Downstream Data Analysis Research Articles

Matrix-localization for fast analysis of arrayed microfluidic immunoassays

High-throughput assays necessitate high-throughput data analysis. Arrayed microfluidic immunoassay shows the capability of high-throughput protein detection. However, its development was restricted by the low efficiency of downstream data analysis. We present herein programming-based image processing through the local recognition of a sub-array followed by the region-growing algorithm to achieve fast, convenient and precise extraction of information with reduced personal bias.

Mining SOM expression portraits: Feature selection and integrating concepts of molecular function

Abstract Background: Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function. Results: Different expression scores based either on simple fold change-measures or on regularized Students t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues, as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and highly expressed genes using SOM data filtering. Conclusions: The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors.

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.

Introduction and Historical Overview of DNA Sequencing

AbstractThe process of DNA sequencing has made tremendous strides in throughput, improved accuracy, ease of production, and lowered cost. As the practice of DNA sequencing has improved, so has the downstream data analysis with sophisticated databases and bioinformatics tools. Together, these advances have enlarged the number of applications upon which DNA sequencing can be brought to bear. This introductory unit provides a description of DNA sequencing with a focus on current and “NextGen” (second and third generation) automated technologies and applications. Curr. Protoc. Mol. Biol. 96:7.0.1‐7.0.18. © 2011 by John Wiley & Sons, Inc.

Incorporating Nonlinear Relationships in Microarray Missing Value Imputation

Microarray gene expression data often contain missing values. Accurate estimation of the missing values is important for downstream data analyses that require complete data. Nonlinear relationships between gene expression levels have not been well-utilized in missing value imputation. We propose an imputation scheme based on nonlinear dependencies between genes. By simulations based on real microarray data, we show that incorporating nonlinear relationships could improve the accuracy of missing value imputation, both in terms of normalized root-mean-squared error and in terms of the preservation of the list of significant genes in statistical testing. In addition, we studied the impact of artificial dependencies introduced by data normalization on the simulation results. Our results suggest that methods relying on global correlation structures may yield overly optimistic simulation results when the data have been subjected to row (gene)-wise mean removal.

SAQC: SNP Array Quality Control

BackgroundGenome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed.ResultsWe developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples.ConclusionsThis study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).

Abstract LB-262: Estimation of tumor content from exome sequencing data

Abstract In cancer genomics studies, variation in sample purity can greatly influence the ability to detect cancer-specific genomic aberrations. Genomics datasets are often analyzed as if samples consist of 100% cancerous or benign cells, which is rarely true in practice. Particularly in prostate tumors, one always expects tumor samples to consist of a mixture of cancer cells, stromal cells, and benign cells. Here we use next-generation exome sequencing data to estimate the proportion of cancer cells by sample, which may then be used for downstream analysis. Using human whole-exome capture data from 14 prostate tumors and matched benign tissues from the same patients, we developed a statistical method for estimating tumor content. Specifically, we generated a list of single-nucleotide variant (SNV) candidates derived from the sequencing data and used these candidates to fit a two-component binomial mixture model. The two components are assumed to consist of a set of experimental artifacts such as sequencing errors which tend to exhibit low fractions of variant reads, and a set of true SNVs whose variant fractions are related to the unknown tumor content. Estimation, achieved via the EM algorithm, results in a probabilistic classification of the SNV candidates as well as an estimated proportion of cancer cells in each sample. As expected, 6 of 7 metastatic samples had high estimated tumor content (>70%). In contrast, among the set of 7 localized cancer samples, most of which exhibited an absence of copy number aberrations by aCGH, only 3 had enough SNVs to reliably estimate tumor content. Tumor content in these samples varied: two of the three samples had tumor content of approximately 70% while the third was estimated to be 35%. Knowledge of a sample's tumor content may be used in any downstream analysis of genomic data; here we point out three applications. First, this method allows for rigorous quality control and exclusion of samples based on tumor content as estimated from the data. Second, it can improve the quality of SNV calling from next-generation sequencing data. To test this, we performed Sanger sequencing on 30 SNV candidates from one of our localized cancer samples and compared validation status with the predictions from our model. Notably, our model predicted validation status perfectly (18 validated; 12 did not) on this set of candidates. Third, precise knowledge of tumor content enabled us to explain variation in copy number profiles by aCGH. We found that log-ratios for regions of gain and loss were smaller in magnitude for samples with lower tumor content; this has major implications for calling aberrant regions from aCGH data. Thus, we anticipate that the methodology described here will be useful in refining many standard methods of analysis so that a clearer picture of aberrations in prostate cancer can emerge. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-262. doi:10.1158/1538-7445.AM2011-LB-262

