Mauve Aligner Download

The mauveAligner and progressiveMauve alignment algorithms have been implemented as command-line programs included with the downloadable Mauve software. When run from the command-line, these programs provide options not yet available in the graphical interface.

mauveAligner and progressiveMauve are included with the Mauve software. On Windows they are located in the directory where Mauve was installed, usually C:\Program Files\Mauve\. On 64-bit Windows platforms, the binaries in the win64 subdirectory may be used instead. On Mac OS X the mauveAligner and progressiveMauve binaries are packaged within the Mauve application. Relative to Mauve.app the paths will be Mauve.app/Contents/MacOS/mauveAligner and Mauve.app/Contents/MacOS/progressiveMauve. The binaries can be copied to a more convenient location. However, if using Mauve versions 2.0.0 or earlier, be sure to also copy the muscle_aed binary to the same location. On Linux the mauveAligner and progressiveMauve binaries are in the top level directory of the software distribution, with 64-bit binaries in the linux-x64 subdirectory.

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Each sequence must have a corresponding Sorted Mer List (SML) file name given. If the SML file does not exist, mauveAligner will create it. Alternatively, mauveAligner can be given the name of a Multi-FastA file containing all the genomes to align. In this case mauveAligner assumes one genome per Multi-FastA sequence entry and will align the entries to each other.

The availability of genome sequences demands methods for aligning long genomic DNA sequences. Several heuristic approaches to align long sequences have been developed under the assumption that highly similar subsequences can be found quickly and are likely to be part of the correct global alignment. These local alignments are used to anchor a global alignment, reducing the number of possible global alignments considered during a subsequent O(n2) dynamic programming step. Some spurious local alignments are typically found because of random sequence similarity, particularly when using a sensitive local alignment method. A method for selecting alignment anchors must be used to filter out spurious matching regions. Alignment tools such as MUMmer, GLASS, AVID, and WABA align pairs of long sequences, implementing various methods to discover local alignments (Delcher et al. 1999; Batzoglou et al. 2000; Kent and Zahler 2000; Morgenstern 2000; Bray et al. 2003). Similar multiple sequence alignment methods for long sequences have been developed and implemented in software packages such as MAVID, MLAGAN, and MGA (Hohl et al. 2002; Bray and Pachter 2003; Brudno et al. 2003a). All of these pairwise and multiple sequence aligners assume the input sequences are free from significant rearrangements of sequence elements, selecting a single collinear set of alignment anchors.

MultiPipMaker, based on BLASTZ, is a tool that can align multiple genomes to a single reference genome in the presence of rearrangements (Schwartz et al. 2003a). MultiPipMaker uses BLASTZ (Schwartz et al. 2003b) on each pair of reference and nonreference genomes to calculate pairwise local alignments. These local alignments are used to construct a rough global alignment that is iteratively refined. Because MultiPipMaker does not provide a mechanism for global alignment of regions not included in the initial local alignments, more divergent homologous regions between local alignments may remain unaligned. As such, MultiPipMaker can best be described as a multiple local aligner for genome sequences, rather than a global aligner. Furthermore, neither Shuffle-LAGAN nor MultiPipMaker provides a means to precisely identify the breakpoints of multiple genome rearrangements.

In addition to missing anchors outside the boundaries of LCBs, the initial anchoring pass may have lacked the sensitivity to find anchors in large regions within each LCB. Because progressive alignment requires relatively dense anchors (at least one anchor per 10 kb of sequence), Mauve performs recursive anchoring on the intervening regions between each pair of existing anchors. Not only does this step anchor more divergent regions of sequence, it also locates anchors in conserved repeats because many k-mers that are not unique in the whole genome are likely to be unique within the intervening regions between existing anchors. Unlike other genome aligners that perform a fixed number of recursive passes with a predetermined sequence of anchor sizes, Mauve calculates a minimum anchor size based on the length of the intervening sequence and stops recursive anchoring when either no additional anchors are found or when the intervening region is shorter than a fixed length, defaulting to 200 bp. During each recursive anchor search, a single collinear set of new anchors in the same orientation as the flanking anchors is selected to cover the region between flanking anchors. For each search, k is calculated as above: k = seed_size(S), where S is the set of intervening sequences, one per genome. By dynamically calculating the value of k, Mauve ensures that k is sized appropriately for the intervening region. Selecting a k too large prevents discovery of multi-MUMs in polymorphic regions, whereas selecting a k too small increases the likelihood that k-mers will not be unique in the intervening region.

Three experiments were performed, each of which consists of numerous simulations. The first experiment evaluates the robustness of Mauve and Multi-LAGAN, a cross-species genome comparison tool, to genomes with high nucleotide substitution and indel rates. A second experiment compares Mauve to Shuffle-LAGAN when aligning pairs of genomes with rearrangements. At the time these experiments were performed, Shuffle-LAGAN was the only publicly available genome aligner capable of aligning genomes in the presence of rearrangement. Our final experiment evaluates the ability of Mauve to align simulated genomes that resemble the nine target enterobacteria.

Summary: Mauve Contig Mover provides a new method for proposing the relative order of contigs that make up a draft genome based on comparison to a complete or draft reference genome. A novel application of the Mauve aligner and viewer provides an automated reordering algorithm coupled with a powerful drill-down display allowing detailed exploration of results.

This tutorial covers the use of the Mauve whole genome aligner in Geneious Prime. You will learn to perform a basic alignment of complete bacterial genomes, order a draft genome against a reference, work with the Mauve viewer, and convert a Mauve alignment into a standard alignment for downstream analysis.

I hope most of you people aware of Mauve aligner tool.When I tried to install Mauve in my laptop I could not able to install it. I am using Windows 8.1 OS .Whenever I am trying to install it showing Mauve requires java 1.4 or greater but currently I am having most updated one java 8.1(I am not sure about version number). When I surfed on google , another guy had a same issue, he solved by re installation of java. I tried but I failed even though I have uninstalled and again installed both the Java and mauve twice.So anybody solved this issue or know how to solve this ,please suggest me a way to solve it?

Segments of DNA between high-scoring alignment anchors can be unrelated, especially in bacteria. Despite that, our method (like many other genome aligners) applies a global alignment algorithm to all inter-anchor segments, navely assuming that homology exists. Our assumption of homology sometimes proves erroneous, so to arrive at an accurate alignment we must detect forced alignment of unrelated sequence. To do so, we apply an HMM posterior decoder that classifies columns in a pairwise alignment as either homologous or unrelated. The HMM structure, transition, and emission probabilities are described elsewhere [34]. The HMM makes predictions of pairwise homology, which we combine using transitive homology relationships. Regions found to be unrelated are removed from the final alignment. Application of the homology HMM is the final step in the alignment procedure, shown as step 8 in Figure 2.

Previous studies of alignment accuracy have used a sum-of-pairs scoring scheme to characterize the residue level accuracy of the aligner [9], [46]. The experiments presented here use sum-of-pairs scoring, but we also define new accuracy measures to quantify each alignment system's ability to predict indels and breakpoints of genomic rearrangement. For each type of mutation, we define True Positive (TP), False Positive (FP), and False Negative (FN) predictions as discussed below. Using these definitions, we can measure the aligner's Sensitivity as and Positive Predictive Value (PPV) as .

For each pair of genomes we also measure whether the aligner correctly predicts LCBs among that pair, yielding a sum-of-pairs LCB accuracy metric. For each pairwise LCB in the true alignment, we record a TP LCB prediction when the predicted alignment contains at least one correctly aligned nucleotide pair in that LCB. Pairwise LCBs lacking any correctly predicted nucleotide pairs are FN predictions. Finally, pairwise LCBs in the predicted alignment lacking any correctly aligned nucleotide pairs are False Positive (FP). Again, we do not measure TN.

As with indels, we define a separate metric to quantify how well each aligner localizes the exact breakpoints of rearrangement. For TP LCB predictions, we record the difference (in nucleotides) between the boundaries of the correct LCB and those of the predicted LCB. The resulting value is negative when the predicted LCB fails to include the full region of homology, and positive when a predicted LCB extends beyond the true boundary.

We downloaded and tested all multiple-genome aligners that were publicly available as of May 2008, when this work was completed. Multi-genome aligners known to handle rearrangements at that point in time included mauveAligner 1.3.0 [9], progressiveMauve 2.2.0, and TBA 28-02-2006 [17]. We did not test two-stage pipelines involving separate synteny mapping and alignment steps, such as MERCATOR+MAVID [49] or Chain-net+TBA [50] but this would be an interesting area for future work. We did test a selection of available multiple-aligners that assume collinear genome sequences as input, including MLAGAN 2.0 [19], MAVID 2.0 [18], and Pecan 0.7 [33], which was available at the time of this work for download but not yet published. In the time since the aligner testing was completed, several new alignment systems have been published, including Enredo+Pecan [33], FSA [51], and an extension of LAGAN for reference-free alignment with duplication and rearrangements [32]. We did not test the duplication alignment accuracy of glocal alignment methods. Our simulation system does not explicitly model gene duplication, and worse, we do not know the true alignment of repeats in the ancestral genomic material, so it is impossible to quantify the accuracy of glocal aligners using our evaluation scheme. Testing of FSA [51] remains as future work. Finally, we did not test any of the numerous pairwise aligners or pairwise synteny mapping methods, as our work focuses on the multiple genome alignment problem. 006ab0faaa