Bioinformatic methods for eukaryotic RNA-Seq-based promoter identification

Jakobi T. Bioinformatic methods for eukaryotic RNA-Seq-based promoter identification. Bielefeld: Bielefeld University; 2014

Revisiting the Zingiberales: Using Multiplexed Exon Capture to Resolve Ancient and Recent Phylogenetic Splits in a Charismatic Plant Lineage

The Zingiberales are an iconic order of monocotyledonous plants comprising eight families with distinctive and diverse floral morphologies and representing an important ecological element of tropical and subtropical forests. While the eight families are demonstrated to be monophyletic, phylogenetic relationships among these families remain unresolved. Neither combined morphological and molecular studies nor recent attempts to resolve family relationships using sequence data from whole plastomes has resulted in a well-supported, family-level phylogenetic hypothesis of relationships. Here we approach this challenge by leveraging the complete genome of one member of the order, Musa acuminata, together with transcriptome information from each of the other seven families to design a set of nuclear loci that can be enriched from highly divergent taxa with a single array-based capture of indexed genomic DNA. A total of 494 exons from 418 nuclear genes were captured for 53 ingroup taxa. The entire plastid genome was also captured for the same 53 taxa. Of the total genes captured, 308 nuclear and 68 plastid genes were used for phylogenetic estimation. The concatenated plastid and nuclear dataset supports the position of Musaceae as sister to the remaining seven families. Moreover, the combined dataset recovers known intra- and inter-family phylogenetic relationships with generally high bootstrap support. This is a flexible and cost effective method that gives the broader plant biology community a tool for generating phylogenomic scale sequence data in non-model systems at varying evolutionary depths

EXFI: Exon and splice graph prediction without a reference genome

For population genetic studies in nonmodel organisms, it is important to use every single source of genomic information. This paper presents EXFI, a Python pipeline that predicts the splice graph and exon sequences using an assembled transcriptome and raw whole-genome sequencing reads. The main algorithm uses Bloom filters to remove reads that are not part of the transcriptome, to predict the intron-exon boundaries, to then proceed to call exons from the assembly, and to generate the underlying splice graph. The results are returned in GFA1 format, which encodes both the predicted exon sequences and how they are connected to form transcripts.Basque Government, Grant/Award Number: predoctoral grant PRE_ 2017_2_0169 and grant IT558-1

Metagenomics : tools and insights for analyzing next-generation sequencing data derived from biodiversity studies

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards

Comparative analysis of plastid genomes in the non-photosynthetic genus Thismia reveals ongoing gene set reduction

Heterotrophic plants provide intriguing examples of reductive evolution. This is especially evident in the reduction of their plastid genomes, which can potentially proceed toward complete genome loss. Several milestones at the beginning of this path of degradation have been described; however, little is known about the latest stages of plastome reduction. Here we analyze a diversity of plastid genomes in a set of closely related non-photosynthetic plants. We demonstrate how a gradual loss of genes shapes the miniaturized plastomes of these plants. The subject of our study, the genus Thismia, represents the mycoheterotrophic monocot family Thismiaceae, a group that may have experienced a very ancient (60–80 mya) transition to heterotrophy. In all 18 species examined, the plastome is reduced to 14–18 kb and is highly AT-biased. The most complete observed gene set includes accD, seven ribosomal protein genes, three rRNA, and two tRNA genes. Different clades of Thismia have undergone further gene loss (complete absence or pseudogenization) compared to this set: in particular, we report two independent losses of rps2 and rps18

Lire les lectures : analyse de données de séquençage

Tous les travaux présentés dans cette HDR concernent l’exploitation de données de séquençage haut débit en absence de génome de référence proche et de bonne qualité.Dans un premier chapitre, nous proposons de nouvelles approches pour extraire des variants biologiques d’intérêt de ces données de séquençage. Dans un second chapitre nous exposons des méthodes de comparaisons de jeux de données de séquençage. Enfin, dans un troisième chapitre, nous proposons une méthode préliminaire à de meilleurs « assemblages » de ces données de séquençage