KBM7, a near-haploid chronic leukemia cell line, and NHEK, a normal diploid human epidermal keratinocyte cell line. We confirm that the contact frequency maps produced by TM3C exhibit features characteristic of existing genome architecture datasets, including the expected scaling of contact probabilities with genomic distance, megabase scale chromosomal compartments and sub-megabase scale topological domains. We also confirm that TM3C captures several known cell type-specific contacts, ploidy shifts and translocations, such as Philadelphia chromosome formation (Ph+) in KBM7. We confirm a subset of the triple contacts involving the IGF2-H19 imprinting control region (ICR) using PCR analysis for KBM7 cells. Our genome-wide analysis of pairwise and triple contacts demonstrates their preference for linking open chromatin regions to each other and for linking regions with higher numbers of DNase hypersensitive sites (DHSs) to each other. For near-haploid KBM7 cells, we infer whole genome 3D models that exhibit clustering of small chromosomes with each other and large chromosomes with each other, consistent with previous studies of the genome architectures of other human cell lines. Conclusion TM3C is a simple protocol for ascertaining genome architecture and can be used to identify simultaneous contacts among three or four loci. Application of TM3C to a near-haploid human cell line revealed large-scale features of chromosomal organization and multi-way chromatin contacts that preferentially link regions of open chromatin. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1236-7) contains supplementary material, which is available to authorized users.

Extracellular recordings with multi-electrode arrays is one of the basic tools of contemporary neuroscience. These recordings are mostly used to monitor the activities, understood as sequences of emitted action potentials, of many individual neurons. But the raw data produced by extracellular recordings are most commonly a mixture of activities from several neurons. In order to get the activities of the individual contributing neurons, a pre-processing step called spike sorting is required. We present here a pure Python implementation of a well tested spike sorting procedure. The latter was designed in a modular way in order to favour a smooth transition from an interactive sorting, for instance with IPython, to an automatic one. Surprisingly enough - or sadly enough, depending on one's view point -, recoding our now 15 years old procedure into Python was the occasion of major methodological improvements., Comment: Part of the Proceedings of the 7th European Conference on Python in Science (EuroSciPy 2014), Pierre de Buyl and Nelle Varoquaux editors, (2014)

We introduce StoDynProg, a small library created to solve Optimal Control problems arising in the management of Renewable Power Sources, in particular when coupled with an Energy Storage System. The library implements generic Stochastic Dynamic Programming (SDP) numerical methods which can solve a large class of Dynamic Optimization problems. We demonstrate the library capabilities with a prototype problem: smoothing the power of an Ocean Wave Energy Converter. First we use time series analysis to derive a stochastic Markovian model of this system since it is required by Dynamic Programming. Then, we briefly describe the "policy iteration" algorithm we have implemented and the numerical tools being used. We show how the API design of the library is generic enough to address Dynamic Optimization problems outside the field of Energy Management. Finally, we solve the power smoothing problem and compare the optimal control with a simpler heuristic control., Comment: Part of the Proceedings of the 6th European Conference on Python in Science (EuroSciPy 2013), Pierre de Buyl and Nelle Varoquaux editors, (2014)