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@sulantha
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/call-for-papers-call-for-papers-special-issue-on-food-crop-proteomics/
Call for Papers: Call for Papers: Special Issue on Food Crop Proteomics
The Journal of Proteomics will devote a Special Issue to food crop proteomics.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/flatten-plus-a-recent-implementation-in-wsxm-for-biological-research/
'Flatten plus': a recent implementation in WSxM for biological research
Summary: Scanning probe microscopy (SPM) is already a relevant tool in biological research at the nanoscale. We present ‘Flatten plus’, a recent and helpful implementation in the well-known WSxM free software package. ‘Flatten plus’ allows reducing low-frequency noise in SPM images in a semi-automated way preventing the appearance of typical artifacts associated with such filters.
Availability and implementation: WSxM is a free software implemented in C++ supported on MS Windows, but it can also be run under Mac or Linux using emulators such as Wine or Parallels. WSxM can be downloaded from http://www.wsxmsolutions.com/.
Contact: [email protected]
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/iongap-integrative-bacterial-genome-analysis-for-ion-torrent-sequence-data/
IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data
Summary: We introduce IonGAP, a publicly available Web platform designed for the analysis of whole bacterial genomes using Ion Torrent sequence data. Besides assembly, it integrates a variety of comparative genomics, annotation and bacterial classification routines, based on the widely used FASTQ, BAM and SRA file formats. Benchmarking with different datasets evidenced that IonGAP is a fast, powerful and simple-to-use bioinformatics tool. By releasing this platform, we aim to translate low-cost bacterial genome analysis for microbiological prevention and control in healthcare, agroalimentary and pharmaceutical industry applications.
Availability and implementation: IonGAP is hosted by the ITER’s Teide-HPC supercomputer and is freely available on the Web for non-commercial use at http://iongap.hpc.iter.es.
Contact: [email protected] or [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/goplot-an-r-package-for-visually-combining-expression-data-with-functional-analysis/
GOplot: an R package for visually combining expression data with functional analysis
Summary: Despite the plethora of methods available for the functional analysis of omics data, obtaining comprehensive-yet detailed understanding of the results remains challenging. This is mainly due to the lack of publicly available tools for the visualization of this type of information. Here we present an R package called GOplot, based on ggplot2, for enhanced graphical representation. Our package takes the output of any general enrichment analysis and generates plots at different levels of detail: from a general overview to identify the most enriched categories (bar plot, bubble plot) to a more detailed view displaying different types of information for molecules in a given set of categories (circle plot, chord plot, cluster plot). The package provides a deeper insight into omics data and allows scientists to generate insightful plots with only a few lines of code to easily communicate the findings.
Availability and Implementation: The R package GOplot is available via CRAN-The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/GOplot. The shiny web application of the Venn diagram can be found at: https://wwalter.shinyapps.io/Venn/. A detailed manual of the package with sample figures can be found at https://wencke.github.io/
Contact: [email protected] or [email protected]
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/civi-circular-genome-visualization-with-unique-features-to-analyze-sequence-elements/
CiVi: circular genome visualization with unique features to analyze sequence elements
Summary: We have developed CiVi, a user-friendly web-based tool to create custom circular maps to aid the analysis of microbial genomes and sequence elements. Sequence related data such as gene-name, COG class, PFAM domain, GC%, and subcellular location can be comprehensively viewed. Quantitative gene-related data (e.g. expression ratios or read counts) as well as predicted sequence elements (e.g. regulatory sequences) can be uploaded and visualized. CiVi accommodates the analysis of genomic elements by allowing a visual interpretation in the context of: (i) their genome-wide distribution, (ii) provided experimental data and (iii) the local orientation and location with respect to neighboring genes. CiVi thus enables both experts and non-experts to conveniently integrate public genome data with the results of genome analyses in circular genome maps suitable for publication.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
Availability and implementation: CiVi is freely available at http://civi.cmbi.ru.nl
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/gener-a-minimal-programming-module-for-chemical-controllers-based-on-dna-strand-displacement/
Gener: a minimal programming module for chemical controllers based on DNA strand displacement
Summary: Gener is a development module for programming chemical controllers based on DNA strand displacement. Gener is developed with the aim of providing a simple interface that minimizes the opportunities for programming errors: Gener allows the user to test the computations of the DNA programs based on a simple two-domain strand displacement algebra, the minimal available so far. The tool allows the user to perform stepwise computations with respect to the rules of the algebra as well as exhaustive search of the computation space with different options for exploration and visualization. Gener can be used in combination with existing tools, and in particular, its programs can be exported to Microsoft Research’s DSD tool as well as to LaTeX.
