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Motivation Protein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. Results Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis. Availability and implementation All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred). Supplementary information Supplementary data are available at Bioinformatics online.

Protein citrullination (deimination) is the post-translational modification of arginine to the non-coded amino acid citrulline and is catalyzed by the peptidyl arginine deiminase (PADI) enzyme family. The most widely expressed of the PADIs family, PADI2, regulates cellular processes that impact several diseases. How PADI2 has evolved in mammals to gain its fundamental function is not fully understood. By performing a systematic evolutionary analysis, we now identify 20 positively selected residues in PADI2, 16 of which are structurally exposed mainly on the N-terminal and middle domains of PADI2. Our integrated evolutionary and structural analyses suggest that these residues have roles in maintaining the PADI2 interactions with cognate proteins. We experimentally demonstrate that one of the loops in the middle domain participates in the interactions of PADI2 with the positive-transcription elongation factor (P-TEFb), which is essential for active transcription and cellular proliferation. This interpretation is supported by our finding that L162 within this loop evolved under positive selection. Our work demonstrates the power of combining sequence-based phylogenetic methods with structural information for investigating the selective function of the middle domain of PADI2 in modulating transcription. This in-depth knowledge could be key to understanding the role of PADI2 overexpression in disease and points to potential targetable regions of the protein.Significance StatementA systematic evolutionary analysis identified positively selected residues in the non-catalytic domain of PADI2. We established a link between the positive evolution of key residues in the PADI2 and their role in transcription. Specifically, we identified the structurally exposed loop encompassing the positively selected L162 in the PADI2 middle domain and its role in transcription and cellular proliferation. This loop contributes to the PADI2 interaction with the P-TEFb complex and cellular proliferation. Our results showcase the use of combining evolutionary and experimental approaches to dissect the dynamic of evolutionary processes.

Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC), The parasite Trypanosoma brucei causes African sleeping sickness that is fatal to patients if untreated. Parasite differentiation from a replicative slender form into a quiescent stumpy form promotes host survival and parasite transmission. Long noncoding RNAs (lncRNAs) are known to regulate cell differentiation in other eukaryotes. To determine whether lncRNAs are also involved in parasite differentiation, we used RNA sequencing to survey the T. brucei genome, identifying 1428 previously uncharacterized lncRNA genes. We find that grumpy lncRNA is a key regulator that promotes parasite differentiation into the quiescent stumpy form. This function is promoted by a small nucleolar RNA encoded within the grumpy lncRNA. snoGRUMPY binds to messenger RNAs of at least two stumpy regulatory genes, promoting their expression. grumpy overexpression reduces parasitemia in infected mice. Our analyses suggest that T. brucei lncRNAs modulate parasite-host interactions and provide a mechanism by which grumpy regulates cell differentiation in trypanosomes., This work was supported in part by Fundação para a Ciência e Tecnologia (FCT) grant, awarded to F.G. and entitled “Long noncoding RNAs as new diagnostic biomarkers for African Sleeping sickness” (PTDC/DTPEPI/7099/2014, start date: 1 January 2016, end date: 31 December 2018); also by Howard Hughes Medical Institute International Early Career Scientist Program (project title: “How parasites use epigenetics to evade host defenses,” project no. 55007419, start date: 1 February 2012, end date: 31 January 2017); and by the European Research Council (project title: “Exploring the hidden life of African trypanosomes: parasite fat tropism and implications for the disease,” project no. 771714, start date: 1 August 2018, end date: 31 January 2024), both awarded to L.M.F. The project leading to these results have received funding from “la Caixa” Foundation under the agreement LCF/PR/HR20/52400019 [project title: “Mechanism and function of epitranscriptomic poly(A) tail modifications in African trypanosomes,” project no. HR20-00361, start date: 1 March 2021, end date: 29 February 2024]. L.M.F. is supported by FCT (IF/01050/2014, project title: “Molecular basis for the efficient biology of trypanossome parasitism,” start date: 1 January 2015, end date: 31 December 2019) and by CEEC institutional program (CEECINST/00110/2018, start date: 1 January 2020, end date: 14 December 2020). C.N. acknowledges the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya. S. Michaeli acknowledges the support of the Israel Science Foundation (ref. 1959/20) from October 2020 to October 2025, entitled “Functional analysis of rRNA processing and the role of rRNA modification for specialized translation in the two life stages of trypanosomes” and U.S. Binational Science Foundation (ref. 2015/219) from October 2015 to October 2019, entitled “The role and mechanism of RNA pseudo-uridylation and sugar methylation (Nm) during the developmental cycle of trypanosomes.” The work done in A.D.’s laboratory was supported by National Science Center SONATA BIS grant, entitled “Non-canonical RNA tailing and other post-transcriptional regulatory mechanisms in T cell-mediated adaptive immunity” (proposal ID: 492777, agreement no: UMO-2020/38/E/NZ2/00372, start date: 22 March 2021, end date: 21 March 2026); National Science Center OPUS grant, entitled “Analysis of the role of cytoplasmic polyadenylation in the regulation of the innate immune response” (proposal ID: 443521, agreement no.: UMO-2019/33/B/NZ2/01773, start date: 2 March 2020, end date: 1 March 2023); and European Union’s Horizon 2020 (H2020-WIDESPREAD-03-2017)–ERAChair, entitled “MOlecular Signaling in Health and Disease - Interdisciplinary Centre of Excellence” (acronym: MOSaIC, agreement no.: 810425, implementation period: start date: 1 November 2018, end date: 31 October 2023).

