Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.     

Data analysis issues for allele-specific expression using Illumina's GoldenGate assay

Background  

High-throughput measurement of allele-specific expression (ASE) is a relatively new and exciting application area for array-based technologies. In this paper, we explore several data sets which make use of Illumina's GoldenGate BeadArray technology to measure ASE. This platform exploits coding SNPs to obtain relative expression measurements for alleles at approximately 1500 positions in the genome.     

QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data

Array-based technologies have been used to detect chromosomal copy number changes (aneuploidies) in the human genome. Recent studies identified numerous copy number variants (CNV) and some are common polymorphisms that may contribute to disease susceptibility. We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArray SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. Other parameters are set via maximum marginal likelihood to prior training data of known structure. QuantiSNP provides probabilistic quantification of state classifications and significantly improves the accuracy of segmental aneuploidy identification and mapping, relative to existing analytical tools (Beadstudio, Illumina), as demonstrated by validation of breakpoint boundaries. QuantiSNP identified both novel and validated CNVs. QuantiSNP was developed using BeadArray SNP data but it can be adapted to other platforms and we believe that the OB-HMM framework has widespread applicability in genomic research. In conclusion, QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies.     

GEDAS - Gene Expression Data Analysis Suite

Currently available micro-array gene expression data analysis tools lack standardization at various levels. We developed GEDAS (gene expression data analysis suite) to bring various tools and techniques in one system. It also provides a number of other features such as a large collection of distance measures and pre-processing techniques. The software is an extension of Cluster 3.0 (developed based on Eisen Lab's Cluster and Tree View software). GEDAS allows the usage of different datasets with algorithms such as k-means, HC, SVD/PCA and SVM, in addition to Kohonen's SOM and LVQ.

Availability     

CGO: utilizing and integrating gene expression microarray data in clinical research and data management

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.     

Enrichment of ultraconserved elements among genomic imbalances causing mental delay and congenital anomalies

Background

In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required.

Results

We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms.

Conclusions

The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.     

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single‐tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large‐scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large‐scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.     

Investigating the utility of combining Φ29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray™ genotyping

Background

Sustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Φ29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray? genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates.

Results

Eighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA.

Conclusions

We conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples.     

GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox

High-throughput gene expression analysis has become a frequent and powerful research tool in biology. At present, however, few software applications have been developed for biologists to query large microarray gene expression databases using a Web-browser interface. We present GENEVESTIGATOR, a database and Web-browser data mining interface for Affymetrix GeneChip data. Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs. Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs. Using GENEVESTIGATOR, the gene expression profiles of more than 22,000 Arabidopsis genes can be obtained, including those of 10,600 currently uncharacterized genes. The objective of this software application is to direct gene functional discovery and design of new experiments by providing plant biologists with contextual information on the expression of genes. The database and analysis toolbox is available as a community resource at https://www.genevestigator.ethz.ch.     

lumi: a pipeline for processing Illumina microarray

Illumina microarray is becoming a popular microarray platform. The BeadArray technology from Illumina makes its preprocessing and quality control different from other microarray technologies. Unfortunately, most other analyses have not taken advantage of the unique properties of the BeadArray system, and have just incorporated preprocessing methods originally designed for Affymetrix microarrays. lumi is a Bioconductor package especially designed to process the Illumina microarray data. It includes data input, quality control, variance stabilization, normalization and gene annotation portions. In specific, the lumi package includes a variance-stabilizing transformation (VST) algorithm that takes advantage of the technical replicates available on every Illumina microarray. Different normalization method options and multiple quality control plots are provided in the package. To better annotate the Illumina data, a vendor independent nucleotide universal identifier (nuID) was devised to identify the probes of Illumina microarray. The nuID annotation packages and output of lumi processed results can be easily integrated with other Bioconductor packages to construct a statistical data analysis pipeline for Illumina data. Availability: The lumi Bioconductor package, www.bioconductor.org     

Recovering filter-based microarray data for pathways analysis using a multipoint alignment strategy

The use of commercial microarrays is rapidly becoming the method of choice for profiling gene expression and assessing various disease states. Research Genetics has provided a series of biological and software tools to the research community for these analyses. The fidelity of data analysis using these tools is dependent on a series of well-defined reference control points in the array. During the course of our investigations, it became apparent that in some instances the reference control points that are required for analysis became lost in background noise. This effectively halted the analysis and the recovery of any information contained within that experiment. To recover this data and to increase analytical veracity, the simple strategy of superimposing a template of reference control points onto the experimental array was developed. The utility of this tool is established in this communication.     

