“PANNZER – a practical tool for protein function prediction”

ABSTRACT The facility of next-generation sequencing has led to an explosion of gene catalogs for novel genomes, transcriptomes and metagenomes, which are functionally uncharacterized. Computational inference has emerged as a necessary substitute for first-hand experimental evidence. PANNZER (Protein ANNotation with Z-scoRE) is a high-throughput functional annotation web server that stands out among similar publically accessible web servers in supporting submission of up to 100,000 protein sequences at once and providing both Gene Ontology (GO) annotations and free text description predictions. Here, we demonstrate the use of PANNZER and discuss future plans and challenges. We present two case studies to illustrate problems related to data quality and method evaluation. Some commonly used evaluation metrics and used evaluation datasets promote methods that that favor unspecific and broad classes over more informative and specific classes. We argue that this can bias the development of automated function prediction methods. The PANNZER web server and source code are available at http://ekhidna2.biocenter.helsinki.fi/sanspanz/. This article is protected by copyright. All rights reserved.Peer reviewe

Novel split quality measures for stratified multilabel cross validation with application to large and sparse gene ontology datasets

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POXO: a web-enabled tool series to discover transcription factor binding sites

We present POXO, a comprehensive tool series to discover transcription factor binding sites from co-expressed genes (). POXO manages tasks such as functional evaluation and grouping of genes, sequence retrieval, pattern discovery and pattern verification. It also allows users to tailor analytical pipelines from these tools, with single mouse clicks. One typical pipeline of POXO begins by examining the biological functions that a set of co-expressed genes are involved in. In this examination, the functional coherence of the gene set is evaluated and representative functions are associated with the gene set. This examination can also be used to group genes into functionally similar subsets, if several biological processes are affected in the experiment. The next step in the pipeline is then to discover over-represented nucleotide patterns from the upstream sequences of the selected gene sets. This enables to investigate the possibility that the genes are co-regulated by common cis-elements. If over-represented patterns are found, similar ones can then be clustered together and be verified. The performance of POXO is demonstrated by analysing expression data from pathogen treated Arabidopsis thaliana. In this example, POXO detected activated gene sets and suggested transcription factors responsible for their regulation

Generation of Gene Ontology benchmark datasets with various types of positive signal

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Trustworthiness and metrics in visualizing similarity of gene expression

BACKGROUND: Conventionally, the first step in analyzing the large and high-dimensional data sets measured by microarrays is visual exploration. Dendrograms of hierarchical clustering, self-organizing maps (SOMs), and multidimensional scaling have been used to visualize similarity relationships of data samples. We address two central properties of the methods: (i) Are the visualizations trustworthy, i.e., if two samples are visualized to be similar, are they really similar? (ii) The metric. The measure of similarity determines the result; we propose using a new learning metrics principle to derive a metric from interrelationships among data sets. RESULTS: The trustworthiness of hierarchical clustering, multidimensional scaling, and the self-organizing map were compared in visualizing similarity relationships among gene expression profiles. The self-organizing map was the best except that hierarchical clustering was the most trustworthy for the most similar profiles. Trustworthiness can be further increased by treating separately those genes for which the visualization is least trustworthy. We then proceed to improve the metric. The distance measure between the expression profiles is adjusted to measure differences relevant to functional classes of the genes. The genes for which the new metric is the most different from the usual correlation metric are listed and visualized with one of the visualization methods, the self-organizing map, computed in the new metric. CONCLUSIONS: The conjecture from the methodological results is that the self-organizing map can be recommended to complement the usual hierarchical clustering for visualizing and exploring gene expression data. Discarding the least trustworthy samples and improving the metric still improves it

Interspliced transcription chimeras: Neglected pathological mechanism infiltrating gene accession queries?

AbstractOver half of the DNA of mammalian genomes is transcribed, and one of the emerging enigmas in the field of RNA research is intergenic splicing or transcription induced chimerism. We argue that fused low-copy-number transcripts constitute neglected pathological mechanism akin to copy number variation, due to loss of stoichiometric subunit ratios in protein complexes. An obstacle for transcriptomics meta-analysis of published microarrays is the traditional nomenclature of merged transcript neighbors under same accession codes. Tandem transcripts cover 4–20% of genomes but are only loosely overlapping in population. They were most enriched in systems medicine annotations concerning neurology, thalassemia and genital disorders in the GeneGo Inc. MetaCore-MetaDrugTM knowledgebase, evaluated with external randomizations here. Clinical transcriptomics is good news since new disease etiologies offer new remedies. We identified homeotic HOX-transfactors centered around BMI-1, the Grb2 adaptor network, the kallikrein system, and thalassemia RNA surveillance as vulnerable hotspot chimeras. As a cure, RNA interference would require verification of chimerism from symptomatic tissue contra healthy control tissue from the same patient

Robust multi-group gene set analysis with few replicates

Background: Competitive gene set analysis is a standard exploratory tool for gene expression data. Permutation-based competitive gene set analysis methods are preferable to parametric ones because the latter make strong statistical assumptions which are not always met. For permutation-based methods, we permute samples, as opposed to genes, as doing so preserves the inter-gene correlation structure. Unfortunately, up until now, sample permutation-based methods have required a minimum of six replicates per sample group. Results: We propose a new permutation-based competitive gene set analysis method for multi-group gene expression data with as few as three replicates per group. The method is based on advanced sample permutation technique that utilizes all groups within a data set for pairwise comparisons. We present a comprehensive evaluation of different permutation techniques, using multiple data sets and contrast the performance of our method, mGSZm, with other state of the art methods. We show that mGSZm is robust, and that, despite only using less than six replicates, we are able to consistently identify a high proportion of the top ranked gene sets from the analysis of a substantially larger data set. Further, we highlight other methods where performance is highly variable and appears dependent on the underlying data set being analyzed. Conclusions: Our results demonstrate that robust gene set analysis of multi-group gene expression data is permissible with as few as three replicates. In doing so, we have extended the applicability of such approaches to resource constrained experiments where additional data generation is prohibitively difficult or expensive. An R package implementing the proposed method and supplementary materials are available from the website http:// ekhidna.biocenter.helsinki.fi/downloads/pashupati/mGSZm.html.Peer reviewe

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

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Defense-related transcription factors WRKY70 and WRKY54 modulate osmotic stress tolerance by regulating stomatal aperture in Arabidopsis

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