k-link EST clustering: evaluating error introduced by chimeric sequences under different degrees of linkage

Clustering algorithms in biomedical research: a review

Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.

Allele identification in assembled genomic sequence datasets

Allelic variation within species provides fundamental insights into the evolution and ecology of organisms, and information about this variation is becoming increasingly available in sequence datasets of multiple and/or outbred individuals. Unfortunately, identifying true allelic variants poses a number of challenges, given the presence of both sequencing errors and alleles from other closely related loci. We outline the key considerations involved in this process, including assessing the accuracy of allele resolution in sequence assembly, clustering of alleles within and among individuals, and identifying clusters that are most likely to correspond to true allelic variants of a single locus. Our focus is particularly on the case where alleles must be identified without a fully resolved reference genome, and where sequence depth information cannot be used to infer the putative number of loci sharing a sequence, such as in transcriptome or post-assembly datasets. Throughout, we provide information about publicly available tools to aid allele identification in such cases.