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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.

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.

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.

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).

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 Rfam database (available at http://rfam.xfam.org) is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.

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.

International audience; Phylogenetic reconstructions are essential in genomics data analyses and depend on accurate multiple sequence alignment (MSA) models. We show that all currently available large-scale progressive multiple alignment methods are numerically unstable when dealing with amino-acid sequences. They produce significantly different output when changing sequence input order. We used the HOMFAM protein sequences dataset to show that on datasets larger than 100 sequences, this instability affects on average 21.5% of the aligned residues. The resulting Maximum Likelihood (ML) trees estimated from these MSAs are equally unstable with over 38% of the branches being sensitive to the sequence input order. We established that about two-thirds of this uncertainty stems from the unordered nature of children nodes within the guide trees used to estimate MSAs. To quantify this uncertainty we developed unistrap, a novel approach that estimates the combined effect of alignment uncertainty and site sampling on phylogenetic tree branch supports. Compared with the regular bootstrap procedure, unistrap provides branch support estimates that take into account a larger fraction of the parameters impacting tree instability when processing datasets containing a large number of sequences.

Ovine forestomach matrix (OFM) biomaterial acts as a biomimetic of native extracellular matrix (ECM) by providing structural and functional cues to orchestrate cell activity during tissue regeneration. The ordered collagen matrix of the biomaterial is supplemented with secondary ECM-associated macromolecules that function in cell adhesion, migration and communication. As angiogenesis and vasculogenesis are critical processes during tissue regeneration we sought to quantify the angiogenic properties of the OFM biomaterial. In vitro studies demonstrated that soluble OFM components stimulated human umbilical vein endothelial cell (HUVEC) migration and increased vascular sprouting from an aorta. Blood vessel density and branch points increased in response to OFM in an ex ovo chicken chorioallantoic membrane (CAM) assay. The OFM biomaterial was shown to undergo remodeling in a porcine full-thickness excisional model and gave rise to significantly more blood vessels than wounds treated with small intestinal submucosa decellularized ECM or untreated wounds.

Ovine forestomach matrix (OFM) is a native and functional decellularized extracellular matrix biomaterial that supports cell adhesion and proliferation and is remodeled during the course of tissue regeneration. Small angle X-ray scattering demonstrated that OFM retains a native collagen architecture (d spacing = 63.5 ± 0.2 nm, orientation index = 20°). The biophysical properties of OFM were further defined using ball-burst, uniaxial and suture retention testing, as well as a quantification of aqueous permeability. OFM biomaterial was relatively strong (yield stress = 10.15 ± 1.81 MPa) and elastic (modulus = 0.044 ± 0.009 GPa). Lamination was used to generate new OFM-based biomaterials with a range of biophysical properties. The resultant multi-ply OFM biomaterials had suitable biophysical characteristics for clinical applications where the grafted biomaterial is under load.

Extracellular matrix (ECM) based biomaterials have an established place as medical devices for wound healing and tissue regeneration. In the search for biomaterials we have identified ovine forestomach matrix (OFM), a thick, large format ECM which is biochemically diverse and biologically functional. OFM was purified using an osmotic process that was shown to reduce the cellularity of the ECM and aid tissue delamination. OFM produced using this technique was shown to retain residual basement membrane components, as evidence by the presence of laminin and collagen IV. The collagenous microarchitecture of OFM retained many components of native ECM including fibronectin, glycosaminoglycans, elastin and fibroblast growth factor basic. OFM was non-toxic to mammalian cells and supported fibroblast and keratinocyte migration, differentiation and infiltration. OFM is a culturally acceptable alternative to current collagen-based biomaterials and has immediate clinical applications in wound healing and tissue regeneration.