"METAGENOTE: a simplified web platform for metadata annotation of genomic samples and streamlined submission to NCBI's sequence read archive".

BACKGROUND: The improvements in genomics methods coupled with readily accessible high-throughput sequencing have contributed to our understanding of microbial species, metagenomes, infectious diseases and more. To maximize the impact of these genomics studies, it is important that data from biological samples will become publicly available with standardized metadata. The availability of data at public archives provides the hope that greater insights could be obtained through integration with multi-omics data, reproducibility of published studies, or meta-analyses of large diverse datasets. These datasets should include a description of the host, organism, environmental source of the specimen, spatial-temporal information and other relevant metadata, but unfortunately these attributes are often missing and when present, they show inconsistencies in the use of metadata standards and ontologies. RESULTS: METAGENOTE ( https://metagenote.niaid.nih.gov ) is a web portal that greatly facilitates the annotation of samples from genomic studies and streamlines the submission process of sequencing files and metadata to the Sequence Read Archive (SRA) (Leinonen R, et al, Nucleic Acids Res, 39:D19-21, 2011) for public access. This platform offers a wide selection of packages for different types of biological and experimental studies with a special emphasis on the standardization of metadata reporting. These packages follow the guidelines from the MIxS standards developed by the Genomics Standard Consortium (GSC) and adopted by the three partners of the International Nucleotides Sequencing Database Collaboration (INSDC) (Cochrane G, et al, Nucleic Acids Res, 44:D48-50, 2016) - National Center for Biotechnology Information (NCBI), European Bioinformatics Institute (EBI) and the DNA Data Bank of Japan (DDBJ). METAGENOTE then compiles, validates and manages the submission through an easy-to-use web interface minimizing submission errors and eliminating the need for submitting sequencing files via a separate file transfer mechanism. CONCLUSIONS: METAGENOTE is a public resource that focuses on simplifying the annotation and submission process of data with its corresponding metadata. Users of METAGENOTE will benefit from the easy to use annotation interface but most importantly will be encouraged to publish metadata following standards and ontologies that make the public data available for reuse.

Accurate estimation of solvation free energy using polynomial fitting techniques.

This report details an approach to improve the accuracy of free energy difference estimates using thermodynamic integration data (slope of the free energy with respect to the switching variable λ) and its application to calculating solvation free energy. The central idea is to utilize polynomial fitting schemes to approximate the thermodynamic integration data to improve the accuracy of the free energy difference estimates. Previously, we introduced the use of polynomial regression technique to fit thermodynamic integration data (Shyu and Ytreberg, J Comput Chem, 2009, 30, 2297). In this report we introduce polynomial and spline interpolation techniques. Two systems with analytically solvable relative free energies are used to test the accuracy of the interpolation approach. We also use both interpolation and regression methods to determine a small molecule solvation free energy. Our simulations show that, using such polynomial techniques and nonequidistant λ values, the solvation free energy can be estimated with high accuracy without using soft-core scaling and separate simulations for Lennard-Jones and partial charges. The results from our study suggest that these polynomial techniques, especially with use of nonequidistant λ values, improve the accuracy for ΔF estimates without demanding additional simulations. We also provide general guidelines for use of polynomial fitting to estimate free energy. To allow researchers to immediately utilize these methods, free software and documentation is provided via http://www.phys.uidaho.edu/ytreberg/software.

Computational estimation of rainbow trout estrogen receptor binding affinities for environmental estrogens.

Environmental estrogens have been the subject of intense research due to their documented detrimental effects on the health of fish and wildlife and their potential to negatively impact humans. A complete understanding of how these compounds affect health is complicated because environmental estrogens are a structurally heterogeneous group of compounds. In this work, computational molecular dynamics simulations were utilized to predict the binding affinity of different compounds using rainbow trout (Oncorhynchus mykiss) estrogen receptors (ERs) as a model. Specifically, this study presents a comparison of the binding affinity of the natural ligand estradiol-17ß to the four rainbow trout ER isoforms with that of three known environmental estrogens 17α-ethinylestradiol, bisphenol A, and raloxifene. Two additional compounds, atrazine and testosterone, that are known to be very weak or non-binders to ERs were tested. The binding affinity of these compounds to the human ERα subtype is also included for comparison. The results of this study suggest that, when compared to estradiol-17ß, bisphenol A binds less strongly to all four receptors, 17α-ethinylestradiol binds more strongly, and raloxifene has a high affinity for the α subtype only. The results also show that atrazine and testosterone are weak or non-binders to the ERs. All of the results are in excellent qualitative agreement with the known in vivo estrogenicity of these compounds in the rainbow trout and other fishes. Computational estimation of binding affinities could be a valuable tool for predicting the impact of environmental estrogens in fish and other animals.

