EAGLE: alignment-free method to compute relative absent words (RAWs)

About

EAGLE is an alignment-free method and associated program to compute relative absent words (RAW) in genomic sequences using a reference sequence. Currently, EAGLE runs on a command line linux environment, building an image with patterns reporting the absent words regions (in SVG) as well as reporting the associated positions into a file. EAGLE has got scripts to run on the current outbreak and the other existing ebola virus genomes (using the human as a reference), including the download, filtering and processing of the entire data.

Citation

Raquel M. Silva, Diogo Pratas, Luísa Castro, Armando J. Pinho & Paulo J. S. G. Ferreira. Bioinformatics (2015): btv189.
DOI: 10.1093/bioinformatics/btv189.

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MENT: Microarray comprEssiOn Tools

About

MENT is a set of tools for lossless compression of microarray images, however, it can be used in other kind of images such as medical, RNAi, etc. This set of tools is divided into two categories, defined by the decomposition approach used:

• Bitplane Decomposition
• Binary-Tree Decomposition

In what follows, we will describe the set of tools available in MENT:

• BOSC06 (Bitplane decOmpoSition Compressor 2006) – Lossless compression tool for microarray images introduced by Neves and Pinho in 2006 (Neves 2006). This tool uses an image-INDEPENDENT context configuration and arithmetic coding.
• BOSC09 (Bitplane decOmpoSition Compressor 2009) – Lossless compression tool for microarray images introduced by Neves and Pinho in 2009 (Neves 2009). This tool uses an image-DEPENDENT context configuration and arithmetic coding.
• BOSC09HC (Bitplane decOmpoSition Compressor 2009 using Histogram Compaction) – Tool inspired on method introduced by (Neves 2009) where it was added an Histogram Compaction unit in order to remove some redundant bitplanes. This Histogram Compaction is usefull for images that have a reduced number of intensities.
• BOSC09SBR (Bitplane decOmpoSition Compressor 2009 using Scalable Bitplane Reduction) – Tool inspired on method introduced by (Neves 2009) where it was added an Scalable Bitplane Reduction unit in order to remove some redundant bitplanes. The Scalable Bitplane Reduction technique was first introduced by Yoo 1999.
• SBC (Simple Bitplane Coding) – Tool inspired on one of Kikuchi’s work (Kikuchi 2009, Kikuchi 2012).
• BOSC09MixSBC (Bitplane decOmpoSition Compressor 2009 Mixture with Simple Bitplane Coding) – Tool based on a mixture of finite-context models. In this particular case, we only considered two different models. The first one used by Neves and Pinho (Neves 2009) and the other one based on a Simple Bitplane Coding inspired on Kikuchi’s work (Kikuchi 2009, Kikuchi 2012).
• BITTOC (Binary Tree decomposiTiOn Compressor) – Tool inspired on Chen’s work regarding compression of color-quantized images (Chen 2002). This tool performance was studied in the context of medical images by Pinho and Neves in 2009 (Pinho 2009) and more recently applied to microarray images (Matos 2014).
• CmpImgs (Compare Images) – An image comparasion tool.

Microarray image sets

• ApoA1 (32 images | 66.4MB | download)
• Arizona (6 images | 694.9MB | download)
• IBB (44 images | 1.03GB | download)
• ISREC (14 images | 26.7MB | download)
• Omnibus – Low Mode (25 images | 2.5GB | download)
• Omnibus – High Mode (25 images | 2.5GB | download)
• Stanford (40 images | 396MB | download)
• Yeast (109 images | 219MB | download)
• YuLou (3 images | 40.7MB | download)

Citation

If you use some tool from MENT, please cite the following publications:

