TRAPID : an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes

Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system

Complete mitochondrial genome of the Verticillium-wilt causing plant pathogen Verticillium nonalfalfae

Verticillium nonalfalfae is a fungal plant pathogen that causes wilt disease by colonizing the vascular tissues of host plants. The disease induced by hop isolates of V. nonalfalfae manifests in two different forms, ranging from mild symptoms to complete plant dieback, caused by mild and lethal pathotypes, respectively. Pathogenicity variations between the causal strains have been attributed to differences in genomic sequences and perhaps also to differences in their mitochondrial genomes. We used data from our recent Illumina NGS-based project of genome sequencing V. nonalfalfae to study the mitochondrial genomes of its different strains. The aim of the research was to prepare a V. nonalfalfae reference mitochondrial genome and to determine its phylogenetic placement in the fungal kingdom. The resulting 26,139 bp circular DNA molecule contains a full complement of the 14 "standard" fungal mitochondrial protein-coding genes of the electron transport chain and ATP synthase subunits, together with a small rRNA subunit, a large rRNA subunit, which contains ribosomal protein S3 encoded within a type IA-intron and 26 tRNAs. Phylogenetic analysis of this mitochondrial genome placed it in the Verticillium spp. lineage in the Glomerellales group, which is also supported by previous phylogenetic studies based on nuclear markers. The clustering with the closely related Verticillium dahliae mitochondrial genome showed a very conserved synteny and a high sequence similarity. Two distinguishing mitochondrial genome features were also found-a potential long non-coding RNA (orf414) contained only in the Verticillium spp. of the fungal kingdom, and a specific fragment length polymorphism observed only in V. dahliae and V. nubilum of all the Verticillium spp., thus showing potential as a species specific biomarker

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching

The identification of nested motifs in genomic sequences is a complex computational problem. The detection of these patterns is important to allow discovery of transposable element (TE) insertions, incomplete reverse transcripts, deletions, and/or mutations. Here, we designed a de novo strategy for detecting patterns that represent nested motifs based on exhaustive searches for pairs of motifs and combinatorial pattern analysis. These patterns can be grouped into three categories: motifs within other motifs, motifs flanked by other motifs, and motifs of large size. Our methodology, applied to genomic sequences from the plant species Aegilops tauschii and Oryza sativa, revealed that it is possible to find putative nested TEs by detecting these three types of patterns. The results were validated though BLAST alignments, which revealed the efficacy and usefulness of the new method, which we call Mamushka.Fil: Romero, José Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Cs. E Ingeniería de la Computacion; ArgentinaFil: Garbus, Ingrid. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Echenique, Carmen Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Cs. E Ingeniería de la Computacion; Argentin

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower

In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways; their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence.Fil: Moschen, Sebastián Nicolás. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Higgins, Janet. The Genome Analysis Centre; Reino UnidoFil: Di Rienzo, Julio Alejandro. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Heinz, Ruth Amelia. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernández, Paula del Carmen. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin