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Multiple Sequence Alignment (MSA) is essential for a wide range of applications in Bioinformatics. Traditionally, the alignment accuracy was the main metric used to evaluate the goodness of MSA tools. However, with the growth of sequencing data, other features, such as performance and the capacity to align larger datasets, are gaining strength. To achieve these new requirements, without affecting accuracy, the use of high-performance computing (HPC) resources and techniques is crucial. In this paper, we apply HPC techniques in T-Coffee, one of the more accurate but less scalable MSA tools. We integrate three innovative solutions into T-Coffee: the Balanced Guide Tree to increase the parallelism/performance, the Optimized Library Method with the aim of enhancing the scalability and the Multiple Tree Alignment, which explores different alignments in parallel to improve the accuracy. The results obtained show that the resulting tool, MTA-TCoffee, is able to improve the scalability in both the execution time and also the number of sequences to be aligned. Furthermore, not only is the alignment accuracy not affected by these improvements, as would be expected, but it improves significantly. Finally, we emphasize that the presented methods are not just restricted to T-Coffee, but may be implemented in any other alignment tools that use similar algorithms (progressive alignment, consistency or guide trees). ; This work was supported by the Government of Spain TIN2011–28689-C02–02, TIN2010–12011-E, Consolider CSD2007–00050 and the CUR of GENCAT. Cedric Notredame is funded by the Plan Nacional BFU2011–28575 and the European Commission FP7, LEISHDRUG Project (No. 223414) and The Quantomics Project (KBBE-2A-222664).

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. ; We acknowledge the support of the "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) under grant numbers BFU2014-55062-P and BFU2017-88264-P, the EMBL partnership, FEDER, the "Centro de Excelencia Severo Ochoa" Programme, and the CERCA Programme/Generalitat de Catalunya. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 635290-PanCanRisk and OpenRiskNet_731076. This reflects only the author's view and the funder is not responsible for any use that may be made of the information it contains. M.D. is supported by DIUE de la Generalitat de Catalunya (Grups consolidats SGR 2017/926), the 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), and the Catalan foundation "La Marató de TV3" (#2016/20-30). The CIBER of Rare Diseases is an initiative of the ISCIII.

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).

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. ; This work was supported by the DG Research of the European Commission FP7 (#HEALTH-F2 2013-602891 and PHECOMP #LHSM-CT-2007-037669), the Spanish 'RETICS-Instituto de Salud Carlos III' (#RD16/0017/0020), the Spanish 'Ministerio de Economia y Competitividad' (#SAF-2014-59648P), the 'Plan nacional sobre drogas' (#PNSD-2013-5068), the Catalan Government 'AGAUR-Generalitat de Catalunya' (#2014-SGR-1547) and the Catalan foundation 'La Marató de TV3' (#2016/20-30). The laboratory of M.D. is supported by DIUE de la Generalitat de Catalunya (Grups consolidats SGR 2014/1125). This work was supported by Fondation Jérôme Lejeune (Paris, France), MINECO (SAF2013-49129-C2-1-R), CDTI ('Smartfoods') and EU (Era Net Neuron PCIN-2013-060). The CRG is a Center of Excellence Severo Ochoa SEV-2012-0208. The CIBER of Rare Diseases is an initiative of the ISCIII. The laboratory of C.N. acknowledges the funding from the Spanish Ministry of Economy and Finance (MINECO), grant number BFU2011-28575. J.E. receivedthe FI grant from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) de la Generalitat de Catalunya

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. ; The laboratory of M.D. is supported by DIUE de la Generalitat de Catalunya (Grups consolidats SGR 2014/1125). This work was supported by Fondation Jérôme Lejeune (Paris, France), MINECO (SAF2013‐49129‐C2‐1‐R; SAF2016‐79956‐R), CDTI ('Smartfoods') and EU (Era Net Neuron PCIN‐2013‐060) and the Catalan foundation 'La Marató de TV3' (#2016/20‐30). The CRG is a Center of Excellence Severo Ochoa SEV‐2012‐0208. The CIBER of Rare Diseases is an initiative of the ISCIII. The laboratory of C.N. acknowledge the funding from the Spanish Ministry of Economy and Finance (MINECO), grant number BFU2011‐28575. J.E. received the FI grant from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) de la Generalitat de Catalunya. The laboratory of R.M. was supported by the DG Research of the European Commission FP7 (#HEALTH‐F2 2013‐602891 and PHECOMP #LHSM‐CT‐2007‐037669), the Spanish 'RETICS‐Instituto de Salud Carlos III' (#RD16/0017/0020), the Spanish 'Ministerio de Economia y Competitividad' (#SAF‐2014‐59648P), the 'Plan nacional sobre drogas' (#PNSD‐2013‐5068), the Catalan Government 'AGAUR‐Generalitat de Catalunya' (#2014‐SGR‐1547) and the Catalan foundation 'La Marató de TV3' (#2016/20‐30).

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 ...

The human genome contains many thousands of long noncoding RNAs (lncRNAs). While several studies have demonstrated compelling biological and disease roles for individual examples, analytical and experimental approaches to investigate these genes have been hampered by the lack of comprehensive lncRNA annotation. Here, we present and analyze the most complete human lncRNA annotation to date, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts. Our analyses indicate that lncRNAs are generated through pathways similar to that of protein-coding genes, with similar histone-modification profiles, splicing signals, and exon/intron lengths. In contrast to protein-coding genes, however, lncRNAs display a striking bias toward two-exon transcripts, they are predominantly localized in the chromatin and nucleus, and a fraction appear to be preferentially processed into small RNAs. They are under stronger selective pressure than neutrally evolving sequences-particularly in their promoter regions, which display levels of selection comparable to protein-coding genes. Importantly, about one-third seem to have arisen within the primate lineage. Comprehensive analysis of their expression in multiple human organs and brain regions shows that lncRNAs are generally lower expressed than protein-coding genes, and display more tissue-specific expression patterns, with a large fraction of tissue-specific lncRNAs expressed in the brain. Expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes. This GENCODE annotation represents a valuable resource for future studies of lncRNAs. ; This work has been carried out under grants RD07/0067/0012, BIO2006-03380, and CSD2007-00050 from the Spanish Ministry of Science, grant SGR-1430 from the Catalan Government, grants 1U54HG004557-01 and 1U54HG004555-01 from the National Institutes of Health, and INBISCIII from Instituto de Salud Carlos III and FEDER. This research project has been cofinanced by the European Commission, within the 7th Framework Programme, Grant Agreement KBBE-2A-222664 (''Quantomics''). C.N. is financed by the Plan Nacional BFU2008-00419. G.B. is supported by the La Caixa Ph.D. program