The Gaggle: An open-source software system for integrating bioinformatics software and data sources

Background  

Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.     

Genome-wide association studies of agronomic and quality traits in a set of German winter barley (Hordeum vulgare L.) cultivars using Diversity Arrays Technology (DArT)

A set of about 100 winter barley (Hordeum vulgare L.) cultivars, comprising diverse and economically important German barley elite germplasm released during the last six decades, was previously genotypically characterized by single nucleotide polymorphism (SNP) markers using the Illumina GoldenGate BeadArray Technology to detect associations with phenotypic data estimated in three-year field trials at 12 locations. In order to identify further associations and to obtain information on whether the marker type influences the outcome of association genetics studies, the set of winter barley cultivars was re-analyzed using Diversity Arrays Technology (DArT) markers. As with the analysis of the SNPs, only polymorphic markers present at an allele frequency >5 % were included to detect associations in a mixed linear model (MLM) approach using the TASSEL software (P?≤?0.001). The population structure and kinship matrix were estimated on 72 simple sequence repeats (SSRs) covering the whole barley genome. The respective average linkage disequilibrium (LD) analyzed with DArT markers was estimated at 5.73 cM. A total of 52 markers gave significant associations with at least one of the traits estimated which, therefore, may be suitable for marker-assisted breeding. In addition, by comparing the results to those generated using the Illumina GoldenGate BeadArray Technology, it turned out that a different number of associations for respective traits is detected, depending on the marker system. However, as only a few of the respective DArT and Illumina markers are present in a common map, no comprehensive comparison of the detected associations was feasible, but some were probably detected in the same chromosomal regions. Because of the identification of additional marker–trait associations, it may be recommended to use both marker techniques in genome-wide association studies.     

AVA: visual analysis of gene expression microarray data

SUMMARY: AVA (Array Visual Analyzer) is a Java program that provides a graphical environment for visualization and analysis of gene expression microarray data. Together with its interactive visualization tools and a variety of built-in data analysis and filtration methods, AVA effectively integrates microarray data normalization, quality assessment, and data mining into one application. AVAILABILITY: The software is freely available for academic users on request from the authors.     

Microarray data analysis made easy

DNA microarrays are valuable tools for analyzing global gene expression. Because of the increasing popularity and the large volume of data produced, tools for facile microarray data analysis are essential. FiRe, a recently introduced computer program, has now solved the seemingly insuperable discrepancy between simplicity and evaluation of DNA microarray data. The program is available as a macro for the popular Microsoft Office Excel software and is user-friendly, interactive, versatile and platform-independent, paving the way for a further push in the evaluation of DNA microarrays.     

Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

This review provides a brief overview of the development of data‐independent acquisition (DIA) mass spectrometry‐based proteomics and selected DIA data analysis tools. Various DIA acquisition schemes for proteomics are summarized first including Shotgun‐CID, DIA, MS^E, PAcIFIC, AIF, SWATH, MSX, SONAR, WiSIM, BoxCar, Scanning SWATH, diaPASEF, and PulseDIA, as well as the mass spectrometers enabling these methods. Next, the software tools for DIA data analysis are classified into three groups: library‐based tools, library‐free tools, and statistical validation tools. The approaches are reviewed for generating spectral libraries for six selected library‐based DIA data analysis software tools which are tested by the authors, including OpenSWATH, Spectronaut, Skyline, PeakView, DIA‐NN, and EncyclopeDIA. An increasing number of library‐free DIA data analysis tools are developed including DIA‐Umpire, Group‐DIA, PECAN, PEAKS, which facilitate identification of novel proteoforms. The authors share their user experience of when to use DIA‐MS, and several selected DIA data analysis software tools. Finally, the state of the art DIA mass spectrometry and software tools, and the authors’ views of future directions are summarized.