Biology subjects

Ciampi, A.

Publications and source records attributed to Ciampi, A..

3 recordsLinked to original sources

bioRxiv2020

SeSaMe PS Function: Functional Analysis of the Whole Metagenome Sequencing Data of the Arbuscular Mycorrhizal Fungi

In this article, we introduce a novel bioinformatics program- SeSaMe PS Function (Spore associated Symbiotic Microbes Position Specific Function) - for position-specific functional analysis of short sequences derived from metagenome sequencing data of the arbuscular mycorrhizal fungi. The unique advantage of the program lies in databases created based on genus-specific sequence properties derived from protein secondary structure, namely amino acid usages, codon usages, and codon contexts of three codon DNA 9-mers. SeSaMe PS Function searches a query sequence against reference sequence database, identifies three codon DNA 9-mers with structural roles, and dynamically creates the comparative dataset of 54 microbial genera based on their codon usage biases. The program applies correlation Principal Component Analysis in conjunction with K-means clustering method to the comparative dataset. Three codon DNA 9-mers clustered as sole member or with only a few members are often structurally and functionally distinctive sites that provide useful insights into important molecular interactions. The program provides a versatile means for studying functions of short sequences from metagenome sequencing and has a wide spectrum of applications.

bioinformatics↗

bioRxiv2020

SeSaMe: Metagenome Sequence Classification of Arbuscular Mycorrhizal Fungi Associated Microorganisms

Arbuscular mycorrhizal fungi (AMF) are plant root symbionts that play key roles in plant growth and soil fertility. They are obligate biotrophic fungi that form coenocytic multinucleated hyphae and spores. Numerous studies have shown that diverse microorganisms live on the surface and inside their mycelia, resulting in a metagenome when whole genome sequencing (WGS) data are obtained from sequencing AMF cultivated in vivo. The metagenome contains not only the AMF sequences, but also those from associated microorganisms. In this article, we introduce a novel bioinformatics program, SeSaMe, designed for taxonomic classification of short sequences obtained by next-generation DNA sequencing. A genus-specific usage bias database was created based on amino acid usage and codon usage of three consecutive codon DNA 9-mers encoding for an amino acid trimer in a protein secondary structure. The program distinguishes between coding sequence (CDS) and non-CDS, and classifies a query sequence into a genus group out of 54 genera used as reference. The average correct prediction percentages of the CDS and the non-CDS test sets at the genus level were 71% and 50% for bacteria, 65% and 73% for fungi (excluding AMF), and 49% and 72% for AMF (Rhizophagus irregularis), respectively. The program provides a means for estimating not only taxonomic diversity and abundance but also the gene reservoir of the reference taxonomic groups associated with AMF. Therefore, the program enables users to study the symbiotic roles of associated microorganisms. SeSaMe can be applicable to other microorganisms as well as soil metagenomes. It is freely available at www.journal.com and www.fungalsesame.org.

bioinformatics↗

bioRxiv2017

Constrained Instruments and their Application to Mendelian Randomization with Pleiotropy

In Mendelian randomization (MR), genetic variants are used to construct instrumental variables, which enable inference about the causal relationship between a phenotype of interest and a response or disease outcome. However, standard MR inference requires several assumptions, including the assumption that the genetic variants only influence the response through the phenotype of interest. Pleiotropy occurs when a genetic variant has an effect on more than one phenotype; therefore, a pleiotropic genetic variant may be an invalid instrumental variable. Hence, a naive method for constructing instrumental variables may lead to biased estimation of the causality between the phenotype and the response. Here, we present a set of intuitive methods (Constrained Instrumental Variable methods [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists, focusing particularly on the situation where pleiotropic phenotypes have been measured. Our approach includes an automatic and valid selection of genetic variants when building the instrumental variables. We also provide details of the features of many existing methods, together with a comparison of their performance in a large series of simulations. CIV methods performed consistently better than many comparators across four different pleiotropic violations of the MR assumptions. We analyzed data from the Alzheimers Disease Neuroimaging Initiative (ADNI) Mueller et al. (2005) to disentangle causal relationships of several biomarkers with AD progression. The results showed that CIV methods can provide causal effect estimates, as well as selection of valid instruments while accounting for pleiotropy.

genetics↗