Computational study of evolutionary selection pressure on rainbow trout estrogen receptors.

Molecular dynamics simulations were used to determine the binding affinities between the hormone 17-estradiol (E2) and different estrogen receptor (ER) isoforms in the rainbow trout, Oncorhynchus mykiss. Previous phylogenetic analysis indicates that a whole genome duplication prior to the divergence of ray-finned fish led to two distinct ER isoforms, ER and ER, and the recent whole genome duplication in the ancestral salmonid created two ER isoforms, ER and ER. The objective of our computational studies is to provide insight into the underlying evolutionary pressures on these isoforms. For the ER subtype our results show that E2 binds preferentially to ER over ER. Tests of lineage specific N/S ratios indicate that the ligand binding domain of the ER gene is evolving under relaxed selection relative to all other ER genes. Comparison with the highly conserved DNA binding domain suggests that ER may be undergoing neofunctionalization possibly by binding to another ligand. By contrast, both ER and ER bind similarly to E2 and the best fitting model of selection indicates that the ligand binding domain of all ER genes are evolving under the same level of purifying selection, comparable to ER.

Reducing the bias and uncertainty of free energy estimates by using regression to fit thermodynamic integration data.

This report presents the application of polynomial regression for estimating free energy differences using thermodynamic integration data, i.e., slope of free energy with respect to the switching variable lambda. We employ linear regression to construct a polynomial that optimally fits the thermodynamic integration data, and thus reduces the bias and uncertainty of the resulting free energy estimate. Two test systems with analytical solutions were used to verify the accuracy and precision of the approach. Our results suggest that use of regression with high degree of polynomials provides the most accurate free energy difference estimates, but often with slightly larger uncertainty, compared to commonly used quadrature techniques. High degree polynomials possess the flexibility to closely fit the thermodynamic integration data but are often sensitive to small changes in the data points. Thus, we also used Chebyshev nodes to guide in the selection of nonequidistant lambda values for use in thermodynamic integration. We conclude that polynomial regression with nonequidistant lambda values delivers the most accurate and precise free energy estimates for thermodynamic integration data for the systems considered here. Software and documentation is available at http://www.phys.uidaho.edu/ytreberg/software.

Advances in the use of terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbial communities.

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a popular high-throughput fingerprinting technique used to monitor changes in the structure and composition of microbial communities. This approach is widely used because it offers a compromise between the information gained and labor intensity. In this review, we discuss the progress made in T-RFLP analysis of 16S rRNA genes and functional genes over the last 10 years and evaluate the performance of this technique when used in conjunction with different statistical methods. Web-based tools designed to perform virtual polymerase chain reaction and restriction enzyme digests greatly facilitate the choice of primers and restriction enzymes for T-RFLP analysis. Significant improvements have also been made in the statistical analysis of T-RFLP profiles such as the introduction of objective procedures to distinguish between signal and noise, the alignment of T-RFLP peaks between profiles, and the use of multivariate statistical methods to detect changes in the structure and composition of microbial communities due to spatial and temporal variation or treatment effects. The progress made in T-RFLP analysis of 16S rRNA and genes allows researchers to make methodological and statistical choices appropriate for the hypotheses of their studies.

MiCA: a web-based tool for the analysis of microbial communities based on terminal-restriction fragment length polymorphisms of 16S and 18S rRNA genes.

A web-based resource, Microbial Community Analysis (MiCA), has been developed to facilitate studies on microbial community ecology that use analyses of terminal-restriction fragment length polymorphisms (T-RFLP) of 16S and 18S rRNA genes. MiCA provides an intuitive web interface to access two specialized programs and a specially formatted database of 16S ribosomal RNA sequences. The first program performs virtual polymerase chain reaction (PCR) amplification of rRNA genes and restriction of the amplicons using primer sequences and restriction enzymes chosen by the user. This program, in silico PCR and Restriction (ISPaR), uses a binary encoding of DNA sequences to rapidly scan large numbers of sequences in databases searching for primer annealing and restriction sites while permitting the user to specify the number of mismatches in primer sequences. ISPaR supports multiple digests with up to three enzymes. The number of base pairs between the 5' and 3' primers and the proximal restriction sites can be reported, printed, or exported in various formats. The second program, APLAUS, infers a plausible community structure(s) based on T-RFLP data supplied by a user. APLAUS estimates the relative abundances of populations and reports a listing of phylotypes that are consistent with the empirical data. MiCA is accessible at http://mica.ibest.uidaho.edu/.