SAMMate: a GUI tool for processing short read alignments in SAM/BAM format

BackgroundNext Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information.ResultsWe have developed a Graphical User Interface (GUI) software tool named SAMMate. SAMMate allows biomedical researchers to quickly process SAM/BAM files and is compatible with both single-end and paired-end sequencing technologies. SAMMate also automates some standard procedures in DNA-seq and RNA-seq data analysis. Using either standard or customized annotation files, SAMMate allows users to accurately calculate the short read coverage of genomic intervals. In particular, for RNA-seq data SAMMate can accurately calculate the gene expression abundance scores for customized genomic intervals using short reads originating from both exons and exon-exon junctions. Furthermore, SAMMate can quickly calculate a whole-genome signal map at base-wise resolution allowing researchers to solve an array of bioinformatics problems. Finally, SAMMate can export both a wiggle file for alignment visualization in the UCSC genome browser and an alignment statistics report. The biological impact of these features is demonstrated via several case studies that predict miRNA targets using short read alignment information files.ConclusionsWith just a few mouse clicks, SAMMate will provide biomedical researchers easy access to important alignment information stored in SAM/BAM files. Our software is constantly updated and will greatly facilitate the downstream analysis of NGS data. Both the source code and the GUI executable are freely available under the GNU General Public License at http://sammate.sourceforge.net.

Processing and analyzing ChIP-seq data: from short reads to regulatory interactions

Chromatin-immunoprecipitation and sequencing (ChIP-seq) is a rapidly maturing technology that draws on the power of high-throughput short-read sequencing to decipher chromatin states with unprecedented precision and breadth. Although some aspects of the experimental protocol require careful tuning, the bottleneck currently firmly lies with the downstream data analysis. We give an overview of the better-established aspects of genome mapping and data normalization and we describe the more recent progress in peak calling and their statistical analysis and provide a brief overview of popular follow-up analyses such as genomic feature categorization and motif search.

Machine learning based prediction for peptide drift times in ion mobility spectrometry.

Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting. When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4). The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php rds@pnl.gov Supplementary data are available at Bioinformatics online.

What Bandwidth Do I Need for My Image?

ABSTRACTComputer representations of real numbers are necessarily discrete, with some finite resolution, discreteness, quantization, or minimum representable difference. We perform astrometric and photometric measurements on stars and co-add multiple observations of faint sources to demonstrate that essentially all of the scientific information in an optical astronomical image can be preserved or transmitted when the minimum representable difference is a factor of 2 finer than the root variance of the per-pixel noise. Adopting a representation this coarse reduces bandwidth for data acquisition, transmission, or storage, or permits better use of the system dynamic range, without sacrificing any information for downstream data analysis, including information on sources fainter than the minimum representable difference itself.

Merging Mixture Components for Cell Population Identification in Flow Cytometry

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project.

A novel peak detection approach with chemical noise removal using short‐time FFT for prOTOF MS data

Peak detection is a pivotal first step in biomarker discovery from MS data and can significantly influence the results of downstream data analysis steps. We developed a novel automatic peak detection method for prOTOF MS data, which does not require a priori knowledge of protein masses. Random noise is removed by an undecimated wavelet transform and chemical noise is attenuated by an adaptive short-time discrete Fourier transform. Isotopic peaks corresponding to a single protein are combined by extracting an envelope over them. Depending on the S/N, the desired peaks in each individual spectrum are detected and those with the highest intensity among their peak clusters are recorded. The common peaks among all the spectra are identified by choosing an appropriate cut-off threshold in the complete linkage hierarchical clustering. To remove the 1 Da shifting of the peaks, the peak corresponding to the same protein is determined as the detected peak with the largest number among its neighborhood. We validated this method using a data set of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances S/N of the peak after the chemical noise removal. We then successfully applied this method to a data set from prOTOF MS spectra of albumin and albumin-bound proteins from serum samples of 59 patients with carotid artery disease compared to vascular disease-free patients to detect peaks with S/N> or =2. Our method is easily implemented and is highly effective to define peaks that will be used for disease classification or to highlight potential biomarkers.

OpenADAM: an open source genome-wide association data management system for Affymetrix SNP arrays

BackgroundLarge scale genome-wide association studies have become popular since the introduction of high throughput genotyping platforms. Efficient management of the vast array of data generated poses many challenges.DescriptionWe have developed an open source web-based data management system for the large amount of genotype data generated from the Affymetrix GeneChip® Mapping Array and Affymetrix Genome-Wide Human SNP Array platforms. The database supports genotype calling using DM, BRLMM, BRLMM-P or Birdseed algorithms provided by the Affymetrix Power Tools. The genotype and corresponding pedigree data are stored in a relational database for efficient downstream data manipulation and analysis, such as calculation of allele and genotype frequencies, sample identity checking, and export of genotype data in various file formats for analysis using commonly-available software. A novel method for genotyping error estimation is implemented using linkage disequilibrium information from the HapMap project. All functionalities are accessible via a web-based user interface.ConclusionOpenADAM provides an open source database system for management of Affymetrix genome-wide association SNP data.

FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data

We present FiD (Fragment iDentificator), a software tool for the structural identification of product ions produced with tandem mass spectrometric measurement of low molecular weight organic compounds. Tandem mass spectrometry (MS/MS) has proven to be an indispensable tool in modern, cell-wide metabolomics and fluxomics studies. In such studies, the structural information of the MS(n) product ions is usually needed in the downstream analysis of the measurement data. The manual identification of the structures of MS(n) product ions is, however, a nontrivial task requiring expertise, and calls for computer assistance. Commercial software tools, such as Mass Frontier and ACD/MS Fragmenter, rely on fragmentation rule databases for the identification of MS(n) product ions. FiD, on the other hand, conducts a combinatorial search over all possible fragmentation paths and outputs a ranked list of alternative structures. This gives the user an advantage in situations where the MS/MS data of compounds with less well-known fragmentation mechanisms are processed. FiD software implements two fragmentation models, the single-step model that ignores intermediate fragmentation states and the multi-step model, which allows for complex fragmentation pathways. The software works for MS/MS data produced both in positive- and negative-ion modes. The software has an easy-to-use graphical interface with built-in visualization capabilities for structures of product ions and fragmentation pathways. In our experiments involving amino acids and sugar-phosphates, often found, e.g., in the central carbon metabolism of yeasts, FiD software correctly predicted the structures of product ions on average in 85% of the cases. The FiD software is free for academic use and is available for download from www.cs.helsinki.fi/group/sysfys/software/fragid.

Bisulfite sequencing Data Presentation and Compilation (BDPC) web server – a useful tool for DNA methylation analysis

During bisulfite genomic sequencing projects large amount of data are generated. The Bisulfite sequencing Data Presentation and Compilation (BDPC) web interface (http://biochem.jacobs-university.de/BDPC/) automatically analyzes bisulfite datasets prepared using the BiQ Analyzer. BDPC provides the following output: (i) MS-Excel compatible files compiling for each PCR product (a) the average methylation level, the number of clones analyzed and the percentage of CG sites analyzed (which is an indicator of data quality), (b) the methylation level observed at each CG site and (c) the methylation level of each clone. (ii) A methylation overview table compiling the methylation of all amplicons in all tissues. (iii) Publication grade figures in PNG format showing the methylation pattern for each PCR product embedded in an HMTL file summarizing the methylation data, the DNA sequence and some basic statistics. (iv) A summary file compiling the methylation pattern of different tissues, which is linked to the individual HTML result files, and can be directly used for presentation of the data in the internet. (v) A condensed file, containing all primary data in simplified format for further downstream data analysis and (vi) a custom track file for display of the results in the UCSC genome browser.

Bisulfite sequencing Data Presentation and Compilation (BDPC) web server--a useful tool for DNA methylation analysis

During bisulfite genomic sequencing projects large amount of data are generated. The Bisulfite sequencing Data Presentation and Compilation (BDPC) web interface (http://biochem.jacobs-university.de/BDPC/) automatically analyzes bisulfite datasets prepared using the BiQ Analyzer. BDPC provides the following output: (i) MS-Excel compatible files compiling for each PCR product (a) the average methylation level, the number of clones analyzed and the percentage of CG sites analyzed (which is an indicator of data quality), (b) the methylation level observed at each CG site and (c) the methylation level of each clone. (ii) A methylation overview table compiling the methylation of all amplicons in all tissues. (iii) Publication grade figures in PNG format showing the methylation pattern for each PCR product embedded in an HMTL file summarizing the methylation data, the DNA sequence and some basic statistics. (iv) A summary file compiling the methylation pattern of different tissues, which is linked to the individual HTML result files, and can be directly used for presentation of the data in the internet. (v) A condensed file, containing all primary data in simplified format for further downstream data analysis and (vi) a custom track file for display of the results in the UCSC genome browser.

Repairing triangular meshes for reverse engineering applications

When an object is digitized and represented in a triangular model, erroneous facets may exist and affect the accuracy of the downstream data analysis algorithms. We here propose an approach to detect and eliminate erroneous facets that might exist in a triangular model. Five types of erroneous facets are identified in this study: degenerate, non-manifold vertices, self-intersection, incomplete connection and inconsistent plane normal. Of these erroneous facets, the first two types must be processed first since they are correlated to the other three types of errors. An individual algorithm is proposed for each type of the errors, and an integrated procedure is then proposed to detect and eliminate all errors automatically. Finally, several examples are presented to demonstrate the feasibility of the proposed method.

Handling missing DNA microarray data by kriging estimators

Microarray gene expression data provide life science researchers with much more sensitive and detailed information about gene expression patterns than conventional methodologies for the purpose of facilitating gene recognition efforts. However, due to insufficient image resolution and noise generated during microarray experiments, gene expression matrices are frequently represented with missing elements. Methods for estimating missing microarray data are therefore needed to allow further analysis. In this paper, we present two kriging estimators for estimating missing values in DNA microarrays. These approaches can be useful for downstream analysis of microarray-based gene expression data.