DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis

Background

Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates could lead false positive finding, misleading clustering pattern or model over-fitting issue, etc in the subsequent data analysis.

Results

We developed a Bioconductor package Dupchecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. A real data example was demonstrated to show the usage and output of the package.

Conclusions

Researchers may not pay enough attention to checking and removing duplicated samples, and then data contamination could make the results or conclusions from meta-analysis questionable. We suggest applying DupChecker to examine all gene expression data sets before any data analysis step.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-323) contains supplementary material, which is available to authorized users.     

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

Background

Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.

Results

We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.

Conclusions

seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-178) contains supplementary material, which is available to authorized users.     

Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots

Background

Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in ¹H NMR spectra has previously been successfully employed. Similar correlation of 2D ¹H-¹³C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results

From 50 ¹H-¹³C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions

Correlation plots prepared by statistically correlating ¹H-¹³C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0413-z) contains supplementary material, which is available to authorized users.     

Kiwi: a tool for integration and visualization of network topology and gene-set analysis

Background

The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.

Results

Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.

Conclusions

Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0408-9) contains supplementary material, which is available to authorized users.     

Proteomic and metabolomic profiles of larval hemolymph associated with diapause in the cotton bollworm,Helicoverpa armigera

Background

Diapause is programmed developmental arrest coupled with the depression of metabolic activity and the enhancement of stress resistance. Pupal diapause is induced by environmental signals and is prepared during the prediapause phase. In the cotton bollworm, Helicoverpa armigera, the prediapause phase, which contains two sub-phases, diapause induction and preparation, occurs in the larval stage. Here, we performed parallel proteomic and metabolomic analyses on H. armigera larval hemolymph during the prediapause phase.

Results

By two-dimensional electrophoresis, 37 proteins were shown to be differentially expressed in diapause-destined larvae. Of these proteins, 28 were successfully identified by MALDI-TOF/TOF mass spectrometry. Moreover, a total of 22 altered metabolites were found in diapause-destined larval hemolymph by GC-MS analysis, and the levels of 17 metabolites were elevated and 5 were decreased.

Conclusions

The proteins and metabolites with significantly altered levels play different roles in diapause-destined larvae, including diapause induction, metabolic storage, immune response, stress tolerance, and others. Because hemolymph circulates through the whole body of an insect, these differences found in diapause-destined larvae most likely correspond to upstream endocrine signals and would further influence other organ/tissue activities to determine the insect’s fact: diapause or development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-751) contains supplementary material, which is available to authorized users.     

Sources of Variability in Metabolite Measurements from Urinary Samples

Background

The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies.

Methods

We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2–3 samples per person from 17 male subjects (age 38–70) over 2–10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies.

Results

Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of “usual” levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations.

Conclusions

The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.     

SFMetrics: an analysis tool for scanning force microscopy images of biomolecules

Background

Scanning force microscopy (SFM) allows direct, rapid and high-resolution visualization of single molecular complexes; irregular shapes and differences in sizes are immediately revealed by the scanning tip in three-dimensional images. However, high-throughput analysis of SFM data is limited by the lack of versatile software tools accessible to SFM users. Most existing SFM software tools are aimed at broad general use: from material-surface analysis to visualization of biomolecules.

Results

We present SFMetrics as a metrology toolbox for SFM, specifically aimed at biomolecules like DNA and proteins, which features (a) semi-automatic high-throughput analysis of individual molecules; (b) ease of use working within MATLAB environment or as a stand-alone application; (c) compatibility with MultiMode (Bruker), NanoWizard (JPK instruments), Asylum (Asylum research), ASCII, and TIFF files, that can be adjusted with minor modifications to other formats.

Conclusion

Assembled in a single user interface, SFMetrics serves as a semi-automatic analysis tool capable of measuring several geometrical properties (length, volume and angles) from DNA and protein complexes, but is also applicable to other samples with irregular shapes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0457-8) contains supplementary material, which is available to authorized users.     