Protein sequence alignments are essential to structural, evolutionary and functional analysis but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly superior to sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis.

Aneuploidy causes broad-scale disruption in the stochiometric balances of transcripts, proteins, and metabolites. These disruptions often lead to detrimental effects for the organism, but for some organisms and in certain environments, it can also cause a fitness advantage. Understanding the complex trade-off between aneuploidy fitness gains and losses requires a systems biological comprehension of its molecular impact. The protozoan parasite Leishmania provides a unique study angle to this problem, as the pathogen lacks transcriptional regulation of individual protein-coding genes and has an unusually high tolerance for aneuploidy. Here, we present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. This 4 layer multi-omics analysis unambiguously establishes that despite this tolerance, aneuploidy in Leishmania globally and drastically impacts the transcriptome and proteome of affected chromosomes, ultimately explaining the degree of metabolic differences between strains. This impact appears to be present throughout the parasite’s life cycle as we observed it in both its proliferative and infectious life stages. We show that, through dosage compensation, protein complex subunits, secreted proteins, and non-cytoplasmic proteins responded less or even not at all to aneuploidy. In contrast to other Eukaryotes, we did not observe the widespread regulation at the transcript level that typically modulate some of the negative effects of aneuploidy. Instead, Leishmania features a surprisingly high number of regulated proteins encoded by non-aneuploid chromosomes, suggesting that aneuploidy results in extensive post-transcriptionally modulation of protein levels. Our results provide the foundation for the integrated molecular understanding of aneuploidy in Leishmania. They also highlight that the parasite is an attractive model to study processes of post-transcriptional modulation of protein levels in the context of aneuploidy and beyond.

Many fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the most popular frameworks for the computation of MSAs. It involves pre-clustering the sequences and aligning them starting with the most similar ones. The scalability of this framework is limited, especially with respect to accuracy. We present here an alternative approach named regressive algorithm. In this framework, sequences are first clustered and then aligned starting with the most distantly related ones. This approach has been shown to greatly improve accuracy during scale-up, especially on datasets featuring 10,000 sequences or more. Another benefit is the possibility to integrate third-party clustering methods and third-party MSA aligners. The regressive algorithm has been tested on up to 1.5 million sequences, its implementation is available in the T-Coffee package.

Many fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the most popular frameworks for the computation of MSAs. It involves pre-clustering the sequences and aligning them starting with the most similar ones. The scalability of this framework is limited, especially with respect to accuracy. We present here an alternative approach named regressive algorithm. In this framework, sequences are first clustered and then aligned starting with the most distantly related ones. This approach has been shown to greatly improve accuracy during scale-up, especially on datasets featuring 10,000 sequences or more. Another benefit is the possibility to integrate third-party clustering methods and third-party MSA aligners. The regressive algorithm has been tested on up to 1.5 million sequences, its implementation is available in the T-Coffee package.

Trypanosoma bruceicauses African sleeping sickness, a fatal human disease. Its differentiation from replicative slender form into quiescent stumpy form promotes host survival and parasite transmission. Long noncoding RNAs (lncRNAs) are known to regulate cell differentiation. To determine whether lncRNAs are involved in parasite differentiation we used RNAseq to survey theT. bruceilncRNA gene repertoire, identifying 1,428 previously uncharacterized lncRNA genes. We analysedgrumpy, a lncRNA located immediately upstream of an RNA-binding protein that is akeydifferentiation regulator. Grumpy over-expression resulted in premature parasite differentiation into the quiescent stumpy form, and subsequent impairment ofin vivoinfection, decreasing parasite load in the mammalian host, and increasing host survival. Our analyses suggest Grumpy is one of many lncRNA that modulate parasite-host interactions, and lncRNA roles in cell differentiation are probably commonplace inT. brucei.

Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this manuscript we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

Summary The growing appetite of behavioral neuroscience for automated data production is prompting the need for new computational standards allowing improved interoperability, reproducibility, and shareability. We show here how these issues can be solved by repurposing existing genomic formats whose structure perfectly supports the handling of time series. This allows existing genomic analysis and visualization tools to be deployed onto behavioral data. As a proof of principle, we implemented the conversion procedure in Pergola, an open source software, and used genomics tools to reproduce results obtained in mouse, fly, and worm. We also show how common genomics techniques such as principal component analysis, hidden Markov modeling, and volcano plots can be deployed on the reformatted behavioral data. These analyses are easy to share because they depend on the scripting of public software. They are also easy to reproduce thanks to their integration within Nextflow, a workflow manager using containerized software., Graphical Abstract, Highlights • Genomic formats can be repurposed to handle behavioral data without information loss • Pergola can store, visualize, and analyze behavioral data with genomics tools • Genomic formats and tools ease standardized, interoperable, and reproducible analysis • Three existing sets of analysis on mouse, fly, and worms are reproduced in this study, Biological Sciences; Genetics; Behavioral Neuroscience; Bioinformatics

A major problem in treating obesity is the high rate of relapse to abnormal food-taking habits after maintaining an energy balanced diet. Alterations of eating behavior such as compulsive-like behavior and lack of self-control over food intake play a critical role in relapse. In this study, we used an operant paradigm of food-seeking behavior on two different diet-induced obesity models, a free-choice chocolate-mixture diet and a high-fat diet with face validity for a rapid development of obesity or for unhealthy food regularly consumed in our societies. A reduced operant performance and motivation for the hedonic value of palatable chocolate pellets was revealed in both obesity mouse models. However, only mice exposed to high-fat diet showed an increased compulsive-like behavior in the absence of the reinforcer further characterized by impaired operant learning, enhanced impulsivity and intensified inflexibility. We used principal component analysis to globally identify the specific behaviors responsible for the differences among diet groups. Learning impairment and inflexible behaviors contributed to a first principal component, explaining the largest proportion of the variance in the high-fat diet mice phenotype. Reinforcement, impulsion and compulsion were the main contributors to the second principal component explaining the differences in the chocolate-mixture mice behavioral phenotype. These behaviors were not exclusive of chocolate group because some high-fat individuals showed similar values on this component. These data indicate that extended access to hypercaloric diets differentially modifies operant behavior learning, behavioral flexibility, impulsive-like and compulsive-like behavior, and these effects were dependent on the exposure to each specific diet.

MicroRNAs (miRNAs) are endogenous small RNAs that recognize target sequences by base complementarity and play a role in the regulation of target gene expression. They are processed from longer precursor molecules that harbor a fold-back structure. Plant miRNA precursors are quite variable in size and shape, and are recognized by the processing machinery in different ways. However, ancient miRNAs and their binding sites in target genes are conserved during evolution. Here, we designed a strategy to systematically analyze MIRNAs from different species generating a graphical representation of the conservation of the primary sequence and secondary structure. We found that plant MIRNAs have evolutionary footprints that go beyond the small RNA sequence itself, yet their location along the precursor depends on the specific MIRNA. We show that these conserved regions correspond to structural determinants recognized during the biogenesis of plant miRNAs. Furthermore, we found that the members of the miR166 family have unusual conservation patterns and demonstrated that the recognition of these precursors in vivo differs from other known miRNAs. Our results describe a link between the evolutionary conservation of plant MIRNAs and the mechanisms underlying the biogenesis of these small RNAs and show that the MIRNA pattern of conservation can be used to infer the mode of miRNA biogenesis. Fil: Chorostecki, Uciel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentina Fil: Moro, Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentina Fil: Rojas, Arantxa Maria Larisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentina Fil: Debernardi, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentina Fil: Schapire, Arnaldo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentina Fil: Notredame, Cedric. The Barcelona Institute of Science and Technology; España. Universitat Pompeu Fabra; España Fil: Palatnik, Javier Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentina. Universidad Nacional de Rosario. Centro de Estudios Interdisciplinarios; Argentina

La reproduction des analyses bio-informatiques de routine est difficile en raison d’une combinaison de facteurs difficiles a controler. Nextflow est un gestionnaire de flux (workflow manager ) qui utilise la technologie des conteneurs pour assurer un deploiement et une reproductibilite efficace des pipelines d’analyse computationnelle. Les pipelines tiers peuvent etre portes dans Nextflow avec un recodage minimum. Nous montrons ici a l’aide d’exemples concrets comment la quantification des niveaux d’expression, l’annotation de genomes et la reconstruction de phylogenie peuvent se reveler non reproductibles lorsqu’elles sont realisees sur des plates-formes UNIX differentes, alors qu’elles deviennent stables lorsqu’elles sont deployees dans Nextflow. Nextflow est disponible sur www.nextflow.io.

