Recent Bioinformatics Tools Research Articles

Chimeras in Merlot grapevine revealed by phased assembly

Chimerism is the phenomenon when several genotypes coexist in a single individual. Used to understand plant ontogenesis they also have been valorised through new cultivar breeding. Viticulture has been taking economic advantage out of chimeras when the variant induced an important modification of wine type such as berry skin colour. Crucial agronomic characters may also be impacted by chimeras that aren’t identified yet. Periclinal chimera where the variant has entirely colonised a cell layer is the most stable and can be propagated through cuttings. In grapevine, leaves are derived from both meristem layers, L1 and L2. However, lateral roots are formed from the L2 cell layer only. Thus, comparing DNA sequences of roots and leaves allows chimera detection. In this study we used new generation Hifi long reads sequencing, recent bioinformatics tools and trio-binning with parental sequences to detect periclinal chimeras on ‘Merlot’ grapevine cultivar. Sequencing of cv. ‘Magdeleine Noire des Charentes’ and ‘Cabernet Franc’, the parents of cv. ‘Merlot’, allowed haplotype resolved assembly. Pseudomolecules were built with a total of 33 to 47 contigs and in few occasions a unique contig for one chromosome. This high resolution allowed haplotype comparison. Annotation was transferred from PN40024 VCost.v3 to all pseudomolecules. After strong selection of variants, 51 and 53 ‘Merlot’ specific periclinal chimeras were found on the Merlot-haplotype-CF and Merlot-haplotype-MG respectively, 9 and 7 been located in a coding region. A subset of positions was analysed using Molecular Inversion Probes (MIPseq) and 69% were unambiguously validated, 25% are doubtful because of technological noise or weak depth and 6% invalidated. These results open new perspectives on chimera detection as an important resource to improve cultivars through clonal selection or breeding.

LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario.

LC/MS-based analysis techniques combined with specialized lipid tool allow for the qualitative and quantitative determination of thousands of lipid molecules. Some recent bioinformatics tools have been developed to study changes in the lipid profile in case-control experiments and correlate these changes to different enzyme activity or gene expression. However, the existing tools have the limitation to treat only the assembled lipid molecules. In reality, each individual molecule can be considered as an assembly of smaller parts, often called building blocks. These are the result of a myriad of biochemical synthesis and transformation processes that, from a systems biology perspective, should not be ignored. Here, we present LipidOne, a new lipidomic tool which highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and among experimental groups. Thanks to LipidOne, even differences in lipid building blocks can now be linked to the activity of specific classes of enzymes, transcripts and genes. LipidOne software is freely available at www.dcbb.unipg.it/LipidOne and https://github.com/matteogiulietti/LipidOne. roberto.pellegrino@unipg.it. Supplementary data are available at Bioinformatics online.

Revisiting global biogeography of freshwater diatoms: new insights from molecular data

The high-dispersal rates of microorganisms have driven to the expectation of their cosmopolitan geographic distribution. However, recent studies demonstrate that microorganisms instead show particular biogeography. Despite the existence of cosmopolitan species, geographically limited microbial groups have been found in aquatic and terrestrial environments worldwide. Diatoms are long time used model to study the biogeography of microorganisms. They are unicellular eukaryotic microalgae that contribute significantly to the aquatic primary production and have huge taxonomic diversity and marked species-specific ecological preferences. Several authors considered that diatoms have no limits in dispersion and are ubiquitously present. On the other hand, recent studies have shown that endemism exists for several genera, and species may have low dispersal capacity. However, all these studies are based on data obtained by microscopy and therefore suffer from the many well-identified biases associated with the optical identification of microorganisms at large scale. Metabarcoding technologies provide an access to taxonomic precision with a higher resolution compared to microscopy and open therefore the possibility of analyzing microbial diversity at genetic level. Recent bioinformatics tools allow reliable and standardized comparison of large datasets originating from distant geographic regions, overcoming issues related to biases in species identification. In this study we used metabarcoding data to revisit central questions in freshwater diatom biogeography. We assembled a large dataset of samples of benthic diatoms collected from rivers in seven different geographic regions. These regions cover the subpolar (Fennoscandia), temperate (France Mainland) and tropical (West Africa, French Guyana, New Caledonia, Tahiti island and Mayotte island) climate zones. The selected geographic regions can also be classified into four continental areas (Fennoscandia, France Mainland, West Africa, and French Guyana) and three islands (New Caledonia, Tahiti and Mayotte). We analyzed diatom alpha, beta and gamma diversity patterns in this dataset to address two main questions: 1) the presence of a latitudinal gradient in diatom diversity and 2) the cosmopolitanism of diatoms. Similarly to results previously reported by Soininen et al. 2016, our data showed a decrease in diatom richness with a decrease in latitude. However, testing the effect of land type (island vs. mainland) showed that this factor explains the actual variability of richness along the climatic gradient and the effect of latitude is not significant. Differences in community structure between regions and climate zones were significant. In multivariate analysis, tropical samples did not overlap with any of the other climate zones, suggesting the specificity of these communities.

Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

Metagenomics Approaches in Discovery and Development of New Bioactive Compounds from Marine Actinomycetes.

Marine actinomycetes are prolific sources of marine drug discovery system contributing for several bioactive compounds of biomedical prominence. Metagenomics, a culture-independent technique through its sequence- and function-based screening has led to the discovery and synthesis of numerous biologically significant compounds like polyketide synthase, Non-ribosomal peptide synthetase, antibiotics, and biocatalyst. While metagenomics offers different advantages over conventional sequencing techniques, they also have certain limitations including bias classification, non-availability of quality DNA samples, heterologous expression, and host selection. The assimilation of advanced amplification and screening methods such as φ29 DNA polymerase, Next-Generation Sequencing, Cosmids, and recent bioinformatics tools like automated genome mining, anti-SMASH have shown promising results to overcome these constrains. Consequently, functional genomics and bioinformatics along with synthetic biology will be crucial for the success of the metagenomic approach and indeed for exploring new possibilities among the microbial consortia for the future drug discovery process.

Identification of potential lipid binding regions in cereal proteins and peptides with the use of bioinformatics

In an era where sustainability becomes increasingly prominent it is clear that agricultural sources are playing an important role in this goal. Cereal science is a well-established research area when it comes to agricultural and food science related topics. Throughout the years a wealth of information has been gathered when it comes to cereals. Recent bioinformatics tools allow detailed scanning of cereal proteins and peptides. The approach presented in this paper will demonstrate a full identification and partial characterization of all potential helical lipid binding regions in a number of proteins and peptides present in cereals. These results will be linked to experimental findings as much as possible and where relevant the contribution to more mechanistic insight in the functionality of the proteins and peptides will be discussed. Finally the use of the approach in helping researcher to fine-tune their search for a better understanding of their protein or peptide of interest will be explained.

Bioinformatics tools for genomic and evolutionary analysis of infectious agents

Genome sequence analysis of infectious agents (IAs) reveals many secrets about their life processes and evolutionary history. Increasing the huge amount of genomic sequence data of various IAs in different biological sequence databases, which are being produced through different sequencing projects, is continuously motivating the genome researchers to unlock the mysteries related to the life of IAs. Furthermore, that information may be helpful for treating the serious illness problem caused by IAs. However, all the genome analysis work requires a good knowledge of bioinformatics tools that may be useful for genome researchers to extract the meaningful and accurate information from the genome sequence data of IAs. In this article, the most recent bioinformatics tools for the genomic and evolutionary analysis of infectious agents have been discussed and compared in detail which will help the genome researchers to select the most appropriate tool for genomic and evolutionary analysis of IAs.

A comparative review of recent bioinformatics tools for inferring gene regulatory networks using time-series expression data

The Gene Regulatory Network (GRN) inference problem in computational biology is challenging. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioinformatics tools capable of performing perfect GRN inference. Here, we review and compare seven recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these seven tools based on both simulated and experimental data sets are generally low, suggesting that further efforts are needed to develop more reliable network inference tools.

Marine metagenomics, a valuable tool for enzymes and bioactive compounds discovery

The enormous potential in diversity of the marine life is still not fully exploited due to the difficulty in culturing many of the microorganisms under laboratory conditions. In this mini-review we underlined the importance of using an omic technique, such as metagenomics, to access the uncultured majority of microbial communities. We report examples of several hydrolytic enzymes and natural products isolated by functional sequenced-based and function- screening strategies assisted by new high-throughput DNA sequencing technology and recent bioinformatics tools. This article ends with an overview of the potential future perspectives of the metagenomics in bioprospecting novel biocatalysts and bioactive compounds from marine sources.

The role of parallelism, web services and ontologies in bioinformatics and omics data management and analysis

The increasing availability of omics data poses new challenges to bioinformatics applications regarding the efficient storage and integration of experimental data, their efficient and high-throughput preprocessing and analysis, the building of reproducible silico experiments, the integration of analysis results with pre-existing knowledge repositories stored into ontologies, like Gene Ontology, or into specialised databases, as those available in pharmacogenomics. This paper presents an overview of how parallelism, service orientation, and knowledge management techniques can be used to face those challenges presenting some recent bioinformatics tools and projects that employ such technologies in different stages of the bioinformatics analysis’s pipeline.

Current trends in the bioinformatic sequence analysis of metabolic pathways in prokaryotes

The study of metabolic pathways is becoming increasingly important to exploit an integrated, systems-level approach for optimizing a desired cellular property or phenotype. In this context, the integration of genomics data with genetic, metabolic and regulatory models is essential because the systematic design of artificial, biological systems requires the identification of robust building blocks like gene promoters, metabolic pathways or genetic circuits taken from natural organisms, and manipulated to develop ad hoc features. Computational tools allowing precise descriptions of natural pathways might thus allow improving the performance of artificial routes. In this review, we introduce the most recent bioinformatics tools enabling detailed characterizations of metabolic pathways in bacteria from different perspectives.