BACA: bubble chArt to compare annotations

Background

DAVID is the most popular tool for interpreting large lists of gene/proteins classically produced in high-throughput experiments. However, the use of DAVID website becomes difficult when analyzing multiple gene lists, for it does not provide an adequate visualization tool to show/compare multiple enrichment results in a concise and informative manner.

Result

We implemented a new R-based graphical tool, BACA (Bubble chArt to Compare Annotations), which uses the DAVID web service for cross-comparing enrichment analysis results derived from multiple large gene lists. BACA is implemented in R and is freely available at the CRAN repository (http://cran.r-project.org/web/packages/BACA/).

Conclusion

The package BACA allows R users to combine multiple annotation charts into one output graph by passing DAVID website.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0477-4) contains supplementary material, which is available to authorized users.     

Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing

Background

V(D)J recombinations in lymphocytes are essential for immunological diversity. They are also useful markers of pathologies. In leukemia, they are used to quantify the minimal residual disease during patient follow-up. However, the full breadth of lymphocyte diversity is not fully understood.

Results

We propose new algorithms that process high-throughput sequencing (HTS) data to extract unnamed V(D)J junctions and gather them into clones for quantification. This analysis is based on a seed heuristic and is fast and scalable because in the first phase, no alignment is performed with germline database sequences. The algorithms were applied to TR γ HTS data from a patient with acute lymphoblastic leukemia, and also on data simulating hypermutations. Our methods identified the main clone, as well as additional clones that were not identified with standard protocols.

Conclusions

The proposed algorithms provide new insight into the analysis of high-throughput sequencing data for leukemia, and also to the quantitative assessment of any immunological profile. The methods described here are implemented in a C++ open-source program called Vidjil.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-409) contains supplementary material, which is available to authorized users.     

mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics

Background

So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis.

Results

mAPKL is an open-source R/Bioconductor package that implements the mAP-KL hybrid gene selection method. The advantage of this method is that selects a small number of gene exemplars while achieving comparable classification results to other well established algorithms on a variety of datasets and dataset sizes. The mAPKL package is accompanied with extra functionalities including (i) solid data import; (ii) data sampling following a user-defined proportion; (iii) preprocessing through several normalization and transformation alternatives; (iv) classification with the aid of SVM and performance evaluation; (v) network analysis of the significant genes (exemplars), including degree of centrality, closeness, betweeness, clustering coefficient as well as the construction of an edge list table; (vi) gene annotation analysis, (vii) pathway analysis and (viii) auto-generated analysis reporting.

Conclusions

Users are able to run a thorough gene expression analysis in a timely manner starting from raw data and concluding to network characteristics of the selected gene exemplars. Detailed instructions and example data are provided in the R package, which is freely available at Bioconductor under the GPL-2 or later license http://www.bioconductor.org/packages/3.1/bioc/html/mAPKL.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0719-5) contains supplementary material, which is available to authorized users.     

Baitmet,a computational approach for GC–MS library-driven metabolite profiling

Introduction

Current computational tools for gas chromatography—mass spectrometry (GC–MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC–MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently.

Results

This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC–MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library.

Conclusions

Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC–MS via freely available spectral data. Baitmet is freely available at http://CRAN.R-project.org/package=baitmet.

     

Integrating protein structural dynamics and evolutionary analysis with Bio3D

Background

Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.

Results

Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

Conclusions

The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0399-6) contains supplementary material, which is available to authorized users.     

Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome

Background

Advances in “omics” technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting “big data.” Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome.

Methodology

Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data.

Conclusions

PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined “modules” of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.     

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq

Background

Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples.

Results

False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values.

Conclusions

Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1767-y) contains supplementary material, which is available to authorized users.     

Simultaneous analysis of large-scale RNAi screens for pathogen entry

Background

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.

Results

We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.

Conclusions

Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1162) contains supplementary material, which is available to authorized users.     

Metabolic pathways for the whole community

Background

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change.

Results

Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools’ performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients.

Conclusions

This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-619) contains supplementary material, which is available to authorized users.