Obesity is an important health problem with a strong environmental component that is acquiring pandemic proportion. The high availability of caloric dense foods promotes overeating potentially causing obesity. Animal models are key to validate novel therapeutic strategies, but researchers must carefully select the appropriate model to draw the right conclusions. Obesity is defined by an increased body mass index greater than 30 and characterized by an excess of adipose issue. However, the regulation of food intake involves a close interrelationship between homeostatic and non-homeostatic factors. Studies in animal models have shown that intermittent access to sweetened or calorie-dense foods induces changes in feeding behavior. However, these studies are focused mainly on the final outcome (obesity) rather than on the primary dysfunction underlying the overeating of palatable foods. We describe a protocol to study overeating in mice using diet-induced obesity (DIO). This method can be applied to free choice between palatable food and a standard rodent chow or to forced intake of calorie-dense and/or palatable diets. Exposure to such diets is sufficient to promote changes in meal pattern that we register and analyze during the period of weight gain allowing the longitudinal characterization of feeding behavior in mice. Abnormal eating behaviors such as binge eating or snacking, behavioral alterations commonly observed in obese humans, can be detected using our protocol. In the free-choice procedure, mice develop a preference for the rewarding palatable food showing the reinforcing effect of this diet. Compulsive components of feeding are reflected by maintenance of feeding despite an adverse bitter taste caused by adulteration with quinine and by the negligence of standard chow when access to palatable food is ceased or temporally limited. Our strategy also enables to identify compulsive overeating in mice under a high-caloric regime by using limited food access and finally, we propose complementary behavioral tests to confirm the non-homeostatic food-taking triggered by these foods. Finally, we describe how to computationally explore large longitudinal behavioral datasets. This work was supported by Fondation Jérôme Lejeune (Paris, France), MINECO (SAF2016-79956-R), CDTI (‘Smartfoods’), EU (Era Net Neuron PCIN-2013-060) and the Catalan foundation ‘La Marató de TV3’ (#2016/20-30) and the EU Joint Programme–Neurodegenerative Disease Research (JPND) project under grant agreement HEROES AC17/00006. The CRG is a Center of Excellence Severo Ochoa SEV-2012-0208. The CIBER of Rare Diseases is an initiative of the ISCIII.

Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. Today, NGS is routinely used to understand many important topics in biology from human disease to microorganism diversity. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: the magnitude of the counts are determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged, and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when comparing heterogeneous samples (e.g., samples collected across distinct cancers or tissues). Instead, methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. In this manuscript, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. In doing so, we review zero replacement, differential abundance analysis, and within-group and between-group coordination analysis. We then discuss how this pipeline can accommodate complex study design, facilitate the analysis of vertically and horizontally integrated data, including multiomics data, and further extend to single-cell sequencing data. In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, “Relative to some important activity of the cell, what is changing?”. Taken together, this manuscript establishes the first fully comprehensive analysis protocol that is suitable for any and all -omics data.

We present a new web application to query and visualize time-series behavioral data: the Pergola web-server. This server provides a user-friendly interface for exploring longitudinal behavioral data taking advantage of the Pergola Python library. Using the server, users can process the data applying some basic operations, such as binning or grouping, while formatting the data into existing genomic formats. Thanks to this repurposing of genomics standards, the application automatically renders an interactive data visualization based on sophisticated genome visualization tools. Our tool allows behavioral scientists to share, display and navigate complex behavioral data comprising multiple individuals and multiple data types, in a scalable and flexible manner. A download option allows for further analysis using genomic tools. The server can be a great resource for the field in a time where behavioral science is entering a data-intensive cycle thanks to high-throughput behavioral phenotyping platforms. Pergola is publicly available at http://pergola.crg.eu/. 'Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat i del Fons Social Europeu', Spanish Ministry of Economy, Industry and Competitiveness (MEIC) [BFU2014-55062-P, BFU2017-88264-P]; EMBL partnership, FEDER, the ‘Centro de Excelencia Severo Ochoa’ Programme; CERCA Programme/Generalitatde Catalunya; European Union's Horizon 2020 research and innovation program [635290-PanCanRisk and OpenRiskNet_731076]; DIUE de la Generalitat de Catalunya [Grups consolidatsSGR 2017/926]; Fondation Jérôme Lejeune (Paris, France), MINECO [SAF2013-49129-C2-1-R;SAF2016-79956-R]; CDTI (‘Smartfoods’) and EU [EraNet Neuron PCIN-2013-060 and JPND AC17/00006]; Catalan foundation ‘La Marato′de TV3’ [2016/20-30]; CIBER of Rare Diseases is an initiative of the ISCIII. Funding for open access charge: CRG institutional core funding.

Multiple sequence alignments (MSAs) are used for structural1,2 and evolutionary predictions1,2, but the complexity of aligning large datasets requires the use of approximate solutions3, including the progressive algorithm4. Progressive MSA methods start by aligning the most similar sequences and subsequently incorporate the remaining sequences, from leaf to root, based on a guide tree. Their accuracy declines substantially as the number of sequences is scaled up5. We introduce a regressive algorithm that enables MSA of up to 1.4 million sequences on a standard workstation and substantially improves accuracy on datasets larger than 10,000 sequences. Our regressive algorithm works the other way around from the progressive algorithm and begins by aligning the most dissimilar sequences. It uses an efficient divide-and-conquer strategy to run third-party alignment methods in linear time, regardless of their original complexity. Our approach will enable analyses of extremely large genomic datasets such as the recently announced Earth BioGenome Project, which comprises 1.5 million eukaryotic genomes6.

Motivation: Most evolutionary analyses are based on pre-estimated multiple sequence alignment. Wong et al. established the existence of an uncertainty induced by multiple sequence alignment when reconstructing phylogenies. They were able to show that in many cases different aligners produce different phylogenies, with no simple objective criterion sufficient to distinguish among these alternatives. Results: We demonstrate that incorporating MSA induced uncertainty into bootstrap sampling can significantly increase correlation between clade correctness and its corresponding bootstrap value. Our procedure involves concatenating several alternative multiple sequence alignments of the same sequences, produced using different commonly used aligners. We then draw bootstrap replicates while favoring columns of the more unique aligner among the concatenated aligners. We named this concatenation and bootstrapping method, Weighted Partial Super Bootstrap (wpSBOOT). We show on three simulated datasets of 16, 32 and 64 tips that our method improves the predictive power of bootstrap values. We also used as a benchmark an empirical collection of 853 1-to-1 orthologous genes from seven yeast species and found wpSBOOT to significantly improve discrimination capacity between topologically correct and incorrect trees. Bootstrap values of wpSBOOT are comparable to similar readouts estimated using a single method. However, for reduced trees by 50% and 95% bootstrap thresholds, wpSBOOT comes out the lowest Type I error (less FP). Availability: The automated generation of replicates has been implemented in the T-Coffee package, which is available as open source freeware available from www.tcoffee.org. Supplementary information: Supplementary data are available at Bioinformatics online. This work was supported by the Spanish Ministry of Science Plan Nacional [BFU2008-00419 to P.D.T. and C.N.]; the Wellcome Trust [WT095908 to P.F.]; the INCEPTION project [PIA/ANR-16-CONV-0005 to O.G.]; the Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2 to J.-M.C.]. We acknowledge support of the European Molecular Biology Laboratory, the Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa 2013-2017” and “The Human Project from Mind, Brain and Learning” of NCCU from the Higher Education Sprout Project by the Ministry of Education in Taiwan

The PSI/TM-Coffee web server performs multiple sequence alignment (MSA) of proteins by combining homology extension with a consistency based alignment approach. Homology extension is performed with Position Specific Iterative (PSI) BLAST searches against a choice of redundant and non-redundant databases. The main novelty of this server is to allow databases of reduced complexity to rapidly perform homology extension. This server also gives the possibility to use transmembrane proteins (TMPs) reference databases to allow even faster homology extension on this important category of proteins. Aside from an MSA, the server also outputs topological prediction of TMPs using the HMMTOP algorithm. Previous benchmarking of the method has shown this approach outperforms the most accurate alignment methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. The web server is available at http://tcoffee.crg.cat/tmcoffee. This work was supported by Plan Nacional [BFU2011-28575 to C.N., P.D.]; Center for Genomic Regulation (CRG); ‘Fundació Obra Social la Caixa’ (to E.W.F, M.C); Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013–2017’ [SEV-2012–0208]; Center for Genomic Regulation (CRG).

