Pure Java desktop application - No fussing around with compiling the program for your OS or configuring your own server or database necessary.
Share your data and plots with colleagues - Simple database import/export function allows you to send your database to colleagues or include it as a supplement to your publication. You can even set up one or more plots before you export the database and they’ll be visible as soon as the user imports the database.
Plays well with others - Any data imported into MochiView or created using MochiView can be exported for use with other programs (and re-imported in one of several simple file formats supported by MochiView).
Support for many kinds of data
Flexible and generalized format - Any data consisting of DNA sequence positional information (with or without associated values) can be imported, manipulated, and viewed in MochiView.
High-density data (e.g. ChIP-Seq / RNA-Seq) - MochiView supports the display of high-density data, including data at a resolution of a single base per value.
ChIP-Chip - Import your tiling array probes and then every time you do a ChIP-Chip experiment you can add a new data set with ease.
DNA binding motifs - Import motifs as either frequency matrices, IUPAC sequences, or affinity matrices. Several libraries of motifs are pre-compiled and available on the website. Even if you have no interest in the MochiView genome browser, MochiView is (to our knowledge) the best available tool for storing/managing/analyzing DNA binding motifs.
Multiple genome alignments - Multiple genome alignment can be displayed in plots and can also be used to enhance motif finding.
Expression data - Import your expression data and color code the genes in the genome browser based on the expression values.
Genome Browser: Overview
Highly customizable plots - MochiView provides a powerful interface for setting up plots using anything loaded into the database. Data can be displayed in a variety of formats, including line graphs, bar graphs, shapes (for displaying Locations), and the color and appearance of these formats is customizable.
Open multiple plots at one time - MochiView uses a tabbed design to allow multiple plots to be open at any given time.
Plots persist across sessions - The state of all open plots is remembered when you close MochiView and they are restored the next time you run the program.
Save/Load plot configurations - Plot ’settings files’ can be saved and loaded with ease.
Genome Browser: Specialized tracks and displays
Gene tracks - Gene tracks are specialized to allow display of multiple isoforms and sub-features such as introns, exons, and UTRs.
Alignment tracks - Display a multiple genome alignment to view sequence conservation.
Motif tracks - Select one or more DNA binding motifs and a motif match score cutoff and all hits that exceed this score will be displayed on the motif track(s).
Hybrid plot with tracks and primary data - In addition to the familiar “track” format, MochiView can also display data above the tracks using a y-axis that spans the full plot. This feature is often used to display ChIP-Chip results and genes above tracks with additional data (e.g. binding regions or motif matches).
Genome Browser: Browsing and interactivity
Smooth scrolling/zooming - It is no small challenge to fetch and display large amounts of data with little to no delay, and a great deal of effort went into making MochiView’s plots load quickly and scroll/zoom smoothly. Zoom in far enough, and the DNA sequence and logos for motif matches are displayed.
Extensive plot mouse interactivity - Virtually every item within a plot is interactive. For example, you can right-click on different sections of a plot to display context-dependent popup menus or left-click and drag the mouse to pan left of right. (A full list of interactive mouse commands is in the manual.)
Extensive keyboard hotkeys - If you love hotkeys like I do, you’ll love the extensive hotkey support for navigating MochiView’s plots (see the manual for a printable list).
Export images of plots centered at regions of interest - The ‘Snapshot Utility’ allows you to rapidly export images (or a single pdf) centered at regions of interest (e.g. ChiP binding regions) using your current plot configuration.
Add annotationsas you browse - Click on a shape in a track and add text annotation that will be displayed on the shape (or import annotations from a text file). If you export the data later the annotations will be included.
Mark and filter items in a track - Shapes in a track set to ‘Edit Mode’ can be toggled between “Yes”/”No”/”Undecided” states, which can then be used to filter down the contents of the track (useful for eliminating spurious ChIP binding region calls, for example).
Genome Browser: Powerful search/navigation tools
Move between areas of interest using the data browser - For most users, this is the single most useful feature in MochiView. Sort any data set by value and jump your plot from location to location with ease. This provides a highly flexible way to navigate your genome of interest. For example, you could sort a set of predicted ChIP binding regions by significance and jump from region to region with ease.
Search for sequences or palindromes - The sequence browser allows searching for sequence matches (including degenerate sequences) and/or palindromes and displays matches in a table. You can then click the table rows to center the plot at the match regions.
Flexible gene search - The gene search window allows re-centering of the plot based on either a gene (the search browser allows matches to aliases as well as wild-cards).
Use MochiView as a motif database - MochiView provides a convenient interface for maintaining, viewing, and annotating a motif library.
Multiple motif import formats - In addition to the simple MochiView frequency matrix format, MochiView supports import of results from the popular motif search programs MEME and Bioconductor. Motifs can also be entered as degenerate sequence (IUPAC symbols). Affinity
Pre-compiled motif libraries - The [MOTIF LIBRARY DOWNLOADS] page on the website provides numerous motif libraries for use in your analyses.
Export visually appealing motif logos - Export motif logos based on the WebLogo concept.