• Luís M. O. Matos, António J. R. Neves, Armando J. Pinho, “Lossy-to-lossless compression of biomedical images based on image decomposition”,in Applications of Digital Signal Processing through Practical Approach, Sudhakar Radhakrishnan (Editor), InTech, pp. 125-158, October 2015. DOI: doi.org/10.5772/60650
• Luís M. O. Matos, António J. R. Neves, Armando J. Pinho, “A rate-distortion study on microarray image compression”, in Proceedings of the 20th Portuguese Conference on Pattern Recognition, RecPad 2014, Covilhã, Portugal, October 2014. DOI: doi.org/10.13140/2.1.3431.2969
• Luís M. O. Matos, António J. R. Neves, Armando J. Pinho, “Compression of microarrays images using a binary tree decomposition”, in Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, Portugal, September 2014. DOI: doi.org/10.13140/2.1.1980.5761
• Luís M. O. Matos, António J. R. Neves, Armando J. Pinho, “Compression of DNA microarrays using a mixture of finite-context models”, in Proceedings of the 18th Portuguese Conference on Pattern Recognition, RecPad 2012, Coimbra, Portugal, October 2012. DOI: doi.org/10.13140/2.1.1061.8245
• Luís M. O. Matos, António J. R. Neves, Armando J. Pinho, “Lossy-to-lossless compression of microarrays images using expectation pixel values”, in Proceedings of the 17th Portuguese Conference on Pattern Recognition, RecPad 2011, Porto, Portugal, October 2011. DOI: doi.org/10.13140/2.1.3553.4403
• Luís M. O. Matos, António J. R. Neves, Armando J. Pinho, “Lossless compression of microarrays images based on background/foreground separation”, in Proceedings of the 16th Portuguese Conference on Pattern Recognition, RecPad 2010, Vila Real, Portugal, October 2010. DOI: doi.org/10.13140/2.1.3815.5843
• António J. R. Neves, Armando J. Pinho, “Lossless compression of microarray images using image-dependent finite-context models”, in IEEE Transactions on Medical Imaging, volume 28, number 2, pages 194-201, February 2009. DOI: dx.doi.org/10.1109/TMI.2008.929095
• António J. R. Neves, Armando J. Pinho, “Lossless Compression of Microarray Images”, in Proceedings of the IEEE International Conference on Image Processing, ICIP-2006, Atlanta, GA, pages 2505-2508, 8-11 October, 2006. DOI: dx.doi.org/10.1109/ICIP.2006.31280

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• MENT

XS: a FASTQ read simulator

About

XS is a skilled FASTQ read simulation tool, flexible, portable (does not need a reference sequence) and tunable in terms of sequence complexity. XS handles Ion Torrent, Roche-454, Illumina and ABI-SOLiD simulation sequencing types. It has several running modes, depending on the time and memory available, and is aimed at testing computing infrastructures, namely cloud computing of large-scale projects, and testing FASTQ compression algorithms. Moreover, XS offers the possibility of simulating the three main FASTQ components individually (headers, DNA sequences and quality-scores). Quality-scores can be simulated using uniform and Gaussian distributions.

Citation

Pratas, D., Pinho, A. J., & Rodrigues, J. M. R. (2014). XS: a FASTQ read simulator. BMC research notes, 7(1), 40.

Web download

Download and install from console

SACO: a lossless compression tool for the sequences alignments found in the MAF files.

About

SACO was designed to handle the DNA bases and gap symbols that can be found in MAF files. Our method is based on a mixture of finite-context models. Contrarily a recent approach, it addresses both the DNA bases and gap symbols at once, better exploring the existing correlations. For comparison with previous methods, our algorithm was tested in the multiz28way dataset. On average, it attained 0.94 bits per symbol, approximately 7% better than the previous best, for a similar computational complexity. We also tested the model in the most recent dataset, multiz46way. In this dataset, that contains alignments of 46 different species, our compression model achieved an average of 0.72 bits per MSA block symbol.

Data sets

• multiz28way
• multiz28wayB
• multiz46way
• multiz100way

Citation

If you use this software, please cite the following publications:

• Luís M. O. Matos, Diogo Pratas, and Armando J. Pinho, “A Compression Model for DNA Multiple Sequence Alignment Blocks”, in IEEE Transactions on Information Theory, volume 59, number 5, pages 3189-3198, May 2013. DOI: dx.doi.org/10.1109/TIT.2012.2236605
• Luís M. O. Matos, Diogo Pratas, and Armando J. Pinho, “Compression of whole genome alignments using a mixture of finite-context models”, in Proceedings of the International Conference on Image Analysis and Recognition, ICIAR 2012, (Editors: A. Campilho and M. Kamel, volume 2324 of Lecture Notes in Computer Science (LNCS)), pages 359-366, Springer Berlin Heidelberg, Aveiro, Portugal, June 2012. DOI: doi.org/10.1007/978-3-642-31295-3_42

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MAFCO: a compression tool for MAF files

About

MAFCO is a lossless compression tool specifically designed to compress MAF (Multiple Alignment Format) files. Compared to gzip, the proposed tool attains a compression gain from ≈ 34% to ≈ 57%, depending on the data set. When compared to a recent dedicated method, which is not compatible with some data sets, the compression gain of MAFCO is about 9%. MAFCO was designed and implemented at IEETA, a research unit of the University of Aveiro, and is available for non-commercial use.

Citation

Luís M. O. Matos, António J. R. Neves, Diogo Pratas and Armando J. Pinho. “MAFCO: a compression tool for MAF files”. PLoS ONE 10(3): e0116082.
DOI: http://dx.doi.org/10.1371/journal.pone.0116082.

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About

FALCON is an alignment-free unsupervised system to measure a similarity top of multiple reads according to a database. The machine learning system can be used, for example, to classify metagenomic samples. The core of the method is based on the relative algorithmic entropy, a notion that uses model-freezing and exclusive information from a reference, allowing to use much lower computational resources. Moreover, it uses variable multi-threading, without multiplying the memory for each thread, being able to run efficiently from a powerful server to a common laptop. To measure the similarity, the system will build multiple finite-context (Markovian) models that at the end of the reference sequence will be kept frozen. The target reads will then be measured using a mixture of the frozen models. The mixture estimates the probabilities assuming dependency from model performance, and thus, it will allow to adapt the usage of the models according to the nature of the target sequence. Furthermore, it uses fault tolerant (substitution edits) Markovian models that bridge the gap between context sizes. Several running modes are available for different hardware and speed specifications. The system is able to automatically learn to measure similarity, whose properties are characteristics of the Artificial Intelligence field.

Citation

Paper was submitted, currently the citation should be addressed to the url (bioinformatics.ua.pt/software/falcon).

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DNAatGlance is a program for the detection of large-scale genomic regularities by visual inspection. Several discovery strategies are possible, including the standalone analysis of single sequences, the comparative analysis of sequences from individuals from the same species, and the comparative analysis of sequences from different organisms. The software was designed and implemented at IEETA, a research unit of the University of Aveiro, and is available for non-commercial use.

Citation

Armando J. Pinho, Sara P. Garcia, Diogo Pratas, Paulo J. S. G. Ferreira (2013) DNA Sequences at a Glance. PLoS ONE 8(11): e79922.
DOI: dx.doi.org/10.1371/journal.pone.0079922.

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• Gui version source
• Gui version manual
• Gui version Windows binaries
• command line source (Linux OS only)
• command line readme

For convenience, we provide a sequence (here in gzip)(here in zip) and the corresponding information profile in WIG format (here in gzip) (here in zip) that can be uploaded to the UCSC Genome Browser as a custom track.

MFCompress: a compression tool for FASTA and multi-FASTA data

About

MFCompress is a compression tool for FASTA and multi-FASTA files. In comparison to gzip and applied to multi-FASTA files, MFCompress can provide additional average compression gains of almost 50%, i.e., it potentially doubles the available storage, although at the cost of some more computation time. On highly redundant data sets, and in comparison with gzip, 8-fold size reductions have been obtained. MFCompress was designed and implemented at IEETA, a research unit of the University of Aveiro, and is available for non-commercial use. For other uses, please send an email to ap@ua.pt.

Citation

Armando J. Pinho, and Diogo Pratas. “MFCompress: a compression tool for FASTA and multi-FASTA data.” Bioinformatics 30.1 (2014): 117-118.
DOI: dx.doi.org/10.1093/bioinformatics/btt594.

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Exploring Human Genetic Variations

About

Variobox is a desktop tool for the annotation, analysis and comparison of human genes. Variant annotation data are obtained from WAVe, protein metadata annotations are gathered from PDB and UniProt, and sequence metadata is obtained from Locus Reference Genomic (LRG) and RefSeq databases. By using an advanced sequence visualization interface, Variobox provides an agile navigation through the various genetic regions. Researched genes are compared to the sequences retrieved from LRG and RefSeq, automatically finding and annotating new potential mutations. These features and data, ranging from patient sequences to HGVS-valid variant description up to pathogenicity evaluation, are combined in an intuitive interface to explore genes and mutations.