Long noncoding RNAs (lncRNAs) are emerging as important regulators of developmental pathways. However, their roles in human cardiac precursor cell (CPC) remain unexplored. To characterize the long noncoding transcriptome during human CPC cardiac differentiation, we profiled the lncRNA transcriptome in CPCs isolated from the human fetal heart and identified 570 lncRNAs that were modulated during cardiac differentiation. Many of these were associated with active cardiac enhancer and super enhancers (SE) with their expression being correlated with proximal cardiac genes. One of the most upregulated lncRNAs was a SE-associated lncRNA that was named CARMEN, (CAR)diac (M)esoderm (E)nhancer-associated (N)oncoding RNA. CARMEN exhibits RNA-dependent enhancing activity and is upstream of the cardiac mesoderm-specifying gene regulatory network. Interestingly, CARMEN interacts with SUZ12 and EZH2, two components of the polycomb repressive complex 2 (PRC2). We demonstrate that CARMEN knockdown inhibits cardiac specification and differentiation in cardiac precursor cells independently of MIR-143 and -145 expression, two microRNAs located proximal to the enhancer sequences. Importantly, CARMEN expression was activated during pathological remodeling in the mouse and human hearts, and was necessary for maintaining cardiac identity in differentiated cardiomyocytes. This study demonstrates therefore that CARMEN is a crucial regulator of cardiac cell differentiation and homeostasis.

This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.

Containers are gaining popularity in life science research as they encompass all dependencies of provisioned tools and simplifies software installations for end users, as well as offering a form of isolation between processes. Scientific workflows are ideal to chain containers into data analysis pipelines to sustain reproducible science. In this manuscript we review the different approaches to use containers inside the workflow tools Nextflow, Galaxy, Pachyderm, Luigi, and SciPipe when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches. We discuss our progress on the OpenRiskNet goal of harmonizing data and metadata within APIs that can be added to already existing analysis and modeling services and data warehouses. We also demonstrate how these APIs can easily be used towards the generation of full risk assessment workflows either using scripting languages or workflow managers. Finally, we show the first approaches to make these APIs semantically rich by annotating data with human- and computer-readable data schemata. OpenRiskNet has initiated the Associated Partners Programme strengthening the working ties between the OpenRiskNet members and other organizations within the scientific community.

International audience; The parasite Leishmania~ donovani causes a fatal disease termed visceral leishmaniasis. The process through which the parasite adapts to environmental change remains largely unknown. Here we show that aneuploidy is integral for parasite adaptation and that karyotypic fluctuations allow for selection of beneficial haplotypes, which impact transcriptomic output and correlate with phenotypic variations in proliferation and infectivity. To avoid loss of diversity following karyotype and haplotype selection, L. donovani utilizes two mechanisms: polyclonal selection of beneficial haplotypes to create coexisting subpopulations that preserve the original diversity, and generation of new diversity as aneuploidy-prone chromosomes tolerate higher mutation rates. Our results reveal high aneuploidy turnover and haplotype selection as a unique evolutionary adaptation mechanism that L. donovani uses to preserve genetic diversity under strong selection. This unexplored process may function in other human diseases, including fungal infection and cancer, and stimulate innovative treatment options.

Drosophila sechellia relies exclusively on the fruits of Morinda citrifolia, which are toxic to most insects, including its sibling species Drosophila melanogaster and Drosophila simulans. Although several odorant binding protein (Obp) genes and olfactory receptor (Or) genes have been suggested to be associated with the D. sechellia host shift, a broad view of how chemosensory genes have contributed to this shift is still lacking. We therefore studied the transcriptomes of antennae, the main organ responsible for detecting food resource and oviposition, of D. sechellia and its two sibling species. We wanted to know whether gene expression, particularly chemosensory genes, has diverged between D. sechellia and its two sibling species. Using a very stringent definition of differential gene expression, we found a higher percentage of chemosensory genes differentially expressed in the D. sechellia lineage (7.8%) than in the D. simulans lineage (5.4%); for upregulated chemosensory genes, the percentages were 8.8% in D. sechellia and 5.2% in D. simulans. Interestingly, Obp50a exhibited the highest upregulation, an approximately 100-fold increase, and Or85c--previously reported to be a larva-specific gene--showed approximately 20-fold upregulation in D. sechellia. Furthermore, Ir84a (ionotropic receptor 84a), which has been proposed to be associated with male courtship behavior, was significantly upregulated in D. sechellia. We also found expression divergence in most of the chemosensory gene families between D. sechellia and the two sibling species. Our observations suggest that the host shift of D. sechellia was associated with the enrichment of differentially expressed, particularly upregulated, chemosensory genes.

This article introduces the Transitive Consistency Score (TCS) web server; a service making it possible to estimate the local reliability of protein multiple sequence alignments (MSAs) using the TCS index. The evaluation can be used to identify the aligned positions most likely to contain structurally analogous residues and also most likely to support an accurate phylogenetic reconstruction. The TCS scoring scheme has been shown to be accurate predictor of structural alignment correctness among commonly used methods. It has also been shown to outper-form common filtering schemes like Gblocks or tri-mAl when doing MSA post-processing prior to phylo-genetic tree reconstruction. The web server is available from http://tcoffee.crg.cat/tcs.

