SimArray: a user-friendly and user-configurable microarray design tool

Background  

Microarrays were first developed to assess gene expression but are now also used to map protein-binding sites and to assess allelic variation between individuals. Regardless of the intended application, efficient production and appropriate array design are key determinants of experimental success. Inefficient production can make larger-scale studies prohibitively expensive, whereas poor array design makes normalisation and data analysis problematic.     

SNPselector: a web tool for selecting SNPs for genetic association studies

SUMMARY: Single nucleotide polymorphisms (SNPs) are commonly used for association studies to find genes responsible for complex genetic diseases. With the recent advance of SNP technology, researchers are able to assay thousands of SNPs in a single experiment. But the process of manually choosing thousands of genotyping SNPs for tens or hundreds of genes is time consuming. We have developed a web-based program, SNPselector, to automate the process. SNPselector takes a list of gene names or a list of genomic regions as input and searches the Ensembl genes or genomic regions for available SNPs. It prioritizes these SNPs on their tagging for linkage disequilibrium, SNP allele frequencies and source, function, regulatory potential and repeat status. SNPselector outputs result in compressed Excel spreadsheet files for review by the user. AVAILABILITY: SNPselector is freely available at http://primer.duhs.duke.edu/     

A multipurpose user-friendly tool for voice analysis: Application to pathological adult voices

Assessing voice quality objectively is of great relevance to clinicians, both for quantifying surgical or pharmacological effectiveness and for detecting and classifying voice pathology. A large number of objective indexes have been proposed in literature and implemented in commercially available software tools. However, clinicians commonly resort to a small subset of these indexes since they may be difficult to set up or understand.This paper presents a new user-friendly voice analysis tool named BioVoice. At present, BioVoice allows for the evaluation of few but important indexes, devoting great effort to their robust and automatic evaluation, although extensions are foreseeable. Specifically, fundamental frequency, along with irregularity (jitter, relative average perturbation), noise, and formant frequencies, is tracked on voiced parts of the signal only. Mean and standard deviation values are also calculated and displayed. This high-resolution estimation procedure is further strengthened by an adaptive estimation of the optimal length of signal frames for analysis, linked to varying signal characteristics.Moreover, BioVoice allows automatic analysis of any kind of voice signal as far as F₀ range and sampling frequency are concerned, with no manual setting required. This new tool is thus feasible for use by non-experts from different scientific fields, thanks to its simple interface.Here, the proposed approach is applied to patients who underwent micro-laryngoscopic direct exeresis to remove cysts and polyps. Pre- and post-surgical indexes were estimated using BioVoice and then compared with the output of one of the most common commercial software tools to both assess voice quality recovery and to evaluate the new method’s capabilities.     

McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems

Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education.     

DECOMP: A PDB decomposition tool on the web

The protein databank (PDB) contains high quality structural data for computational structural biology investigations. We have earlier described a fast tool (the decomp_pdb tool) for identifying and marking missing atoms and residues in PDB files. The tool also automatically decomposes PDB entries into separate files describing ligands and polypeptide chains. Here, we describe a web interface named DECOMP for the tool. Our program correctly identifies multi­monomer ligands, and the server also offers the preprocessed ligand­protein decomposition of the complete PDB for downloading (up to size: 5GB)

Availability

http://decomp.pitgroup.org     

ChIPOTle: a user-friendly tool for the analysis of ChIP-chip data

ChIPOTle (Chromatin ImmunoPrecipitation On Tiled arrays) takes advantage of two unique properties of ChIP-chip data: the single-tailed nature of the data, caused by specific enrichment but not specific depletion of genomic fragments; and the predictable enrichment of DNA fragments adjacent to sites of direct protein-DNA interaction. Implemented as a Microsoft Excel macro written in Visual Basic, ChIPOTle uses a sliding window approach that yields improvements in the identification of bona fide sites of protein-DNA interaction.     

SRMBuilder: a user-friendly tool for selected reaction monitoring data analysis

With high sensitivity and reproducibility, selected reaction monitoring (SRM) has become increasingly popular in proteome research for targeted quantification of low abundance proteins and post translational modification. SRM is also well accepted in other mass-spectrometry based research areas such as lipidomics and metabolomics, which necessitates the development of easy-to-use software for both post-acquisition SRM data analysis and quantification result validation. Here, we introduce a software tool SRMBuilder, which can automatically parse SRM data in multiple file formats, assign transitions to compounds, match light/heavy transition/compound pairs and provide a user-friendly graphic interface to manually validate the quantification result at transition/compound/sample level. SRMBuilder will greatly facilitate processing of the post-acquisition data files and validation of quantification result for SRM. The software can be downloaded for free from http://www.proteomics.ac.cn/software/proteomicstools/index.htm as part of the software suite ProteomicsTools.     

PrimerIdent: A web based tool for conserved primer design

Conserved primers across multiple species and simultaneously specific for a certain isozyme can be rare and difficult to find. PrimerIdent was developed aiming to automate this primer design and selection process in a given nucleotide sequence alignment, providing an intuitive, easy to interpret graphical result, which offers a list of all possible primers that meet the user criteria, with a colour-code identity to each sequence in the alignment. The software here presented is a simple and intuitive web based tool that is suitable for distinguishing very similar nucleotide sequences, such as isozymes-coding sequences, to enable the conserved primer design across multiple species, necessary for approaches that rely on knowing if a primer is suitable for a certain set of pre-aligned sequences, to design a specific primer to a certain sequence variation, or a combination thereof. This extremely useful software can, therefore, be used as a tool for the specific amplification of individual members of multigenic families across related species and also to evaluate the differential expression of isogenes for a given species. AVAILABILITY: http://primerident.up.pt.     