Citing

To cite this tool use the following publication:

Variobox: Automatic Detection and Annotation of Human Genetic Variants. Paulo Gaspar, Pedro Lopes, Jorge Oliveira, Rosário Santos, Raymond Dalgleish, José Luís Oliveira. Human Mutation, 2014

Download

VarioBox is available for all the main operating systems (Windows [XP, 7, 8]; Linux; MacOS) that support Java. The current version of the software is 1.4.4. Click the link bellow to download:

Download Variobox

To run, first unpack all the files to any folder. Then, if you’re on Windows, double click the Variobox file inside the folder. On Mac or Linux, start a terminal, change the directory to the created folder, and run java -jar variobox.jar

Tutorial

Step 1 The initial layout

This is the initial VarioBox workspace that shows up when you open the application. At the bottom of the workspace you can find a separator, “Home”, created automatically. Here will be as many separators as searches performed, each one identified by the searched HGNC code. At the centre you can see the logo and a panel, where searches for reference genes can be performed, using a valid HGNC symbol. To work with Variobox, a reference gene is always the starting point. After obtaining the reference, a sequence can be loaded to the application to be aligned with the sequence, and analysed.

Step 2 Making a quick search

By default there are two genes bellow the search box: Collagen, type I, alpha 1 (COL1A1), and Myotubularin 1 (MTM1). Click on COL1A1 or type it at the search box and hit search. A progress bar will show up indicating the progress of the loading process. A new tab (with the name of the searched HGNC code), like the one below, will show up once the reference gene is automatically retrieved from the web servers:

The right zone is formed by two distinct panels:

• The top one, titled Protein Viewer is where the 3D protein conformation of the selected gene is shown, if available, using JMol.
• The bottom one, titled Information Panel, which will display additional information on selected items, such as mutations and exons.

On the top of the window there is a large genomic viewer with a movable and resizable window that allows specifying a region to be explored in the centre zone. This viewer distinguishes exons (blue) and introns (purple), and allows quickly jumping through the gene. The centre zone is populated with gene data and information, in three distinct panels, described below:

• Gene panel

In this panel you can see the codon sequence and the decoded polypeptide sequence, labelled Reference Sequence and Translated Sequence respectively, and also the Known Mutations for the gene, as retrieved from WAVe. A zoomed genomic viewer is also displayed to further facilitate the exploration of the gene.

Mutations are identified by different colours, and shown next to the corresponding nucleotides. Additional information about a mutation can be obtain by clicking on the mutation. The Information Panel (right side of the workspace) will display details regarding the selected mutation’s position, source, type, annotation, etc.

• Navigation panel

The navigation panel is a simple feature that allows the easy exploration of the gene through mutations and exons. Clicking on the next or previous buttons will centre the sequence in the appropriate item (a mutation or exon):

The Navigation Panel also permits filtering what mutation types are to be shown in the Gene Panel. For instance, if you only check Substitutions, all mutations besides SNPs will be hidden.

• Gene Details panel

This panel shows you a quick information about the gene that you are analysing. The current information supported is the following:

• Number of mutations: displays the total number of mutations found in the reference gene. No information will be displayed if no mutations are known;
• Number of exons: total number of exons found in the gene;
• Sequence size: total size of the reference sequence;
• Date of creation: the date and time when this gene was created;
• Loaded files: the files that were selected by the user to be aligned with the reference sequence.

Step 3 Loading mutated sequences

To load a gene sequence and align it with the reference gene, click the menu Genes → Load gene file. Alternatively, go to the menu File → Load gene file. You will be prompted with a new window to select the file you want to load. For the current version we support the file types:

After selecting the file (or files, if you choose the forward-reverse format), click Load selected file and VarioBox will read them. Once the file is correctly loaded, an alignment with the reference gene is automatically performed. This alignment will also display found mutations, as compared to the reference gene. The analysis of the loaded sequence is described in the next step.

Step 4 Analysing mutated sequences and saving results

After the files are loaded, the Gene Panel will be updated with the mutated sequence as well as the calculated mutations, as depicted in the following figure:

Step 5 Final Features

If you want to register a new patient in VarioBox, make the following steps: Go to Patients → New patient and fill the Patient Details panel (shown bellow) with all the required information(note that only one field is mandatory). After that just click Save patient and a new record will be created.

GReEn: a tool for efficient compression of genome resequencing data.

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