It is now over 10 years since the first avian genome [International Chicken Genome Sequencing Consortium, 2004] and the first complete avian karyotype [Masabanda et al., 2004] were both published; however, until 2014, avian cytogenetics has focused heavily on descriptive studies [e.g. Griffin et al., 2007, 2008; Skinner et al., 2009; Volker et al., 2010] with less attention to its functional relevance. Last year, however, saw 2 landmark efforts in the chromosomal studies of birds: a special issue of Chromosome Research in April and the announcement of recently completed sequences of multiple new avian genomes in Science and the BMC journals (taking the total number sequenced to over 50) in December. Studying the chromosomes of birds is, perhaps for the first time, telling us more about avian biology, function and evolution than it ever has... Conclusions. The most recent advances in avian cytogenetics have culminated in great promise not only for the study of bird karyotypes, but also for providing insight into the mechanisms of chromosome evolution in general. New avenues for investigation include gene regulation; for instance, it will become necessary to map accurately the physical location of polyadenylation and transcription start sites, important reference points that define promoters and post-transcriptional regulation. It will also become possible to sequence full-length transcripts, to allow accurate identification of alternate splicing events and their controlling elements. The ENCODE (Encyclopedia of DNA Elements) project has helped to define functional elements of the human genome, including those aforementioned as well as other chromatin signals, e.g. active chromatin, enhancers, insulators, methylation domains, etc. An effort of agENCODE is underway to include agriculturally important birds such as chicken, turkey, duck, quail, and perhaps ostrich. The study of cytogenetics will be essential here in helping to define higher-order structures in nuclear organization that show regulatory interactions within and between chromosomes. Finally, reconstruction of evolutionary events allows us to study genome organization and function not only in extant but, by extrapolation, in extinct species also. Reconstruction of avian-reptilian ancestral karyotypes will allow us to define chromosomal rearrangements in long-dead species that have captured the public imagination. Here be dragons!

Multiple sequence alignment (MSA) is crucial for high-throughput next generation sequencing applications. Large-scale alignments with thousands of sequences are necessary for these applications. However, the quality of the alignment of current MSA tools decreases sharply when the number of sequences grows to several thousand. This accuracy degradation can be mitigated using global consistency information as in the T-Coffee MSA-Tool, which implements a consistency library. However, consistency-based methods do not scale well because of the computational resources required to calculate and store the consistency information, which grows quadratically. In this paper, we propose an alternative method for building the consistency-library. To allow unlimited scalability, consistency information must be discarded to avoid exceeding the environment memory. Our first approach deals with the memory limitation by identifying the most important entries, which provide better consistency. This method is able to achieve scalability, although there is a negative impact on accuracy. The second proposal, aims to reduce this degradation of accuracy, with three different methods presented to attain a better alignment. This work has been supported by the Government of Spain TIN2011-28689-C02-02. Cedric Notredame is funded by the Plan Nacional BFU2011-28575 and The Quantomics project (KBBE- 2A-222664).

Most evolutionary analyses or structure modeling are based upon pre-estimated multiple sequence alignment (MSA) models. From a computational point of view, it is too complex to estimate a correct alignment. Hence, increasing or identifying signal inside sequence alignment has intensified over the last few years. During the presentation, I would like to share two approaches, homology extension and sampling, on this topic. The first part, transmembrane proteins (TMPs) constitute about 20~30% of all protein coding genes. The relative lack of experimental structure has so far made it hard to develop specific alignment methods and the current state of the art (PRALINE™) only manages to recapitulate 50% of the positions in the reference alignments available from the BAliBASE2-ref7. We show how homology extension can be adapted and combined with a consistency based approach in order to significantly improve the multiple sequence alignment of alpha-helical TMPs. TM-Coffee is a special mode of PSI-Coffee able to efficiently align TMPs, while using a reduced reference database for homology extension. Our benchmarking on BAliBASE2-ref7 alpha-helical TMPs shows a significant improvement over the most accurate methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. The second part, homology and evolutionary modeling are the most common applications of MSAs. In this work, we show how this problem can be partly overcome using the transitive consistency score (TCS), an extended version of the T-Coffee scoring scheme. Using this local evaluation function, we show that one can identify the most reliable portions of an MSA, as judged from BAliBASE and PREFAB structure-based reference alignments. We also show how this measure can be used to improve phylogenetic tree reconstruction using both an established simulated data set and a novel empirical yeast data set. Our approach relies on the T-Coffee framework; it uses libraries of pairwise alignments to evaluate any third party MSA. We compared TCS with Heads-or-Tails, GUIDANCE, Gblocks, and trimAl and found it to lead to significantly better estimates of structural accuracy and more accurate phylogenetic trees. References: PSI/TM-Coffee: a web server for fast and accurate multiple sequence alignments of regular and transmembrane proteins using homology extension on reduced databases. Nucleic acids research 44, W339–343(2016). TCS: a web server for multiple sequence alignment evaluation and phylogenetic reconstruction. Nucleic acids research 43, W3–6 (2015). TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction.Molecular biology and evolution 31, 1625–37 (2014). Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee. Bmc Bioinformatics 13, S1 (2012). Website: PSI/TM-Coffee http://tcoffee.crg.cat/tmcoffee, TCS http://tcoffee.crg.cat/tcs

