"TreeBeST is an original tree builder for constrained neighbour-joining and tree merge, an efficient tool capable of duplication/loss/ortholog inference, and a versatile program facili- tating many tree-building routines, such as tree rooting, alignment filtering and tree plot- ting. TreeBeST stands for ‚Äò(gene) Tree Building guided by Species Tree‚Äô. It is previously known as NJTREE as the first piece of codes of this project aimed to build a eighbour-joining tree.
TreeBeST is the core engine of TreeFam (Tree Families Database) project initiated by Richard Durbin. The basic idea of this project is to build a full tree constrained by a manually verified seed tree. The tree builder must know how to utilize the prior knowledge provided by human experts. This demand disqualifies any existing softwares. Given this fact, we devised a new algorithm to control the joining step of traditional neighbour-joining. This is origin the constrained neighbour-joining.
When trees are built, they are only meaningful to biologists. Computers generate trees, but they do not understand them. To understand gene trees, a computer must be equipped with some biological knowledges, the species tree. It will teach a computer how to discriminate a speciation from a duplication event and how to find orthologs, provided a correct gene tree.
Unfortunately, gene trees are not always correct. Since the advent of UPGMA algorithm in 1958, we have tried to find a ideal model for nearly half a century. But we failed. Evolution is so complex a thing. A model best fits in one lineage might mean a disaster in another. A unified model is far from being discovered. TreeBeST aims at improving the accuracy of tree building, but it does not try to set up a new model in a traditional way. Instead, it integrates two existing models with the help of species tree, finding the subtree that best fits the models and merging them together to build a new tree incorporating the advantages of the both. This is the tree algorithm." (treebest.pdf)
“Proper identification of repetitive sequences is an essential step in genome analysis. The RECON package performs de novo identification and classification of repeat sequence families from genomic sequences. The underlying algorithm is based on extensions to the usual approach of single linkage clustering of local pairwise alignments between genomic sequences. Specifically, our extensions use multiple alignment information to define the boundaries of individual copies of the repeats and to distinguish homologous but distinct repeat element families. RECON should be useful for first-pass automatic classification of repeats in newly sequenced genomes.” (http://selab.janelia.org/recon.html)
"RAxML is a fast implementation of maximum-likelihood (ML) phylogeny estimation that operates on both nucleotide and protein sequence alignments."
mpiBLAST is a freely available, open-source, parallel implementation of NCBI BLAST. mpiBLAST takes advantage of distributed computational resources, i.e., a cluster, through explicit MPI communication and thereby utilizes all available resources unlike standard NCBI BLAST which can only take advantage of shared-memory multi-processors (SMPs).
"PAML (for Phylogentic Analysis by Maximum Likelihood) contains a few programs for model fitting and phylogenetic tree reconstruction using nucleotide or amino-acid sequence data." (doc/pamlDOC.pdf)
"'Fitmodel' estimates the parameters of various codon-based models of substitution, including those described in Guindon, Rodrigo, Dyer and Huelsenbeck (2004). These models are especially useful as they accommodate site-specific switches between selection regimes without a priori knowledge of the positions in the tree where changes of selection regimes occurred.
The program will ask for two input files: a tree file and a sequence file. The tree should be unrooted and in NEWICK format. The sequences should be in PHYLIP interleaved or sequential format. If you are planning to use codon-based models, the sequence length should be a multiple of 3. The program provides four types of codon models: M1, M2, M2a, and M3 (see PAML manual). Moreover, M2, M2a and M3 can be combined with 'switching' models (option 'M'). Two switching models are implemented: S1 and S2. S1 constraints the rates of changes between dN/dS values to be uniform (e.g., the rates of changes between negative and positive selection is constrained to be the same as the rate of change between neutrality and positive selection) while S2 allows for differents rates of change between the different classes of dN/dS values.
If you are using a 'switching' model, 'fitmodel' will output file with the following names: your_sequence_file_trees_w1, your_sequence_file_trees_w2, your_sequence_file_trees_w3 and your_sequence_file_trees_wbest. The w1, w2 and w3 files give the estimated tree with probabilities of w1, w2, and w3 (three maximum likelihood dN/dS ratio estimates) calculated on each edge of the tree and for each site. Hence, the first tree in one of these files reports the probabilities calculated at the first site of the alignment. Instead of probabilities, the wbest file allows you to identify which of the tree dN/dS is the most probable on any give edge, at any given site. A branch with label 0.0 means that w1 is the most probable class, 0.5 indicates the w2 is the most probable and 1.0 means that w3 has the highest posterior probability." (README.txt)
"Bowtie is an ultrafast, memory-efficient short read aligner geared toward quickly aligning large sets of short DNA sequences (reads) to large genomes. It aligns 35-base-pair reads to the human genome at a rate of 25 million reads per hour on a typical workstation. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: for the human genome, the index is typically about 2.2 GB (for unpaired alignment) or 2.9 GB (for paired-end or colorspace alignment). Multiple processors can be used simultaneously to achieve greater alignment speed. Bowtie can also output alignments in the standard SAM format, allowing Bowtie to interoperate with other tools supporting SAM, including the SAMtools consensus, SNP, and indel callers. Bowtie runs on the command line." (http://bowtie-bio.sourceforge.net/manual.shtml)
Several of our software packages require that users complete a form that is then kept on record here before we can grant access to the software. Academic users should download, print, complete, and return the form to OSC. Non-academic users should contact OSC Help for assistance.
Python is a high-level, multi-paradigm programming language that is both easy to learn and useful in a wide variety of applications. Python has a large standard library as well as a large number of third-party extensions, most of which are completely free and open source. We highly recommend using Python 2.7.1, as we have added a lot of Python packages and tuned them to perform well on our systems.
OSC has over two petabytes (PB) of disk storage capacity distributed over several file systems, plus almost 2PB of backup tape storage. (A petabyte is 1015, or a quadrillion, bytes.) This guide describes the various storage environments, their characteristics, and their uses.