Multiple motif scoring options - Motifs can be scored using LOD scores, p-values, cumulative approaches, as well as affinity scores.
Integrated motif finder - MochiView contains its own motif finder (it uses Gibbs Sampling and a Markov model), which benefits from seamless connection to the other contents of your database. For example, you can select a set of locations such as ChIP binding regions, apply a filter (e.g. p-value cutoff), press the start button, and a table will then display motifs as they are found.
Simple to export data for MEME (and other) motif searches - MochiView’s motif finder is not as powerful as MEME, so MochiView makes it easy to export sequence and background Markov models in a format that can be read by MEME. You can either export sequence from a location set in your database or paste in a list of genes and export the promoter sequences. It is equally simple to re-import the MEME results.
Motif enrichment table - This utility addresses the question “is my set of locations enriched for any motifs in my library”? Motif match frequencies at a variety of motif score cutoffs are displayed in a table for one or more sets of locations. The table displays the number of motif matches, the average number of bases per match, and a Fisher’s exact test p-value comparing the match frequency to a control set of locations.
Motif enrichment plot - This utility is the complement to the Motif Analysis Utility described above, providing a visual representation of motif frequencies in different sets of locations (and an optional ROC plot). Apart from the display format, this utility differs from the Motif Analysis Utility in that it scores for motifs on a per-location basis (rather than using frequency of occurrence across all sequence covered by the set of locations). P-values are displayed as dashed lines in the plot.
Motif comparison utility - This utility answers the question “does my motif resemble any of the other motifs in the library?”, and is based on the TOMTOM algorithm.
Motif distribution utilities - These utilities address the question “is the positional distribution of my motif relative to some other landmark non-uniform?”. The most common usage is to measure position of motif matches relative to the start codon or relative to matches to a different motif.
Motif GO enrichment - Test whether strong matches to a motif tend to occur upstream of genes with common function.
Convert motif matches to a data set for browsing - Search a set of locations (or the whole genome) for matches to a motif that exceed a score cutoff. These matches are stored in the database, and can then be used in the Data Browser when viewing plots to quickly survey regions of the genome with strong motif matches.
Score a set of locations for motif matches - Creates a new data set from a set of locations (such as ChIP binding regions or promoters) in which the location is assigned a score based on either a cumulative scoring metric or the maximum motif match score.
Create a gene-to-motif scoring matrix - This utility (found at ‘Export->Location Set->Format: MochiView (with optional Motif Scans)’) allows (among other things) the ability to instantly create a tab-delimited Excel-ready file that contains a score for each motif in your library for every promoter region in the genome.
Extract enriched regions from high-density (wiggle) data - Identify and browse enriched regions from RNA-Seq or ChIP-Seq data by uploading a wig file of read counts and then using the ‘Create Data Set by extracting enriched regions from Tiled Set‘ utility.
Map high-density data onto regions of interest - Use the ‘Map Tiled Set(s) to Location Set‘ utility to, For example, map the median RNA-Seq read counts from an uploaded wig file onto a set of genes, or to map ChIP-Seq counts onto a set of promoters.
Combine/manipulate high-density data sets - Utilities are provided to combine (mean, medium, minimum, maximum, smooth) high-density data sets or manipulate them by adding/subtracting one data group from another.
Filter your data and location sets - Filter by value or by one of several location overlap criteria. For example, you could filter a set of putative ChIP binding regions to remove any regions that overlap with regions bound in a control experiment.
Smooth data for display - A data smoothing utility can create a high-density data set of smoothed data. For example, you can collapse expression data from multiple replicates of a ChIP-chip experiment into a single smoothed data set to reduce plot clutter.
Extract peaks from your data - The ‘Extract peaks from Data Set(s)’ utility can extract fixed-size peak regions from one or more ChIP-chip data sets. The utility offers a sampling-based significance test and the option to designate control set(s) that must lack significant enrichment in the candidate peak region.
Annotation locations with proximal genes - The ‘Annotate with gene proximity’ utility can assign genes to a set of locations (typically ChIP binding regions) using user-defined criteria.
Set operations - Combine and manipulate sets of locations using Union, Intersection, and Subtraction set operations. For example, create a set of intergenic locations by subtracting a gene set from the genome as a whole.
Location set summary statistics and comparison - Get detailed summary statistics on genome coverage and distribution of a set of locations as well as the extent of overlap between two location sets.
Create a location set based on sequence matches - This utility allows rapid identification of all of the locations within the genome (or a sub-region thereof) that match a given sequence.
Sample fixed length locations from a location set - Generate appropriate control sets for motif analyses.
GO Enrichment analysis -Test whether a set of locations is distributed near genes with common function. Alternatively, just paste in a list of gene names for a conventional GO term enrichment analysis.
Map locations between genomes - A rapid way to transfer the contents of an existing database over to a new version of a genome or a closely related species. Sorry, small genomes only!
Build a promoter set - A simple utility to create a location set corresponding to the upstream regions of genes. Useful for motif searches.