Reproducing routine bioinformatics analysis is challenging owing to a combination of factors hard to control for. Nextflow is a flow management framework that uses container technology to insure efficient deployment and reproducibility of computational analysis pipelines. Third party pipelines can be ported into Nextflow with minimum re-coding. We used RNA-Seq quantification, genome annotation and phylogeny reconstruction examples to show how two seemingly irreproducible analyzes can be made stable across platforms when ported into Nextflow.

The dependence of life scientists on software has steadily grown in recent years. For many tasks, researchers have to decide which of the available bioinformatics software are more suitable for their specific needs. Additionally researchers should be able to objectively select the software that provides the highest accuracy, the best efficiency and the highest level of reproducibility when integrated in their research projects.Critical benchmarking of bioinformatics methods, tools and web services is therefore an essential community service, as well as a critical component of reproducibility efforts. Unbiased and objective evaluations are challenging to set up and can only be effective when built and implemented around community driven efforts, as demonstrated by the many ongoing community challenges in bioinformatics that followed the success of CASP. Community challenges bring the combined benefits of intense collaboration, transparency and standard harmonization. Only open systems for the continuous evaluation of methods offer a perfect complement to community challenges, offering to larger communities of users that could extend far beyond the community of developers, a window to the developments status that they can use for their specific projects. We understand by continuous evaluation systems as those services which are always available and periodically update their data and/or metrics according to a predefined schedule keeping in mind that the performance has to be always seen in terms of each research domain.We argue here that technology is now mature to bring community driven benchmarking efforts to a higher level that should allow effective interoperability of benchmarks across related methods. New technological developments allow overcoming the limitations of the first experiences on online benchmarking e.g. EVA. We therefore describe OpenEBench, a novel infra-structure designed to establish a continuous automated benchmarking system for bioinformatics methods, tools and web services.OpenEBench is being developed so as to cater for the needs of the bioinformatics community, especially software developers who need an objective and quantitative way to inform their decisions as well as the larger community of end-users, in their search for unbiased and up-to-date evaluation of bioinformatics methods. As such OpenEBench should soon become a central place for bioinformatics software developers, community-driven benchmarking initiatives, researchers using bioinformatics methods, and funders interested in the result of methods evaluation.

Gene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. A computationally efficient implementation of the approach in R has been released as an addendum to the propr package.1

Obesity represents an important risk factor contributing to the global burden of disease. The current obesogenic environment with easy access to calorie-dense foods is fueling this obesity epidemic. However, how these foods contribute to the progression of feeding behavior changes that lead to overeating is not well understood and needs systematic assessment. Using novel automated methods for the high-throughput screening of behavior, we here examine mice meal pattern upon long-term exposure to a free-choice chocolate-mixture diet and a high-fat diet with face validity for a rapid development of obesity induced by unhealthy food regularly consumed in our societies. We identified rapid diet-specific behavioral changes after exposure to those high-caloric diets. Mice fed with high-fat chow, showed long-lasting meal pattern disturbances, which initiate with a stable loss of circadian feeding rhythmicity. Mice receiving a chocolate-mixture showed qualitatively similar changes, though less marked, consisting in a transient disruption of the feeding behavior and the circadian feeding rhytmicity. Strikingly, compulsive-like eating behavior is triggered immediately after exposure to both high-fat food and chocolate-mixture diet, well before any changes in body weight could be observed. We propose these changes as behavioral biomarkers of prodromal states of obesity that could allow early intervention.

Reproducibility has become one of biology’s most pressing issues. This impasse has been fuelled by the combined reliance on increasingly complex data analysis methods and the exponential growth of biological datasets. Nextflow is a pipeline orchestration tool that has been designed to ease deployment and guarantee reproducibility across platforms. It allows the seamless parallelization and deployment of any existing application with minimal development and maintenance overhead, irrespective of the original programming language. These capabilities guarantee consistent results over time and across different computing platforms.