FastME 2.0: A Comprehensive, Accurate, and Fast Distance-Based Phylogeny Inference Program.

FastME provides distance algorithms to infer phylogenies. FastME is based on balanced minimum evolution, which is the very principle of Neighbor Joining (NJ). FastME improves over NJ by performing topological moves using fast, sophisticated algorithms.

-8p12-23 and +20q are predictors of subtypes and metastatic pathways in colorectal cancer: construction of tree models using comparative genomic hybridization data.

A substantial body of evidence suggests the genetic heterogeneous pattern and multiple pathways in colorectal cancer initiation and progression. In this study, we construct a branching tree and multiple distance-based tree models to elucidate these genetic patterns and pathways in colorectal cancer by using a data set comprised of 244 cases of comparative genomic hybridization. We identify the six most common gains of chromosomal regions of 7p (37.

Getting a tree fast: Neighbor Joining, FastME, and distance-based methods.

Neighbor Joining (NJ), FastME, and other distance-based programs including BIONJ, WEIGHBOR, and (to some extent) FITCH, are fast methods to build phylogenetic trees. This makes them particularly effective for large-scale studies or for bootstrap analysis, which require runs on multiple data sets. Like maximum likelihood methods, distance methods are based on a sequence evolution model that is used to estimate the matrix of pairwise evolutionary distances.

Construction and analysis of tree models for chromosomal classification of diffuse large B-cell lymphomas.

Aim: To construct tree models for classification of diffuse large B-cell lymphomas (DLBCL) by chromosome copy numbers, to compare them with cDNA microarray classification, and to explore models of multi-gene, multi-step and multi-pathway processes of DLBCL tumorigenesis.

Methods: Maximum-weight branching and distance-based models were constructed based on the comparative genomic hybridization (CGH) data of 123 DLBCL samples using the established methods and software of Desper et al. A maximum likelihood tree model was also used to analyze the data.

A comprehensive continuous-time model for the appearance of CGH signal due to chromosomal missegregations during mitosis.

Aneuploidy, the gain or loss of large regions of the genome, is a common feature in cancer cells. Irregularities in chromosomal copy number caused by missegregations of chromosomes during mitosis can be visualized by cytogenetic techniques including fluorescence in situ hybridization (FISH), spectral karyotyping (SKY) and comparative genomic hybridization (CGH). In the current work, we consider the propagation of irregular copy numbers throughout a cell population as the individual cells progress through ordinary mitotic cell cycles.

A newly discovered human alpha-globin gene.

A previously undefined transcript with significant homology to the pseudo-alpha2 region of the alpha-globin locus on human chromosome 16 was detected as part of an effort to better define the transcriptional profiles of human reticulocytes. Cloning and sequencing of that transcript (GenBank AY698022; named mu-globin) revealed an insert with a 423-nucleotide open reading frame. BLASTP and ClustalW and phylogenetic analyses of the predicted protein demonstrated a high level of homology with the avian alpha-D globin.

Construction of tree models for pathogenesis of nasopharyngeal carcinoma.

Pathogenesis of nasopharyngeal carcinoma (NPC) is a multistep and multipathway process that cannot be fully explained by a fixed linear progression model. We used distance-based and branching-tree methods to construct more general tree-like models for NPC carcinogenesis from 170 comparative genomic hybridization (CGH) samples previously published in five smaller studies. Imbalances were classified into "overlap regions," each containing the most commonly gained or lost band on each chromosome arm as well as adjacent bands that were gained or lost almost as often.

Tumor classification using phylogenetic methods on expression data.

Tumor classification is a well-studied problem in the field of bioinformatics. Developments in the field of DNA chip design have now made it possible to measure the expression levels of thousands of genes in sample tissue from healthy cell lines or tumors. A number of studies have examined the problems of tumor classification: class discovery, the problem of defining a number of classes of tumors using the data from a DNA chip, and class prediction, the problem of accurately classifying an unknown tumor, given expression data from the unknown tumor and from a learning set.

Theoretical foundation of the balanced minimum evolution method of phylogenetic inference and its relationship to weighted least-squares tree fitting.

Due to its speed, the distance approach remains the best hope for building phylogenies on very large sets of taxa. Recently (R. Desper and O.

Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle.

The Minimum Evolution (ME) approach to phylogeny estimation has been shown to be statistically consistent when it is used in conjunction with ordinary least-squares (OLS) fitting of a metric to a tree structure. The traditional approach to using ME has been to start with the Neighbor Joining (NJ) topology for a given matrix and then do a topological search from that starting point. The first stage requires O(n(3)) time, where n is the number of taxa, while the current implementations of the second are in O(p n(3)) or more, where p is the number of swaps performed by the program.