OrFin: A web tool for detection of putative orthologs

Identification of ortholog is one of the important tasks to understand a novel genome. It helps to assign functional annotations, from one organism to another organism. To identify the putative ortholog, Reciprocal Best BLAST hit (RBBH) method is known to be an efficient approach. OrFin makes use of the same approach to identify pair of orthologous proteins for a given set of sequences of two species. It is a user-friendly web tool which works with user defined parameters to search RBBHs. Results are produced in both html and text format.

Availability

This web tool is freely available at http://bifl.uohyd.ac.in/orfin     

GeneFizz: A web tool to compare genetic (coding/non-coding) and physical (helix/coil) segmentations of DNA sequences. Gene discovery and evolutionary perspectives

The GeneFizz (http://pbga.pasteur.fr/GeneFizz) web tool permits the direct comparison between two types of segmentations for DNA sequences (possibly annotated): the coding/non-coding segmentation associated with genomic annotations (simple genes or exons in split genes) and the physics-based structural segmentation between helix and coil domains (as provided by the classical helix-coil model). There appears to be a varying degree of coincidence for different genomes between the two types of segmentations, from almost perfect to non-relevant. Following these two extremes, GeneFizz can be used for two purposes: ab initio physics-based identification of new genes (as recently shown for Plasmodium falciparum) or the exploration of possible evolutionary signals revealed by the discrepancies observed between the two types of information.     

Next generation modeling in GWAS: comparing different genetic architectures

The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.     

webFOG: A web tool to map genomic features onto genes

A large number of new genomic features are being discovered using high throughput techniques. The next challenge is to automatically map them to the reference genome for further analysis and functional annotation. We have developed a tool that can be used to map important genomic features to the latest version of the human genome and also to annotate new features. These genomic features could be of many different source types, including miRNAs, microarray primers or probes, Chip-on-Chip data, CpG islands and SNPs to name a few. A standalone version and web interface for the tool can be accessed through: http://populationhealth.qimr.edu.au/cgi-bin/webFOG/index.cgi. The project details and source code is also available at http://www.bioinformatics.org/webfog.     

Relevance of genetic relationship in GWAS and genomic prediction

The objective of this study was to analyze the relevance of relationship information on the identification of low heritability quantitative trait loci (QTLs) from a genome-wide association study (GWAS) and on the genomic prediction of complex traits in human, animal and cross-pollinating populations. The simulation-based data sets included 50 samples of 1000 individuals of seven populations derived from a common population with linkage disequilibrium. The populations had non-inbred and inbred progeny structure (50 to 200) with varying number of members (5 to 20). The individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait controlled by 10 QTLs and 90 minor genes showing dominance. The SNP density was 0.1 cM and the narrow sense heritability was 25%. The QTL heritabilities ranged from 1.1 to 2.9%. We applied mixed model approaches for both GWAS and genomic prediction using pedigree-based and genomic relationship matrices. For GWAS, the observed false discovery rate was kept below the significance level of 5%, the power of detection for the low heritability QTLs ranged from 14 to 50%, and the average bias between significant SNPs and a QTL ranged from less than 0.01 to 0.23 cM. The QTL detection power was consistently higher using genomic relationship matrix. Regardless of population and training set size, genomic prediction provided higher prediction accuracy of complex trait when compared to pedigree-based prediction. The accuracy of genomic prediction when there is relatedness between individuals in the training set and the reference population is much higher than the value for unrelated individuals.     

Genetic variation, predator-prey interactions and food web structure

Food webs are networks of species that feed on each other. The role that within-population phenotypic and genetic variation plays in food web structure is largely unknown. Here, I show via simulation how variation in two key traits, growth rates and phenology, by influencing the variability of body sizes present through time, can potentially affect several structural parameters in the direction of enhancing food web persistence: increased connectance, decreased interaction strengths, increased variation among interaction strengths and increased degree of omnivory. I discuss other relevant traits whose variation could affect the structure of food webs, such as morphological and additional life-history traits, as well as animal personalities. Furthermore, trait variation could also contribute to the stability of food web modules through metacommunity dynamics. I propose future research to help establish a link between within-population variation and food web structure. If appropriately established, such a link could have important consequences for biological conservation, as it would imply that preserving (functional) genetic variation within populations could ensure the preservation of entire communities.     

MPSQ: a web tool for protein-state searching

MPSQ (multi-protein-states query) is a web-based tool for the discovery of protein states (e.g. biological interactions, covalent modifications, cellular localizations). In particular, large sets of genes can be used to search for enriched state transition network maps (NMs) and features facilitating the interpretation of genomic-scale experiments such as microarrays. One NM collects all the catalogued states of a protein as well as the mutual transitions between the states. For the returned NM, graph visualization is provided for easy understanding and to guide further analysis.     

Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.