Availability and implementation: Gener is available for download at the Cosbi website at http://www.cosbi.eu/research/prototypes/gener as a windows executable that can be run on Mac OS X and Linux by using Mono.
Contact: [email protected]
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/the-swisslipids-knowledgebase-for-lipid-biology/
The SwissLipids knowledgebase for lipid biology
Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it.
Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge.
Availability: SwissLipids is freely available at http://www.swisslipids.org/.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/chippcr-an-r-package-to-pre-process-raw-data-of-amplification-curves/
chipPCR: an R package to pre-process raw data of amplification curves
Motivation: Both the quantitative real-time polymerase chain reaction (qPCR) and quantitative isothermal amplification (qIA) are standard methods for nucleic acid quantification. Numerous real-time read-out technologies have been developed. Despite the continuous interest in amplification-based techniques, there are only few tools for pre-processing of amplification data. However, a transparent tool for precise control of raw data is indispensable in several scenarios, for example, during the development of new instruments.
Results: chipPCR is an R package for the pre-processing and quality analysis of raw data of amplification curves. The package takes advantage of R’s S4 object model and offers an extensible environment. chipPCR contains tools for raw data exploration: normalization, baselining, imputation of missing values, a powerful wrapper for amplification curve smoothing and a function to detect the start and end of an amplification curve. The capabilities of the software are enhanced by the implementation of algorithms unavailable in R, such as a 5-point stencil for derivative interpolation. Simulation tools, statistical tests, plots for data quality management, amplification efficiency/quantification cycle calculation, and datasets from qPCR and qIA experiments are part of the package. Core functionalities are integrated in GUIs (web-based and standalone shiny applications), thus streamlining analysis and report generation.
Availability and implementation: http://cran.r-project.org/web/packages/chipPCR. Source code: https://github.com/michbur/chipPCR.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/bindnase-a-discriminatory-approach-for-transcription-factor-binding-prediction-using-dnase-i-hypersensitivity-data/
BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data
Motivation: Transcription factors (TFs) are a class of DNA-binding proteins that have a central role in regulating gene expression. To reveal mechanisms of transcriptional regulation, a number of computational tools have been proposed for predicting TF-DNA interaction sites. Recent studies have shown that genome-wide sequencing data on open chromatin sites from a DNase I hypersensitivity experiments (DNase-seq) has a great potential to map putative binding sites of all transcription factors in a single experiment. Thus, computational methods for analysing DNase-seq to accurately map TF-DNA interaction sites are highly needed.
Results: Here, we introduce a novel discriminative algorithm, BinDNase, for predicting TF-DNA interaction sites using DNase-seq data. BinDNase implements an efficient method for selecting and extracting informative features from DNase I signal for each TF, either at single nucleotide resolution or for larger regions. The method is applied to 57 transcription factors in cell line K562 and 31 transcription factors in cell line HepG2 using data from the ENCODE project. First, we show that BinDNase compares favourably to other supervised and unsupervised methods developed for TF-DNA interaction prediction using DNase-seq data. We demonstrate the importance to model each TF with a separate prediction model, reflecting TF-specific DNA accessibility around the TF-DNA interaction site. We also show that a highly standardised DNase-seq data (pre)processing is a requisite for accurate TF binding predictions and that sequencing depth has on average only a moderate effect on prediction accuracy. Finally, BinDNase’s binding predictions generalise to other cell types, thus making BinDNase a versatile tool for accurate TF binding prediction.
Availability and implementation: R implementation of the algorithm is available in: http://research.ics.aalto.fi/csb/software/bindnase/.
Contact: [email protected]
Supplementary information: Supplemental data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/pdbest-a-user-friendly-platform-for-manipulating-and-enhancing-protein-structures/
PDBest: a user-friendly platform for manipulating and enhancing protein structures
Summary: PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility.
Availability and implementation: PDBest installation packages are freely available for several platforms at http://www.pdbest.dcc.ufmg.br
Contact: [email protected], [email protected], [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/genome-scale-strain-designs-based-on-regulatory-minimal-cut-sets/
Genome-scale strain designs based on regulatory minimal cut sets
Motivation: Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype.
