Shared probe design and existing microarray reanalysis using <Emphasis Type="SmallCaps">PICKY</Emphasis>

Background  

Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations.     

The Longhorn Array Database (LAD): An Open-Source,MIAME compliant implementation of the Stanford Microarray Database (SMD)

Background  

The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms.     

Avoiding inconsistencies over time and tracking difficulties in Applied Biosystems AB1700™/Panther™ probe-to-gene annotations

Background  

Significant inconsistencies between probe-to-gene annotations between different releases of probe set identifiers by commercial microarray platform solutions have been reported. Such inconsistencies lead to misleading or ambiguous interpretation of published gene expression results.     

ArrayIDer: automated structural re-annotation pipeline for DNA microarrays

Background  

Systems biology modeling from microarray data requires the most contemporary structural and functional array annotation. However, microarray annotations, especially for non-commercial, non-traditional biomedical model organisms, are often dated. In addition, most microarray analysis tools do not readily accept EST clone names, which are abundantly represented on arrays. Manual re-annotation of microarrays is impracticable and so we developed a computational re-annotation tool (ArrayIDer) to retrieve the most recent accession mapping files from public databases based on EST clone names or accessions and rapidly generate database accessions for entire microarrays.     

Combining gene expression data from different generations of oligonucleotide arrays

Background  

One of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public repositories. Through a comparative analysis on a variety of datasets, a more comprehensive view of the underlying mechanism or structure can be obtained. However, as we discover in this work, continual changes in genomic sequence annotations and probe design criteria make it difficult to compare gene expression data even from different generations of the same microarray platform.     

MIMAS: an innovative tool for network-based high density oligonucleotide microarray data management and annotation

Background  

The high-density oligonucleotide microarray (GeneChip) is an important tool for molecular biological research aiming at large-scale detection of small nucleotide polymorphisms in DNA and genome-wide analysis of mRNA concentrations. Local array data management solutions are instrumental for efficient processing of the results and for subsequent uploading of data and annotations to a global certified data repository at the EBI (ArrayExpress) or the NCBI (GeneOmnibus).     

Comparing functional annotation analyses with Catmap

Background  

Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis.     

BASE - 2nd generation software for microarray data management and analysis

Background  

Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2.     

<Emphasis Type="Italic">μ</Emphasis>-CS: An extension of the TM4 platform to manage Affymetrix binary data

Background  

A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.     

ZCURVE_V: a new self-training system for recognizing protein-coding genes in viral and phage genomes

Background  

It necessary to use highly accurate and statistics-based systems for viral and phage genome annotations. The GeneMark systems for gene-finding in virus and phage genomes suffer from some basic drawbacks. This paper puts forward an alternative approach for viral and phage gene-finding to improve the quality of annotations, particularly for newly sequenced genomes.     

The Use of Edge-Betweenness Clustering to Investigate Biological Function in Protein Interaction Networks

Background  

This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.     

Automatic detection of false annotations via binary property clustering

Background  

Computational protein annotation methods occasionally introduce errors. False-positive (FP) errors are annotations that are mistakenly associated with a protein. Such false annotations introduce errors that may spread into databases through similarity with other proteins. Generally, methods used to minimize the chance for FPs result in decreased sensitivity or low throughput. We present a novel protein-clustering method that enables automatic separation of FP from true hits. The method quantifies the biological similarity between pairs of proteins by examining each protein's annotations, and then proceeds by clustering sets of proteins that received similar annotation into biological groups.     

Correction: Disease candidate gene identification and prioritization using protein interaction networks

Background  

Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses.     

Integrating sequence and structural biology with DAS

Background  

The Distributed Annotation System (DAS) is a network protocol for exchanging biological data. It is frequently used to share annotations of genomes and protein sequence.     

VisualRepbase: an interface for the study of occurrences of transposable element families

Background  

Repbase is a reference database of eukaryotic repetitive DNA, which includes prototypic sequences of repeats and basic information described in annotations. Repbase already has software for entering new sequence families and for comparing the user's sequence with the database of consensus sequences.     

TabSQL: a MySQL tool to facilitate mapping user data to public databases

Background  

With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases.