Biology subjects

Sagot, M.-F.

Publications and source records attributed to Sagot, M.-F..

7 recordsLinked to original sources

bioRxiv2021

The genome sequence of the cereal pest Sitophilus oryzae: an unprecedented transposable element content

BackgroundThe rice weevil Sitophilus oryzae is one of the most important agricultural pests, causing extensive damage to cereal in fields and to stored grains. S. oryzae has an intracellular symbiotic relationship (endosymbiosis) with the Gram-negative bacterium Sodalis pierantonius and is a valuable model to decipher host-symbiont molecular interactions. ResultsWe sequenced the Sitophilus oryzae genome using a combination of short and long reads to produce the best assembly for a Curculionidae species to date. We show that S. oryzae has undergone successive bursts of transposable element (TE) amplification, representing 72% of the genome. In addition, we show that many TE families are transcriptionally active, and changes in their expression are associated with insect endosymbiotic state. S. oryzae has undergone a high gene expansion rate, when compared to other beetles. Reconstruction of host-symbiont metabolic networks revealed that, despite its recent association with cereal weevils (30 Kyear), S. pierantonius relies on the host for several amino acids and nucleotides to survive and to produce vitamins and essential amino-acids required for insect development and cuticle biosynthesis. ConclusionsHere we present the genome of an agricultural pest beetle, which may act as a foundation for pest control. In addition, S. oryzae may be a useful model for endosymbiosis, and studying TE evolution and regulation, along with the impact of TEs on eukaryotic genomes.

genomics↗

bioRxiv2020

BrumiR: A toolkit for de novo discovery of microRNAs from sRNA-seq data.

MicroRNAs (miRNAs) are small non-coding RNAs that are key players in the regulation of gene expression. In the last decade, with the increasing accessibility of high-throughput sequencing technologies, different methods have been developed to identify miRNAs, most of which rely on pre-existing reference genomes. However, when a reference genome is absent or is not of high quality, such identification becomes more difficult. In this context, we developed BrumiR, an algorithm that is able to discover miRNAs directly and exclusively from sRNA-seq data. We benchmarked BrumiR with datasets encompassing animal and plant species using real and simulated sRNA-seq experiments. The results demonstrate that BrumiR reaches the highest recall for miRNA discovery, while at the same time being much faster and more efficient than the state-of-the-art tools evaluated. The latter allows BrumiR to analyze a large number of sRNA-seq experiments, from plants or animals species. Moreover, BrumiR detects additional information regarding other expressed sequences (sRNAs, isomiRs, etc.), thus maximizing the biological insight gained from sRNA-seq experiments. Finally, when a reference genome is available, BrumiR provides a new mapping tool (BrumiR2ref) that performs an a posteriori exhaustive search to identify the precursor sequences. The code of BrumiR is freely available at https://github.com/camoragaq/BrumiR.

bioinformatics↗

bioRxiv2019

Wengan: Efficient and high quality hybrid de novo assembly of human genomes

The continuous improvement of long-read sequencing technologies along with the development of ad-doc algorithms has launched a new de novo assembly era that promises high-quality genomes. However, it has proven difficult to use only long reads to generate accurate genome assemblies of large, repeat-rich human genomes. To date, most of the human genomes assembled from long error-prone reads add accurate short reads to further polish the consensus quality. Here, we report the development of a novel algorithm for hybrid assembly, WO_SCPCAPENGANC_SCPCAP, and the de novo assembly of four human genomes using a combination of sequencing data generated on ONT PromethION, PacBio Sequel, Illumina and MGI technology. WO_SCPCAPENGANC_SCPCAP implements efficient algorithms that exploit the sequence information of short and long reads to tackle assembly contiguity as well as consensus quality. The resulting genome assemblies have high contiguity (contig NG50:16.67-62.06 Mb), few assembly errors (contig NGA50:10.9-45.91 Mb), good consensus quality (QV:27.79-33.61), and high gene completeness (BO_SCPCAPUSCOC_SCPCAP complete: 94.6-95.1%), while consuming low computational resources (CPU hours:153-1027). In particular, the WO_SCPCAPENGANC_SCPCAP assembly of the haploid CHM13 sample achieved a contig NG50 of 62.06 Mb (NGA50:45.91 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50:57.88 Mb). Providing highest quality at low computational cost, WO_SCPCAPENGANC_SCPCAP is an important step towards the democratization of the de novo assembly of human genomes. The WO_SCPCAPENGANC_SCPCAP assembler is available at https://github.com/adigenova/wengan

bioinformatics↗

bioRxiv2018

MOMO - Multi-Objective Metabolic mixed integer Optimization: application to yeast strain engineering

In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering.\n\nProduction of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. The multi-objective programming framework we developed, called Momo, is open-source and uses PolySCIP{ddagger} as underlying multi-objective solver. Momo is available at http://momo-sysbio.gforge.inria.fr

systems biology↗

bioRxiv2018

Sparse network-based regularization for the analysis of patientomics high-dimensional survival data

Data availability by modern sequencing technologies represents a major challenge in oncological survival analysis, as the increasing amount of molecular data hampers the generation of models that are both accurate and interpretable. To tackle this problem, this work evaluates the introduction of graph centrality measures in classical sparse survival models such as the elastic net.\n\nWe explore the use of network information as part of the regularization applied to the inverse problem, obtained both by external knowledge on the features evaluated and the data themselves. A sparse solution is obtained either promoting features that are isolated from the network or, alternatively, hubs, i.e., features that are highly connected within the network.\n\nWe show that introducing the degree information of the features when inferring survival models consistently improves the model predictive performance in breast invasive carcinoma (BRCA) transcriptomic TCGA data while enhancing model interpretability. Preliminary clinical validation is performed using the Cancer Hallmarks Analytics Tool API and the String database.\n\nThese case studies are included in the recently released glmSparseNet R package1, a flexible tool to explore the potential of sparse network-based regularizers in generalized linear models for the analysis of omics data.

bioinformatics↗

bioRxiv2017

FAST-SG: An alignment-free algorithm for hybrid assembly

Long read sequencing technologies are the ultimate solution for genome repeats, allowing near reference level reconstructions of large genomes. However, long read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods which combine short and long read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. In this paper, we propose a new method, called FAST-SG, which uses a new ultra-fast alignment-free algorithm specifically designed for constructing a scaffolding graph using light-weight data structures. FAST-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how FAST-SG outperforms the state-of-the-art short read aligners when building the scaffolding graph, and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using FAST-SG with shallow long read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878).

bioinformatics↗

bioRxiv2017

MassBlast: A workflow to accelerate RNA-seq and DNA database analysis

SummaryCurrent workflows for sequence analysis heavily depend on user input and manual curation. New specialized tools and methods are appearing all the time, but the actions required for a full analysis are disconnected and very time-consuming. The software we propose, MassBlast, combines BLAST+ and an automated workflow analysis to filter the results and significantly improve the annotation of multiple sequencing databases for exploring new biosynthetic pathways and new protein families, among other applications. MassBlast is fully configurable and reproducible.\n\nAvailability and ImplementationThe MassBlast package is written in Ruby. Source code and releases are freely available from Github (https://github.com/averissimo/mass-blast) for all major platforms (Linux, MS Windows and OS X) under the GPLv3 license.\n\nContactandre.verissimo@tecnico.ulisboa.pt

bioinformatics↗