Results: We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. Because of vastly enlarged search spaces in genome-scale networks, we developed strategies to (optionally) preselect suitable candidates for flux regulation and novel algorithmic techniques to further enhance efficiency and speed of cMCSs calculation. We illustrate the cRegMCSs approach by a simple example network and apply it then by identifying strain designs for ethanol production in a genome-scale metabolic model of Escherichia coli. The results clearly show that cRegMCSs combining reaction deletions and flux regulations provide a much larger number of suitable strain designs, many of which are significantly smaller relative to cMCSs involving only knockouts. Furthermore, with cRegMCSs, one may also enable the fine tuning of desired behaviours in a narrower range. The new cRegMCSs approach may thus accelerate the implementation of model-based strain designs for the bio-based production of fuels and chemicals.
Availability and implementation: MATLAB code and the examples can be downloaded at http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html.
Contact: [email protected] or [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/pez-phylogenetics-for-the-environmental-sciences/
pez: phylogenetics for the environmental sciences
Summary: pez is an R package that permits measurement, modelling and simulation of phylogenetic structure in ecological data. pez contains the first implementation of many methods in R, and aggregates existing data structures and methods into a single, coherent package.
Availability and implementation: pez is released under the GPL v3 open-source license, available on the Internet from CRAN (http://cran.r-project.org). The package is under active development, and the authors welcome contributions (see http://github.com/willpearse/pez).
Contact: [email protected]
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/addressing-false-discoveries-in-network-inference/
Addressing false discoveries in network inference
Motivation: Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles.
Results: We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson’s paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation.
Conclusions: CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well.
Availability and implementation: Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/tax4fun-predicting-functional-profiles-from-metagenomic-16s-rrna-data/
Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data
Motivation: The characterization of phylogenetic and functional diversity is a key element in the analysis of microbial communities. Amplicon-based sequencing of marker genes, such as 16S rRNA, is a powerful tool for assessing and comparing the structure of microbial communities at a high phylogenetic resolution. Because 16S rRNA sequencing is more cost-effective than whole metagenome shotgun sequencing, marker gene analysis is frequently used for broad studies that involve a large number of different samples. However, in comparison to shotgun sequencing approaches, insights into the functional capabilities of the community get lost when restricting the analysis to taxonomic assignment of 16S rRNA data.
Results: Tax4Fun is a software package that predicts the functional capabilities of microbial communities based on 16S rRNA datasets. We evaluated Tax4Fun on a range of paired metagenome/16S rRNA datasets to assess its performance. Our results indicate that Tax4Fun provides a good approximation to functional profiles obtained from metagenomic shotgun sequencing approaches.
Availability and implementation: Tax4Fun is an open-source R package and applicable to output as obtained from the SILVAngs web server or the application of QIIME with a SILVA database extension. Tax4Fun is freely available for download at http://tax4fun.gobics.de/.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/inspect-a-computational-tool-to-infer-mrna-synthesis-processing-and-degradation-dynamics-from-rna-and-4su-seq-time-course-experiments/
INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments
Motivation: Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking.
Results: INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design.
Availability and implementation: INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1.
Contact: [email protected]
Supplementary Information: Supplementary data are available at Bioinformatics online.
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/ksnp3-0-snp-detection-and-phylogenetic-analysis-of-genomes-without-genome-alignment-or-reference-genome/
kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome
Summary:We announce the release of kSNP3.0, a program for SNP identification and phylogenetic analysis without genome alignment or the requirement for reference genomes. kSNP3.0 is a significantly improved version of kSNP v2.
Availability and implementation: kSNP3.0 is implemented as a package of stand-alone executables for Linux and Mac OS X under the open-source BSD license. The executable packages, source code and a full User Guide are freely available at https://sourceforge.net/projects/ksnp/files/
Contact: [email protected]
New Post has been published on Bioinformatics Research Feeds
New Post has been published on http://bioinformatics.researchfeeds.com/inferring-data-specific-micro-rna-function-through-the-joint-ranking-of-micro-rna-and-pathways-from-matched-micro-rna-and-gene-expression-data/
Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data
Motivation: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult.
Results: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature.
Availability and implementation: This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.