Clustering Expressed Sequence Tags Research Articles

A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family

BackgroundClustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes.Methodology/Principal FindingsWe propose a hybrid distance measure that combines the global and local features extracted from ESTs, with the aim to address the clustering problem faced by ESTs derived from the same gene family. The clustering process is implemented using the DBSCAN algorithm. We test the hybrid distance measure on the ten EST datasets, and the clustering results are compared with the two alignment-free EST clustering tools, i.e. wcd and PEACE. The clustering results indicate that the proposed hybrid distance measure performs relatively better (in terms of clustering accuracy) than both EST clustering tools.Conclusions/SignificanceThe clustering results provide support for the effectiveness of the proposed hybrid distance measure in solving the clustering problem for ESTs that originate from the same gene family. The improvement of clustering accuracies on the experimental datasets has supported the claim that the sensitivity of the hybrid distance measure is sufficient to solve the clustering problem.

An overview of the wcd EST clustering tool

Summary: The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d 2 distance function or edit distance, improving existing implementations of d 2. It supports merging, refinement and reclustering of clusters. It is ‘drop in’ compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. It is distributed with an EMBOSS wrapper allowing wcd to be installed as part of an EMBOSS installation (and so provided by a web server).Availability: wcd is distributed under a GPL licence and is available from http://code.google.com/p/wcdestContact: scott.hazelhurst@wits.ac.zaSupplementary information: Additional experimental results. The wcd manual, a companion paper describing underlying algorithms, and all datasets used for experimentation can also be found at www.bioinf.wits.ac.za/~scott/wcdsupp.html

Identification of polyadenylation signals and alternative polyadenylation in Vitis vinifera based on ESTs data

Polyadenylation, one of the post-transcriptional processing events, requires some sequence signals on the 3′ end of pre-mRNA to determine the polyadenylation site (PS). Some pre-mRNAs contain multiple polyadenylation sites, producing mature transcripts with 3′ end of variable length. To elucidate polyadenylation signals and alternative polyadenylation in Vitis vinifera, we extracted 62,811 3′ expressed sequence tags (ESTs) of V. vinifera in each UniGene entry and aligned the clustered ESTs of each transcription unit. After filtering artificial poly(A) sites, a total of 27.9% of the sequences examined were found to contain alternative polyadenylation sites. To clarify the pattern of polyadenylation signals, three datasets of sequences containing polyadenylation site were built. Analysis of sequence features in the datasets indicated that the content of uracil begins to increase up to 40% 100 nucleotides (nt) before the PS. Nearly half of the sequences examined showed stringent requirement of CA or UA at cleavage and poly(A) addition sites. The sequence at −15 nt to −25 nt upstream of the PS has been characterized as an A-rich motif, corresponding to the strict polyadenylation signal, AAUAAA, in animals. In grape, most A-rich motifs are variants of AAUAAA. PS commonly occurs following and preceding U-rich element, without a dominant specific hexanucleotide motif. The whole pattern of polyadenylation signals in V. vinifera is more similar to yeasts than animals.

The transcript composition of egg cells changes significantly following fertilization in wheat (Triticum aestivum L.)

Here, we report the transcript profile of wheat egg cells and proembryos, just after the first cell division. Microdissected female gametophytes of wheat were used to isolate eggs and two-celled proembryos to construct cell type-specific cDNA libraries. In total, 1197 expressed sequence tags (ESTs) were generated. Analysis of these ESTs revealed numerous novel transcripts. In egg cells, 17.6% of the clustered ESTs represented novel transcripts, while 11.4% novel clusters were identified in the two-celled proembryo. Functional classification of sequences with similarity to previously characterized proteins indicates that the unfertilized egg cell has a higher metabolic activity and protein turnover than previously thought. Transcript composition of two-celled proembryos was significantly distinct from egg cells, reflecting DNA replication as well as high transcriptional and translational activity. Several novel transcripts of the egg cell are specific for this cell. In contrast, some fertilization induced novel mRNAs are abundant also in sporophytic tissues indicating a more general role in plant growth and development. The potential functions of genes based on similarity to known genes involved in developmental processes are discussed. Our analysis has identified numerous genes with potential roles in embryo sac function such as signaling, fertilization or induction of embryogenesis.

A set of 840 mouse oocyte genes with well-matched human homologues.

GenBank contains 14 477 expressed sequence tags (EST) derived from mouse oocyte cDNA libraries: 3499 of these are from two unfertilized oocyte libraries and 10 978 are from two fertilized oocyte libraries. Gene expression profiles were obtained for these libraries by matching library EST to UniGene clusters. The 14 477 EST identified 4226 UNIGENES: These were screened using HomoloGene to identify 1386 homologous UniGene clusters in two other species with one of the matches being human. Within these human matches, 840 encoded named proteins, 223 encoded hypothetical proteins, and 323 encoded clustered EST. The set of named genes provides the first step in establishing a database of genes expressed in mouse oocytes and, by extension, human oocytes.

D2_cluster: a validated method for clustering EST and full-length cDNAsequences.

Several efforts are under way to condense single-read expressed sequence tags (ESTs) and full-length transcript data on a large scale by means of clustering or assembly. One goal of these projects is the construction of gene indices where transcripts are partitioned into index classes (or clusters) such that they are put into the same index class if and only if they represent the same gene. Accurate gene indexing facilitates gene expression studies and inexpensive and early partial gene sequence discovery through the assembly of ESTs that are derived from genes that have yet to be positionally cloned or obtained directly through genomic sequencing. We describe d2_cluster, an agglomerative algorithm for rapidly and accurately partitioning transcript databases into index classes by clustering sequences according to minimal linkage or "transitive closure" rules. We then evaluate the relative efficiency of d2_cluster with respect to other clustering tools. UniGene is chosen for comparison because of its high quality and wide acceptance. It is shown that although d2_cluster and UniGene produce results that are between 83% and 90% identical, the joining rate of d2_cluster is between 8% and 20% greater than UniGene. Finally, we present the first published rigorous evaluation of under and over clustering (in other words, of type I and type II errors) of a sequence clustering algorithm, although the existence of highly identical gene paralogs means that care must be taken in the interpretation of the type II error. Upper bounds for these d2_cluster error rates are estimated at 0.4% and 0.8%, respectively. In other words, the sensitivity and selectivity of d2_cluster are estimated to be >99.6% and 99.2%.