Services and software

The bioMoby project aims to provide bioinformatics resources through the web. In this regard, we have designed and implemented a set of Web services that are compliant with bioMoby specifications. We have been focusing on providing genome analysis resources. These resources are of various types:

• Sequence analysis
  • Gene prediction (i.e. runGeneIDGFF, runSGP2GFF)
  • Signal predicition (transcription regulatory elements or splicing elements) [i.e. runMatScanGFF, runMetaAlignmentGFF]
  • ESTs assembly
• Sequence data retrieval (promoter sequences from Ensembl database)
• Data format conversion

A more complete list of Web resources can be found in the WEB SERVICES section We have also set up some pipelines of analysis to illustrate the use of these Web resources. Here are the main pipelines of computational analysis that were implemented:

• Promoter analysis
• ESTs assembly

More information about our pipelines of analysis can be found in the WORKFLOWS section.

ETE is a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. Besides a broad set of tree handling options, ETE  provides specific methods to analyze phylogenetic and clustering trees. It also supports large tree data structures, node annotation, independent editing and analysis of tree partitions, and the association of trees with external data such as multiple sequence alignments or numerical matrices.

ETE is available at http://ete.cgenomics.org

Visualizing pair-wise alignments with annotated axes from GFF files.

We are proud to announce the new version of gff2aplot, that has been re-implemented in perl. Visit the program's web page downloading section to obtain v2.0. You can obtain the full distribution tarball from there.

Although the "gff2aplot User's Manual" is not finished yet, you can start using it as we have written several HTML tutorials that will introduce you in how to use this program. We hope you will enjoy them.

A snapshots web page is also available, listing few examples of what can be done with gff2aplot.

meta is a program to produce and to align the TF-maps of two gene promoter regions. meta is very useful to characterize promoter regions from orthologous genes, or from co-regulated genes in microarrays, as it reduces the signal/noise ratio in a very significant manner, still detecting the real functional sites.

• meta homepage
• meta server

A database of tetrapod mitochondrial tRNAs. The database includes secondary structure based alignments of mt-tRNAs from 277 species of completely sequences tetrapod mitochondrial genomes as of 2007.

overlap is a program that computes the overlap between two sets of genomic features. More precisely it takes two gff files of genomic features as input and for each feature of the first set, says whether it is overlapped by a feature of the second set (basic mode, however more and more precise information can be retrieved).

EXECUTE INSTRUCTIONS:

After getting the overlap executable, type:

./overlap

without any argument to get the help.

This will give you all the possible options of overlap.  Basically the output will be equal to the first input file (file1) with additional information about the overlap of its features with the features of the second file (file2).  There are 4 basic modes:- Mode 0 (option -m 0) is to report boolean overlap: 1 if the feature of file1 is overlapped by a feature of file2, 0 otherwise.- Mode 1 (option -m 1) is to report quantitative overlap: the number of file2 features overlapping a file1 feature. - Negative mode (option -m -1 for example) is to report the list of coordinates of file2 features overlapping a file1 feature.- Value mode (option -m n where n>=10 and n is even) is to report the list of values located in field 10 in file2 associated to file2 features overlapping a file1 feature.

You can also ask for inclusion instead of general overlap, this is the option -i, or make a stranded overlap, this is the option -st.

Do not hesitate to contact sarahqd at gmail dot com for more information.

• overlap (64 bit linux)
• overlap (32 bit linux)

PhylomeDB is a public database for complete collections of gene phylogenies (phylomes). It allows users to interactively explore the evolutionary history of genes through the visualization of phylogenetic trees and multiple sequence alignments. Moreover, phylomeDB provides genome-wide orthology and paralogy predictions which are based on the analysis of the phylogenetic trees. The automated pipeline used to reconstruct trees aims at providing a high-quality phylogenetic analysis of different genomes , including Maximum Likelihood or Bayesian tree inference, alignment trimming and evolutionary model testing. PhylomeDB includes also a public download section with the complete set of trees, alignments and orthology predictions.

• Homepage

Protein aggregation causes many devastating neurological and systemic diseases and represents a major problem in the preparation of recombinant proteins in biotechnology. Major advances in understanding the causes of this phenomenon have been made through the realisation that the analysis of the physico-chemical characteristics of the amino acids can provide accurate predictions about the rates of growth of the misfolded assemblies and the specific regions of the sequences that promote aggregation. More recently it has also been shown that the toxicity in vivo of protein aggregates can be predicted by estimating the propensity of polypeptide chains to form protofibrillar assemblies.

Fast predictions of RNA-Protein interactions and domains

Online service: catRAPID v2.0

                              catRAPID v1.0 

Please note:

Query sequences  should be pasted in the 'Protein sequence' and 'RNA sequence' text area. 

The catRAPID server accepts only amino acid and nucleic acid sequences, defined as lines of sequence data, without the FASTA definition line, sequence identifiers and/or other symbols; eg:

MSEYIRVTEDENDEPIEIPSEDDGTVLLSTVTAQFPGACGLRYRNPVSQCMRGVRLVEGILHAPDAGWGNLVYVVNYPKDNKRKMDETDASSAVKVKRAVQKTSDLIVLGLPW KTTEQDLKDYFSTFGEVLMVQVKKDLKTGHSKGFGFVRFTEYETQVKVMSQRHMIDGRWCDCKLPNSKQSPDEPLRSRKVFVGRCTEDMTAEELQQFFC

General guidelines are reported here below:

1. Use one word to identify your protein and your RNA

2. Use standard IUB/IUPAC amino acid and nucleic acid codes. 

The nucleic acid codes supported are:

A  adenosine; C  cytidine; G  guanine; U  uridine.

