Quantitative gene expression profiles in real time from expressed sequence tag databases.

An accumulation of expressed sequence tag (EST) data in the public domain and the availability of bioinformatic programs have made EST gene expression profiling a common practice. However, the utility and validity of using EST databases (e.g., dbEST) has been criticized, particularly for quantitative assessment of gene expression. Problems with EST sequencing errors, library construction, EST annotation, and multiple paralogs make generation of specific and sensitive qualitative arid quantitative expression profiles a concern. In addition, most EST-derived expression data exists in previously assembled databases. The Virtual Northern Blot (VNB) (http: //tlab.bu.edu/vnb.html) allows generation, evaluation, and optimization of expression profiles in real time, which is especially important for alternatively spliced, novel, or poorly characterized genes. Representative gene families with variable nucleotide sequence identity, tissue specificity, and levels of expression (bcl-xl, aldoA, and cyp2d9) are used to assess the quality of VNB's output. The profiles generated by VNB are more sensitive and specific than those constructed with ESTs listed in preindexed databases at UCSC and NCBI. Moreover, quantitative expression profiles produced by VNB are comparable to quantization obtained from Northern blots and qPCR. The VNB pipeline generates real-time gene expression profiles for single-gene queries that are both qualitatively and quantitatively reliable.

Finding local communities in protein networks.

BACKGROUND: Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. RESULTS: We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. CONCLUSION: The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to investigate functional modules that a particular protein is a part of.

Spectral affinity in protein networks.

BACKGROUND: Protein-protein interaction (PPI) networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. RESULTS: We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. CONCLUSION: We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to quickly find nodes closest to a queried vertex in any protein network available from BioGRID or specified by the user.