The accepted amino acid codes are:

A  alanine; C  cystine; E  glutamate; D  aspartate; F  phenylalanine; G  glycine; H  histidine; I  isoleucine; K  lysine; L  leucine;

M  methionine; N  asparagine; P  proline; Q  glutamine; R  arginine; S  serine; T  threonine; V  valine; W  tryptophan; Y  tyrosine.

3. Blank lines are not allowed in the middle of sequence input (no spaces or next line)

4. Use of sequence identifiers, such as simply accession, accession.version or gi's (e.g., p01013, AAA68881.1, 129295) is not supported in the protein and RNA sequence fields.

project is a program that projects genomic features onto their sequences. Please contact Sarah Djebali (sarah dot djebali at crg dot es for any question).

• project (64 bit linux)
• project (32 bit linux)

Selenoproteins are a group of proteins that contain selenocysteine (Sec), a rare amino acid inserted co-translationally into the protein chain. The Sec codon is UGA, which is normally a stop codon. In selenoproteins UGA is recoded to Sec in presence of specific signals on selenoprotein gene transcripts. Due to the dual role of the UGA codon, gene prediction programs fail to predict correctly selenoproteins. Selenoprofiles is an homology-based in silico tool able to scan genomes for members of the known selenoprotein families, thus finding both selenoproteins and cysteine homologues. Selenoprofiles is built in python, and it internally runs psitblastn, exonerate, genewise and SECISearch.

Selenoprofiles is tuned to search for selenoprotein genes, and comes out-of-the box with profile alignments for each known selenoprotein and selenocysteine-related family (Note: profiles will be released soon. The current release contain only the program and a single profile for example).

Selenoprofiles can be used to search for any protein family (also non-selenoprotein), given an input profile alignment. This pipeline combines standard gene prediction tools to provide a clean and fast way to scan genomes for protein families, and provides a wide repertoire of output formats which can also be extended by the user. The program allows for a deep level of customization, and provides many built-in methods to filter spurious hits.

Selenoprofiles version 2.2 is now available. Version 1 is no longer maintained -- if you need help with version 1, contact us by mail. This version features major improvements on the previous ones, such as:

• improved workflow control
• prediction by blast can be output, allowing use of selenoprofiles in bacterial genomes (exonerate and genewise are eukaryote specific)
• lazy computing implemented
• pre-clustering of the profile alignment: multiple blast are run if the profile is highly variable
• an SQLite database is used to store results, allowing to search for a high number of families without producing an enourmous amount of files, since they can be deleted at the end of computation
• improved customization of the options used with the slave programs, which can potentially be different for each profile
• improved filtering of results: all filtering procedures are defined as pieces of python code which are run internally in selenoprofiles. Several methods useful for filtering are provided. Filtering can be customized for each family
• intra-family and inter-family redundancy of results is removed
• tag blast and gene ontology extensions implemented for filtering (see manual)

Tools for graphical representation of selenoprofiles results are under development and will be released in the next few months.

MANUAL

Download the last version of selenoprofiles manual here: http://genome.crg.es/~mmariotti/selenoprofiles_manual.pdf 

INSTALLATION:

For selenoprofiles to work, all the slave programs that it utilizes must be  already installed in your machine (blastall, exonerate, genewise). You will also need some external python modules if you want to use all its functionalities. These additional modules are needed if you want to scan genomes for selenoproteins, but may be omitted if you want to scan for your protein family of interest. In this page you can find help to install the slave programs and the additional python modules.

You can either use svn to fetch the latest selenoprofiles package:

  svn co --username guest --password guest svn://svn.crg.es/big/selenoprofiles/trunk selenoprofiles

or download this tarball. Then, follow the instructions in the README.

CITATION:

Selenoprofiles was published in Bioinformatics. To read the article or access the online data, check this page. Please cite:

Mariotti M, Guigo R - Selenoprofiles: profile-based scanning of eukaryotic genome sequences for selenoprotein genes.Bioinformatics. 2010 Nov 1;26(21):2656-63. Epub 2010 Sep 21

Contact: marco.mariotti@crg.eu

SymCurv is a computational ab initio method for nucleosome positioning prediction. It is based on the structural property of natural nucleosome forming sequences, to be symmetrically curved around a local minimum of curvature. The method takes as input the primary DNA sequence, calculates the expected curvature from which it deduces possible centers of nucleosomal sequences, by imposing symmetry constraints. SymCurv's performance is comparable to existing tools but offers the additional advantages of predicting nucleosome positions under two assumed-states (stationary and dynamic) providing insight on the remodelling potential of nucleosomes of possible regulatory function.

• SymCurv WebServer
• SymCurv Documentation

The Flux Capacitor predicts abundances for transcript molecules and alternative splicing events from RNAseq experiments. Additionally, there is a simulation pipeline that is capable to simulate whole transcriptome sequencing experiments.

• homepage
• bugtracker
• forum

TreeKO is a python package used to compare phylogenetic trees. Currently it contains two different programs:

Tree comparison The tree comparison algorithm has been designed in order to be able to compare trees that have undergone gene loss and gene duplication processes and therefore do not necessarily have the same number of leaves. TreeKO computes all the possible pruned trees in each original tree by splitting the trees by the duplication nodes and reassembling the trees with combinations of pruned trees. The pruned trees are then compared all against all, pruning the leaves that are not common in both pruned trees. TreeKO offers two distance measures that are modifications of the Robinson & Foulds distance. The speciation distance will compute the distance between two trees without penalizing for gene loss and duplication events. On the other hand the strict distance will compute the distance between the complete structures of the two trees.

Phylome support The phylome support algorithm has been designed as a way to identify conflicting nodes and to incorporate genome-wide information on species trees. The algorithm is able to map gene-tree variability levels of large groups of gene trees (e.g. a whole phylome) on the nodes of the species tree.